FreakTakes

A close reading of the early history of the Advanced Research Projects Agency (ARPA) — the ARIA-like agency in the US that funded the early internet and autonomous vehicles — yields a key lesson: exceptional projects were usually the result of exceptional contractors. World-changing ARPA successes like the ARPAnet and early autonomous vehicles were the result of a common shape of R&D org, one structured, incentivized, and staffed differently than typical academic labs or VC-funded startups. These orgs pursued exceptionally ambitious North Star technical visions, fueled by a mix of contracts and grants. We call orgs of this shape frontier research contractors (FRCs).

FRCs pursue their technical visions by building technology to solve difficult real-world problems, using customer demand to guide their research direction. History shows us that FRCs can be exceptional platforms for ambitious, applied-minded scientists to move their field of R&D forward. Two historical examples that inspire us include:

• J.C.R. Licklider’s early BBN computing team at Bolt, Beranek and Newman. Their technical vision was to build a world of interactive computing, which led them to implement the first version of the internet in the 1960s.

• Early Carnegie Mellon’s autonomous vehicle team. Their technical vision of self-driving vehicles led them to successfully take a vehicle cross-country 98.5% autonomously in 1997.

FRCs can be nonprofit or for-profit R&D groups, with technical visions ranging from building individual tools to bootstrapping frontier labs into existence. What they all have in common is their commitment to fueling their ambitious technical work through a mix of contracts and grants.

As government R&D funders such as ARIA increasingly chase early ARPA-style outcomes, we need ambitious FRCs to do key portions of the work. That’s why we created the Frontier Research Contractor Launchpad, an experimental programme in which FRC founders working in ARIA technical areas were given (1) £100k and (2) a year of operational + strategic guidance in building and expanding their FRCs.

The FRCs involved ranged from a team with only one employee and less than a year old to teams of almost two dozen that were approximately three years old. Upon selection, each founder had a clearly honed North Star technical vision and a sense of what types of customers and funders could fuel their work. All teams, regardless of age, planned to use their participation in the programme to establish a foothold in some new market. And each team was selected with the belief that if they succeeded in doing this, their field would be materially better for it.

Beyond the £100k in financial resources, the programme made a special effort to help founders overcome the temptation to chase funding piecemeal, application by application, and instead build a core customer strategy to reliably fund progress towards their technical visions. We complemented this funding and strategy work with additional resources including founder group workshops, 1:1 sessions, and active network-building on behalf of each FRC.

Each of the five teams in the FRC Launchpad was selected with the belief that their success in establishing a foothold in a new market or research area would materially improve their entire field. Each set a goal for what they wanted to accomplish with the programme.

Nine months in, the results are exceptionally promising. The following per-team subsections describe the technical ambition of these organisations and progress towards their goals thus far.

Cultivarium. Cultivarium began its life as a Focused Research Organization (FRO). FROs are often called “nonprofit startups”, and tend to be founded in areas where startup-like efforts are optimal for progress, but in which the massive upside of the effort is public benefit-related, rather than a $10 billion+ commercial upside. Cultivarium was founded to build the tools and models essential to making it faster, cheaper, and easier to work with non-model organisms — enabling greater access to the entire tree of life in biological investigations. Through Cultivarum’s (Cultivarium CEO) programme participation, Cultivarium has succeeded in transitioning from a time-bound, philanthropically funded FRO to a self-sustaining FRC — that can exist indefinitely if the org continues to solve problems for funders with applied needs. Through Cultivarum’s participation in participation in the the FRC Launchpad, Cultivarium won two large contracts: one with an organism-focused philanthropy to ensure Cultivarium’s models and tools speed up the work of the funder’s PIs using a particular organism, and a second large contract of a similar structure.

Living Physics. Living Physics was funded into existence by an ARIA Nature Computes Better opportunity seed - ARIA’s highly flexible ‘fast grant’ funding mode designed to support speculative scientific research with immense potential in a lighter-touch manner than ARIA’s programme funding.. ARIA’s Nature Computes Better opportunity space is dedicated to scaling computational efficiency, and has looked for inspiration to natural systems — which are often orders of magnitude more efficient than modern computers. David Jordan (Living Physics founder) is a biologist who can be thought of as having the mindset of a physicist, the hands of an engineer and the expertise of a biologist. Using the perch his ARIA opportunity seed created, David plans to turn Living Physics into a center of excellence at the intersection of engineering physics and biology. Specifically, his group is building a suite of instruments (e.g., Raspberry Pi-powered bioreactors) and partnerships to move forward the state-of-the-art in theoretical biology, such as understanding how information is processed in biological systems. Biology is a complex subject in which improved measurement and perturbation are often key capacity bottlenecks. Through the FRC Launchpad, David is finding early success in scaling his org from a purely research endeavor to one that accelerates science for others by building instrumentation for ambitious researchers and partnering with research groups that require the skills of people like him. These contracts, which Living Physics achieved through the FRCL, are allowing Living Physics to operate as a self-sustaining R&D org that can take their research into real-world applications. In addition, Living Physics is leveraging the operational know-how gained through the FRC Launchpad to spearhead applications for larger contracts that take advantage of this center of excellence he is building outside of the university and VC ecosystems, one that pairs an exceptionally ambitious research agenda with an intense desire to build artifacts of applied technology.

Syntato. Syntato was also funded into existence by an ARIA grant, from ARIA’s Synthetic Plants programme that is looking to build fully synthetic plant genomes and chloroplasts. Syntato was founded, in part, to address a key programme need: building better applied crop genetics tooling. This was necessary because in agriculture, the venture capital model is not well-suited to areas like “tools for transgenic crop design”. Syntato has built an integrated team of engineers and researchers that is as ambitious as any basic research group, but focused on building good tools for industrial crop biotechnology. During Syntato’s FRC Launchpad participation, Syntato has secured additional ARIA funding, a four-year Bezos PhD studentship, with several larger applications for research projects. These applications add up to over £5M in opportunities, with a high probability that at least £1M will be secured in the next few months.

Basis Institute. Basis builds reasoning engines: AI systems that automatically construct simulators and world models from knowledge and data, and use them to predict the future, explain the past, and reason about counterfactuals. Basis’s North Star technical ambition is a “universal reasoning engine”: a new foundation for AI that unifies probabilistic, causal, and mathematical reasoning over computational models of every kind. Basis has funded itself for several years through a mix of philanthropic support from those who believe in its vision, contracts from funders like ARIA and DARPA, and experimental contracts with companies. While Basis is building towards a general set of technical capabilities, it views working on targeted challenge problems (particularly in the sciences) as an ideal way to make progress. It believes this sustained problem-solving will surface lessons enabling it to steadily generalize its methods. In effect, Basis is trying to use its base of contracts and grants to build a new frontier lab, one with a path to simultaneously being self-sustaining, solving fundamental research problems, and building scalable technology that solves real-world problems. During its FRCL participation, Basis has established several promising contract leads that will expand its capacity to tackle challenges in the UK R&D ecosystem.

Topos Institute. The Topos Institute is an FRC focused on problems of applied category theory and mathematical software engineering, founded with the deep belief that category theory can be a useful, coherent modeling language in the sciences, particularly in problems that require communicating across disciplines. Similar to Basis, Topos seeks to increase the generality of its systems by continually solving specific challenge problems. Topos has also funded itself for several years with a funding mix similar to Basis. During its FRCL participation, the org has succeeded in diversifying the variety of contract partners in its Oxford office, and is actively considering how it might use the corps of talent it has built up to build increasingly useful pieces of technology for users. Many Topos employees would be at home in either top mathematics departments or AI labs. Its success in building a financially sustainable FRC enables this world-class group to marshal their talents in service of a technical goal that is simultaneously big-if-true, but not on the radar of the VC ecosystem.

Due to the success of the first FRCL cohort, ARIA is expanding our next cohort to eight teams! Read more about the programme here [link to RenPhil website]. We’ll also be hosting a webinar for any interested applicants on Aug 10 at 5pm BST: register here! The FRC Launchpad team — Eric Gilliam and Janelle Tam — are eager to chat with any team that can substantially move the work of any ARIA opportunity space forward. Feel free to reach out to them at [email protected] and [email protected].

Strong applications will have both a big-if-true technical vision and a clear thesis for a set of contracts and grants which can fuel the vision indefinitely. And these orgs should be building towards goals that could not be built within academic labs or the VC-funded path.

Existing ARIA Creators looking to find a way to use the perch offered by their ARIA to turn their efforts into self-sustaining FRCs are particularly encouraged to apply. The shape of each founder’s technical ambition will vary — FRO-like, frontier lab-like, center of excellence in some area, ambitious CRO-like efforts, etc. What’s important is the founders’ commitment to building an R&D organization that is simultaneously ambitious and able to financially sustain itself through a mix of contracts and grants.

If that sounds like you, check out the application here! Applications close Aug 21.

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When it comes to the philanthropic ecosystem, we live in exciting times. A duo of recent posts — one from one of SF’s great thinkers, the other from one of its great general managers — are dedicated to this fact.

Last week, Dwarkesh Patel closed submissions for a blog prize searching for the best answers to the question: How do we deploy (possibly) hundreds of billions of dollars to “make AI go well?” He specifically wanted to know how readers might approach the problem of converting money into impact from the POV of the OpenAI Foundation.1

In a second blog post, The third wave of American philanthropy, Stripe Climate GM Nan Ransohoff put the scale of the opportunity in context. She points out that if $50 billion from AI donors were to enter the philanthropic ecosystem each year, that would be enough to fund the annual budgets of the following organizations:

• 6 Gates Foundations (~$9B/yr), or

• 67 Coefficient Givings, formerly OpenPhil (~$1B/yr), or

• 100 GiveWells (~$500M/yr), or

• 333 Arc Institutes (~$150M/yr), or

• 5000 Institutes for Progress (~$10M/yr)

Nan then breaks down the challenge into a set of seven subproblems that, from an ecosystem design perspective, we must overcome to use these dollars well. Today’s piece zeroes in on a solution to one of these problems: figuring out how these new funders can leverage their unusually large risk appetites to world-changing effect.

Incidentally, this is exactly what my submission to Dwarkesh’s blog competition was about. In it, I profiled the best scientific philanthropist of all time, Warren Weaver, and his (exceptionally risk-tolerant) playbook for willing new fields into existence. Like great VCs, he only took big swings. Unlike VCs, he did next to no diversification. To Weaver, field creation was an endeavor that required focus; responsibility was not about hedging your bets, but giving carefully selected fields everything they needed to flourish.

Over the course of twenty years, Weaver bootstrapped the field of molecular biology into existence. He then funded a course of crop research that would grow into the Green Revolution. Weaver had the courage to live with concentrated bets for years on end, and the world is immeasurably better for it.

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Given the blog competition word count, I’m briefer than usual in many spots. If you’re curious to learn more about any topic mentioned, check out my long piece on Weaver, my many Weaver tweets, or ping me. And if you have the urge to act on today’s piece, contact me — [email protected], or on my Twitter. If urgent, I can fly to SF.

Two more of Nan’s subproblems — (1) the need to create new philanthropic capital allocators and (2) to make the problems that need solving more legible — will be the subject of a coming piece.

To become a technology of historical consequence, we must proactively make good things happen using AI. In the not-so-distant past, we find kinks in mortality graphs representing hundreds of millions of lives saved — kinks sparked by the action of a few smart people. Human brains did that. In partnership with a superhuman AI, we should have no less ambition.

Our ambition should extend beyond longer, healthier lives. I take a somewhat Robert Gordonian view of human flourishing: people working less without getting poorer, finding ways to make housing and food more affordable, and so on.

In pursuit of these goals, the philanthropic efforts of the AI labs should focus on problems that take advantage of two Silicon Valley comparative advantages: R&D and risk-tolerant capital allocation.2

Key individuals at the OpenAI Foundation have the opportunity to cement themselves as historically great, as more consequential than even J.C.R. Licklider or Bell Labs’ Mervin Kelly. But to do so, they must internalize the lessons of history’s best research funders. Why? Turning money into impact is closer to a problem of organized complexity than one of simplicity. Working from empirical data trumps “first principles” in areas of this sort. Funders need to decide on some heroes, and learn from them.

If I ran the OpenAI Foundation, a photo of Warren Weaver would hang in the lobby. From his perch at the Rockefeller Foundation, which he took over in 1932, Weaver made two contributions that cement him as the greatest scientific grant funder of all-time. He:

1. Funded molecular biology into existence.

2. Was key in funding the Green Revolution into existence.

(If the reader wonders whether Weaver simply got lucky twice, he also spotted the computing wave. Purchase the book-form of Claude Shannon’s The Mathematical Theory of Communication, and you’ll find that Weaver is co-author. Search for the grant that funded the 1956 Dartmouth Summer AI Conference, “Weaver.”)3

I’d structure the foundation to empower modern-day visionaries deploying the Weaver playbook. The playbook, in a line: true specialization in an extremely young field.

How young is young?

• In the case of molecular biology, ~80% of Weaver’s Natural Sciences Division budget went into the field for ~two decades pre-Watson and Crick. For the first five years, many key experiments didn’t run or barely worked. They fell forward. Even in 1948 — 15 years into this focused bet, 5 years pre-Watson and Crick — one could find Leo Szilard hemming and hawing over whether it was too risky to enter such a young field.4

How did Weaver choose to specialize in molecular biology?

• Weaver, an applied mathematician, worked in physics during the era in which physics’ tools and models fruitfully invaded chemistry.5 He believed a branch could be similarly willed into existence at the intersection of biology and physics.6 Biology, at that point, was a ~single instrument field — the optical microscope. Biologists didn’t work at the small scales physicists had now learned to study. A biological question on the scale of heredity was the perfect place to start.

Why specialize?

• Before Weaver arrived, the Natural Sciences Division operated somewhat typically. They let scientists line up in general areas, and funded the best ideas until money ran out. Weaver saw that as inefficient, and believed focus would be super-additive. “A highly selective procedure is necessary if the available funds are not to lose significance through scattering.”7

It worked. By 1965, 18 Nobel Prizes would be given out for molecular biology. 15 of them were beneficiaries of Rockefeller Foundation funding, on average receiving their funds ~2 decades in advance of the prize — in an era in which scientists won the prize in middle age. Weaver would then move all his chips into his next branch, one that would grow into the Green Revolution.8

I’ve simplified a bit.9 Still, the simplicity of the approach begs the question: “Why doesn’t everyone do it?”

The answer is likely also simple: courage and trust are in short supply. The field Weaver tied up almost all his money in did not have a name — he named it “molecular biology” in 1938. The bet was overseen by someone named “Weaver,” not “Rockefeller.” And he didn’t have all that much to show for it in the early years. The bet is unthinkable at almost every large philanthropy today. Most who tried would get impatient, scared, or fired before the seeds bore fruit.10

But it can be done. AI’s philanthropists have enough money to fund multiple Weavers at once, but not enough to diversify their way to Weaver.11 The work will require courage and a steady hand; it’s a Berkshire Hathaway-style portfolio of focused bets.12

How to choose which could-be Weavers to empower? Focusing on areas where the R&D funding ecosystem systematically under-invests is a natural approach. Several include:

• New Field Creation

• Instrumentation

• Doing-Heavy Discovery Work

AI might give us better next experiment ideas in biology, but that doesn’t change the structure of the NIH or the incentives of VC. NIH panels can err towards consensus work in fields that already exist, and VCs toward things that are VC-profitable within 10 years. Neither specializes in creating new molecular biologies. The OpenAI Foundation can, if it chooses.

On instrumentation, neither academia nor VC fund biological tooling at a societally optimal level. The status quo may deliver super-human intelligence that is wildly under-sensed. There’s surely a Weaver who would dedicate themselves to the problem.

Opportunity also exists beyond AIxBio. Doing-heavy discovery work is under-emphasized in general; for example, crop test fields for construction are not a university specialty. A related historical example worth dredging up comes from the memoir of James Killian, my least favorite dead MIT President. Polaroid cofounder Edwin Land once proposed that, at MIT’s Sloan School, Killian “establish a model company — a practice school — to explore other ways to create other noble prototypes of industry.” That strikes me as a good problem for a Weaver in the age of AI.

Thanks for reading.

Stay tuned for my piece on founding new philanthropic capital allocators that scale, and doing so in not-yet-legible areas of R&D, coming in early June.

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FreakTakes is, in many ways, a ~200,000-word exploration of exactly this — from several dozen different angles.

Naturally, this piece is primarily focused on those eager to focus their philanthropy on R&D. This is, after all, an R&D history Substack. And even among those giving to R&D, there are obviously heroes you can pick other than Weaver! J.C.R. Licklider (of early BBN and ARPA fame) and MIT c. 1920 are both worthy heroes, for example. I’ve written longer FreakTakes pieces on both.

In the next couple of months, I’ll also publish a piece breaking down why Weaver, in the 1940s, thought it was clear that the computer would eventually cement itself as one of (if not the) most important instruments in the study of biology.

This Substack’s original Weaver piece spends many thousands of words detailing what I had to distill in a few sentences here. So if you want to hear stories from the nascent labs, breakdowns of Rockefeller budgets before and after Weaver’s arrival, and how he used the other 20% of his portfolio to explore areas that could become his next branch, see that piece.

Many who read this Substack will be familiar with the fact that the physicists and chemists won each other's Nobels quite frequently, in this period.

To my eye, this heuristic for field creation feels timeless. Heuristics only take you so far, but it’s a great place to start.

Weaver felt that philanthropic divisions (like his) were not above the sort of specialization expected of firms. He focused his funds where they were needed, giving the young (speculative) field he was building the resources it needed to flourish. New Silicon Valley funders will be well-placed to impose this focus on specific activities happening in an organization. But doing so in the service of long-term, (often) not very measurable goals might be a bit of a balancing act. Empowering single individuals, as companies do with executives, is essential. But extending them more rope than is typical of executives is also essential if you want a Weaver. Most philanthropies, frankly, do not have the stomach to fund a Weaver. I write this piece in the hopes that a few will have the stomach and steady hand to do so.

As I wrote in the conclusion of my original Weaver piece: “Weaver was not committed to molecular biology at the expense of all other things. He was committed to creating high-value branches at the expense of all other things.

The Division would not abandon its young branch — molecular biology — because it was not publicly producing in a way that would make headlines, but they would abandon their baby if they felt it didn’t need them anymore. There had to be some new, undiscovered branch out there in need of their support. The follow-on funders could take it from there.

For some people, like Weaver and Szilard, life’s too short to spend basking in the glory of what they’ve built. They’d rather do the damn thing again.”

Of course, Weaver didn’t do it alone. He relied on excellent field strategists, most prominently Max Delbrück and Salvador Luria, who recruited and pitched great young people into the field one at a time, over many years. And many other key Weaver practices contributed to the success. For example, he deeply studied the field, maintaining a sense of what failures were productive vs. dead ends. Etc. Etc. Read the longer Weaver piece to learn more about all of this.

This is a real problem. The solution to which one might be just as likely to find in religion and folk wisdom as in any business or R&D history book. The mechanism by which courage and patience, for the long haul, are created in individuals is curious. For modern exemplars of steady-handed confidence of this form, I’d recommend watching several dozen hours of Warren Buffett and Charlie Munger reacting to the manias, panics, and fads of their times.

Upon reading Nan’s piece, I might hedge this statement a bit. I was imagining yearly giving numbers more in the ~$5 billion range, give or take. But even if it were ~$1 billion per year, upon further reflection, that might be enough to diversify your way to Weaver in cheaper subject areas. A social science-flavored focus would be one example where this could be possible. Computational law, computational applied history and experimental history, and the “noble prototypes of industry” idea mentioned in the conclusion are all areas that could potentially warrant their own Weaver.

It should be noted that Weaver, Buffett, and Munger are all aggressively Midwestern — a factor this Midwestern author finds material. In his life, Munger made an unwieldy number of quips along the lines of, “The big money is not in the buying or the selling, but in the waiting.”

Biotechnology is filled with promising young scientists who want to work on big problems, but whose ideas do not neatly fit within academia, venture capital, or even large-scale philanthropy. Academics tend to work within individual laboratories, rather than big teams. And they usually work toward publishable results instead of scalable technologies. (Most papers stop at the proof-of-concept stage.) Venture capital expects large returns within a ten-year period. Focused research organizations take tens of millions of dollars to get off the ground.

Fortunately, there is a narrower (albeit more delicate) path, as long-time FreakTakes readers know: BBNs.

Originally named after an ARPAnet contractor named Bolt, Beranek & Newman, a BBN is just a funding mechanism for scientists to work on ambitious ideas. Unlike academia, BBNs focus on applied problems. Unlike VCs, BBNs are not concerned with making billions of dollars in the next decade. And unlike FROs, BBNs can launch quickly — often with a few hundred thousand dollars — and extend their runway by winning grants and contracts. BBNs and FROs can share essentially identical technical ambitions, so the distinction is mainly funding. FROs begin with a large upfront endowment ($10–100M) that lets them pursue a big goal from day one, whereas BBNs pursue the same kinds of goals but more steadily.

A few weeks ago, when Eric Gilliam and I sat down to lunch, I riffed on some BBN-shaped ideas for biotechnology. He asked me to write them down, and I agreed. There were two reasons for my doing so: First, I think more people ought to know what a BBN-shaped idea is; and second, ideas are a limiting ingredient in launching more BBNs.

In this short article, I riff on three biotechnology ideas which might be suited to a BBN: “Flowers by Design,” “A Biosensor for Everything,” and “Proteins for Pennies.” Readers may reject or disagree with elements of these ideas, but my hope is that, by writing them out explicitly, people will at least be inspired to start BBNs.

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Eric is in Boston through March 27th, and he’s eager to talk to anyone with interesting BBN ideas. If you’re around, reach out at [email protected].

The first idea, “Flowers by Design,” is not my own; there is already a small yet serious group of people using genetic engineering, pathogens, and other molecular tools to create entirely new types of flowers. (I wrote about one such flower designer for Asimov Press.)

These designers, all of whom are also plant biologists, make engineered flowers with unique patterns, colors, and shapes. They tend to be brilliant not only because they are adding beauty to the universe (a goal one should not scoff at), but also because, by mixing these methods together, they are making important discoveries about how plants actually work. The “big” goal of a flower design BBN might be to develop a general theory of plant programming, such that one can control the traits and growth patterns of a wide array of plant species.

Any tools created to engineer flowers would also unlock capabilities in food crops. Flower designers work across dozens of species by necessity; far more than the handful of model organisms that academic labs use. Also, the DNA delivery methods used to engineer a dahlia or a morning glory are the same methods needed for sorghum or cassava, two crops where transformation efficiency is currently so poor that most genetic engineering efforts fail.

The same transcription factor proteins that control flower structure — petal number, organ boundaries, and symmetry — are also closely related to the genes which determine grain size, seed coat thickness, and fruit morphology in food crops. Engineering flowers is thus a way to create beauty in the universe while discovering the developmental and genetic principles that matter for food yield.

Unfortunately, flower design does not easily fit into existing career paths. It is not an obvious VC play because it can take several years to circumvent regulatory barriers alone, and returns often take more than a decade. (The Juliet Rose took 15 years to create, at a cost of $3 million, but is profitable today.) If any genetic engineering methods are used to make a flower, scientists must abide by USDA regulations overseeing how that flower is shipped across state lines.

More important than regulations, though, is the simple fact that everyone I’ve met who calls themselves a “flower designer” is extremely idealistic; almost allergic to business. They see themselves as artists and scientists first and foremost, and they don’t want to spend several years making one plant just to sell it repeatedly. Instead, they want to make hundreds of different flowers, with thousands of bespoke designs, and this takes a lot of time and money.

This isn’t an obvious fit for academia, either. The goal of flower design is to make beautiful objects, rather than publish papers. It also isn’t an obvious “pure” philanthropy play, though philanthropy would be better suited to this idea than academia or business. It just isn’t clear, to me, that a wealthy person would give $10 million to a person making flowers when that money could instead go toward curing cancer or something with more “social capital.”

Fortunately, there are many contracts and grants available for flower design, which could be used to bootstrap a BBN. Japan funded an effort to make a genetically-engineered morning glory for the 2020 Olympic Games, though the project later fell through. (Japan, in fact, has one of the largest flower industries in the world, commercializing about 40,000 new varieties each year.) A single USDA SBIR Phase II grant (~$2M over two years), paired with one or two annual contracts from plant biotech companies interested in new ways to engineer traits in flowers (typically $100–300K each), could plausibly sustain a small team of three to five people indefinitely.

My second idea is called a “Biosensor for Anything.”

Biology has evolved, over billions of years, to sense millions of unique molecules. Our noses can differentiate two molecules differing by a single atom, or even mirror images of the same molecule (S-carvone smells like spearmint; R-carvone smells like caraway seeds.) And we don’t only sense molecules, either, but also other ‘forces.’ Some birds migrate thousands of miles by sensing magnetic fields, for example, and proteins inside cells can sense individual metal atoms, like zinc or iron.

I suspect there is a BBN-shaped idea around using biotechnology — especially engineered cells or AI-designed proteins — to sense a wider array of molecules and forces. (There is a related ARIA program focused on building so-called “Hypersensory Intelligence.”) Whole-cell biosensors can be created using genetic circuits, where one protein binds to a molecule and a second protein emits an output, such as fluorescence or a color change, in response. A platform technology BBN, focused on building tools to make virtually any biosensor, would help us respond to outbreaks more quickly, or create cheaper diagnostic tests. (About 500,000 children in the U.S. have lead poisoning, for example, and the main way to test for it is with a $5,000 device.)

Specifically, I’m envisioning a BBN that systematically collects large datasets on protein sensors — the biological molecules that detect a signal — and the output modules that report what they’ve found, perhaps by emitting light or changing color. These datasets would be used to validate which sensors work reliably across many different cell types and experimental conditions, and to build predictive models that help scientists design better sensors without having to test every possible combination by hand. The most promising sensors would then be put through field trials in the messy, real-world environments where they’d actually be deployed: sand, soil, water, blood, and so on.

A few years ago, there was lots of excitement about paper-based diagnostics. The basic idea was that scientists could engineer a biosensor in living cells, and then kill those cells and extract their “juices,” including the synthetic DNA. This “juice” could then be coated onto a piece of paper and freeze-dried. When scientists added liquid back to the paper, the genetic circuit would reactivate and change color if the molecule being sensed was present. These paper biosensors cost pennies to make, yet I’m not aware of any companies that have successfully commercialized them, either because the paper strips were not accurate or reliable enough for clinical diagnostics or because — in places where accuracy matters less — the amount they could be sold for was too small to generate large returns.

This idea, much like flower design, isn’t obviously suited for pure academia or philanthropy. The work requires deep expertise across multiple disciplines, including computational protein design, genetic circuit engineering, and materials science. One lab can’t do all of this. And even though philanthropists might fund the creation of a specific diagnostic test for a narrow disease, they’re less likely to fund a much broader effort to make “biosensors for anything.”

That’s why this feels like a BBN-shaped idea to me. A small team could win contracts and grants to build biosensors for underrated diseases, using that money to engineer proteins and prototype devices. Domain-specific contracts — from funders like Arnold Foundation on lead, or from the EPA on environmental monitoring — could cover much of the operating cost of a BBN in this space, but the group would also need about $300–500K in unrestricted funding each year to keep doing R&D and make sure they don’t drift down a narrow diagnostic path. Over time, the BBN would use what they learn to build better reporters, more stable freeze-dried systems, and also better computational design tools to speed up prototyping.

A third idea, and my personal favorite, is “Proteins for Pennies.” The overarching aim here is to make a “protein fabricator” such that scientists can create any protein, of any amino acid sequence, for a few pennies; ideally within a few hours. We are nowhere near achieving this, but doing so would not require breaking any laws of physics. Cells “eat” nutrients, like sugars and nitrogen, and turn them into useful biomolecules. Indeed, a single cell synthesizes tens of thousands of proteins simultaneously, using only the atoms repurposed from its environment. Why can’t we do the same, but synthetically?

There are currently two main ways to make proteins in the laboratory. The first option is to order a DNA sequence encoding a protein of interest. Each nucleotide costs about $0.07 to order from a DNA supplier. Given that an average protein has about 300 amino acids, this means scientists must order a gene with at least 900 nucleotides (three “letters” of DNA encode one amino acid) to make such a protein. In this scenario, the DNA sequence alone costs at least $63. And even after the DNA sequence is made, scientists must then clone it into a microbe, grow those microbes in large batches, and purify the protein. This takes time and money.

The second option is chemical synthesis, where chemists click amino acids together, piece by piece, until the full protein is completed. The problem with this approach is that each step has a particular efficiency — say 99% — and a single error is enough to ruin the entire molecule. (For a theoretical protein with 300 amino acids, a per-step accuracy of 99% would yield just 5% of proteins with a perfect sequence.)

A BBN could work towards lowering the costs of protein synthesis by 100x. Protein-based therapeutics, some of which are currently too expensive to manufacture for rare diseases, could be made on-demand. The cost of protein design would also fall sharply, since wet-lab testing of computer-generated designs is the main cost — and most time-intensive part — of the whole process.

A BBN focused on protein costs could begin by miniaturizing and automating existing protein synthesis technologies. With enough scale and multiplexing, perhaps costs could fall by 3-5x. A recent paper from the Baker lab, for example, describes a semi-automated workflow for purifying and characterizing hundreds of designed proteins per day using standard laboratory equipment. This reduced the cost of testing each protein design to just five dollars per construct. Still, my suspicion is that radical price reductions will require a fundamentally new technology; something with near-100% accuracy (like living cells have) without requiring a DNA template to build the proteins.

Previously, I wrote about one “Protein Printer” idea together with Julian Englert, CEO of AdaptyvBio, a protein testing company. Englert’s idea was to build a circular loop of RNA containing a codon for each amino acid, attach a ribosome to it, and then move that ribosome back-and-forth to each codon to make a particular protein. Perhaps this could be done using flashes of light, where a “blue” wavelength triggers the ribosome to move one codon forward, and a different wavelength triggers it to move back one codon; akin to a rotary telephone. In this way, scientists would not need to synthesize an original DNA template for each protein; they could just use these engineered ribosomes and circular loops of RNA to make any protein using light. But this is not the only idea to make a “protein printer.”

Grants could easily be used to bootstrap this BBN. Many funders are excited about the intersection of AI and biology and, again, protein synthesis costs are a major bottleneck for testing computationally-designed proteins. Therefore, the cheaper we can make proteins, the larger the datasets we can collect for a given amount of funding, and the faster we can improve AI models.

ARIA recently announced a £50M Universal Fabricators program, specifically to develop scalable protein-based manufacturing. If a team of three to four people could win some grants to build a fence around R&D efforts, they could then go chase after contracts for more narrow efforts (like bespoke protein manufacturing for therapeutics companies.)

Everything in this essay is me shooting spitballs against the proverbial glass window. But I hope this article inspires others to think of BBN-shaped ideas in their own fields. Which ideas would move your field forward in a meaningful way, yet are not well-suited to academia, VC, or large-scale philanthropy? Write them down!

Niko McCarty is a fellow at Astera Institute and a founding editor of Asimov Press.

Today’s piece is a guest post by Ivan Jayapurna. Ivan is a Programme Director (PD) at the UK’s Advanced Research + Invention Agency (ARIA). This week marks the launch of his £50m program: Universal Fabricators.

Ivan’s program has some very BBN-shaped pieces of work, and he believes some of his most ambitious applicants might see his call for proposals as their chance to launch a BBN of their own. If that sounds like you, and you can solve the problems Ivan outlines below, reach out to him here. (To learn more about “BBNs,” see this prior FreakTakes post.)

As a bit of context — for those that did not see my interview with ARIA’s founding CEO — ARIA PDs are empowered to fund brand new R&D orgs into existence if it suits their program’s needs. The org covered in the most recent FreakTakes post, Syntato, is a case in point. ARIA’s Programmable Plants work needed a group to build tools to transgenically gene-edit crops. Using its ARIA contract to do just that, Syntato was formed.

Ivan’s program may offer similar opportunities. In our discussions, Ivan was particularly excited about the idea that, at their best, BBNs can be technology-maximizing firms. I hope that a few of your pitches live up to that bar.

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Two quick notes.

1. I initially intended this series of BBN-related guest posts to be a weekly series. That was overly ambitious. I get over-eager sometimes, sue me. The new cadence will be two or three pieces a month.

2. In the UK, many have begun to call BBNs “Frontier Research Contractors” (FRCs). Ivan uses that terminology below.

By: Ivan Jayapurna

Ages of human history are defined by new materials: Stone, Bronze, Iron. However, civilisation-defining materials, from steel to optical fibres, changed the world not at discovery, but only when processes were developed to make them cheap and abundant. Our physical world is still dominated by ancient materials (wood, concrete) and antiquated ones (steel, plastic) built using brute-force heat and pressure. Continued reliance on manufacturing paradigms from the Iron Age and 1900s chemical engineering has trapped us in brittle global supply chains, resource conflicts, and a tiny corner of the total possible materials design space. Despite advances in material synthesis, many desired electromagnetic, thermal, optical, and mechanical properties remain inaccessible.

Mass manufacturing with molecular precision is crucial for state-of-the-art material performance, and proteins represent a uniquely powerful toolkit to achieve this. Yet despite recent breakthroughs in protein engineering (e.g., Alphafold, de novo design, directed evolution, non-canonicals, cell-free synthesis), subsequent investment and applications have primarily been in pharmaceuticals and biocatalysis, leaving the potential of proteins in materials assembly severely underexplored. Today, most protein engineers only design drugs and enzymes. If this programme is successful, they will design next-gen materials across electronics, energy, infrastructure, and more — and proteins will become “universal fabricators.”

As an ARIA programme director, I don’t have a lab of my own. My role is to design the programme, select contractors to fund, and orchestrate them towards the programme’s North Star technical vision. As such, success is almost entirely dependent on the programme’s contractors, who will develop proteins into universal fabricators. I need people and labs that are excited to become universal fabricators. It’s possible that FRCs — or BBNs as they’re also known — could be ideally-suited to do substantial portions of work for this ambitious, engineering-heavy, and radically interdisciplinary programme.

As Eric has written in a prior FreakTakes piece:

[FRCs] will tend to have ambitious goals that are too engineering-heavy, multidisciplinary, or applied for academia. And they will tend to focus on markets ill-suited to VC — either (a) because these are not billion-dollar markets, or (b) because the financial impact of the work could be massive, but will not be realized within the ~10-year life of a VC fund.

Of course, there are some select academic research groups able to overcome these barriers. Often, these labs are well-funded, highly interdisciplinary, and have worked with ARPA-style agencies before (DARPA, ARPA-H, etc.). These labs have a culture of spinning out companies, allowing for R&D to be distributed between basic scientific research in the main lab and more “engineering-heavy” or “user/product-focused” work in spinouts. However, these labs are few and far between; and where they do exist, their spinouts are often subject to the constraints of venture capital funding.

For example, it is all too common a tale that an exciting materials startup with dreams of building revolutionary platform technologies is pushed by market and investor incentives towards a niche in the pharmaceutical industry, or incremental advancements on existing systems. These are all useful, but tend to fall short of the founder’s original ambitions. For profit-maximising firms this is understandable, but once you narrow in the technical scope to only those markets that fit VC constraints, many lines of societally exciting technical inquiry are lost.

I view FRCs as technology-maximising organizations that seek contract revenue from customers not as the objective to maximize, but as fuel to develop coupled science-engineering breakthroughs. These groups should seek to do work at the edge of the impossible. For that reason, FRC-like groups are a great fit for a programme like ours.

The majority of our £50m programme will fund a small number (<10) of teams developing proteins into universal fabricators. There are multiple different renditions of what an FRC that could successfully build a universal fabricator could look like. One variant might look something like a team made up of the following archetypes:

• Protein Engineer — Designs the building blocks and interactions on the molecular scale.

• Complex Soft Matter Expert — Manipulates short- and long-range interactions between the building blocks, such that they assemble at the right length scales and time scales.

• Inorganic Materials Mineralization Expert — Programs nucleation and growth of crystalline or amorphous inorganic matter into highly valuable functional materials.

• Process Engineer — Designs reaction environments to create “error-correcting” assembly systems that impose selective pressures for assembly through fields and flows.

No matter how they are structured and composed, these teams should be able to rapidly iterate design-test cycles — quickly going from a sequence, to the design of single molecules, to ensemble design, to field/flow-aligned assembly (liquid → slurry → solid).

To further emphasise, this all-star team is just one vision (mine) of how to design a universal fabricator FRC. We’re looking for creatives with their own takes on team composition, and imaginations that exceed our expectations to fill the white space that sits between:

• Biology and solid-state physics,

• Protein assembly + templated mineralisation (sequence → structure → function) and scalable materials manufacturing (processing → structure → performance)

If successful, I hope these universal fabricators can do the work that defines the next era of human history. I want to read proposals from any team with a vision of how to make that happen.

The programme will likely benefit from other FRC-shaped groups, beyond teams building protein-programmed fabrication platforms. One big challenge that will need to be solved is scaling up protein sequence-to-function design-test cycles. Today, in silico screening and automated assaying is done efficiently at the molecular scale. Scaling this up by many orders of magnitude, to the materials scale, today, would optimistically take months. Ideally, we want to shrink this timeline to under a week, to match the current state-of-art in drug design pipelines.

An FRC focusing on advancing rapid turnaround — large quantity, bespoke, engineered protein production — would likely be one of the most important suppliers for our programme. Specifically, I’m looking for a group to develop a system where, if I give you an arbitrary protein sequence, within a week you can give me enough material to e.g. template the assembly of a magnet large enough to measure (BH)max. Today, this would likely be done by living systems, but it’s conceivable that it could be achieved by drastically improved non-living production systems (e.g., cell free, protein printer). If a group has the capacity and ambition to substantially improve the state-of-the-art, it would be a valuable organisation for us to potentially nucleate into existence.

We expect this service to be exceptionally useful to a variety of customers, beyond the needs and length of our material science programme. In the near-term, we expect that a sustainable inflow of contracts could also be sourced from markets ranging from pharmaceuticals and biocatalysts to food, fibres, and packaging. Contracts like these, in addition to the sizable needs of our own program, would allow for the steady funding required to knock down technical barriers in pursuit of ambitious long-term goals in this space.

These are just two examples of possible FRCs aligned with my programme’s needs. I’d love to read your pitches for more of them. For example, FRC proposals in an area like metrology would be very welcome. Is there an FRC-shaped group that could quickly measure defect-free incorporation of proteins into macroscopic structures?

Prior to joining ARIA, I was a researcher trapped at the interface of materials and biotechnology — the only non-healthcare company in a biotech incubator, the only biotechnology in a climate accelerator — as well as between academia and profit-maximising startups. ARIA offered me an alternative, more direct path towards achieving my North Star of ‘Manufacturing Abundance’. Through the Universal Fabricators programme, I hope to open alternative paths for others. If this piece resonated with you, let’s go on this journey together.

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Today’s piece is a guest post by Karen Sarkisyan.1 Karen is the founder of Syntato, an applied crop genetics BBN. BBNs pursue ambitious North Star technical visions with a mix of contracts and grants. As Karen puts it, Syntato’s North Star technical vision is “computer chip design and manufacturing — but for plants.”

Karen is the perfect founder to present the first example of a modern BBN in this series. Syntato has both an extremely ambitious technical goal and a great customer — one that has enabled the org to be a break-even operation from day one. Beyond those attributes, Karen is an exceptionally effective writer.

While Karen’s name may be new to readers, his work might be more familiar; Karen previously co-founded Light Bio. Light Bio inserts genes from fungi into petunias to make them glow (see below). Syntato’s work builds on methods developed at Light Bio and ideas from Karen’s research group at London’s MRC Laboratory of Medical Sciences.

In today’s piece, Karen will paint a picture of where Syntato hopes to go. In reading it, I hope readers develop confidence that BBNs can swing big in the modern era while covering their own costs.

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This piece is a part of a FreakTakes series on BBNs and the BBN Fund. To read more about the BBN Fund itself — and how great BBNs from ARPA history contributed to breakthroughs like the ARPAnet and early autonomous vehicles — see the opening piece in this series. TLDR: the BBN Fund will be a time-bound, thesis-driven fund at Renaissance Philanthropy, headed by Janelle Tam and me.

Our goal is to be for the BBN ecosystem what the best ARPA PMs are to speculative areas of R&D. As an alternative framing — to steal a new term from Nan Ransohoff’s excellent piece from a few weeks ago — we will be “General Managers” for the modern BBN experiment, directly responsible individuals dedicated to finding ways to lower the barriers to founding ambitious BBNs in the modern era. For us, success will mean paving the way for dozens of BBNs like Syntato to be founded per year. And we are actively seeking our first funders to make this happen. So if you are a funder interested in partnering with us, please reach out at [email protected] and [email protected]!

With that, I’ll be out of your hair! Enjoy:)

By: Karen Sarkisyan

— How can we design a novel crop?

— Or program the behaviour of a house plant?

— Or turn a field into a super-organism that breaks the growth-defense trade-off through division of labour?

— Can we domesticate marine plants to produce food without deforestation?

Today, our ability to engineer plant traits is primitive. We are able to write one, two, and sometimes three lines of the lowest-level DNA code, and these programs are typically species-specific. None of the engineered plants on the market goes beyond the line:

In the future, we may be able to design plant traits we need. But with the current rate of progress, this will not happen before our existing agriculture practices and need for energy and materials destroy most of the planet. (This image of Borneo deforestation, below, is one of the saddest things one can find on the internet — and there seems to be no realistic solution, except possibly an economic shift driven by biotechnology.)

— Is it possible to have a truly novel crop on the market in 10 years, by 2036?

— Probably not. Not with the current tools. And which team is even in a position to work on that?

Surprisingly, across the world, there are almost no competent teams working on ambitious plant engineering from first principles. Even the startups supposedly doing “cutting-edge” plant biotechnology almost always create yet another version of “overexpress genes A-G”. And most effort — shaped by regulations — goes into modifying existing plant genomes, one small edit at a time.

Several factors limit accessibility of deep plant re-engineering to biologists, most importantly:

1. difficulty of transforming and regenerating most plants, especially non-model cultivars,

2. lack of predictable building blocks to program plant traits,

3. immature “hardware” technology: molecular tools to build, host and update large DNA programs in plants.

Fortunately, plant transformation and regeneration (=1) has seen a lot of progress in recent years: a variety of novel tissue-culture-free (as well as tissue-culture-based) approaches have been developed [1, 2], with many studies already expanding way beyond model plants [3, 4, 5, 6]. We are certain that in the next 5-10 years, hundreds of academic groups will be trying out diverse approaches, largely removing this bottleneck without the extra help.

In contrast, predictable building blocks for programming plant traits (=2) remain scarce. Over the last decade, great work has been done standardising molecular infrastructure and creating collections of lowest-level DNA parts (promoters, genes, terminators) for different species of dicots [7, 8, 9, 10, 11] and monocots [12, 13]. However, very little work has been done at the higher level of genetic circuits and metabolic pathways.

Configuring pathways into openly accessible and easy-to-use building blocks lowers the technical “activation energy” barrier — with a non-linear, catalytic effect on their adoption. An illustration of this is the reporter RUBY which encodes biosynthesis of a red pigment: after being configured into the easy-to-use form of a single transcription unit in 2020, it is on the way to become one of the most popular reporters in plant biology [14, 15].

Let’s review a hypothetical program (on the left) and the building blocks needed to execute it (on the right). This program enables a farmer to deliver a critical weather forecast using a drone-sprayed chemical signal, helping plants survive environmental stress and maintain crop yield. To reduce costs for the farmer, the plants themselves propagate the information from the spray site throughout the field

Some of the building blocks required for such programmable traits have not been developed, despite the availability of components; others have only been shown to work as a proof of concept, but not as robust plug-and-play tools. An assessment of block compatibility and joint performance within higher-level programs has not even been attempted. There is no validated library of low-level genetic abstractions one can use to build a DNA program in plants.

Plant “hardware manipulation” technologies (=3) are at a similar place: we do not have solutions to routinely build and manipulate plant synthetic chromosomes, let alone do so at a cost compatible with iterative prototyping of large DNA programs. (This problem is the focus of Syntato’s ongoing ARIA-funded work — to create a technology to inexpensively build plant synthetic chromosomes.)

Due to GM regulations and the cost of obtaining regulatory approvals [16], there is little economic drive for enabling more complex plant programming. In the absence of such a drive, the current lack of tools and low-level genetic abstractions is unlikely to change soon: we are stuck without having the tools, despite the technology components already in place. A well-funded academic lab may attempt to work on this, but as the effort does not necessarily yield scientific novelty, but instead requires large-scale multi-year iterative refinement of genetic designs, it will be misaligned with the academic incentive structure. A startup cannot afford to do this general-purpose work, as it is too broad, expensive, and far from the market. A large seed company is unlikely to approve such spending as it’s not directly aligned with the goals of their crop development efforts. Without a focused effort for large synthesis of available technology to make good tools (“molecular hardware” & “DNA software libraries”), the activation barrier will stay high enough to prevent crop design from becoming accessible to a broad range of bioengineers.

By focusing on building tools, we aim to help move the field from the current primitive plant engineering towards the design of novel complex, adaptive traits — and eventually, just like we design computer chips today, we will design and manufacture genomes of new plants.

Syntato is the lead contractor on an ARIA-funded project to create plant synthetic chromosomes technology. It is a 3-year £4.1M+VAT contract, the budget of which is shared with collaborators and subcontractors. The contract with ARIA allowed us to buy equipment, build a small team, launch lab operations, and create a frontier screening platform for plant engineering work – all in under 9 months. This initial investment into the robotic infrastructure and technology created a basis to amplify the value of future grants and contracts: an opportunity to follow a BBN-style organisational path.

The platform we developed enables high-throughput experimentation in fully intact plant cells, with the highest reproducibility we’ve seen among plant biology transgenesis platforms. We offer two types of contracts:

• Standard screening of customer’s genetic designs on our pipeline (cheaper, simple paperwork, no custom assay development, no IP ownership).

• Tailored co-development of custom plant-based assays (more expensive, IP may be shared).

Syntato’s platform is universal and can be adapted to any plant species. It can be used for large-scale herbicide screenings, testing of AI-designed protein binders, directed evolution of proteins in plant cells, genetic circuit prototyping, and optimization of natural product biosynthesis. These services are particularly valuable for organisations without a strong plant engineering team or high-throughput plant testing capabilities. A use-case example would be a player in the horticulture industry interested in engineering a biosynthetic pathway to produce a pigment in an ornamental plant. Or a newly established startup that chooses to derisk their proof-of-concept on our platform instead of launching a fully functional lab. Or a seed company that outsources their protein engineering work instead of building that expertise in-house.

Essentially, if an organisation has a trait design project, we are excited to work with them. What makes the contract worthwhile for us is the customer's willingness to fund the upstream R&D necessary to make the desired outcome possible — allowing us to build tools and methods that get us closer to our technical vision. In that context, contracts that help move the whole field forward, such as those from philanthropic or government funders, are especially interesting.

In Eric’s BBN Fund post, he wrote how the long-run organisational goals of BBNs will vary. One of the goals he mentioned was the possibility of using a BBNs base of contracts and grants to build up the equivalent of an academic department. This resonates with Syntato’s vision: we aim to use our contracts and grants to eventually grow into a larger research organisation, an engineering bureau that designs and creates plants. Doing this well would not just mean building up the technical equivalent of an academic department, but an entire research institute.

What would it take to reach that stage? We estimate a required volume of contracts and grants in the range of $3-7 million per year for about 10 years. We would love to work with a philanthropic funder willing to accelerate this work — please reach out to [email protected] if you have a lead. At the same time, we also feel that the customer-focused BBN path — the one which keeps us closely tied to industry’s real challenges — is the best path for Synato.

Thanks for reading:)

Karen’s LinkedIn, Twitter, and email — [email protected] .

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Please let me know if you found this structure of today’s BBN write-up useful. The structure I personally tend to use is a bit more detailed, but in the same spirit. It goes something like: technical vision, contracts and grants, initial steps, distinct advantages, recruitment strategy, growth strategy, key winners, midterm assessment, and asks. We presented a simplified structure today, but I’m happy to adjust/expand based on reader feedback!

Karen is a part of RenPhil’s Frontier Research Contractors (FRC) pilot with ARIA in the UK, which helps BBN founders in the ARIA ecosystem better pursue their goals. You can read a bit more about this program in the opening post in this series. And if you’d like to learn more about the program, just reach out to me on Twitter or via email — [email protected].

A year ago, I wrote a post saying I’d be taking a half-step back from this Substack in 2025. I adore writing FreakTakes, but I needed more time to throw myself into what I felt was the most important problem I could be working on: building more BBNs.

Since then, some version of the following paragraph has graced the top of an astonishing number of operational documents I’ve written.

A close reading of ARPA’s early history yields a key lesson: exceptional projects were usually the result of exceptional contractors. Now-famous ARPA success stories like the ARPAnet and early autonomous vehicles depended on a common shape of R&D organization — one structured, incentivized, and staffed differently than typical academic labs or VC-funded startups. I call this shape of organization a BBN-model org — named after ARPAnet contractor Bolt, Beranek & Newman (BBN). If R&D funders plan to chase more early ARPA-style outcomes, we should build more BBNs to do it.

Like FROs, BBNs pursue ambitious technical goals, ill-suited to the incentives of VC, that are too engineering-heavy or multidisciplinary for academia. Unlike FROs, BBNs primarily fund this work with a mix of contracts and applied grants, rather than a $10 to $100 million upfront fundraise.

This past year, I’ve embraced the role of a ‘field strategist’ for the BBN ecosystem.1 In this period — Stage 1 of the modern BBN experiment — I sought to verify that there was both demand for BBNs from ARPA-like funders and a supply of top researchers eager to found BBNs. Thanks to the UK’s Advanced Research + Invention Agency (ARIA), both have now been resoundingly verified. To provide just one data point in support: according to ARIA’s most recent fiscal year data, a small set of scrappy BBNs won more in ARIA funding during the year than every lab at the University of Cambridge combined. Stage 1 of the modern BBN experiment is now complete.

It is now time for Stage 2 of the experiment: building a “Convergent Research for BBNs.” The BBN Fund’s objective will be simple: seed a modern ecosystem of BBNs and work to maximize their overall technical ambition.2 If successful, we will forge a new pathway for today’s best applied, ambitious researchers to pursue ambitious R&D agendas — as Convergent Research has done with FROs. The team’s functions will fall into two basic buckets.

• Capital Deployment. Using the capital we raise for the BBN Fund, we will deploy funds to drive the creation or growth of the most promising BBNs — using a mix of financial instruments including revenue-sharing agreements, revolving loans, and undirected R&D grants.

• Field Building. Using the time of BBN Fund staff and affiliates, we will cultivate BBN founders, source new BBN customers and funders, and develop the scaffolding needed to grow the BBN ecosystem.

I will be founding the BBN Fund at Renaissance Philanthropy with Janelle Tam, formerly Convergent Research’s Head of Programs. She also built and ran Y Combinator’s Series A program, where she advised hundreds of early-stage startups on fundraising and growth. We are aiming to raise an initial philanthropic fund of $10 million to fuel this mission, and are looking for our first funders. Beyond the baseline costs of staff and field building work, for each additional $1 million, we believe we can help found or scale another ~5 BBNs. If we succeed, the J.C.R. Lickliders of our time will be able to build their life’s work within a BBN, rather than shoehorning their best ideas into academic-paper- or VC-shaped boxes.

In today’s post, I will paint a picture of our grand ambitions for the BBN ecosystem, and how the BBN Fund can get us there. The first section summarizes the BBN model for those who have not read prior FreakTakes pieces on the topic. The second section provides an overview of Stage 1 of the BBN experiment — ARIA’s embrace of BBNs and its partnership with Renaissance Philanthropy to provide grants and support to fledgling BBNs.3 I will end by presenting our vision for the BBN Fund.4

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Today’s piece marks the start of a series. Each Friday for the next two months, I will turn the Substack over to someone in the growing BBN community, usually a founder, to write a piece on a new BBN-related topic. This will include:

• Janelle Tam, my cofounder, on the potential to unlock large pools of capital for BBNs by demonstrating their potential as an investable asset class.

• Karen Sarkisyan, a BBN founder in the UK, on his new applied crop genetics BBN.

• Henry Lee, CEO of Cultivarium — the non-model organism FRO — on the prospect of transitioning his FRO into a BBN.

• Tom Milton, CEO of Amodo Design — a BBN with the desire to fold toolmakers back into the early stage science process — on the rate at which BBNs can grow, and what drove his org’s ability to increase its largest contract by an order of magnitude four quarters in a row.5

• And more! Stay tuned.

If you are (or know people who are) interested in becoming a BBN funder, founder, or customer, reach out to us at [email protected] and [email protected].

Simply put, BBNs pursue ambitious North Star technical visions with a mix of contracts and grants. The shape of technical ambition will vary. It can range from an FRO-like technical goal, such as developing methods to cheaply map mammalian brains, to building a center of excellence in an area of R&D in which academia struggles, such as bespoke biotech instrumentation or chip building.6 Some BBNs may even pursue the speculative work necessary to help establish new subfields of research, e.g. computational law. What unites BBN founders will not be the shape of their technical visions, but their capacity to fund them by solving real problems for paying customers en route to these visions.

At the original BBN, J.C.R. Licklider leveraged the model masterfully to pursue his technical vision: building a future of interactive computing. It was at Bolt, Beranek & Newman that Licklider took his first step away from his MIT professorship and towards building this future. Similarly, CMU’s 1980s autonomous vehicle teams leveraged the BBN model to tackle their own FRO-like problem, succeeding in building autonomous vehicles where DARPA’s prime contractors could not and academic computer science would not.

In pursuit of Licklider’s vision, a series of technical problems related to both UX and real-time computing needed to be knocked down. The BBN computing group did just that in the 1960s by utilizing a set of cleverly assembled contracts and grants from groups like the NIH, NASA, ARPA, consulting projects with industry, and other contract partners. The group did this while building a research culture at the firm that earned the admiration of MIT professors and affiliates, many of whom described the original BBN using phrases like “the third great university of Cambridge,” “the Cognac of the research business,” and the true “middle ground between academia and the commercial world.” Licklider and others left positions like MIT professorships to join the firm. They felt it was a far more resonant research environment for their applied, ambitious research than MIT.

BBN’s computing team was able to strike this balance between ambition and contracting because they only worked with customers whose needs were exceptionally aligned with their technical vision. One example of an aligned contract was the firm’s contract to build an NIH-funded hospital time-sharing system. This contract, which was key in funding BBN’s real-time computing progress, was ostensibly a contract to build an administrative computing system for Massachusetts General Hospital. While BBN did not necessarily care that much about building computers for doctors, the team embraced the work because it funded years of real-time computing R&D en route to its technical ambition — R&D that was then too speculative for industry and ill-suited to MIT.

Like FROs, BBNs will tend to have ambitious goals that are too engineering-heavy, multidisciplinary, or applied for academia. And they will tend to focus on markets ill-suited to VC — either (a) because these are not billion-dollar markets, or (b) because the financial impact of the work could be massive, but will not be realized within the ~10-year life of a VC fund. The latter scenario was likely the case for both the ARPAnet and autonomous vehicles.

Like FRO founders, common BBN founder archetypes will include applied-minded postdocs, research-minded engineers, defected professors, and former deep tech founders who crave the longer R&D timelines and research flexibility that the BBN model allows. BBNs won’t build and hire as fast as FROs on day one, as they will usually have much less capital. In choosing the BBN pathway, founders trade the practicality of an upfront ~$10 million FRO fundraise for a different kind of practicality: finding a set of customers and applied funders whose needs are aligned with their technical vision. Like startups, if they are able to tap into a sustainable base of funding, they can steadily expand in pursuit of their technical vision, learning as they grow. Many inspired technical visions will not have a corresponding customer base, of course. But wherever you have an ambitious researcher with vision and an aligned set of customers/funders, you might have a BBN.

Early ARPA’s best BBNs shared three distinct traits, rarely found together in R&D labs. Each:

1. Was novelty-seeking or technically ambitious, with a strong preference for projects that pushed the technological frontier forward substantially.

2. Built useful technology for paying customers. This entailed professional contract management and a willingness to focus on difficult systems engineering tasks. Many BBNs were relatively indifferent to market size, so long as they could find adequate contracts and grants to pursue their North Star technical vision

3. Used more flexible team structures than academia. When compared to academia, they more effectively hired, organized, and incentivized researchers, engineers, and other experts to collaborate on applied projects in a common-sense fashion.

With a set of aligned contracts and grants, BBN founders can often begin working towards their technical goals with only a few hundred thousand dollars — some BBNs can even be bootstrapped. This path comes with a level of ongoing financial risk not present in FROs — at least not FROs that have successfully fundraised. But fundraising for FROs is a bottleneck that few presently pass. The BBN model empowers talented, practical individuals to get in the game using a scrappier model. We believe that by lowering the barrier to entry, in five years, dozens of BBN teams could be founded each year. That means dozens of founders or technical leads setting out on the path towards their ambitious technical vision in a research environment that is both resonant with their goals and cost-offsetting.

Stated plainly: the goal of The BBN Fund is to enable the work of the next J.C.R. Licklider. And based on the exceptional results of BBNs with ARIA so far, we believe this is a realistic goal.

(For a thorough piece synthesizing the BBN model, see A Scrappy Complement to FROs: Building More BBNs.)

Stage 1 of the BBN experiment sought to verify the demand for BBNs from modern customers and the supply of talented potential founders. The UK’s Advanced Research + Invention Agency (ARIA), led by fellow metascience nerd Ilan Gur, was singularly important in running this stage of the BBN experiment. But Ilan and ARIA were not specifically trying to run this experiment. They were just trying to find some way, as Ilan might frame the goal, “to find the right people, in the right institutional environments, with the right incentives” to drive ARIA’s technical agenda forward.7

ARIA’s flexible procurement rules made this possible. Its Programme Directors (PDs) are empowered to make large bets — even multi-million dollar bets — on orgs that are young or brand-new, run by individuals ARIA feels are the best on Earth to get a job done. How flexible were these policies, exactly? As Ilan proudly shared when I interviewed him for Asimov Press, when individuals like postdocs fill out ARIA grants, ARIA has taken the unusual step of enabling them to check a box that allows them to accept the grant as new, independent research orgs. They can staff themselves with multidisciplinary researchers and engineers, negotiate contracts without involving university admins, tackle projects academia does not incentivize, pay people market rates, etc. In essence, they can spin up BBNs.

Less than two years into funding R&D projects, ARIA-funded BBNs are performing exceptionally. To contextualize how exceptionally, I’ve put together the following table using the agency’s recent fiscal year data. The table seeks to answer the question, “If the few BBNs in the ARIA ecosystem were a university, how would the ARIA funding they win compare to other UK universities?” The answer: BBNs won more ARIA contracts than every university in the UK except one, outperforming even the University of Cambridge.

Amodo Design — whose CEO will write a guest post in this series — was, on its own, neck-and-neck with the University of Oxford. And excluding Amodo, the rest of the combined set of BBNs still won more contracts than top universities like the University of Edinburgh. And more BBNs are coming. Syntato, an ARIA-funded BBN with a technical ambition to build an ARM chip design for plants — whose proposal you will read in Week 3 of this series — was not even included in this data, for example. While any specific ranking in that table should be taken with a grain of salt — contract award data is lumpier than reality — the fact that BBNs immediately find themselves in the mix with the UK’s historical research giants is a major early data point indicating that BBNs might have as big a role to play in this century as they did in the last one.

The question is no longer, “Is there any demand for BBNs from modern funders?” Stage 1 of the modern BBN Experiment has been completed. Ilan Gur and the team at ARIA — in their search to have their problems solved by aligned R&D groups — have seen to that.

Momentum is building. ARIA has even set aside a yearly pot of £400k for a program with Renaissance Philanthropy to help found and scale BBNs aligned with ARIA’s goals. Several individuals from ARPA-like agencies in other governments have also begun reaching out to discuss similar programs to serve their own needs. Most importantly, the BBN founders themselves — a boundless source of ideas — are constantly finding ways to reach new types of customers — from hedge funds to academic labs — that can preserve their technical ambition and pay the bills. The time is ripe to undertake Stage 2 of the BBN experiment: the BBN Fund.

The BBN Fund will act as the vehicle to orchestrate this experiment in “applied metascience.”8 Our focus will be to help build more and more ambitious BBNs. In operating this experimental, nonprofit fund, we plan to deploy funds in ways that answer questions relevant to BBN-curious funders — both nonprofit and for-profit. How technically ambitious can BBNs become with a modest amount of undirected R&D grants? Under what circumstances can investments in BBNs not only push the frontier of scientific progress, but also beat index fund returns on a risk-adjusted basis?

We plan to answer questions like this with economy and speed. The capital we raise will be used to seed BBNs, and we will learn, by doing, how to found and manage BBNs in the modern era.9 The two levers we will rely on to do this work will be the time of BBN Fund staff and capital from the BBN Fund itself. The rest of this section will summarize our approach to using both.

An exciting characteristic of BBN founders is their ability to balance practicality and grand ambition. From day one, BBN founders design their org with practical realities, such as customer sales cycles, in mind. But even with practical planning from the founder and a set of aligned customers, several financial problems hamper the creation and growth of BBNs. The BBN Fund will leverage three financial instruments to overcome these problems. Each instrument, in its own way, will enable us to leverage the practicality of the BBN model to stretch R&D funding dollars abnormally far.10

But before discussing these instruments, let’s talk about the problems. The first is temporary cash flow problems. Even if a new BBN wins some contract, it may have cash flow problems for several months (at least), as contract expenses are often front-loaded. The second problem is startup capital needs. Many BBNs will require a modest injection of funds — the equivalent of a seed round — to do the technical and operational work necessary to become self-sustaining organizations.11 The third problem is ongoing research funding needs. For many BBNs, the flexibility provided by winning something like ~20% of their budget as undirected, NSF-style R&D funds — for investigations, core technology development, etc. — can make the org ~twice as technically ambitious.

The three financial instruments the BBN Fund will experimentally deploy to help founders overcome these problems are low-or-no-interest loans, revenue-sharing investments, and undirected R&D grants. While loans and revenue-sharing agreements are peculiar instruments in the world of philanthropy, we believe they might enable us to stretch a fixed pot of capital exceptionally far. Below, I describe the promise of each instrument in more detail.

A Revolving Loan Fund. Low-or-no-interest loans with no collateral requirements will enable far more founders to spin up BBNs. Several existing BBNs had to bootstrap, including Amodo Design. That is admirable, but presents a barrier to entry for many prospective BBN founders, limiting the scalability of the model. Judiciously applied, low-interest loans can provide instant access to capital to cover the salaries, equipment, leases, and other expenses BBN founders have to pay upfront to win and execute contracts. These instruments will have the same limited downside risk for founders as VC funding. Given out as loans instead of grants, the revolving loan fund offers the opportunity to spend dollars multiple times. Any repayment above 0% will drive a higher ROI than the status quo. That being said, with prudent decision-making, most of the capital loaned out can be paid back into the fund — many BBNs asking for loans will have a high payback rate, as they will already have warm contract leads. In the case where ~80% of funds loaned out are revolved back into the fund within 18 months, we can create ~five times as many BBNs using a fixed pot of capital, compared to deploying the funds as undirected R&D grants.12

Revenue-Sharing Agreements. The BBN Fund will experiment with BBNs as an investable asset class. We will identify scenarios and strategies in which investing in BBNs can be attractive to certain private investors and philanthropic funders. In my discussions with BBN founders, revenue-sharing agreements have been the most common category of instrument proposed to facilitate these investments. And I have yet to meet a BBN founder who, for a fair price, is categorically against partnering with a for-profit investor, sharing the upside with them. To lower the barrier to engaging in these revenue-sharing agreements, the BBN Fund will design a fit-for-purpose, YC-SAFE-style instrument to facilitate these agreements. Ideally, this instrument would allow the following deal terms to be customized to match any given situation:

• Percent of Revenue Shared

• Revenue Base (i.e., is revenue calculated from overall revenue, a certain line of business, etc.)

• Sunset Clauses (e.g., no payments after 8 years, once 5x the original investment is paid back, no sunset, etc.)

• Grace Period (e.g., no repayment expected for first 18 months)

Even in cases where the returns on these agreements do not outperform index funds on a risk-adjusted basis, they can still prove to be an exceptionally cost-effective tool for philanthropists and impact investors.

Undirected R&D Grants. Many BBNs require modest levels of undirected R&D funds to effectively pursue their technical visions. These funds do not need to be the majority of their budget, but can often be in the range of ~20%. This minority of funds, and the flexibility they provide, can be vital in differentiating the work of certain BBNs from a typical, unambitious contract research organization. While it will only be a minority of the fund, when it is cost-effective, the BBN Fund should deploy undirected R&D grants to BBNs. One exciting characteristic of even this ‘free money’ is its relative cost-effectiveness when compared to undirected R&D grants to labs that don’t take on contracts. E.g. If a BBN can fund ~80% a course of R&D via contracts that other groups would have asked philanthropists or the NSF to wholly fund via undirected grants, the extra ~20% in undirected grants needed to top off their budget should come as an obvious bargain.

Through our efforts, we hope to make BBNs a legible vehicle for investment for both philanthropic funders and for-profit investors. While we are a nonprofit fund that will reinvest any revenue-sharing profits into BBNs, we hope as many types of BBNs as possible prove to be profitable. That is because one of our goals is to stimulate a BBN investment market into existence. Profitable investments from our portfolio will serve as a proof-of-concept to investors that certain types of BBNs are a profitable asset class. And in those areas in which funding BBNs carries a negative profit margin, and seek philanthropic backing, we will obsess over cost-effectiveness all the same. We aspire to become one of the most cost-effective forms of scientific philanthropy on Earth — the group that, while ruthlessly selecting for ambition, still found a way to spend each dollar multiple times.

If the goal of the BBN Fund’s capital deployment is to create and amplify the ambition of BBNs via financial investments, BBN “Central Office” will do so through the direct effort of a small group. This group — Janelle and I for now — will be the hub of BBN field building, dedicated to addressing the non-financial bottlenecks that currently limit the growth of the BBN ecosystem.

In many ways, Central Office will be looking to scale and expand on my own field strategy efforts over the past year. At Renaissance Philanthropy, wearing my BBN field strategist hat, I spent significant time on the following tasks:

• Sourcing new BBN funders and customers, helping them understand which of their problems are BBN-shaped, and how I might source a BBN founder to solve those problems.

• Cultivating new BBN founders, finding exciting founders with aligned ambitions, and helping them understand that a BBN might be an exciting vehicle in which to pursue their life’s work.

• Field Building. In addition to my ARIA work, I’ve been able to use FreakTakes as a vehicle to excite readers — some of whom inhabit important positions in the R&D ecosystem — about the possibilities of the BBN model. This has driven an increasingly impressive flow of founders and funders my way, as well as helped bring existing BBNs together under one banner, with one common vocabulary.

Through my and Renaissance Philanthropy’s field strategy efforts, we’ve already had a hand in unlocking over $10 million in funds for BBNs.13 Our goal is for every dollar spent on BBN Central Office to unlock 10X as many funds from other customers and R&D funders through similar efforts. So far, we’re on track.

Scaling the above tasks will include accomplishing key goals like ensuring that, for every BBN founder, every aligned ARPA-style PM or philanthropic grant officer on Earth is one warm introduction away, and ideally is briefed on what BBNs can do for them before calls for funding go out.14 We’re excited to find ways to tackle these problems. In addition, we will concern ourselves with a variety of other bottlenecks that repeatedly come up when talking to founders. Each could unlock an order-of-magnitude more capital for BBNs than they cost to address. A sample of these tasks includes:

• Sourcing aligned, practical mentors. While BBNs are meant to be far more ambitious than typical contract research organizations (CROs), some have CRO-like revenue streams. That’s great, if it can fund their technical vision! But there’s a problem: many PhDs from places like MIT have never met anyone who runs a CRO. As top researchers now look to build BBNs in this area, we need to find mentors to show them the ropes.

• Sourcing aligned contractors. Similarly, Central Office will establish relationships with individuals needed to make commercial sales. Some BBNs have floated the idea of selling data from instruments they build to commodity traders, to offset costs. If this proves to be a common need, we will find data brokers who can explore and facilitate these contracts.

• Unlocking NSF-style grant funding. NSF and NIH grants can prove to be reliable sources of R&D funding for BBNs. But accessing NSF-style funds is not yet as straightforward as it should be. Certain BBNs claim they cannot win NSF grants at the rate they did at universities. One advisor believes this might be a problem of know-how, and is eager to work with a few BBNs to try to increase their win rate in applying for these grants.15

One way or another, it is our job to solve these problems for BBNs.

The biggest upside of funding BBNs is not even their cost-effectiveness, but their potential to unlock categorically different research than existing institutions can provide. The original BBN delivered the first four nodes of the ARPAnet on time and under budget, for approximately $8 million today. This was a contract that companies like IBM ‘no-bid’ because they thought it was impossible. Early CMU’s autonomous vehicle teams accomplished technical breakthroughs that DARPA’s prior prime contractor failed at with 10x their budget.

The 20th Century’s great BBNs demonstrated that in R&D, with a differentiated model, small teams can cast a large shadow. Charting their own research path with a mix of contracts and grants, BBNs have a measure of freedom to direct their research agendas towards differentiated outcomes — with no worry for what’s unpopular in academia or ill-suited to venture incentives.

For years, I’ve obsessively studied historically-great R&D operations of many shapes and sizes. Based on my experiences with BBN founders and customers this year, I believe the BBN model can be a vehicle for the right founders, in the right situations, to fund a range of these orgs into existence. This includes:

• Bootstrapping new academic departments. The BBN model can enable founders to bootstrap (what are essentially) new academic departments into existence, outside the university.16 17 18

• Funding industrial R&D labs into existence. BBNs are, in many ways, self-sustaining frontier labs. Under the right circumstances, the BBN model may prove to be a way to make a dent in the problem of funding more groups like early DeepMind into existence — without the pressure to appease investors and exit to a high bidder.19

• An alternative approach to deep tech VC. With a bit of taste, we might demonstrate that investors can reliably pick modestly profitable firms when making revenue-sharing investments into BBNs. If savvy investors can reliably identify 3x outcomes, with 20x outcomes from time-to-time, a world of possibilities opens up in which BBNs can not just beat index fund returns from a financial perspective, but fund wildly ambitious R&D projects along the way.20 (Janelle will dedicate next week’s post to painting a picture of this possibility.)

The BBN Fund is being created to run this experiment and find out what’s possible. If the model proves capable of creating any of these types of orgs, in bulk, that would be a big deal. As of now, we believe the model might be capable of doing all of the above, at least some of the time.

While the Midwesterner in me blushes to speak of grand ambitions, in philanthropy, I believe ambition is a moral duty. We will not aim small. Our ambitions, over the long run, should be measured in metrics like Turing Awards per dollar, number of BBNs doing the most interesting work in their area, or the number of entirely new industries created. If the NSF and NIH annually spend $50 billion to return ~4 Nobel prizes, we will strive to be 100x as efficient.21

These are grand goals, but they are not crazy goals. The $8 million spent on the ARPAnet seems to be less than 1% of that year’s NSF budget. CMU’s initial NavLab vehicle breakthroughs were done for ~1/1000th of that year’s NSF budget. These were exceptionally cost-effective uses of R&D dollars — maybe some of the best in the history of US public R&D — and they were done by BBNs.22 Let’s build more of them.

If you are an interested funder, customer, or aspiring BBN founder, please reach out — [email protected] and [email protected], or dm me on Twitter.

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As always, thank you to my faithful editors, Toren Fronsdal and Tristan Wagner. They have edited this and every other FreakTakes piece. Every piece would be longer and less intelligent without them.

And thanks to Adam Marblestone,23 Tom Kalil, Janelle Tam, and Tom Milton for each —in different ways — changing how I conceptualize what might be possible with the BBN model.

If you’d like to read more about R&D groups from history that worked in BBN-like ways, or with lessons to teach modern BBNs, check out the following FreakTakes pieces. Each paints a picture of how orgs fueled by contracts can raise the ambition of the R&D ecosystem.

• “The Third University of Cambridge”: BBN and the Development of the ARPAnet

• An Interview with Chuck Thorpe on CMU: Operating an autonomous vehicle research powerhouse

• Edwin Cohn and the Harvard Blood Factory, for Asimov Press

• A Progress Studies History of Early MIT— Part 2: An Industrial Research Powerhouse

• ILLIAC IV and the Connection Machine24

• MOSIS: The 1980s DARPA ‘Silicon Broker’

• Managing Lockheed’s Skunk Works

• How did places like Bell Labs know how to ask the right questions?25

Through my role at Renaissance Philanthropy, finding ways to grow the BBN ecosystem with philanthropies, ARPAs, etc.

This will be in the form of what Renaissance Philanthropy calls thesis-driven philanthropic funds. You can read more about this structure here.

This work — a part of RenPhil's (broad) ARIA Activation Partnership and its (BBN-specific) Frontier Research Contractor Launchpad program is specifically focused on assisting BBNs aligned with ARIA's technical agenda. This agenda is represented by areas of technical opportunity in which ARIA is particularly excited to run programs, which it calls "Opportunity Spaces.

The quote that accompanies the image was written by BBN cofounder Leo Beranek, describing Lick’s obsessive use of BBN’s first computer, a $30,000 Royal-McBee. I was happy to find an image is of Lick demonstrating this trademark vigor along with the smiling nature, which Beranek also noted. In the image, Lick is using the PDP-1 he had the firm buy two years after they had bought their first machine, the Royal-Mcbee.

And, as Tom noted in our WhatsApp correspondence, “doubling year on year since then.” For reference, Amodo was a bootstrapped org that built some of its first contracts out of a founder’s living room.

To provide a historical example demonstrating that, with an aligned set of customers, BBNs can pursue FRO-like technical goals, take the early CMU autonomous vehicle teams. In another world, these teams could have operated with an FRO structure. But in practice, in the 1980s, they operated using a model more similar to BBN’s contract research model. And this group made so much progress that they were able to drive a vehicle cross country 98.5% autonomously in 1995, using a neural net-powered steering system and other advances funded by their DARPA contracts.

Wherever possible, Renaissance Philanthropy and I, in our field strategy efforts, attempted to connect potential founders who might serve ARIA's needs to ARIA, worked with fledgling founders on questions of BBN strategy, and worked to understand Programme Director’s problems that might be solved by BBNs. More details on this field strategy work come in a following section.

A Renaissance Philanthropy fund, to be clear.

With success, in the next five years, we hope to create a world where dozens of BBN teams are created each year. And we hope many of these BBNs do not rely on undirected philanthropic grants to survive, but, rather, a more sustainable mix of contracts and grants.

If curious, a wonderful complement to this section is Alex Obadia’s — former tech entrepreneur and current ARIA PD — piece: Wanted: New Instruments to Fund BBNs

As with the loans, this can include capital to cover the salaries, equipment, leases, and other expenses BBN founders have to pay upfront to win and execute contracts. In general, both of these instruments will be used to fund a variety of the work needed to survive a key early period which must be overcome before an org can be self-sustaining. E.g. to cover a brief period in which academic users are eagerly using your tools, but they need to write you into their next grant to pay you adequately.

Another question we’d seek to answer using the BBN Fund: If a philanthropy hands out $1 million in low-interest loans to help found BBNs with warm customer leads in X area, how much money is paid back on average?

The above field strategy tasks represent some of the work Renaissance Philanthropy and I have undertaken with ARIA in this work.

For reference, when I meet with ARIA programme directors (PDs), the value add/pitch goes something like, “The goal of my work is to make it so that, as you design your program, you don’t have to take the contractor ecosystem as fixed.” This makes a difference. People are often happy to tell you about the specific problems they are working through. And if there’s alignment, I try to help. These are not secrets, but it’s also not as if every philanthropic program officer has their own Substack to explain their current thinking on some matter. You have to go out of your way to understand their problems, just as entrepreneurs do with customers. As a side note, it’s been a real pleasure to have these discussions with the ARIA PDs — some of the more fun interactions of my professional career.

We hope to build towards a future where BBNs can attain the majority of their basic research budget from NSF-style entities. Given recent NSF initiatives ranging from TechLabs to the TIP Directorate, we are optimistic.

The Topos Institute, an applied category theory BBN covered in an earlier piece on this Substack, has built up the kind of culture in which a Turing Award winner regularly comes for visits, often just for fun, as researchers often do to academic departments

Pre-World War II MIT is an obvious example that great research departments can be largely industry-funded. The FreakTakes Progress Studies History of MIT Series, — particularly Part 2: An Industrial Research Powerhouse — covers this at some length. MIT’s Technology Plan — a program in which MIT made a coordinated push to provide contract R&D and other services to industry — is the clearest example of this. As William Walker, one of the founders of modern chemical engineering practice, framed the work, “There could be no more legitimate way for a great scientific school to seek support than by being paid for the service it can render in supplying special knowledge where it is needed.” At one point, during this period, MIT’s applied chemistry department became 6/7ths industry-funded.

BBNs can also be a great vehicle to spin up what I call “Compton Model Research Departments.” This model of academic research department — named after WWII-era MIT President Karl Compton — in which one or a few great individuals control an academic department and its resources, steering it towards a specialized set of goals. This is in contrast to the status quo in which each professor gets their own small budget, their own small teams, and departments attempt to hire somewhat broadly to try and cover the breadth of what is going on in a field at a given time. Compton — a Princeton physicist and longtime GE Research contractor — pushed for the model because, as he saw it, the effective management and coordination of these departments towards shared goals enabled a situation in which “the output has greatly exceeded the individual capacities of the research workers.”

Many in the progress studies community wonder how we can build “modern-day versions of Bell Labs.” While certain aspects of Bell Labs model are not straightforward or not worth the effort to replicate, the general point is a phenomenal one. And, in many cases, relying on the VC ecosystem to fund these orgs into existence doesn’t make a lot of sense, incentive-wise. In many instances, what’s optimal is a field strategist with great technical vision to run the group and control its governance, as J.C.R. Licklider did at BBN. It’s possible that these groups might wish to raise VC money after some breakthrough — or facilitate a VC-funded spinout — but working through many iterations of R&D before entering the VC ecosystem can be preferable, even in VC markets.

This is an approach to deep tech venture creation similar to that of early MIT — whose golden era of deep tech VC coincided with its golden era of contract R&D. In addition to the FreakTakes Progress Studies History of MIT, I’ve covered early MIT’s approach to deep to deep tech venture creation, specifically, in An Alternative Approach to Deep Tech VC.

Their goals are more complicated than this, as ours will be. I was simply taking this moment to put into the reader’s mind the overall budgets of the NSF and NIH and the single biggest marker of success in academic research. As you’d expect, for somebody who routinely writes 10,000-word Substack posts on niche R&D history topics, I have a three-page internal document summarizing just a few of these goals. So do not be concerned, we will be measuring ourselves using much more than Nobels per dollar — which are given out on a delay, only exist in certain areas, etc. etc.

In the introduction to a later piece, I will explore some of the (accidents of) history behind how the BBN model waned in popularity in the later 20th Century. Firstly, as technical universities (e.g. midcentury MIT) abandoned R&D contracting to focus on winning NIH and NSF grants, it eliminated a key pipeline for prospective BBN founders — PhDs who, in working on these contracts, developed the know-how and relationships needed to build contract businesses. Secondly, the best BBN customers of the prior generation (e.g. DARPA, NIH, NASA) often grew more bureaucratic, particularly in their procurement processes, making them more difficult for young, hungry founders to work with. Thirdly, this coincided with the rise of venture capital, in which VC-funded startups replaced BBNs as the main path for talented graduate students to start firms.

Things are now different. New customers have sprung up that are as flexible as early ARPA — like ARIA, AI labs, hedge funds, and billion-dollar philanthropies run by exited tech founders. For one reason or another, disenchantment by young researchers with what they see as the academic rat race, disappointing academic problem selection, etc. have created a generation of applied-minded graduate students often seeking alternative paths to do research. Additionally, after some trial-and-error, it’s become clear that venture is often a poor way to fund the risky, long-term research core to ambitious science. It’s a tool for lucrative, special cases. Through these shifts — along with the late 20th Century shift away from ambitious, corporate industrial R&D labs — we have gotten to observe those areas in which the American R&D ecosystem has become hollowed out in response to the shifts described. Our goal with the BBN Fund is to learn from this history and capitalize on the current moment. We intend to build a dynamic ecosystem of BBNs fueled by ambitious (often young) researchers looking to adopt this old new model of research org; they will use it to fuel their own ambitions.

It was, in fact, a conversation with Adam that allowed me to steel my nerve and feel comfortable throwing myself into this work. I felt nobody would know better than him what I should do. His encouragement went a long way.

It’s possible that the ill-fated Thinking Machines Corporation may have had a less tumultuous existence as a BBN, compared to the complications it experienced as a venture-funded firm, in which many of its first-rate research staff — and possibly its CEO — cared more for technical ambition than building for the most lucrative business use cases.

This piece contains lessons on how BBNs might find aligned customers, choose research questions, etc.

The Spotify version of today’s episode can be found at this link.

Today’s piece is an interview with Jano Costard, Head of Challenges at SPRIN-D — which some call “a German ARPA.” The interview will give readers (or listeners) a peek inside the SPRIN-D experiment, from the perspective of one of its leaders. It should be a particularly interesting listen for those who aspire to be PMs of ARPA-style organizations, procedural nerds in the R&D community, and those who simply want to understand what is going on at ARPA-like organizations around the world.

I’d love to highlight two things before diving into the interview. First, one piece of the interview that fascinated me was Jano’s obsessions with SPRIN-D’s administrative processes, such as their speed. “Fourteen days time-to-money,” the amount of time from a research group submitting a bid to receiving a decision and the funds hitting their account, is exceptionally impressive on its own. But it’s also the sort of improvement which can have fascinating knock-on effects when applied to large chunks of an R&D ecosystem. Speed and operational excellence on that level not only save existing researchers substantial amounts of time, but can also bring entirely new sets of people and organizations to the table, ones that might have little willingness to tolerate typical government R&D funders.

Second, regarding today’s interview, I had to put it together in a bit of a rush. I had a death in the family followed by a work trip. Things happen, of course. I’d love to have taken the time to read more and interview additional SPRIN-D employees to better understand SPRIN-D’s processes, scientific perspectives, contractor ecosystem, etc. But the folks at SPRIN-D and I were very excited to put this interview out in public quickly. The reason why is simple: SPRIN-D is looking for aspiring program managers, and there’s an application deadline coming up in two weeks. (More details below)

So, while I’d love to do my usual and spend 100 more hours understanding SPRIN-D before publishing a piece on them, this is meant to be a practical Substack. And, for today, what’s practical is sharing this (relatively in-depth) interview on the unfolding SPRIN-D experiment with the ambitious researchers and engineers who read FreakTakes — many of whom might want to become program managers, at some point.

I really hope that any of you who are interested will throw your hat in the ring not just for this program, but other opportunities like it. Having the curiosity to both spend a large portion of your life becoming world-class in a technical area and read something like FreakTakes in your spare time, simply out of curiosity, makes many of you precisely the kind of people places like SPRIN-D want. If you’re interested, please know there’s a good chance they’ll be interested in you.

Enjoy:)

The cleaned up transcript — courtesy of my wonderful colleague Ales and my lovely girlfriend Katherine, which I did not have any time to work on myself for the reasons listed above — is below.

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Big if True Science Accelerator. Applications for a program which trains and mentors bright researchers and engineers on how to design and pitch effective ARPA/SPRIN-D-style programs close August 1. To learn more about the program, called BiTS, check out this link. My colleagues at RenPhil are helping run it. I will not be involved in running it, but I had a fun time writing some of the reading materials for the participants.

Eric: Welcome to another edition of The FreakTakes Podcast. Thanks for being here. Today we have Jano Costard, who is the head of challenges at SPRIN-D, which some people call “a German DARPA.” But, of course, as with ARIA from the last FreakTakes podcast, it's a metascience experiment all its own. And [I’m] excited to talk to Jano about it today.

So, Jano, do you want to give us a bit about you first, before I jump into the questions?

Jano: Yeah, absolutely. As you mentioned, I'm head of challenges at SPRIN-D. That means I came to SPRIN-D, which is the German version of an ARPA, to build a challenge program. I'm an economist by training, and I have been dealing with competition and innovation. I now have the chance to actually run and design innovation competitions, which is really exciting for me.

Eric: And how did you fall into this, exactly? Because some people would think of an economist as a natural pick for this, and some people might find it odd. So how did it come to be?

Jano: So in my previous post, I was with a consulting body of the German government, with the Commission of Experts for Research and Innovation.
And our job was to consult the German government with regard to policies of science and technology. And that body was one of the leading actors that demanded that something like SPRIN-D should be created. And then SPRIN-D was created! And I thought it would be really interesting to move from just consulting the government to actually go to implementation, and try to do something differently.

Eric: And when there was this push internally in Germany for something like SPRIN-D, what was the exact reason? Oftentimes, there's some founding story or quick tagline that informs…the very complicated…it's the simplified form of a very complicated goal of the org. So, like, you'll hear DARPA people repeat and repeat, "to create and prevent strategic surprise." Like ARIA people talk about standards of living and growth in the UK. Is there something analogous for SPRIN-D?

Jano: So I think, in general, different cultures are motivated in different ways. One might say that the German way of being motivated is being pessimistic. [laughter] And so one of the perspectives was that Germany used to be a place where new technologies came into being that ultimately created whole industries. If you think of the automotive industries, chemistry, pharmaceuticals, and things like that. But that was like 140 years ago.

I think Germany is still living pretty well off of that. But I think we're moving in the direction where we need new technologies, where we see that those industries might not sustain our wealth or sense of living for the decades to come.

We need to find new ways of creating new industries, to create the surroundings for a wealthy society, for a healthy and happy society — as well as create solutions that tackle the grand challenges of our time. I think these two things really belong together.

But looking at how we did in that respect over the last decades, I think our record is not that stellar. If you think about the breakthroughs that Germany created over the last decades, I think not many come to mind.

Eric: And I guess for the listeners, me being history-brained and 140 years not being very long to me, I still look at Germany with a lot of awe. For example, when you read a ton of the — we'll call them the MIT founding fathers, like the early MIT professors and presidents — they all would continuously take tours of German universities, and just copiously take notes. Because in the very beginning of that institution, all they could think is, “If we could create something as good as a German technical institute, what a success that would be!”

And you see similar things with a lot of the early industrial R&D labs, like them really admiring the German chemical industry and things like that. And so it's interesting that you talk about German pessimism, because you have an optimistic mission that they've laid out for you.

Has the pessimism found its way into the structures of how this [SPRIN-D] has been laid out in some way? Because different cultures go at these things differently, and there are strengths, there are weaknesses, et cetera. But I'm just curious, in that, [with] a German ARPA, even if you wanted to create something identical, culture will seep in.
So I'm very curious about how the culture has seeped in as you've been there, from planning to where you are today.

Jano: So the motto of Rafael Laguna, our Director, is "Pessimism is a waste of time." And that really is the SPRIN-D spirit.

In that sense, it somewhat breaks with that German pessimism. He is a software entrepreneur, not by training but by practice. He’s been an investor for many years, spent loads of time in the US, and therefore went back and forth between Germany and the US quite a bit. He has been educated and shaped in his experiences quite a bit by both continents.

And I think that really shows with SPRIN-D, with how SPRIN-D operates, what our values are. We really are techno-optimists in the sense of believing and truly believing that technology can be a great source for finding solutions for the grand challenges of our time, and that that can be created from within Germany and Europe. It's not that we have a pessimism that says, "Okay, we didn't perform as well as we wanted to. And unfortunately, that's how it goes, downhill from here." I think the foundation is there to create outstanding things out of Europe.

Eric: And what is SPRIN-D in a nutshell? And also talk a little bit about how people should think about SPRIN-D programs and why you've gravitated towards the structures that you have.

Jano: Yeah, SPRIN-D's mission is to create what we in Germany call Sprunginnovation, or breakthrough innovation. And that is something that we define as innovation that really changes the lives of people for the better, in contrast to incremental innovation that really takes what we know and can do, and makes it a little bit better. And with that, we share many things with other ARPA-style institutions in the sense of, you know, we are willing and able to take a lot of technological risk in trying to achieve these great breakthroughs, even if that means that most of our projects will fail.

That is something that does not come naturally to public institutions, at least in a setting that has not been exposed that much to ARPA or DARPA-style institutions such as the US. But I think it was really, really important when SPRIN-D was set up that we gained that freedom from typical ways of doing science and innovation funding.

Eric: And can you talk about one or two failed projects that you're very happy with? SPRIN-D has done 10 challenges, something like that so far?

Jano: Yeah, I think we are at 12 challenges right now. So one of the challenges that failed in the sense of… so every challenge, typically, runs in multiple stages. It might very much differ how long those stages are, how many there are. It's completely different between different fields. One that we ran was on tissue engineering. What we wanted to see was complex tissues, artificially generated, that not only reach a certain size, but also a certain functionality at a level that has not been demonstrated before.

And we stopped that challenge after stage one, because we didn't see the progress that made us believe that in that setting we would reach the goal of that challenge. In that sense, it was not successful in reaching those milestones. But what we still saw in all those teams is that they really made good use of that money and our support in the sense of… They built stronger teams. They hired new people that eventually made them progress further, acquired other funding, and raised funding rounds. And so even though we didn't meet the goal of that challenge, I think it was really beneficial for the teams themselves and would give us another shot as a founder at SPRIN-D or other funders to really take that further again.

Eric: And could you talk a little bit about the types of teams that were participants in that challenge?

Jano: So it's typically, as so often in our challenges, it's a mixture between small startups and academic institutions. So universities, non-university research centers, and the team I was referring to, that was a small startup. They raised a seed round before. And with our money — that was really an amount of money that made us one of their primary funders in that startup. That's also something where we therefore typically have a very close relationship with the teams. Not only because we are really focusing on observing the progress that they have, but also because it is a close relationship once you are really the primary funder, and ultimately are essential for that startup either living or not living.

Eric: And I'll ask you more about financial instruments in a few rounds of questions, but for now, I'd love to stick on structure. In one of the pieces of writing you sent over to me, you talked about being agnostic — the challenge model being agnostic towards which approach is used to pursue some challenge, some goal. But some might say prizes are even more agnostic. Can we chat about that a bit? Because, practically, I think I know why you all chose the challenge model, but I'd love to give you first crack at addressing that argument.

Jano: Yeah. Just to differentiate between challenges and prizes — or sometimes it's more confusingly called “challenge prizes,” and it's completely unclear what the difference might be.

So in our case, a SPRIN-D challenge is a setup where we formulate a highly ambitious goal where we think that if it could be achieved, it would be highly impactful — a breakthrough where most people would agree that would really be a very beneficial breakthrough. But equally, many people would agree that it's currently probably impossible to actually achieve that.

And then we fund a range of different teams, with different approaches, in parallel to try to achieve that goal. Do the R&D. Do everything that's around that to achieve that goal. Whereas with a prize competition, you equally formulate a very ambitious goal, but then set up a prize, in the sense that whoever proves first that he or she reached that milestone, they will receive that cash prize, or whatever that prize might be.

The difference being that, for one, we fund teams along the way for the R&D work, and everything else that they need to have to sustain that work towards that goal. This is typically not the case in prize competitions.

Another difference being that prize competitions are typically more open to a wider set of contestants, in the sense of potentially not limiting entry at all. So, anyone could submit any kind of solution and proof of that solution to claim that prize. Whereas we start with a call, and we might select an initial set of teams. And then we continue to work with that set of teams.

And I think there is certainly something that speaks to both approaches. Looking at our settings, the reason why we fund R&D along the way primarily is because there is a lack of funders that would support that R&D until they reach that milestone for the prize. So I think we need to…

Eric: I remember one time I was talking to somebody who did — it was an economist who did market shaping. It was in the life sciences, where a lot of your contestants would be the Mercks, the Pfizers of the world, with a big war chest.

And when I raised the issue of — I think a lot about small R&D groups that have difficulty getting funded but are very resonant, like BBNs, which I've written a lot about a lot on the Substack. And the economist kind of just looked at me weird and said, “Why would you want to condition on or incentivize something that's not the outcome of note?” Which, in a purely theoretical way, I guess is true, if you have a big enough war chest or you make the outcome prize big enough where other people will come. But practically, there are a lot of times where I think of valleys of death as just areas where there are not a lot of incentives or funding. So, to me, it makes a lot more practical sense if you're dealing with small, resource-constrained R&D groups to take this [challenge] approach.

Jano: And I'm very sympathetic to that prize idea in general. But if you talk about the Mercks and Pfizers of this world, you would need to have a substantial prize to make them move. And even then, I have been working on market shaping in drug development, on infectious disease antiviral therapeutics, in particular. And that is an area where I think market shaping, for example, in the form of advanced market commitments, is really something that is necessary to make sure that new technologies actually reach the market.

But in the conversations I had with pharma, for example, it became very clear that even if there is a cash prize, however large it might be, that would not be sufficient. Because what they are looking for are natural markets, or natural markets that might occur due to those instruments. Because any kind of cash prize would need to compete with the natural markets they are in: in oncology, in other blockbusters that give them way more return on investment. And competing against that with a one-time payment will be very hard.

Eric: Yeah. And can you talk about how you think about follow-on funders when you're coming up with challenges? Like SPRIN-D can't fund these things forever. Usually, there's some end state, whether it's a VC-funded company or whatever. So how do you give those instructions, or what line do you toe internally when it comes to follow-on funders?

Jano: So I think the most important thing is that we think about that, right from the start. And within SPRIN-D, you know, we don't have a transfer department. We don't do our R&D funding, and then the project, and then push it to the next building, or something, to other folks who work on transfer.

It goes so far that our challenges, and the goals of our challenges, are shaped by how we might think about commercialization. One example of that is we have been active in a space that is called carbon dioxide removal. That is about removing CO2 from the atmosphere.

That is something that — from all these climate institutions — we know we need to do if we want to reach our climate goals. From an economist's perspective, it's a difficult thing to do, because removing CO2 has large societal value, but pretty much no private value.

So if you are a company like Climeworks, working in Iceland and pumping CO2 underground that mineralizes there in the basalt, there is no private value for a company to do that. They don't have a product that they could sell. So, currently, that is very much sustained by voluntary contributions by industry, but that is not a condition that is ultimately scalable at a global level.

So what we tried to do, and funded in that challenge, were approaches that remove CO2 from the atmosphere, and do that in close relation to existing product markets. For example — including that carbon in long-lasting and valuable products. Think about carbon-negative concrete, for example. Concrete has an established product market. It is one of the largest flows of man-made material on earth. And if you were able to develop carbon-negative concrete, it would not only avoid emissions, but due to that negative effect, it would essentially remove carbon from the atmosphere, in principle, the more you build.

And that is where we start. And then it is something where we very closely look at how we select teams — whether they might have the capability to eventually go to market. And that's also something that we focus a lot on when we mentor and support teams.

Eric: And can you talk a bit about SPRIN-D — is it ‘challenge leads’ in your department, and do they go by' program managers or something elsewhere? Or how do you all name them? The equivalent of DARPA program managers.

Jano: So, at SPRIN-D, we call them innovation managers.

Eric: Innovation managers, yes.

Jano: Yeah. And we have a group of innovation managers in all different kinds of fields. And so they have a high degree of freedom on which problems to tackle, and how to tackle them. So it's not that we have specific innovation managers that only do challenges, or only do whatever kind of instrument. Because I think what's really worthwhile, with a set of financial instruments that is as varied as ours, is that we think good and hard about how we best combine them, to get the most out of the potential that we have there for developing new technologies.

Eric: And where have you historically found your innovation managers? And can you talk a bit about how you're looking for innovation managers currently? Can you tell us about that?

Jano: It's a rare type of people, let's say that! Because what we are looking for is a very interesting combination of deep technological and scientific knowledge and skill, but that combined with what I sometimes call multi-sector experience. So we wouldn't want to have someone that only spent his time in his academic lab all his life. We would want to look for someone who has additional experience from industry, from founding their own companies, from being an investor, because these are all aspects that matter when we eventually want to commercialize those technologies.

And as I mentioned, it's not something — that commercialization aspect — something that we do at the very end of our funding process. But we want to bake it in right from the start, do it in a very integrated manner. We need to have people who not only have that experience, but also have the perspective of what it means to cross those different sectors, which is not easy.

Eric: And are there fields where you've had an easier — they're hard to find — are there fields where you've had an easier time finding innovation managers? Just because some circumstances of the field have made it that more of these are likely? For example, when I talk with some venture capitalists who do deep tech VC, some tend to have a pretty good time with electrical engineers. There's something about the field and the circumstances and the history where they go, "Oh yeah, we actually find quite a lot of founders in this area." And they have more difficulties in some other areas. So I'd love to hear your experiences on that.

Jano: I think there was no field where it was easy. Obviously, one thing that clearly plays a role is competition out there, for certain talents. In the field of AI, getting world-class people is not easy. And I think that's also something that's very important that we think about as a public institution. How do we attract the best talent, both for our employees, but also out there in the teams that we fund?

For some reason — I actually cannot say for sure why it happens — a large share of our innovation managers have a background in physics. Even though they might have done something else in the meantime. That just happens to be the case. I don't know whether it is random or whether there's a larger trend behind that.

Eric: And how should potential innovation managers conceptualize the opportunity? Oftentimes, they might have a vague sense of what's “ARPA-shaped,” what's “ARIA-shaped,” what's “SPRIN-D-shaped,” etc. But you've probably done a lot of thinking about this, and can make it quite clear for them! If you wanted to tee it up for a thousand potential innovation managers, how they should think about the opportunity, and why this is a chance even more exciting than founding a lot of companies to gain a foothold and do scalable innovation management.

Jano: Yeah, I think the kind of theoretical aspect of that is that, in pretty much all that we do, just having one shot might be so risky that if you really want to change the world, you better have multiple shots. And you probably have a hard time having multiple shots if you just found a startup. Then you do your one shot.

If you come to SPRIN-D, or any other ARPA, and run programs, you have multiple shots on goal, and therefore also increase your probability of actually having success.

Plus, I think specifically for SPRIN-D, if I look at concrete examples, one being one of our innovation managers, Patrick Rose. He used to be Chief Scientist at the Office of Naval Research Global, based in London. And he came to SPRIN-D because we have a toolset and an environment in Europe that was unique for what he wanted to achieve. For example, he came to run one challenge, that is still running, which is called Circular Biomanufacturing. And that started at a point where we observed that industrial biotech is currently good at producing niche products with large margins.
And it does that built on feedstocks such as highly refined sugars. The societal impact of that is somewhat slim. What these processes cannot do is do bulk products — so, mass markets that typically have very low margins — and do that based on other feedstocks.

What he came up with was that challenge, where the goal is to produce these kinds of low-margin, high-volume products based on real waste streams. And I'm talking about the real messy things, like supermarket waste, food waste, whatever it is, whatever goes in the garbage bin, take it out and produce new materials from that.

And so that is something where — the way we work at SPRIN-D — was something where he thought it would give really the best chance to actually achieve those goals. As well as having the know-how on bioprocess design, as well as fermentation infrastructure in Europe, was a combination that he had been searching for and found here.

And that is something where I think also, for future innovation managers or challenge leads, that is their chance to use the framework that we have at SPRIN-D and leverage the know-how, the technical know-how that we have across Europe. And I think that is something where we can really push the boundary of what's possible.
And for me, it's really about matching talent and opportunities in the best way possible.

Eric: And if I can give you a slate of three or four questions, and you could probably answer them all at once, but I'll put them all on your plate. So what, are there any particular types of innovation managers you're currently looking for? Are any technical areas in versus out? Is anything too technically risky? And also, how many are you looking to hire or onboard? What are the timelines in which you do that? And I'll stop there.

Jano: Yeah. So, very good questions, because I think we might be operating a bit differently from other ARPA's in that regard, that it’s relevant for those questions.
The one thing is, in terms of topics and fields that we cover, I would say we are somewhat opportunistic in the sense of… we don't have siloed budgets. We don't have, I don't know, a rule where we spend a third on biotech, a third on AI, and a third on computers or whatever it is. We try to run the best programs we can, even if that would mean that for a year or two we spend a very large fraction on life sciences, and then you know, that weight shifts over time. It really comes down to finding the right people for the right program at the right time. We're willing to back and implement whatever we think has the most impact.

Also in that regard, it's hard to put a number on how many people we would want to hire. We just want to see the best people and then we'll onboard them, because it's also the case that the size of our programs varies quite a bit. So, just for challenges, the smallest challenge might be just a million euro in total. Whereas other challenges are larger, tens of millions.

And you could think of challenges, and we're actually having some in the pipeline, where it will be more about the hundreds of millions for a challenge. Obviously, that has an impact on how many of those you could run and how many people you onboard for that.

That is the pragmatism that we have. We are trying to find the best people, and then we'll find a way to work with them.

Eric: And let's say five really interesting innovation managers saw this podcast and reached out and they had programs in the 10 to 30 million range. Is that the kind of thing where if they were all interesting, you have the capacity to bring on that many, or is that kind of reaching the breaking point where you'd go, we'd hire some and we'd maintain relationships with others?

Jano: That's the fun part about being in a public institution currently. Especially, we are not in the same situation in the US and Germany, but we are certainly in a situation where we don't have an approved federal budget in Germany as of now. That means we actually don't know what our budget is this year and we don't know what it's going to be next year. Economists will know the debate about the debt brake in Germany, that it's limited how much debt Germany can make. That is a huge discussion currently.

But what we are currently creating is an exception from that debt brake for investments. So there is something where there's going to be investments in the hundreds of billions. And part of that will go into research and development and innovation. But we are currently at a point where we don't know what our budget exactly will be next year. So therefore, I think it is a great time and a great opportunity to think about the question of whether SPRIN-D might be a good place to really push those programs because we might really be able to implement them and run them.

Eric: And in terms of growing the supply of interesting innovation managers with actionable ideas, do you want to talk about the partnership you have spun up with my colleagues at Renaissance Philanthropy? Because of course, the program they've spun up comes from this line of thinking — that of the number of really interesting technical minds who could do the technical side of being an ARPA-style PM, a lot of them don't learn to think appropriately in terms of servicing a follow-on funder, thinking in demand from day one of an ambitious program. Because programs at SPRIN-D, or ARIA, or ARPA are applied and ambitious and it has been hard to marry the two. So can you chat a little bit about that and what you're doing with BITS?

Jano: Absolutely. BITS, standing for Big If True Science — it's a program by Renaissance Philanthropy, a US-based philanthropy.

And the purpose of that BITS program, that accelerator program, is to take on people who have a clear vision for future breakthroughs, about capabilities that really might change the lives of the people, and how technology could support us getting there. And the mentoring program supports them in designing R&D programs that enable firms, any kind of institution, to really take those steps to achieve that goal.

As we mentioned, it's a rare type of person who has the skillset that is necessary to lead such a program. Therefore, it's absolutely necessary to look for those people globally. And that's what we do. We look for the best people globally. It is really also a special skill and knowledge set, designing those programs, because that is not something you would typically do at a startup or in an academic institution or as a policymaker or in any other kind of position. Plus, the number of such programs that are run are — I would say — very limited globally. Therefore, there are not a ton of people who have very good experience and insights into what works, what does not work. So therefore, combining our experience at SPRIN-D with the experience and expertise of Renaissance Philanthropy, which can really draw strongly on experience from experienced DARPA program managers, for example, or other people who used to work at different ARPAs — that is something that I think is super critical in designing the best programs. Because in my perspective, designing the best programs matters strongly for our actual success probability in the end.

Eric: Yeah, and we're going to talk a lot about know-how as we get into the nerdy metascience bits of this conversation, including in a couple of minutes.
But essentially, if somebody technically thinks they have what it takes to be something like a SPRIN-D innovation manager, it's a kind of program meant to help you get up to speed on the know-how. And if you're interested, you can email Joshua, JP, or Ales at [email protected].

But now to get into some very nerdy bits that you know a lot about and SPRIN-D has been very impressive on. Do you want to talk about the range of financial instruments you've come up with, and why they're a big deal in your eyes, in unlocking a lot of the upside that an organization like SPRIN-D is meant to go after?

And I have it printed out here too, in case we miss anything.

Jano: Absolutely. In contrast to many other ARPA-style institutions, we have a very broad mandate in the sense of we might be working on life sciences, drug development, biotech, computing — in principle, everything that has civilian or dual-use cases.

That means we could potentially tap into every kind of field of technology or application. And I think there are fundamental differences between different fields of technology that we need to take into account when we want to design effective programs. Plus, we might start funding different kinds of institutions at different TRL levels, at different levels of maturity of that technology.

Taking all that into account, for me, means there is not a one-size-fits-all, "we just hand you over cash" kind of way of doing things. And therefore, I think we are pretty happy that we got a whole range of different financial instruments. So, different kinds of grants, different kinds of R&D contracts, equity investments, convertible loans, and I think that is something where we can really tailor our support to the needs of that particular team. An academic institution will have completely different needs than a startup raising a Series A or a Series B, and if we think they need our support, then we must be able to provide them the best support possible.

Eric: Could I run a couple of hypotheticals by you and you walk me through if they're possible at SPRIN-D and what the R&D piping to make it happen would look like? The first one is somewhat niche — but I think important at an organization like SPRIN-D.

Let's say there's a university with a postdoc and they’re in some niche area that's exceptionally important to an area you'd like to run a challenge in. And this postdoc, or them and a group of their colleagues, are just the best people. And what would make the most sense is for them and a team of two or three to leave and set up shop as a brand new company or nonprofit, and it would be inordinately important to the challenge being viable.

But what it might take is a half-million euro grant to this org that, while these people are proven commodities as postdocs — this is a day-zero org. It has no track record, it's never filed taxes. Can you do that? How would you do that?

Jano: So we can absolutely do that. That's not an issue. It's something that we see very often — that when teams apply with us, they might actually still be a postdoc, at whatever kind of institution, at an academic institution.

But they tell us, if you fund us, we are going to create a spin-out, and then that funding should go to the spin-out. So we can fund those kinds of no-track-record startups, institutions, people that have very little track record or no track record in that kind of sense. So technically, that's no issue at all.

What we would really focus on is a question of whether these people are actually willing to do it. And really, the part of being willing is way more important than the question of whether they're currently capable — in the sense of, when you have people who have spent their whole career so far in academia, they will not have a clue on how to build a company. Which is okay. That is to be expected.

What we really would need to see are signs that they're coachable, that they have potential and can develop. And that is certainly also something that is very crucial for us when we think about follow-on funding.

Eric: And so to turn two knobs a bit and see where this becomes difficult.
Is there an amount of seed funds where you'd say, “Oh, that's going to be difficult. We can do that, but doing it with 500,000 euros versus X are just qualitatively different things.” Like, where does it stop?

Jano: That's a very difficult question. I think it, again, it's also very case-dependent. So if we talk about nuclear fusion, there's probably nothing very cheap to be gained there. You would probably also be looking at very technical people that you would need to support there.

And so it would be a combination of the technical profile of the people and the amount of money that you would probably not be willing to spend on — for example, a software project where you have a purely academic person and hand them millions as funding — that's probably something that we'd never do.

And so I think, as an economist, as an academic, it depends.

Eric: Yeah. And is there, do they have to be in Germany? Do they have to be in the EU? Is the UK okay? Is the US okay?

Jano: We have a broad understanding of Europe and we fund all across Europe. So the UK, from my perspective, and from a personal perspective, I consider the UK still a part of Europe.

We fund in non-EU member states as well, including the UK, Switzerland, Norway, countries like that. Also including Israel. So for a technical definition of that geography — it's EU member states, states of the European Free Trade Area, plus the UK and Israel. That's our typical geography in which we fund.

Occasionally, we go beyond that, especially also when we run challenges where we have what we call a non-funding track. So sometimes, we would open up the challenge for teams that we don't fund but might still want to integrate.

So for example, we had a challenge on, let's say, digital infrastructure, digital proofs of identity. And we had Samsung and Google Android as participants, but we didn't fund them. So that was one of the cases where we extended our geography through that non-funding track.

Eric: And when you're selecting, let's say, postdocs who want to start a new organization, you brought up "do they want to leave?" as one big question.
And practically, of course, that's one of the big questions. But when VCs are funding firms, they also ask, “Are you a founder? Do you have the stuff?” And at VC firms, they're remarkably unstructured a lot of the time. Like even at the biggest firms, they make a judgment call on that. A lot of people maybe think a judgment call is the best way to do that, but they also don't think a random person making a judgment call is synonymous with how government typically does things.

So can you give us a sense of how this goes internally at SPRIN-D?

Jano: Yeah. So if I'm focusing in on challenges — what I really think is super important about challenges is that stage-based approach, that gives us the opportunity to take on a range of different teams where we might be uncertain about some of their qualities.

Obviously, there needs to be something in them that makes us really want to give them a chance. But we try to be very explicit on what it is that we give them a chance on. And that gives us a chance to — either we see someone who has an amazing track record in actually executing stuff, multiple companies founded, brought things to market, but might be somewhat lacking in the tech aspect. Or the other way around — maybe technically super strong, but no track record whatsoever in terms of actually implementing things that can go to market.

If we see the potential in those blind spots, we give them the chance, take them on, fund them for a bit, and give them the chance to demonstrate that they can do what we're unsure about their qualities. And then, at the end of the stage, during that stage, we might be able to pull the plug and say, okay, either they didn't perform as well as we hoped for, and then that journey is over, or they really outperformed.

And there might really be very positive surprises, because every once in a while we get those people who come out of academia, who have never been in any kind of other role than being a postdoc, PI, whatever. And suddenly, it's an awakening and they really have a very strong development up to the point where they say, “If I would have known earlier what I know today, I would've founded five companies before.”

Giving those chances through the challenge-based approach where we can just say, okay, we admit 10 and we see how it goes. It's something that I very much like — and I think it's very important if we want to be able to solve problems that apparently are so hard that no one else has solved them.

Eric: And in terms of the makeup of the performer pool, there's one — like a piece of what some people would think of as operational or contracting minutiae that to me is at the core of the SPRIN-D model. Can you talk a bit about output monitoring, not input monitoring? This is particularly fascinating to me, because in reading a bunch of the early DARPA oral histories, right around the 1980s, you start to get gripes from a lot of the long-time contractors, places like BBN, where essentially they have long complaints about the nature of procurement they’re changing and “there being more accountability for the pennies than accountability for the results.”

So this is precisely what you're going after with output monitoring. Can you talk a bit about how it works, why you thought it was essential, and how it changes the makeup of your contractor ecosystem?

Jano: Yeah. So I think if you look at how science and innovation funding in Germany would classically look like, especially from public institutions — you’d have some kind of call, you write a proposal. In that proposal, you write what you're going to do and what you're going to spend your money on, that’s typically in a very detailed manner. You would typically be required to follow that plan, and it might be a three- or five-year plan, a very detailed one.

And — I'm not kidding — you have to tell the funder how much your train tickets will cost three, four years from now. [laughter] That’s just weird. But that also means that, for one, you're very much focused on what your plan initially has been.

In theory, you might be able to change it, but then you have to reapply and ask for permission. It also means that you have to prove all your expenses were in accordance with that plan you originally submitted. And that is just super cumbersome. Everyone who has filed travel expenses or had that kind of grant funding knows how cumbersome that is.

And typically it involves people who would be better off working on innovation. I think it's really mind-boggling that we keep the best people of our generation — and I think that is the people that we want to address — busy with sorting receipts and doing those expense claims.

It’s highly inefficient for a project that is run. And I think it has a very strong selection outcome in the sense of, you know, certain types of people might not expose themselves to those mechanisms.

For example, I was very curious to see how our challenges would run in the field of AI, because people there just have very strong outside options. They really don't need funding from any kind of public institutions, unless it's really in the hundreds of millions and might be something different. But at our typical ticket size, it's nothing that they would require. I was talking to one of the teams that we fund in one of our challenges called Composite Learning. They are Y Combinator alumni. They are venture-funded. They don't need us.

And what they told me is that they specifically made a change in their policy to not apply for public funds because they saw that we act differently. That we don't waste the time of their most precious resource, which is the people. But we really support them in a way that makes sense. I think what's interesting is that we have to square that with our responsibility towards taxpayers. We cannot just get rid of input monitoring and then say, “Here's the money, have fun,” because that would be neither legal nor responsible.

So what we do is focus on what you mentioned, which is output monitoring. We are very close to the teams, so we have regular update calls, meetings, we visit them. And what we care about is new data being generated, demonstrators being built — something that is tangible, where we can see actual progress. Most of that will mean learning things that don't work, which is a great way to actually know what progress can eventually look like.

And by replacing that input monitoring with output monitoring, we actually make that monitoring mechanism worthwhile for the teams as well. Because if we are in a stage where we have people at SPRIN-D that are so good that the teams expect us to actually be helpful in overcoming their hurdles, they will voluntarily share with us what went wrong, because we might help them to actually solve those things.

That gives us a transparency that I think is way, way higher than if we would just look at how much you spent on a computer or on copy paper or whatever. I think it's way more productive.

Eric: And if sometimes groups will enter a challenge competition and they pick one technical path to pursue, and the path itself proves to be unfruitful — often for no fault of the team's own, other than a not-perfect initial hypothesis. But that's why you get in this game, is to test the hypothesis, not to be right off the bat.

If they proved to be an exceptional R&D team who just got unlucky with the hypothesis they explored, do you all have a way to keep them around and throw them at further stages of the challenge, if you thought that they proved themselves to be the right team to do that, and they just need to build off another team's learnings or something?

Jano: Obviously, it somewhat depends on the extent of that. But to give you a concrete example, in one of our challenges in drug development for antiviral therapeutics, one of the teams was expecting to use a specific type of delivery mechanism. It's one of the essential building blocks of the final product that will have huge consequences on whether it might be effective, what side effects might look like, things like that.
It's a really crucial part. And they changed — completely changed — what their delivery mechanism will look like mid-stage. I don't remember whether it was stage one or stage two of that challenge. And the process for that, if you can call it a process, is they call me or the innovation manager running that challenge and say, "Look, that's the data we generated. We think we have a better solution. We need a switch." And then we say, "Of course, you need to. Let's do it."

And it's the other way around [as well]. If, at the end of a stage, they would write, as they do, a written report and they pitch, and if they come to us and say, "You know what, months ago we figured XY is not working. Can we change it?" And then we would ask them, "What did you waste your time on, the last couple of months?" Because once you figure it out, that's the time when you need to change it.

Eric: And the government should care, 'cause that's time you paid for, too.

Jano: Absolutely. Yep. But it's not the typical logic. The typical logic is, we need to be able to make sure that money is spent the way we allowed it to be spent. And that keeps you on your path even if it might be completely bonkers. And that's the rule, not the exception, in our business, because I'm 100% sure that none of the plans of people that apply to us and get into a challenge can follow that path 100%. 'Cause technological uncertainty is just so high that it's impossible that everything will work out the way they planned. If that would be the case, they either lied to me or they weren't ambitious enough.

Eric: Yeah. And this is an approach that sounds common sense, reasonable, to most engineers and researchers. But also the economists love measuring things, and you're at least somewhat sympathetic to this.

You can imagine one variable, like an outcome metric of success here being, "Do you all get an inordinate percentage of people who are talented and are able to win a grant who have never won an EU grant before?" Because there's a certain bit of know-how involved in winning and maintaining an EU grant in the same way that, similar to SBIR grants in the US, where a lot of the researchers will roll their eyes and they'll say winning that is its own skill, and being good at research is just one thing they're meant to do, but it's probably not the primary skill.

So is this something you all track or would be interested in tracking or could care less about because you're busy with other things?

Jano: You mean in terms of it being a positive KPI if we attract people who never got an EU grant, because then they're the right type of people?

Eric: Yeah. Or something of that sort! You might think of a better one right now. I'm sure you will.

Jano: The difficulty there is, it's not a skill issue — for the pure fact that there are consultancies out there who write that application for EU funding for you. In fact, there has just been a study that was published on evaluating EU innovation funding.
One thing they found is that the effect of that innovation funding was very small, if at all positive, at those institutions that they funded. The largest effect that they saw was in consultancies that helped those companies and institutions get those grants in the first place.

And that's pretty much the only significant effect that they found on firm success — those consultancies.

Eric: So is something related to a consultancy popping up or being the major coefficient in your paper, I guess, a metric you would be interested in, then?

[laughter]

Jano: No, it's really bad in the sense of — a large share of funds that were supposed to be for innovation actually end up in consultancies, because it's so complicated to apply to most EU grants that you've got to have a consultancy that does it for you.

And I think that is a very bad state to be in.

Eric: Yeah. And can you talk a little bit about 14-day time-to-money, which is something you're very proud of and should be?

Jano: So 14-day time-to-money refers to how long it takes us in SPRIN-D challenges to go from the deadline of application to having teams selected and having contracting done.

If I zoom out a little bit, going from the ultimate goal we have, which is fostering breakthrough innovation — we care about what are success factors. Some of those success factors are attracting the best teams out there. And then, as we talked about, that might involve working in a way that is attractive to the best performers out there. I think therefore, time-to-money or speed of processes, lean processes, no unnecessary bureaucracy, is really crucial to address the best performers.

I think it is something that far too little attention goes into. It's also something that is not straightforward, in the sense that certain aspects of what is required to go at that speed are formalized. So for example — what are the legal requirements in procurement law or state law or whatever, what do our contracts look like? That is something that we worked out, that we can share, and that in principle every other institution could do.

What I think is the way more tricky bit, is doing it hands-on and focusing on the processes that are necessary to arrive at that goal. And that goes very deep down to what is the operating system of the organization that you are.

To give one example, how do we arrive at those 14 days? For a two-stage selection process that we typically have, it means that within a week we need to be able to analyze all applications that come in. That means I need to have a team, and typically it's a SPRIN-D internal team, part of our technical team at SPRIN-D, who have to be able to analyze all applications within a week. That means, for example, I need to be able to block their capacity so that four or five people, however many I need, are really there full-time, no nine-to-five, and work on those applications.

It also means I need to have in place a jury, because final selection at challenges is done with juries. I need to have that in place beforehand. I need to have those jury members, they need to be informed about what's coming, what the setting is, what we are looking for. You know, it's up until event organization. 'Cause at the end of that second week, we have pitch events where if we have 20 teams on the shortlist, we have an event where 20 teams are pitching over the course of two days.

Then we'll need to have a way of arriving at a funding decision at the end of that second day, at the end of those two weeks. Plus, we are looking at contracting as well. I need to be able to do contracting, in principle, overnight. That means at the end of the selection process, my team in the SPRIN-D office is preparing the contracts, sends them over to our directors, and they really sign those — I don't know, 15 contracts or however many teams we take on — overnight. That means the teams then the next morning have those signed contracts in their inbox.

That is something where, for example, as a counterfactual, I would have a very hierarchical organization with very strict limits. I would not be able to tell my team of analysts, "That's a week that you have to block full-time to do analysis." I would not be able to tell my directors, "You need to be there to sign 15 documents overnight." That very much goes to what I would call the operating system of that institution. It's first the ability and then also the shared perspective that this is something that is important.

Eric: And can you talk about your partnership with NSF and how you imagine transferring know-how on these matters back and forth?

Jano: I think that is something I would call tacit knowledge. Those formalized aspects, what are the legal underpinnings of what we do? What are the contracts? That is something that we frequently share. And anyone who wants to see our contracts can do so. In fact, we publish them all the time. But I think that for pretty much most institutions, it would be impossible to copy the results. Just because you have the legal basis and the contract, you will not be able to run this process in 14 days, or a week, or three weeks.

I think that requires very much hands-on process knowledge, experience, and knowing the limits of your organization. And that is also something where I think very little knowledge is shared, just because it is tacit knowledge. Also because such programs are not run that often. I've been running 12 challenges. I was talking to a DARPA program manager who said he was running a challenge and once he did that, he was trying to find someone at DARPA who used to run a challenge before.

But if running programs is so rare that you have to struggle to find someone who did it before, I think it's a very bad starting point for learning at a very professional level. And that's why, for example, in our partnership with NSF, what that encompasses is that we jointly run a challenge. So it will be run in that way that NSF funds teams in the US, SPRIN-D funds the European teams, but we will very strongly integrate all our operations down to the process level. Because I think that is one of the ways in which we can really share that tacit knowledge, the process knowledge, the know-how, and really understand how the other side ticks, what works for them, what might be applicable in each other's settings, and how we can actually improve on that.

That is also an aspect that I really love about the BITS program, because that is another way of really deeply integrating experiences, sharing experiences down to that level of operations that we otherwise very rarely have.

Eric: And are there any genres of, or sorts of innovation managers who you haven't had at SPRIN-D yet? For whatever reason, you've just failed to get a certain archetype that you would really like to come through that BITS program or hire as innovation managers at some point? I'm imagining, let's say, a potential innovation manager reading your site and saying, "Oh, they've never had anybody who looks like me before. This is not meant for me." I'm trying to give you a chance to flag that you would love X sort of person or Y sort of person.

Jano: You know, I wouldn't want to focus on one particular profile because I'd rather want to put it the other way around. I think there are potentially many people out there who might not know that they might be a great fit.

And they might come in various different shapes and forms. What is, I think, really crucial is that they have a strong commitment to change a field, that they have a strong conviction that what most people think would be impossible or might not even have thought about approaching or trying to deliver, that that is something we would really need to have.

In that sense, I really don't care whether it's in AI, computing, life sciences, or any other field. It is a strong conviction. And what I want to see as a track record is the ability to execute.

Pure theory will bring us, at least in this business, nowhere.

Eric: Yeah. And in selecting people like this, you do not have quite as finite an amount of money as maybe something like DARPA's BTO, which probably gets handed a specific budget, but the funds are somewhat finite.

While the specific number is hard to pin down and you have to make really difficult judgment calls between fields — like computer science and electrical engineering versus quantum biology, or how to generate more life sciences data. How do you go about making those judgments? What does that look like internally at SPRIN-D?

Jano: It's interesting in the way that I've been talking about the pragmatism that we try to have, and that has been supported, in very practical terms, by SPRIN-D's budget increasing from year to year. We are a young institution. And I think so far, for most programs we really wanted to run, we eventually were able to find the funds.

You know, we started out with a budget that came solely from our Federal Ministry for Science and Technology. What happened in the meantime was that, for one, more ministries from the federal German government saw what we're able to do, and that they're not able to do the same R&D funding, and gave us extra amounts of money to be active in certain fields. As well as other governments starting to approach us to run programs, just like the NSF collaboration was born out of the fact that NSF observed that we are able to run programs in a manner that's way different from how NSF would typically run programs.

That is something that we see increasingly — that we have other governments, mostly from across Europe, but also the US, trying to work with us. That also gives us the chance to have a much broader portfolio of programs that we can support.

Therefore, I really have yet to see the case where we say, "Okay, that's a great program, a great innovation manager, but unfortunately we cannot fund it." So far, in pretty much all the cases, we've found the funding. Because hopefully, if you can really talk about the impact that a successful program, a successful challenge in that field would have, and can show some of the track record that we have in the sense of — we really manage to bring people into the game that typically would not be addressed by public funding, and that really giving us an increased probability of success.

That is something that's very convincing to policymakers or funders — to actually go after that combination of topic and method.

Eric: So is there any really germane constraint that you would want innovation managers to know about before reaching out?

Like whether it's — is there a TRL you work in less, or is the answer, "Oh, your program needs to be a bridge to something interesting and it could be lower or higher TRL as long as it's a good bridge." Is there any quick framework that you think should shape their thinking?

Jano: Yeah. A very short answer would be, we do TRL three to seven, and that's it. But that's not doing justice to the complexity that we are in. When we talked about the difference between challenges and prizes — in prizes, you have that big honeypot at the end that might be very tempting, and we don't have that.

But our perspective is that we try to design challenges such that if a team is successful over the course of that challenge, then that should unlock follow-on funding. That might mean different things in different fields. It might mean, in certain fields, that point is coming earlier.

In biotech and AI, for example, there might be cases depending also on one aspect that is market risk, that we have talked about, but I think it's very crucial to differentiate between technological risk and market risk in how you run programs.
We try to think about that kind of handover point before we start a program, knowing that if we learn that other funders would not be able to take over funding in cases where we see great potential, then we would have the means, the instruments, and the budget to further support those programs, those teams. That's also what we did do in the past. For example, you might see teams coming out of challenges but having trouble raising the necessary funds due to excessive market risk. And then we will be able to catalyze funding rounds getting together by being able to fund actually up to 70% of a funding round.

Eric: And I'd love to ask you — take the time and ask you three niche metascience questions that you could have quick answers to. Or you could dive into them if you think they're important! And so the first one would be, in understanding the opportunities and constraints of the innovation manager position, are there scenarios in which they might really want to do something where somebody will stand in their way, or them wanting to do it is not sufficient for SPRIN-D to do it?

Jano: That might be the case. For example, what we have in our group of innovation managers is very strong experience and knowledge from the investment community.
There might be innovation managers out there or at SPRIN-D who have less experience in that regard when it comes to structuring funding rounds, clearing up cap tables, and things like that. So there might be a case where we go into that discussion and decide to make sure that we need to see certain requirements fulfilled before we can do follow-on funding, for example.

And that is something that, due to the experience that we have in our group of innovation managers, we also can very much lean on. Therefore, I'd rather see it as arriving at a better result due to our internal processes, rather than forbidding a program manager to do something.

Eric: And who picks the juries that decide on program applications? Who makes up the juries? Are they looking for unanimous approval from the jury? Or can high-variance reviews on applications be good?

Jano: Juries are one thing that we only do in challenges. And it's typically a combination of the innovation manager and me looking at jury members and selecting them.

What is very important to me in that decision-making process is, for one, that juries are somewhat small, meaning typically around six to eight people. What I tried to do when we set that up was try to combine a broad set of perspectives and experiences, both in tech and commercialization, while avoiding having a large committee.

Because large committees typically tend to agree on the smallest common denominator. And that is exactly not what we want. We want to take those bets — those high-risk bets that might actually be able to really pay off. Therefore, the decision-making process within that jury very much embraces that variance in opinion.

It also very much depends on the field, on how much variance we might have in there. One measure that we take to embrace that variance is to have a system of golden tickets. So, for example, for the initial selection for stage one teams, the average jury member has a golden ticket, and if he or she thinks that team X is really so exciting in one category, “I really want to see them perform, I want to see that thing demonstrated” — then we fund them.

We do not have a majority vote typically for the decision-making where we say, “Okay, the majority is for the team, we take it on, and the majority is against, we don't.” We try to arrive at a consensus within the jury, where we have a shared opinion on the strengths and weaknesses and risks of a certain team, and decide why we want to give that team the chance to perform or why that's not the case.

Because that also gives us a basis to evaluate them against our decision, over the course of that stage.

Eric: And who gets final say, once you incorporate all of that and you say, "Oh, we've generally frozen on an agreed-upon list of strengths and weaknesses," whose job is it to look at that and say, "We're going to do X"?

Jano: It is the jury. The jury will arrive at a consensus, even if it means to agree to disagree on certain aspects of a team. It is my job to moderate these juries to arrive at that consensus. And it's really something where, from afar, it might look somewhat weird.

For example, I was in the US, I was talking to a former office director of DARPA, and I was telling her about our selection procedure. And she was like, "Juries? That's not how it's done," coming from a DARPA background. Then she and several other people joined us for a certain jury session.

And she was in a jury session, saw the discussion, saw how we arrived at that consensus, and saw how we tried to aggregate that information that each and every single jury member brings, and build a consensus about what kind of the best-performing teams might be down the road.

And then came to the conclusion that actually, that is a very nice process.

Eric: And do you have something written down on what you would do in the event of a hung jury?

Jano: Never happened. I don't know. I was like in like — I don't know, like 30 jury sessions — never happened.

Eric: This is fascinating. This is also one of those areas where I can imagine there being cultural differences. Like when I learned how Swiss children vote on what field trips to go on, even from exceptionally young ages. It just wouldn't work in the US. [laughter]

Jano: I don't know. But I mean, we recruit jury members internationally.
It's not just Germans sitting there and agreeing with each other. So we have international juries. European, North American, Asian, it's really diverse. And that is exactly what I mean with tacit knowledge and something that you might only be able to fully grasp with all its pros and cons when you are in the process, because it's highly context-dependent.

And that is something where, from my perspective, we don't have — so far — a very good way of learning. And it's also something where we typically don't have the scale to do proper learning. I was talking about the amount of times that we run challenges, other institutions run challenges.

If you think about the fact that we run or start a new challenge maybe on average three times a year, what is the process where you would think that, on a professional level, you get really really good at things that you only do three times a year? And I think it's something that we do more often than other institutions in the field.

Therefore, I'm really concerned about that. How do we make sure we learn across institutions, especially those aspects of tacit knowledge? Because I think these really matter. All that I said about how the operating system of a funding institution might have a critical influence on success factors such as speed — it's nothing that I think we have a good way of learning from one another.

If I look at other fields where you have a strong dependence on tacit knowledge —think about the culinary world, restaurants. That is something where I would like to compare the way we structure processes to. There is this French term called mise en place that describes the situation where chefs would prep their kitchen and station beforehand, to be able to execute when service time is and execute both with quality and speed. It's a very deliberate process about what you prepare beforehand, what you focus on in the moment, to achieve the highest quality at the highest speed. And that is something that we try to do when we try to achieve those 14 days time-to-money. And it's something that you cannot easily copy.

But in that culinary world, with all that tacit knowledge and hands-on experience, we have systems like apprenticeships, mentorships, where you go through stations, you go from restaurant to restaurant to learn all that tacit knowledge. I think we don't have that to that degree in our world.

That's also part of why I think that collaboration with the BITS program, with NSF, to really go down to that level of operations is really exciting. Because that might be one step of trying to do that learning and then transferring of insights on those elements of tacit knowledge.

Eric: Yeah. And so, I'm going to move us along a little, skip a few questions because we're running out of time, but I have a few I really want to get to with you. So one of them would be — we're going to have a lot of West Coast listeners in the US, and Boston and the UK, but I have a pretty West Coast center of gravity. And they're going to have a lot of curiosity about the German R&D ecosystem in general. And also, you might want innovation managers and things in cohorts coming from American institutions and things like that! So I would love to tee you up with something and I would love to force you to step out of your own skin slightly here.

So — I get the sense that Germans don't really brag enough. For example, I read the Fraunhofer-Gesellschaft annual reports that come out, and as an American, I see a lot in there that makes me jealous. And I think there are things in there that make the UK folks jealous as well. Like, I think Catapults and initiatives like that were overtly Fraunhofer-based.

But what I've brought up Fraunhofer to — and for those who don't know, Fraunhofer is the applied version of the Max Planck Institutes, in very simplified form, in Germany, and they have a whole ecosystem of them. But when I bring up Fraunhofer to Germans, and like really fantastic metrics from annual reports, like they have an 80 or 90% spin-off survival rate after five or 10 years, German folks often bring up to me all the things Fraunhofer's not doing or could be doing better. But if I was going to force you to put on your West Coast of the US hat and brag for a bit, could I hear your most optimistic pitch for the sorts of ARPA-like programs that are possible in Germany, that are difficult elsewhere?

'Cause you have a different set of institutions, often with very different focuses or that exist in places that are the valley of death in the US in certain fields.

Jano: Yeah, if you think about the innovation that led to what now is ASML and EUV lithography — that is a degree of engineering that I think is very hard to find elsewhere. If you think about the fact that you have countless technologies at the highest level of engineering, that you then need to integrate to build something like the lithography machines of ASML, that is super hard. If you think about the precision that is necessary to shoot those lasers at those tin droplets that create the radiation that is used for that lithography process, it was probably a 10-year R&D effort of major companies and institutions involved at an unprecedented level. And I think that is something where you really need those sustained levels of engineering at the highest quality in areas that have never been tried before. I think that is where we really have great institutions in Europe.

Eric: And would you care to, for the listeners who won't know precisely what you want to talk about, can you share 3, 5, 10 — whether it's areas, specific centers of engineering excellence — where you would just want people to know if they're thinking of anything in that area? Like a strong argument that Germany is the best game in town, sometimes the only world-class game in town, to do work in that specific area. Like Americans might off the top of their head think of a company like Zeiss and understand that we don't have things like that, but there's probably all sorts of not-famous firms in areas that you think Germany is world-class at. And people don't force you to brag on a podcast like I am right now, often enough to talk about that.

Jano: I think it's really not about those singular, stellar companies or institutions. I think the real strength of Europe is its very high level of quality across the board. It's the diversity that we have with very strong centers of excellence.

For example, life sciences in Denmark and Copenhagen, engineering in Germany, places like Munich and Berlin, AI also in France as well as the southwest of Germany. And I think the real strength is that all these places are really, from American standards, not that far away from one another.

Plus you have a really strong industrial base that could take on things and really scale. And I think that is a thing that we talk not enough about. As I mentioned, our innovation manager, Patrick Rose, came to Europe to run challenges with SPRIN-D because we have that infrastructure in biotech that really enables us to not only develop, but scale things.

Eric: Okay, fantastic. Thank you for that. I have a bunch of questions that you could probably run through each of them in under a minute that I would love to get you on the record talking about, but none of them is that important. This is the list of things I wanted to catch at the end here.

And so one of them was, when you're putting together challenges, you can understand why people think, "Oh, you don't want to condition not on the outcome or constrain things in that way," but it's often essential for practical reasons. Is coming up with those intermediate benchmarks painful, or is it actually often quite straightforward and forces you to think hard about it?

Can you talk about the trade-offs and the emotions involved in coming up with those?

Jano: It's very difficult. Very hard to do well. And from my perspective, it cannot be done purely based on quantifiable metrics and KPIs, because the technological uncertainty is too high to be able to exactly pinpoint both the exact KPIs that you want to use and the right threshold levels. But it also brings in a factor of appreciating all the work that the teams do in different dimensions. The combination of seeing teams in parallel gives us then the opportunity to not just like a team because we like the people, but really compare that to competitors and really show who is ahead.

Eric: And so I talked with Ilan Gur about the idea of one ARIA program leading to another, and he said, "Oh, I actually really hope we do something like that." Is that a tool that SPRIN-D thinks about, or is that a bit far away? 'Cause you're giving yourself a lot of leeway to not think in translation.

I'd love to know how you consider that, 'cause there are obviously pros and cons to the approach.

Jano: Yeah, no, absolutely. And we do sequels. For example, when we look at autonomous systems research and development, we did a sequel to a challenge that we ran last year and run version two this year. Not unlike the famous DARPA Grand Challenge, where pretty much everyone failed in the first attempt, in the first challenge, and then you have a shot at it again in a slightly different way. But no, that's certainly something that we do.

Eric: And so to sit on that DARPA Grand Challenge point, that's an area where DARPA was a workhorse funder of autonomous vehicles from the early eighties up until the Grand Challenges. So that's almost two decades where they had a typical like, "gardening, we don't know what is there in this space yet, we have practical outcomes we're looking for, but also we're developing a tech branch here," that ended in a challenge.

If you had an area that felt like the one approach was ideal for one decade, and then a completely different approach would be ideal a decade or two from then, how might SPRIN-D go about attacking that field in terms of its program structure? If you were going to do sequels, what would that look like, if you all were trying to do the same thing?

Jano: You're talking to an institution that is five years old. [laughter] So I think most of it is still to be developed. Let me give you another example, and that is nuclear fusion. We have been funding technologies for nuclear fusion for a couple of years now, and that certainly will not be finished next year or the year afterwards.

So we continuously evaluate what the right next step is. That depends on tech development. That depends on the whole ecosystem developing, how other funding efforts are structured. And that is certainly something where we think a certain field of technology is both super important and would have a great impact, but wouldn't be able to move along without us.

Then we are certainly willing to continuously support that development.

Eric: And SPRIN-D exists in part to fund stuff that kind of presently ends up falling into the valley of death more than it should. Let's say there was some contractor where it maybe wasn't worth any given program or challenge officer funding the org into existence, but SPRIN-D itself would benefit from some contractor existing and being good for the rest of the ecosystem.

And a specific example from DARPA history — I think of early MOSIS, where DARPA kind of created their own silicon broker at the USC Information Sciences Institute. What they did was enable like really small chip runs for all of DARPA's researchers. I don't know if you had to spend that out of your program, if you would do it as a program manager, but like from the office level, it makes sense. Structurally, how would you all go about doing that?

Is there some straightforward mechanism? For example, ARIA has activation partnerships to do things like that. How would you all handle it?

Jano: So it would be something that we would steer from within SPRIN-D. We don't rely as much on activation partners as ARIA does. We do more of that work SPRIN-D internally, but it certainly would be something that we would look at and would be willing to do.

When we figure out that there is a certain bottleneck that is in the way of achieving an important breakthrough, then we would address that bottleneck. Even if it would mean that that work in isolation would not be a breakthrough. We're just aware that oftentimes those breakthroughs are different puzzle pieces fitting together, things building on top of each other.

It's not just the one problem that we solve and then it's a straight line to a breakthrough.

Eric: And if SPRIN-D's budget 10x'd, would we see 10 times as many challenges and programs, or is it not so linear?

Jano: So I think the one question is, do our ticket sizes get bigger? And the other question is, how broad are we going to be? I think there is a limited scope for having bigger ticket sizes. The amount of funding, the ticket sizes in a challenge — I think that will not change a lot. What we will look at is, if we have a 10x budget, are there technologies out there that need bigger ticket sizes?

Because, for example, we might need to fund phase two clinical trials in certain cases because no one else is going to do that, participate in Series B rounds because they would otherwise not come together. Really, I think there is a natural limit of what certain institutions or team startups at a given time can productively work through in terms of money. Having money is necessary, but it can make people lazy and slow if they have too much money at the wrong point in time. So it would rather be probably a combination where we have more programs and are able to address more challenges.

Eric: And if SPRIN-D was the only group funding some area for X amount of time, how long can X be before you start getting uncomfortable and saying, "Are we just making ourselves feel good here, and nobody actually values this area as much as we do?"

Jano: I think that can be a long time, especially when…

Eric: Is it five years? Is it 50 years?

Jano: Asking a five-year-old institution about what we are going to do when we run things for 50 years is like looking into a crystal ball.

[laughter]

But I think we have the commitment and the willingness to really follow paths as long as we are convinced that's the right way to do it. Because we start programs when everyone else would be against that. So we can continue programs when everyone else would be against that.

Eric: Yeah. And okay, just three more and then I'll let you go. And thank you for all the time you've spent with me today! First one, if you have a really wildly successful challenge, whether you had a huge technical success or you learned something that showed you the promise in this area and you said, "Oh, the expected value is even higher than we thought," or we got a good outcome — to what extent, what does doubling down for SPRIN-D look like?

And at what point is some percent of your budget maybe too much? Like I constantly bring up this Warren Weaver at the Rockefeller Foundation example, because it was 80% of Rockefeller's natural sciences budget. And I think a lot of orgs would say, "Oh, that's a bit too much." But I would love to know where expansion is natural and good if you're doing a good job, and where it might be too much of your budget or there's a sort of soft threshold that you wouldn't want to pass.

Jano: Yeah. That's a very tough question. I mean, if we spend a budget share of like 80% on a given technology, it would really warrant a question of why is no one else committing that amount of money into that project? I think it's very important to think about opportunity costs because spending 80% of money on a given project means not spending that money on a range of other topics and teams.

And I think that is something that we should be aware of and where we should have somewhat of a balance, especially given the broad mandate that we have. I think it's important to be aware that when we spend 10 million on contributing to some Series A, this might mean not funding 10 teams in any other fashion.

Eric: Second to last question. SPRIN-D is a metascience experiment in and of itself. And that being the case, I'm sure you look at all sorts of hyperparameters you can tune or things that could be improved. Are you all currently dealing with any bottlenecks for which you'd love some kind of organizational innovation to address? Or maybe you have one in mind and you're about to test it?

Jano: What I'm really concerned with is really the learning of that tacit knowledge and setting up ways to do that across institutions. I think that is something that really has great, untapped potential. Continuing that work with other ARPAs out there is really super important and I think can be greatly beneficial to the work that we all do.

Eric: Okay. And last question. So SPRIN-D as an organizational experiment, a government R&D experiment, and a scientific experiment — what's an example of a success of each where if in 20 years X happened, you would say, “We've succeeded. This has been a great experiment. I'm so happy I spent that portion of my life working on that, or this whole group of people spent this portion of their life working on that.”

Jano: I think one thing would be if we still exist and still work differently than the rest of government, that would be one, in terms of that government dimension. One thing that I start to see is that people change, for example, other funders changed their reporting requirements because teams that we fund would go to them and say, "SPRIN-D is not requiring that. Apparently, it's the German government, that seems legit, so why do you require it?" Therefore we see other funders in other countries lower their reporting burden on teams, especially that input monitoring.

That is something — when we can really have that push in the ecosystem, in the funders' ecosystem — that would be something where I would say that experiment went well, when we have that push to change how we operate, also beyond the ARPAs.

Eric: Yeah. There are a lot of scientists in the US who desperately hope you're right and that does come to pass. And then on the technical end, is it a Turing award? Is it a Nobel Prize? Is it another ASML?

Jano: It certainly would rather be the ASML than the Nobel Prize and the Turing Award. You know, one example that might be world-changing if it happens is, if any one of our teams from our very first challenge actually succeeds. Because that is about creating a new technological paradigm to treat virus infections. That's something that is really striking me in the sense of, we apparently just gave up on the fact and don't even notice it, that we are typically sick for half the year in the cold season due to some kind of virus infection. And we do not have the tools in antiviral therapeutics as we have with bacterial infections where we're just used to having broad-spectrum antibiotics, but we don't have that same thing for viruses.

We're just used to being sick, having a cold, having the flu. Not to speak of pandemics. And if we could solve that, really make those new technologies in that field successful, we would not only be able to avoid future pandemics but also do away with the flu, with the cold season.

Not suffer from not being at full performance for a couple of weeks in winter. And I think that would be something that might really be world-changing.

Eric: All right. Jano Costard, thank you so much. And I'll stop the recording there.

Jano: Thank you.

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Today’s piece is a guest post by Alex Obadia. Alex is a new program director at the UK’s Advanced Research and Invention Agency (ARIA). As Alex puts it, his focus is “currently on designing new enabling trust primitives for a world where many more substrates (e.g. DNA, materials, thoughts) are programmable.”

I met Alex last month at the ARIA Summit. At the Summit, I got to spend several days talking with ARIA staff, incoming PDs, and ARIA ‘Creators’ — ARIA’s word for R&D contractors. My discussions with the ARIA creators and incoming PDs who read FreakTakes were particularly informative. In these discussions, we were able to quickly get into the weeds on ideas explored on FreakTakes that these researchers had already read, considered, and about which they had practical thoughts they wanted to discuss.

My brief interaction with Alex was one example. Alex attended a session at the Summit in which we discussed not just the possibilities for BBNs in the ARIA ecosystem, but the practical financial problems BBNs might encounter. Several days after the session, Alex reached out because he had drafted a blog post outlining his understanding of the financial problems and several concrete ways they might be overcome.

I loved Alex’s framing of the problem and shared his excitement to move the brainstorm into a public forum. I quickly shared a summary of Tom Kalil’s thoughts on the four biggest financial hurdles great BBN founders would need to deal with, and, with that, told Alex I’d be happy to hand over the reins to FreakTakes for the day.

I’ll let Alex take it from here!

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By: Alex Obadia

BBNs are entities that provide services critical to R&D breakthroughs. The acronym ‘BBN’ (coined in this blog!) comes from a famous example — Bolt, Beranek, & Newman, which was instrumental to the creation of ARPAnet. BBNs are often compared to Focused Research Organizations (FROs) because they’d attract similar people. But while FROs require significant funding, BBNs would be the consulting-ish approach to FROs, contributing to R&D by servicing others in need of R&D support.

Like FROs, BBNs do not fit into purely the academic funding bucket, nor do they fit into the VC funding bucket as they’re not aiming to make heaps and heaps of money. Rather, they want to do impactful R&D in an organization that can stand on its own. The closest type of org that I use to conceptualize BBNs is simply a traditional business, but with a focus on frontier tech and R&D, which is not traditional — hence the need for a new name for them!

In a sense, BBNs are a more realistic FRO — one where someone can get started building ~today instead of looking for altruistic funding. One (imperfect) analogy to think of them: FROs are like venture-backed startups, BBNs are bootstrapped startups.

At last week’s ARIA Summit, I attended a workshop on the topic of BBNs led by Eric. While the conversation was initially focused on unearthing what BBN-shaped problems exist out there, it quickly moved to a more “horizontal” focus on whether there are any organizational or coordination innovations that could enable more BBNs — much like the YC SAFE was a coordination technology that allowed early stage startups to raise unpriced rounds easily on standardized founder friendly~ish terms. (And similarly, their series A template).

A few ideas flew around, such as the possibilities of new legal entities like Asterisk Labs’ cooperative model or a standardized university grant contract to make it easier to fund academic researchers rather than the lengthy back & forth with universities on terms — sometimes creating a period of multiple months between getting the “yes” on funding to getting any money.

One idea that stuck with me — and hence why I am writing this post — is the idea of an R&D lending facility [1]. While BBNs seek sustainability (i.e., to make more money than they spend), just like other businesses, they have capital needs that sometimes do not match their inflows from contracts and grants. Four foreseeable categories of problem that might stand between exciting BBNs being founded or continuing to exist include:

• (Temporary) Cash Flow Problems. The BBN has won some contracts/grants, but will have a cash flow problem for at least several months.

• Startup Capital Needs. Some BBNs might be very solid operations if somebody could buy them some expensive equipment they need to get started. E.g., a BBN that brings in contracts via a CRO-style service might need a $500k mass spectrometer to get going.

• Rainy Day Fund. Many BBNs might require rainy day funds in the case that contracts or grants in some area temporarily dry up, for one reason or another.

• Risk Capital Needs. Just about all BBN founders could use a more robust risk capital budget to pursue their ambitious technical visions. For a large percentage of existing BBNs, something like ~30% of their budget existing as undirected funds for ambitious investigations or core technology development could make them something like twice as ambitious. [2]

I can already hear you say, “We have SME loans already.” That’s true. Yet it turns out that today’s system may not be fully equipped to lend to this type of org. [3]

And so the question becomes: Do there exist 3-4 key financial products that are widely needed among budding BBNs and require an R&D-knowledgeable lender to price risk (and therefore premiums) appropriately? [4] If the answer is yes, and if we can spec out those products, it could allow us to fund BBNs outside of solely philanthropy, and to potentially create a set of new financial products (R&D bonds anyone?), that can be bought by the public, allowing a thousand BBNs to bloom.

Once upon a time, dividend-style arrangements fueled the ambitious building and expansion of things like canals and electric utilities in early America. While times and the state of finance have changed, the lessons of these groups in financing work that was ambitious might be evergreen.

To get the brainstorm started, Eric and I put together some concrete ideas, past experiments, and additional context in the footnotes. Have a look! Eric is hoping to figure out what it might look like to raise funds and pilot solutions to each of these problems, and he's eager for your input.

If you’re a would-be BBN who could use some of this funding, a BBN who wishes these products had existed when it started, a funder interested in piloting solutions to these problems, a lender who groks deep-tech risk and has a good idea of what these products could look like, or are simply enthusiastic about the topic, Eric and I would love to hear from you! Reach out at [email protected] or on Twitter.

Thanks to Eric for letting me guest post on his blog and helping shape the post, and to Tom Kalil, Ilan Gur, and Tom Milton for their suggestions and reviews!

[1] From Ilan Gur, the CEO of ARIA, founding PM at ARPA-E, and founder of Activate:

We prototyped this at Activate — we called it a “working capital fund” for early science-based startups. The implication for this sort of working capital fund is much bigger than BBNs — it’s any research based startup that is pre-venture or not venture aligned, and especially low risk capital for those that have been awarded a grant from a reputable org (that pays in arrears) — that’s one of the big reasons we did this at Activate i.e. because our fellows were winning lots of grants but didn’t have working capital because they hadn’t raised yet.

[2] From Tom Milton, CEO of Amodo Design. Amodo is a BBN-style org doing exceptional bioengineering and electrical engineering work for ARIA. Tom says:

Today, the paths for this are A) retained earnings/profits, B) traditional external funding, and C) sometimes raising VC funds. Some examples: A) Argon&Co used its retained earnings to develop its own IP and spends ~30% of their time running their own projects, B) applying to an ARIA funding call, C) Cambridge Consultants launching a $10m spinout fund that invests exclusively in the technologies they’ve developed in-house

[3] From Eric, purveyor of FreakTakes:

I view the question of whether SME loans can be used to found new BBNs to be a hole in our knowledge; it’s the kind of hole we hope this piece might help fill. To this point, no BBN founders I’ve come across (that I can recall) have leaned on SME loans. Whether this has been because a founder did not wish to put personal assets at risk or there did not exist a lender technically savvy enough to price the deal appropriately, these instruments have simply not yet been an enabler of new BBNs thus far. If there’s someone out there eager to help us figure out how to use existing instruments/loans differently to act as enablers for BBNs, that’s great! E.g. I can imagine some Will Manidis-type, obsessed with bespoke banking stuff, maybe having an idea.

[4] From Tom Kalil, CEO of Renaissance Philanthropy. The following are some of Tom’s (paraphrased) suggestions to the problems listed above, to get your creative juices flowing:

• Revenue-Based Finance. Instead of fixed payments that may hamper a young BBN or prove too risky, loans are repaid as a small percentage of monthly revenue. This aligns the incentives of the lender and the BBN — the lender obtains revenue if the BBN does. These loans would often include a grace period to allow the BBN to find its footing. There may also be some reasonable repayment cap (e.g. 5X the principal) or time horizon in which payments are no longer owed (e.g. 8 years), depending on what is desirable for both founders and investors.

• Low-Interest Loan. A below-market rate, low-interest loan from a philanthropy or government agency. The purpose of the loan would be to help fund one-time, start-up costs involved in creating a new BBN or a new R&D capability within a BBN. While this pool of capital would not achieve the financial returns a private market investor might look for, it’s an exciting way to stretch a pool of philanthropic capital much further than it might go as individual grants. Given a BBN’s goal to both pursue ambitious work and substantially offset their costs in the process, low or no-interest loans from philanthropic entities might be a natural, capital-efficient instrument to catalyze new BBNs.

• Revolving Door Loan Fund. This instrument would help solve the 'grant-to-cash' gap. A BBN may win a major grant, but the first payment is months away. This fund provides immediate access to capital to cover salaries, equipment, a lease, and other expenses. When the grant money arrives, the BBN repays the loan, and that capital is “revolved” back into the fund for the next BBN facing a similar problem. This instrument acts as a crucial liquidity bridge, smoothing out the lumpy reality of R&D funding.

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The Spotify version of today’s episode can be found at this link.

Some time in 2023, Tom Kalil told me he thought it would be a good idea to carve out a chunk of time and get to work on some ARPA project histories. The ARPA model was proliferating, and Tom felt these pieces might find a ready audience of ARPA emulators and fans eager to make use of the actionable information. Tom, who was then at Schmidt Sciences and is now President of Renaissance Philanthropy, has good taste. When he suggests something, I’ve learned you should listen. So I trusted him and threw myself into the work.

If any of you have enjoyed this series and wondered how the sausage is made, “How does one figure out how a 60-year-old R&D project — with no book written about it — was managed?” The answer is often: oral histories. The documentation that enabled many of these histories came to be because, at some point, committed historians sat down with a set of DARPA Directors, officer directors, staff, program managers, and funded researchers to record interviews with them on the practical details of their work.

Usually, I’m just a consumer of these oral histories. In today’s piece, in a departure from my usual role, I get to deliver you all an oral history — one I think is ideally suited to FreakTakes readers. It’s an oral history with possibly the best guest I could have asked for: Ilan Gur. Ilan is the founding CEO of the UK’s new Advanced Research and Invention Agency (ARIA). And, better yet, he’s a huge metascience nerd! The two-hour interview, recorded in Berkeley, attempts to unpack the metascience experiment that is ARIA.

I can’t thank Asimov Press enough for funding the interview and facilitating its recording. The full audio, video, and transcript are here on FreakTakes. And I also worked with Asimov to write up a much snappier, abridged version of the interview. You can find that piece in Asimov Press today.

Enjoy!

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If you prefer to listen to podcasts on Spotify, the Spotify version of this interview can be found here.

And since the text formatting for podcast transcripts on Substack can be annoying, I’ve also published the written transcript as its own piece, in addition to including it below.

Eric: Ilan, the CEO of the [UK] Advanced Research and Invention Agency. Thank you so much for being here and doing the Asimov interview today.

Ilan: Thanks for having me. I don’t think I have ever been interviewed by someone who geeks out about the same things I do as much as you.

Eric: Well that’s exhilarating, because this is my first interview. We’ll see how it goes! [Laughter]

First question. We’ll start with a fun one. As grandiose as it sounds, ARIA’s mandate is to aim big and try to create technologies as important as the ARPANET or spark new fields the way the Rockefeller Foundation did with molecular biology. Do you consciously think about that to yourself — that you have to be or find the next Warren Weaver or JCR Licklider? Does that keep you up at night?

Ilan: [Laughter] What keeps me and our team up at night is actually having a massive enough impact. Do we think about trying to be or find the next Weaver or Licklider? Probably not. I use an analogy often of being a catalyst or an enzyme. Whether it's me or our team, we're one thing you introduce into the system that hopefully mobilizes a bunch of other stuff to make an impact.

Eric: In that case, if you think about being a catalyst, what makes a reaction big enough?

Ilan: That’s a great question. You’ll appreciate this: when we have program directors that come into ARIA, some of the pre-reads we give them before they start are interviews with Bob Taylor or stories from DARPA or otherwise. And the main thing is to try and get in their heads, “What does a win look like?” For a lot of places if the research you fund leads to a new product that makes an impact, that's a big win. I often say, “That would be a loss for ARIA.” Because what we really need to do is catalyze something that is bigger than a product, bigger than a company. It should essentially be like a movement, an entirely new technology platform that didn't exist, an entirely new industry that didn't exist. And I think the way you catalyze that can be really different for different people. So if I think about our program directors, they're all different. They all have different superpowers. Just like if you think about Licklider...I mean you're a history geek, right? So just like if you think about Licklider and Bob Taylor, right, you could talk about them each as having catalyzed this massive transformation, but in very different ways.

Eric: Are there any reactions that have been catalyzed in the past 10 or 20 years or so where you look at them and you say “Wow that's the kind of thing we should be going for”? I’m definitely guilty of thinking too far in the past on some of these things, so I’d love to hear your examples.

Ilan: One of our advisors for ARIA is Aslem Turechi, one of the founders of BioNTech. And so you think about their story of having sort of worked so hard on mRNA-based vaccines and systems — initially for cancer. And then you think of Dan Wattendorf at DARPA who basically started the program thinking about vaccines for pandemics. All of that was in the mix, and then all of a sudden you had the pandemic event that actually turned it all into the world-changing impact. So I think that's such an easy one to go to. I also want us to be thinking differently at ARIA around how those transformations might take place.

Interestingly, for example, you think of ASML as a company. But it really is a company that manifests, in that one company, a massive transformation in the field, in terms of UV lithography. Especially with ARIA being something based in the UK...you can't be picky in terms of how you're gonna get that world changing of a transformation within an ecosystem that is bound like the UK. I mean, we’re working globally, but I think ASML is such a compelling example of, “Out of some fodder and set of reactants, back to the catalyst, something catalyzed this massively transformative company and then field shift.”

Eric: Do you have any program in the current cohort that you think, like, their way of scaling to impact might be in an ASML-shaped box?

Ilan: That’s a great question. I think it may be for...any of them. [Laughter] In the sense that, and we may talk about this more later on, one of the big things about ARIA that we're trying to build in...that maybe differentiates it from, say, ARPA agencies or DARPA (which started seventy years ago) is recognizing just how important entrepreneurship and startups are to cutting-edge research in today's world. Our bet with ARIA is that probably the biggest driving force for impact will be through entrepreneurs and entrepreneurship, in some way. Whether you end up catalyzing a whole ecosystem of entrepreneurs and different companies, or entrepreneurs that are focused on government, or whatever else. Or what emerges from that is one singularity of a company that changes the world, or like an “Ozempic moment.” Again, I don’t think we can be picky. I think you could imagine that happening in just about any of our spaces. For example, I think about neurotech, I think about programmable plants, and certainly in robotics. It's just a question of do we get lucky and nucleate that right thing at the right time.

Eric: On that point. So I read your House of Lords testimony and a lot of things of that sort…

[Laughter]

Eric: I’d love to parrot back to you how I understand ARIA, from those materials. You can tell me what’s right, what’s wrong, what I need to say differently, etc. Also, if you could use examples from some of the current programs, that would be great. I’m sure there are many listeners who may have seen a program announcement, but that’s all they know. So as I understand it: ARIA programs must be 1) “big if true” and 2) differentiated. You don't want to fund programs that somebody else in the ecosystem would fund otherwise. And while each project doesn't have to succeed — the vast majority won't in the traditional sense — they should all succeed in materially changing the conversation regarding what's possible in a given space. In terms of your mandate in the UK — in the long term, these programs are meant to help drive economic output, quality of life improvements, health benefits, etc. Is that more or less accurate, how you'd conceptualize it? If someone says, “Isn’t that what deep tech VCs do?” what would you say?

Ilan: First of all, is that right? I think that’s right. We're meant to take bold bets that kind of amplify different parts of the research system in new ways. I think about our programs as funding like a constellation of teams that can lead to massively transformative outcomes at the intersection of quality of life and economic growth — not just for the UK, but for the world. I think that ‘for the world’ is really about the scale of ambition that we are talking about.

You were asking, “How’s that different from a VC?”

Eric: If somebody says, “How’s that different from a VC? Or “...than if the NIH spins up a new study section?” Or something of that sort.

Ilan: I think those are two very different questions. Let’s talk about VCs. There are a lot of VCs who are just in it to make money...but in terms of the mindset and the motivations...a lot of VCs, I think, do see venture capital as a platform to change the trajectory of the future and world in positive ways. I think the big difference with VC is — when talking about the idea of ‘big-if-true’ — there’s a sense of scale, right? You have to think about the scale of “how big is ‘big’?” — and how speculative are you willing to get on the ‘if-true’?

In talking to venture capitalist friends about what I do and what we do at ARIA, sometimes you hear this metaphor around startups and VCs: “surfing a wave.”

That’s one way to think about this. What does a VC do? A VC is meant to be like a surfer in the water, to look out at the wave set that’s coming and ask themselves, “Oh, okay, is that wave forming? Is that going to be a really big one?” The size of the wave is, kind of, how big of a market they think might emerge. And the bet they’re trying to make is, “I bet that’s a big enough wave.” And then they’re trying to bet on the timing, like, “Okay, this is when I should grab a surfboard and start paddling.” And you can think of the surfboards as the companies they’re investing in.

I think the big difference [between ARIA and VC] is, that’s being very reactive to the system. You’re trying to find the trends you can hop on and surf, so that you can maximize the ROI of your investment in a very concrete, direct way.

I see our job as more of putting the energy in to create the wave. A venture capitalist can only fund into a thesis once there’s enough momentum where they can start reading, “I see the market forming, I think there are technologies that can work here.” And most importantly, “There are companies.” Like, most VCs are waiting for companies to come find money. So there need to be enough entrepreneurs and companies doing that.

If you look at the spaces that we’ve carved out [at ARIA], we’ve said, “Right now everyone looks out and just sees flat water, but we believe that by pushing a little bit here or there we can start to build a wave.” And if we can catalyze the formation of a wave that’s big enough in one of these spaces, then actually that’s going to catalyze all the VCs to want to jump on that wave and to want to invest in companies. Then you get a win-win and, ultimately, change the world. [Laughter]

Eric: And so…

Ilan: Was that helpful, that analogy? I probably went a little too much on that.

Eric: No no no, it was great! So I have some questions. Sometimes, in terms of creating the wave...I’m sure there are times where you put a certain amount of money in a space, and there's enough teams out there that already care about this and have the proper background and the right incentives to just throw themselves into it once the capital is there. And then there's probably other cases where more legwork is necessary. Because that's not quite what a university would do — or usually a startup would do X task, but this isn’t quite a VC-scale market. How do you approach those two different types of areas?

Ilan: There’s so much we can talk about here. I’m glad this is a long form interview, because we’ll have time to get into a bunch of this. Part of this has to do with how we organize what we focus on, and maybe we’ll come back to that. I think it’s worth just talking about an example here. What came to mind as we were talking about the wave and VCs, I was thinking about one of our program directors, Angie [Burnett]. This might be particularly interesting to this audience. Angie’s a plant physiologist by background, had worked in academia, a national lab, she worked for the UN for a little bit. She came into ARIA initially thinking about food security and what we could do in food security. Fast forward — where did she end up with her opportunity space, which is what we call our focus areas, and then the program that she launched? The opportunity space that she launched is called Programmable Plants.

We tend to think for all of these [opportunity spaces], there’s some insight there. For us, an opportunity space needs to be, like you said, big-if-true. And we need to be able to make an argument relative to the potential impact. So if it’s highly consequential for society and relative to that impact you can argue it’s underexplored...you know, not enough funding or not enough of the right type of ideas or bets being made in that space. And that for some reason, it’s ripe for transformation. That defines a very big [opportunity] space that we are now going to start working in — and basically buying options through the early research that we’re funding to learn more and see if we find something that could become one of those big waves.

Angie's insight on this space was that if you just think from first principles, when you look at some of the biggest problems and opportunities we have in the world, obviously, agriculture is one of them. You think about food security, you think about climate change. The tough part about these segments is that they're massive problems, right? You need to talk in terms of gigatons of CO2 or carbon, in either case. And her view is that, actually, you can think of plants as a technology platform, and plants are one of the few technology platforms we know of that, actually, we have a system that operates at that scale — of gigatons of carbon.

And what do I mean by technology platform? A plant is a piece of hardware. It has certain functionality. Interestingly, in many plants, like you go to the store and you buy a cob of corn, we have actually engineered the functionality of that plant over many years. And you have distribution channels and the ability to deliver these things at a massive global scale, commensurate with those problems — food security, climate, etc.

The problem is...it's like a really shitty technology platform, right? We're very limited in what functionality we can build in. Instead of 18 months for a new iPhone, you take 18 years to get a new crop that's actually viable. Interestingly, if you talk to most investors, they will say, “Well, in the ag space...you've got the top [incumbent] ag companies and [given the incumbents and structure] it's basically the hardest, most entrenched space to innovate in.” It's just not a great place to invest. Massive capital, massive entrenched interests, there’s regulations, etc.

The bet that we're making is that actually...on first principles, when you look at what’s happening in synthetic biology, when you look at the problems and the forcing functions that are coming, requiring a change to how we do agriculture...there’s a pretty strong argument to be made that, sometime in the next few decades, there will be a shift in how we approach global agriculture. You can think about it as something like, “There will be an ASML-type company that completely disrupts the big five ag companies in the world, and does things radically differently.”

Eric: And what's Angie's job over the course of five years? And I guess also tie in, what is a program director? And how important is it that they have a point of view? And does that come out as you're picking them? ...I asked you a lot of questions.

Ilan: No, that’s fine. Angie’s job was first — together with me and the cohort we had — beating up the question of, “How do you define a space that’s interesting and in line with this idea of ‘plants as a technology platform that’s underappreciated’?” Synthetic biology has been this massive vector of technology progress, which is not being leveraged for plants and ag to anywhere near the extent it’s being leveraged for human health. So now there’s like this arbitrage [opportunity]: “We’re making all this progress in synbio, we have this massive problem opportunity in ag.” But they’re not really connecting the dots.

Eric: And how many years behind do people perceive ag as, in terms of the cutting edge in synbio? I’ve heard 10 or 20 years, but you’d have a better...

Ilan: Well, let’s fast forward. So, what is Angie’s job? Her first job is to lay out that space. The next job was to say, “Okay, what’s a funding program we’re going to launch?”

She had a bunch of different ideas. Where she ended up is a program that we’ve now launched — and we’ve just announced the people and groups we’re funding. That program is called Synthetic Plants. [See this link for more information.]

And the idea was, if you look at the real frontier edge of synthetic biology, and we're talking about things like de novo genome synthesis, like actually thinking about synthetic organelles. You have, in the UK and other parts of the world, really amazing progress happening. But when you go and talk to those researchers working in those areas, and you say, “Well, we're thinking about doing a plant program,” the reaction Angie got from all of them was, “Why? There's so much to be done in mammalian systems, we're making all this progress...like, plants are hard! They have multiple copies of the genome, it's impossible to figure out how to transform them, it’s XYZ...”

And instead of getting discouraged by that, Angie got more excited by that. She said, “Well, wait a second, let me tell you a little bit about the potential impact we could make if you start working on plants.” Long story short, she held a workshop. And I got to be at this workshop, where she brought together a combination of top synbio people, top ag people in the UK and otherwise. There were probably 50 people in the room. One of the questions I asked was, “How many other people in the room did you know when you walked in?” And I had said [gesturing his hand upward], “Raise your hand if you only knew one person, raise your hand if you only knew X...”

And I think most people knew like five out of the 50...and none of the synbio people had talked to any of the plant people.

What we’ve found is, now, there are all these new collaborations. We have synbio folks who are basically saying, “I’m shifting my work to plants because I’m just convinced this is the most important thing I can do.”

Eric: And when you say ‘folks,’ how many are thinking of shifting their work to plants?

Ilan: Well, you’ll see how many synbio people we fund in this program when it gets announced. But I know of at least two specific cases Angie’s mentioned where, when she started the process, the person was so averse to the idea of doing anything in plants and thought it was a waste of time...who have built really meaningful collaborations and — whether they get our funding or not — are going to start working on that. So, it’s cool.

Eric: When is high variance [in reactions] among the adjacent experts exciting? When is it less exciting?

Ilan: Oh yeah. How do you know when people just say, “Oh, that's a horrible idea.” How do you know when it actually is a horrible idea? [Laughter]

Yeah, I mean this is an interesting thing. You talk about variance, right? Someone from DARPA once shared this with me, and it's a framework that I really love, which is...if you think about the role DARPA plays, more important than anything else, it is a mechanism to increase variance in the system.

It’s actually something I think a lot about for ARIA. The idea there is, more than anything else, ARIA was created to do things differently. The idea is: do things differently, and by doing things differently, by shifting the modality of research, you can get different outcomes. We can spend a lot of time coming back and talking about that.

But if you want to do things differently, first of all, you need people who see things differently. It’s one of the reasons the PD [program director] model is very helpful. But secondly, you have to break what is, I think, a pretty strong and homogeneous sort of truism of how we (largely governments) fund research. Which is something like, “We’re pretty good at funding the well-known disciplines. And we’re pretty good at funding the obvious problems or opportunities.” But that's all very linear, like, “More funding for biology, or synthetic biology, or semiconductors, because we see what's happening geopolitically.”

If you want to do something different, you need to find some way to cut and slice through that in a different way. Interestingly, the ARPA model says, “Okay, you find a program director, like Angie. You have them look across the system and say, ‘Well, what’s an actual thesis of something new that we could do, that’s not a single discipline? Maybe it is, but it cuts across TRLs [Technology Readiness Levels].” That’s the other thing. People sometimes ask, “Is this low TRL or high TRL?” It’s like, no. We’re mixing these modes in new ways.

Eric: You’re ambitious and applied...

Ilan: Yeah, and you have sort of a spectrum of portfolio. What that means is, what we end up funding on the backend of one of these theses we’ve developed, is going to be a very different set of people with a very different set of incentives — in terms of what they are going to do — than would otherwise get created.

And the variance is...if everyone thought it was a good idea, we would be introducing no variance into the system. If no one thought it was a good idea, we probably shouldn’t do it because it probably is not a good idea. But if you get spiky reactions to anything you’re doing, it’s the sort of thing where...okay, you have some minority that actually think this could work. But in a consensus panel, peer review, that minority voice would probably not win out or be there. So it would never happen otherwise. And, actually, I think the hardest job of a program director is to be able to actually face the criticism of so many people saying, “You don’t know what you’re talking about!”

Eric: You need to have courage to live with your bet, for years on end...

Ilan: You need to have courage to live with your bet, yeah.

Eric: And so some people...

Ilan: Wait, I’ve been talking a lot! I feel like it’ll be good for this audience...as we’ve been talking, what comes up for you as you think about some of the history that you’ve learned about for program managers at DARPA, variance, etc.?

Eric: So, I guess what I’ve been really impressed by, in working with the ARIA PDs, and ARIA in general, is when I read about early ARPA, 1960s and 1970s in particular, it seems like they really did trust the PMs to have an opinion. Or to say, “Here’s why this contractor’s best. You have to see it [the vision], and here’s why there’s only one group to do X thing.”

It seems like ARIA has given its PDs a long enough leash to have the point of view and follow it wherever it may lead. When I talk to folks at a place like DARPA, or something, I often get the sense that the PMs do have a strong point of view, but it’s an older organization. Like, it’s a 60 year old government agency. I think a part of the magic of DARPA is, so many government organizations only get more bureaucratic over time. They’ve [DARPA] been phenomenal at, like every five or ten years, finding ways to clear out a lot of the red tape, scar tissue, etc. But it does still build up a little bit.

So it’s been really cool. Something I try to impress upon people is that 2025 DARPA is not 1965 DARPA. You need to pursue different programs and do different things [today]. And when people ask me, “Well, do we have an early ARPA [today]?” Before ARIA, I guess I’d say, “Maybe OpenPhil.” OpenPhil lets people be free like this. But as ARIA has come into existence, it seems like you all live in the vein of early ARPA much more closely than anyone else. But, saying that, I also know you want to do something new...

Ilan: No, it’s interesting though, because I think this relates to the variance point. I’m trying to keep this dear to ARIA in the early days. It’s not about, “Did we find the right bet?” Because actually, given the variance point, in the early days, it’s not going to be obvious which is the right bet.

So it’s really about, “Are we being true to a commitment to increasing the variance of what gets funded?” And then, “Are we going to have the muscle to see the right bets emerge from that work?”

I actually had a [ARIA] board member recently say, “Isn’t one of our biggest risks right now that we created the wrong programs — like we’re funding the wrong programs?” And I said, “No, I don’t think that’s a risk at all, in part because we decided to create these opportunity spaces.” The whole idea behind an opportunity space for us, in these focus areas, is that our whole job is to take speculative bets. What that means is we can’t know upfront whether this is going to lead to impact. We can’t engineer that. The only way we get it wrong is if we take a speculative bet in an unproductive direction. So the point of an opportunity space is to say, “Let’s define what we think is a productive direction.”

An opportunity space is going to have certain beliefs that bound it. And the point is, if you can get yourself to believe these things...e.g. “Pressure on food security and climate is going to force us to do some things different in agriculture,” “Synthetic biology is moving...” Right? If you believe those things, you have to imagine there is the potential for enormous value — economic value and social impact — that can happen in this space. And now that we’ve bound that space and set that opportunity space, now we can take steps, big and small, and make bets in that area without having to worry, like, “Is synthetic plants the right program?” I’m not worried about it at all. Because we’re making a bet in a direction that’s productive.

The only thing we now have to worry about is that we don’t do what we were made to do. What makes ARIA unique — and what’s meant to make a place like DARPA unique, certainly in the early DARPA days — is that you make a bet, you take a step, and then you learn something, and you can pivot and say, “Oh! Actually it’s this 5% of what we’ve done so far that is starting to feel really valuable. Let’s minimize the rest and 10X that 5%.” That’s the thing that...I think that’ll be one of the big muscles we have to create for ARIA in this next phase.

Eric: So, you’ve moved to the UK, and ARIA is...

Ilan: I’m sorry, I have to stop! I’m like, I’m sitting here wondering if people watching this are excited about it. It’s just like — the idea that there’s now a community that’s excited to hear this conversation — that’s something I’ve been searching for for a while.

Eric: [Excited laughter] Okay, so, in that case...

Ilan: How many subscribers do you have?

Eric: Like 4,000. But there’s people like Matt Clancy who have like 15,000, or something like that. [Correction: Matt has over 18,000.]

Ilan: But those are high impact people.

Eric: Yes, I am continually wowed by the subscribers I have...and when I write something, the email outreach I get. It’s very much a dream for someone like me who...I wish I could be a great researcher [gestures at Ilan], but it was not in the cards for me.

Ilan: [Laughter] I wish I could be a great researcher too. Wasn’t in the cards for me. Definitely more of a Bob Taylor than a Licklider.

Eric: So you’ve been doing this for so much longer than many people. A lot of people have maybe gotten interested in this space in the past 5–6 years or something.

Ilan: Wait, what’s “this space”?

Eric: We’ll call it this “applied metascience” space. People who want to really consciously experiment with how you build R&D ecosystems differently, or research labs differently. In many ways, you’ve been doing this for possibly the majority of your professional career. Can you tell people about the different stops you’ve taken? Maybe different things you’ve learned along the way? Different gripes you’ve picked up? ... [I imagine] you only throw yourself into this if there’s stuff to be fixed and you have ideas.

Ilan: Yeah, I mean, I sometimes describe it as, like, I got thrown into this just because I felt like a misfit in all of the different environments to do research. And we can talk more about that. But I think that is a motivating factor, right? What motivates me, and probably what motivates most of the folks who would listen to a podcast like this, is just realizing that you can take the things we’re learning at the cutting edge of science and discovery, and turn them into stuff that’s useful, real, and awesome.

I mean I had — actually, you’ll probably appreciate this story — I was one of a rare set of people who was an undergraduate major in material science. Material science is kind of a weird field that most people, at least when I went to college, didn’t really know about.

Eric: They’d pick it up as a masters...

Ilan: Yeah, exactly. I think there were maybe like five people at Berkeley who came into the undergrad material science program. But the reason I got into material science...I grew up in Pittsburgh and went to a public school, which just happened to have a couple of phenomenal science teachers. And one of them encouraged me in 10th grade to put in an application for this program that CMU [Carnegie Mellon] did, where they would host high school students in a lab.

And I still remember, like, they gave you a questionnaire, and it was like, “What are your interests?” And I was in 10th grade, you know, I was not a polymath who was way ahead of their time. So my interests are like, ”I like physics [shrugs]...maybe chemistry?” You know, you just say pretty banal, normal stuff. Anyway, they paired me with a material science lab. And I remember showing up and saying, “Why did I get the oddball thing? Why couldn’t I just be paired with a normal lab?” And I didn’t think it was going to be cool or exciting.

The project — and this is the piece that I think you’ll appreciate — the project that I got paired with was a lab that worked in magnetics. What they wanted to do was come up with a new magnetic storage medium. At the time, you know, a hard drive was, what, 16 megabytes? You didn’t have flash drives, etc. And so the question was like, “How could you get much higher density?” So what they were doing is they were taking a material called MCM 41 — this is the part you’ll love — MCM 41 was one of a class of what are called molecular sieves. It’s a material that has a microporous structure, a lot of core space, sort of a zeolite material used as a molecular sieve/filter/etc. Basically it had like honeycomb pockets. The project was, through different means — chemically, sputtering, etc. — to fill these honeycomb pores with magnetic material, with some iron compound. And then the idea is that the ceramic honeycomb borders would be the separations between domains of a magnetic storage medium.

I still remember, at the time — you’d go around and say things like, “You could imagine putting a gigabyte on something the size of your hand!” But it was mind blowing! Just the whole idea that you have these nano honeycombs, and then you’d have a head that would sort of scan them.

Eric: Did you believe it? Or did you feel like that was crazy?

Ilan: No, I totally believed it! And the reason you’ll like the story is because MCM 41 stands for “Mobile Corporation Material #41.” So I’m still waiting for you to do a post — maybe you’ve done one and I’ve missed it — on all of the amazing discoveries, fundamental and otherwise, that came out of the oil and gas research labs. Mobile Corporation did all this incredible material science work. And, of course, they named all the compounds, like, “MCM 3, MCM 5.” So MCM 41 was, like, something everybody knows about...or at the time did!

Eric: That actually would be an interesting post too. Because I was just yelling [injecting historical stories into random conversations] at some people on Twitter the other day about the origins of TI [Texas Instruments], as just like, one of the early oil and gas firms that had a ton of early MIT ties. They just got very good at making their own instrumentation. And that’s a pretty high margin business. It’s a little less speculative. And they just like...I forget if it [TI] was a spinoff or if the firm [the original oil and gas firm] entirely pivoted. But yeah, maybe I should. If you have ideas!

Ilan: Oh yeah! Materials, lithium ion batteries, came out of an oil and gas lab. A bunch of solar cell work. I think there’s something cool there, but...

Videographer: Can we take a quick break?

[Camera Break]

Ilan: You were asking about the trajectory of ARIA?

Eric: Yeah, it would be good if we could discuss how...you were at ARPA-E. What in the world is Activate? All of these things, probably like, being dissatisfied in different ways. Or being happy with what you did, but dissatisfied drove you...?

Ilan: I think there’s a story of navigating, you know, continuing to basically try and do bigger and bigger metascience experiments, if you call it that. There’s a version of that which is, like, me being disillusioned by different institutional structures, or different silos, and how we do research. But maybe a more positive or interesting version is that, as I think over that time, is...I talked about being at CMU. When I did that research project at CMU, what flipped on for me was this idea of, “Holy shit, the wonder of the ways that we can take scientific discoveries and turn them into massive impact!” For me, the ultimate [thing] at the time — and this probably went all the way through my undergrad and into my PhD — the most valued currency for me in terms of changing the world with science was the ideas. And the instruments and the processes and the methods. I was so obsessed and in love with that, and I saw that as the big driver.

Interestingly, I had this experience 18 months into my PhD where we had written a Science paper. We were on the cover of Forbes for this new approach to creating solar cells much cheaper, you know, II-VI nanoparticles that we were going to print like newspaper. There was all this excitement. And I spent a couple of days with some business school folks with technical backgrounds, and realized it was all BS. We actually weren’t solving the right problems.

And I became...you could think about it as being disillusioned, but actually I think it’s more [that] I recognized that the world of ideas that I was in, where that was the currency, there was something missing there, in terms of how you drive impact.

And then I ended up in startups, VC-backed startups. My mindset moved from like, “Actually, ideas are pretty cheap, you know? Ideas are pretty easy to come across.” It’s more about, “How quickly can you iterate through them?”

But I also got obsessed with currency as the currency for driving impact. Because my job running a startup was, like, “Raise money.” And you could see that the more money you could raise, the more of an opportunity you had to drive and get to faster outcomes relative to competitors, or whatever else. So I really was in this mindset of money.

And having a chance to go to ARPA-E, one of the things I found very compelling was that it was a different mode from venture capitalists. I thought venture capital was sort of too narrow of an impact model for doing early-stage science, especially for industrial markets. But I also found compelling, like, “Oh wow. We’re going to be the biggest Skunk Works innovation funder in the US, for anything climate-related.” And I was attracted to this big money question. Money is like the driving force you have for reactions.

And now I think, through that time for me, at ARPA-E, my biggest recognition was that we had...

We had an incredible mission: to drive translational science into big impact in climate and energy. We had access to a lot of funding to do it, so we had the driving force. We had access to all these brilliant ideas, meaning every researcher who worked in any of these areas in the country would apply to our programs if we could just pick and choose the ideas. And the thing I realized was that, actually, none of those was actually the thing that mattered. What really mattered was people and institutions and incentives. And you can have all the money you want, and you can have all the ideas you want,  but if you don't have the right people in the right institutional environments with the right incentives to drive progress, you're pushing on a rope.

Eric: At ARPA-E, were you ever wowed by some set of people and institutions and incentives that you got to deal with?

Ilan: Well, in the end, I came back to...I left my startup feeling somehow like I had been misled. The idea of, “Venture Capital-backed startups are going to change the world.” We were working on batteries for EVs, industrial markets, very low margin business. Probably our timing was off. But I was like, I don’t see how we can develop the technology, get it to scale, and actually have it converge for someone like Vinod Khosla, who was like, “Either this needs to be a trillion dollar…” it was at the time billion dollar, now like, “...trillion dollar company, or it’s not worth doing.”

And so I kind of had a feeling when I went to ARPA-E, like, “Actually venture-backed startups are not the way to do this. And startups might not be the way to do this. Let’s figure out all the other institutional modes.” What I found in the end was that, to your question, of all the environments that I was funding research, the ones that felt most resonant and most productive were either the startups or the folks within academic labs that were already operating as though they were spinning out a company.

And what I realized was that the venture capital funding model has constraints, but if you don’t worry about the funding model for a second and you just think, “Well, what is a startup?” A startup is a vehicle for getting the right people into the right environment with the right incentives completely aligned with some translational research mission.

If you think about it, [for] everyone in an early-stage startup, the goal is to create value in a very concrete way. You’re probably doing something very speculative, if it’s science-based. Everyone who’s there in the early days has decided they’re going to commit the next chapter of their life to that pursuit. The incentive is clear. The alignment is clear. You often are getting people from different backgrounds, in interdisciplinary ways, to do it. So I think startups are massively valuable and important vehicles for R&D.

And the question becomes, and we can have a conversation about this, the question becomes, “How can we fund them? How can we fund them as R&D centers?” Venture capital is not a great way to do that. Some VCs actually do fund very applied R&D and take speculative bets. And I think those are companies that tend to change the world. But relative to the, like, $70 billion of US funding to applied and basic research — that number is probably dated right now — you don’t have anywhere near that going into super science-y startups.

Eric: To understand the problem...You love the shape of VC-funded startup groups — the people, environment, incentives they create — for the problems they want to attack. But there’s all sorts of problems that...

Ilan: They can do it if they see the wave coming and they’re just hopping on the wave. So when that happens, they work great. But why can’t you have the same aligned incentives, full-on commitment, before the wave even exists? And couldn’t that be a mechanism to create some of those waves?

Eric: And how do you think about making that happen…

Ilan: I have no idea.

Eric: ...at ARIA?

Ilan: Oh, at ARIA!

Eric: Yes. And of course all of my readers will know I’m biased. I’ve spent this huge amount of time writing about the best early ARPA contractors, like BBN or the CMU autonomous vehicle groups...groups that were very startup-related, but really embraced technical ambition over market size. Their constraint was that they need some amount of grants [or contracts] to fund it. But I’m sure you have all sorts of theories on how to make this work for you [ARIA] in spots where there are parts of the R&D ecosystem that could use a little bolstering — at least in terms of ARIA’s incentives.

Ilan: Yeah. So one of the things we’re doing at ARIA, which I think is probably different than DARPA, is being really open-minded and attuned to, “How do we try and fund not just the best people and ideas, but with resonance in terms of the environments and incentives where they are doing the work?”

What does that mean? That means a few things. One is that we are very comfortable with the idea that, using ARIA funding, a researcher might decide to go start a company. They might decide to go, leave their academic lab, and just do the research as an independent researcher, because that’s what they think is the right thing.

We have something interesting, which is we do these seed funding awards. I mentioned those opportunity spaces where we say, “This is just the direction that we think is fertile.” A program director will create a program, which is their thesis, and that’s where they’re going to focus a lot of energy and where we’ll focus a lot of funding initially. But then what we do is we say, “Well, we’re not the only smart people around. And it’s all about increasing variance. So why don’t we find other people and let them start seeding ideas that could lead to the massive breakthroughs...[unintelligible]”

And this is like, you know, give us a three-page application, tell us why you’re obsessed with this idea, and [why] no one else will fund it. And we’ll give you up to £500k. So just start running at it. And if you come back with something compelling, we can double down with you. [We can] either make it a bigger project or actually have it inspire a whole program.

Eric: So Jenny said something in one of her interviews. Jenny is the PD on the robotic dexterity program. She said at one , “I was almost disappointed that I didn’t get the chance to fund, like, some random person in their garage.” Because the funds are open to them. Can...

Ilan: Anything.

Eric: Can you explain what it would look like to be...like if I’m a random guy in Birmingham and I want to…?

Ilan: [Gestures at Eric] As opposed to a random guy in…

Eric: Yeah, in Chicago. [Laughter] And if I wanted to run it out of my garage, how much paperwork is involved? It seems to be a very fair [reasonable] amount.

Ilan: So this is really interesting. What we started with at ARIA is, we said, we wanted to make clear that we are completely agnostic. We wanted to fund the best people in the best environments. Apply. We don’t care if you’re at a university. Just tell us you’re obsessed with the idea, what the idea is, and show us that with our funds you can get access to the equipment you need to make it work. And we wanted to be really agnostic.

Now, there’s a piece of this, which is we have a brilliant, awesome, founding CFO for ARIA. Her name is Antonia [Jenkinson]. She and her team, I went to them and I said, “This is a little hard to do as a government agency. You got to do due diligence on these people. How are they using their money?” And they said, “Yeah, but this is what we’re built to do, so we’ll just figure out how to do it.”

Eric: And is it true that she walks around with the [ARIA] founding mandate in her pocket?

Ilan: [Laughter] She has been known to carry the ARIA Act around, which I love. So we started and said, “Yes, we can fund this [alternative applicants].” And seeds were a three-page application. We would tell you within three weeks whether you got the funding.

Eric: Three pages? Like...[makes a show of counting out three pages]

Ilan: Three pages, yeah. It was a three-page application, three weeks to funding decisions. And when we did it, we did have this. We had people apply. And then we had a few people who said...we had one person at a well-known university in the UK who said, “I’m really glad I’m getting this seed. To be honest, I don’t think I want to do this in my academic lab. For a number of reasons.” I think they were actually a postdoc. They were, sort of, earlier [career]. And they said, “What do you think about me, just like, finding and renting some space to do this work as an independent researcher?” The program director talked through it with them and said, “Actually, that makes sense. You’ll probably do a lot better. You’ll probably be more motivated.” Whatever it was. And so we just figured out how to give that person the award as an individual to do it in this way.

And we saw a few of those. Then we paused, and we said, “Well, wait a second. People are funded and excited to work on these projects.” And when we tell them we’re open to wherever you do it, they’re telling us, “Well, maybe I’ll do it some other way.” So the next time we did a seed call, as part of the questions we said, “What’s the institution you’re in now? If we award you the seed, where would you like to do the work? It doesn’t have to be the same place.” And the options were, “Will you do it in an academic environment?” You know, “my current university, another university, a company,” wherever else. And then we [included], “still undecided”.

First of all, I think we’re the only government funding agency in the world that gives that option. Secondly, the fact that there was an undecided bucket means, “We’re cool...like, we’ll talk through it.” I think we had like 23% of applicants in the next seed call say “Undecided.”

Which for me is such a big deal, because it suggests that being prompted with the question of like, “Eric, there’s a big project...it could be the most important thing you do in your life. Stop and think about, ‘What is the best environment for you to do this work and have it succeed and have a chance to change the world?’” And what we learned is like 23% of people [think], “Maybe it should be a startup, maybe this is my moment.” Or, “Maybe I should move to the UK and try to do this at a university in the UK,” which has happened now with ARIA grants. So, that was pretty cool.

Eric: Yeah…

Ilan: That felt like a...I warned you that if you get me excited about something, I’m just going to keep talking about it!

Eric: No, no, no! That was great! So I have a question [from my notes] lingering here, I don’t know where to put it. So I’ll just ask you now.

Ilan: Go ahead!

Eric: So this one comes from Tom Kalil. He said, “In what specific ways do you desire to transcend the DARPA model?” Because you’re not looking to set up some version of an American ARPA in the UK?

Ilan: I think that’s right.

Eric: There’s lessons you’re taking, but…

Ilan: Yeah, I mean look...I think DARPA is such incredible inspiration. You know what I mean. Like people argue about, “Is DARPA past its heyday?” “Is it doing good or bad?” But we have this beautiful gift, which is just the history and the reality and the myth of DARPA all wrapped in. And, honestly, if you can’t be inspired by that, like, forget you! Right? And I think that inspiration is so important and true.

We’ve touched on some of the things that I’m hoping with ARIA...ARPA, like you said, established 70 years ago in a very different time. It’s changed in many ways. I think for me, the most important things — one of them is one of the things we talked about earlier, which is having the intellectual humility to, and the eye on part of our job as just to increase variance and learn and pick up on threads that are valuable. That is really important to me.

One of the things I’ve noticed — and this is good and bad — but when our program directors...we pair them up with DARPA PMs, former and current, to give advice and whatever else. And oftentimes I will hear a DARPA PM say, “Wait, 37 performers for this program? You can’t do that. That’s not how we do things. That’s not the right way to do it.” What we’ve had the chance to do is kind of go to first principles on everything. And that’s a real case. I don’t think it’s 37. But davidad, who runs our Safeguarded AI program...for the first technical area of his program, he wants to, as quickly as possible, answer some theory questions and develop some frameworks which he thinks are best done as open source. And, actually, where that led him is we’re funding a lot of teams with small awards, to which normally people would say, “It’s not worth the management,” or “It’s not cohesive enough.” But the first principles suggest that, yeah, that’s the right thing to do there.

So, it’s kind of like having the intellectual humility to say, “Okay, I don’t know if this is exactly right. But it feels like it’s matching the first principles. And across what we’re doing, it gives us variance. We’ll learn more on the go.” I think that’s one thing that I’m hoping gets preserved at ARIA.

This other thing we’re talking about is probably the other most important one, which is...DARPA, rightfully, has a mantra of, “We don’t have our own labs. We don’t create institutions. We fund research projects.” And DARPA increasingly has done more with startups, which I think is great and one of the biggest impact vectors it has. But I think for us, for ARIA, being able to say from the beginning, “Actually, the people and the institutions matter. And there may be new institutions that we need to form — or that we catalyze forming. And we’re not going to be, sort of, scared about that,” is important.

Eric: Yeah. This is also something I think early DARPA was a bit friendlier to. They wouldn’t necessarily found an org, but they would write a pretty big check to an org that, like, didn’t seem to have an office yet or something like that.

Ilan: Was RAND DARPA?

Eric: Was RAND DARPA...

Ilan: I remember reading something that...

Eric: No, I think RAND would’ve been...I do think the USC ISI [Information Sciences Institute] was very clearly heavily early DARPA funded [from its earliest days, which helped create the organization of largely RAND alums]. They ran DARPA’s early MOSIS initiative, which was one of the first fabless [services]. That’s definitely a case of...those guys may have left RAND to form...I’d have to go double check some of my notes. But that’s a case of them [DARPA] writing [very early checks into an org] and making some promises, and it was an institution they needed.

Ilan: Yeah, it’s actually interesting! MOSIS came up recently at ARIA because, one of the programs that we’ve launched...we’re already starting to see that, within this...

[Camera Break]

Eric: You were talking about MOSIS and how it fits into one of your programs that you were talking about recently.

Ilan: Yeah, the opportunity space is Nature Computes Better. One of the theses there is that there have been lots of attempts to think about how you disrupt general computing. But it’s such an impossible thing to think about doing because of the massive supply chain and all the different forms of general compute. Suraj Bramhavar, one of our program directors, his insight was, “Actually, AI is very unique in the history of computing because you have a compute mode with a pretty narrow set of mathematical primitives, but potentially massive applicability, value, and impact. And that narrow mode of AI could be a new foothold to think about alternative types of computation — with alternative physics, alternative hardware, alternative substrates, etc.”

And the other piece of this is...when we think about, “Is there a program or opportunity space here,” one of the important questions to ask is, “What is the fundamental limit of performance that could happen in this space? And where are we now?” And one of the things we know about computation is,[even though] we’ve had Moore’s law for as long as we have, we are still orders of magnitude from the fundamental limits of computation. Whether it’s [the fundamental limit based on] Shannon’s information theory or what we see happening in nature. So the idea behind the space is, “Can we find new approaches inspired by physical systems, natural systems, other ideas that researchers have, etc. to use AI as a foothold for next gen compute?” The program that we’ve shaped aims to demonstrate that you can show, as a first step, AI training at a thousandth the cost and energy consumption than state of the art today.

And there are a bunch of really interesting systems questions around, like, “How do you even prove that in sort of a demo system?” We’ve now funded a set of teams across academia, startups, big companies, and a number of approaches that we think, in coordination, can get there. And from that work we’re already seeing, “Wow, there might be a next phase of this given some of the early excitement that we have.”

How would you take advantage of it? Maybe you need some MOSIS-like institution that is focused on this area. It’s still very early, but it’s cool because, like, you had your post on MOSIS and it actually is making a difference, being able to give us [ARIA] a catalyst of things to chew on.

Eric: And can you paint a picture of what this MOSIS would be doing? Like if a lot of these contractors are point solutions or different approaches, what would you need them [this MOSIS] to do?

Ilan: [Laughter] I don’t think I could paint a picture of that for Suraj’s program because he’s literally just in the early stages of thinking about it and I don’t think I could actually talk through it without disclosing things that he probably wouldn’t want me to. But I do think, you know, one of the things we talked about earlier was the question of, “Where are there new institutions, and how could they help ARIA?” We talked about our Creators. (DARPA talks about the people they fund as ‘performers.’) We thought that was a little odd. Actually, you might know the origin story of where ‘performers’ came from. Maybe it’s like: you want performance, so you have performers? Anyway, we call the people and institutions we fund ‘Creators.’ The idea being, the work we do at ARIA, I think you need equal doses of creativity and creation. This word ‘create’ is interesting because you can think about it in terms of creativity — which is like pie in the sky thinking — or you can think about it as like, “No! Go create something, like building!” And our view is you need both of those to come together.

Anyway, we talked about new institutions around Creators. We’ve also been thinking about new institutions to help ARIA do better. You might have seen our Activation Partners call.

Eric: Yes. Can you explain what that is?

Ilan: I can. [Laughter] This might be a divergence, but...

Eric: We can come back to it later if you prefer.

Ilan: No, let’s do it! When we think about the ARIA model as a whole, we talked about finding the opportunity spaces, being able to inject energy, catalyze these big waves — which are essentially new movements of technology...but then talent and capital that can transform the world in one of these spaces.

There is a question. We’re not DARPA. One of the things that allows DARPA to get things from early idea/technology into practical systems in the field is that they have this massive lever that is the Department of Defense budget. [Laughter]

Eric: Yeah, they have a built-in customer.

Ilan: Yeah, they have a built-in customer. And there is no bigger driving force than the procurement power of the Department of Defense, if wielded in the correct way. ARIA’s mandate is much broader. We’re not just defense, we’re actually not doing any defense right now — it’s quality of life and economic growth, broadly speaking. We don’t expect to have a built-in customer from government. There might actually be cases where government, like the NHS [National Health Service] could be an amazing partner and uptake vehicle for technologies we develop. But outside of having purchasing power as the driving force, our bet is that the biggest driving force that exists to get new ideas into the world at scale is going to be entrepreneurship.

It’s that whole idea of, “If you, as the researcher and a team of people are willing to commit their life for a stretch of time, yes, you could change the world.” We know it. We’ve seen that happen.

So, one of the questions we’re trying to grapple with is, we’ve created these opportunity spaces specifically to be places where the wave doesn’t yet exist. So, there probably aren’t a lot of startups in these spaces because venture capitalists aren’t super active in all these spaces. Maybe there are some. What can we do to increase the amount of reactants that are entrepreneurial in the opportunity spaces we have?

And we thought about a few things. When I was at ARPA-E, actually, we ran an experiment that I helped launch, which is called the technology to market program. We basically built, within a government funding agency, a business analysis/biz dev type function. The idea being, “Let’s figure out how to inject into our programs some thinking around how you translate the technologies.” DARPA does some of this.

Our view is actually, for ARIA, there are some things we can do internally...but to think about how technology’s transition to market, especially through startups and entrepreneurship, the best people in the world doing that are going to be people doing it in the field. And you might be able to attract one or two of them, here and there, into a government funding agency, but you’re not going to be the best in the world at doing that. So what we did was we put out a call, and we said, “Listen, we think there is going to be massive opportunities and value created in these spaces. It’s still early. If you are an organization that deals with venture talent creation, entrepreneurship, rapid prototyping...anything related to tech translation, transitions, and value creation...and you’re amazing at what you do, could you imagine focusing your energy on some of our spaces? And just do what you do really well, but inject more reactants into that system.”

My colleague Pippy [James], who’s our Chief Product Officer, was really the visionary behind driving this forward. Where we ended up was, we have a handful of organizations. Some of them are venture capital firms that are saying, “We will, for the first time, focus on the UK and look at opportunity creation in these spaces.” They’re doing things like running fellowship programs for scientists who want to be entrepreneurs.

Renaissance Philanthropy is another Activation Partner thinking about, “How do we create a two-sided marketplace between the technologies that start to emerge and philanthropic goals/philanthropic funding?”

Eric: A lot of people don’t think about it, philanthropic demand is demand all the same. They [philanthropists] often have particular technologies they want to bring into existence. But it’s often unclear how to solicit demand from philanthropic...the mass of philanthropies. It’s hard to understand what they want.

Ilan: Yeah, and in a lot of these spaces it’s not clear that when we’re done with our program, the commercial value is going to be right there. It may be that we’re still in a tragedy of the commons, market failure mode, and we need to band together with others to do more in those spaces. So I think that’s going to be important.

But one of the things that came to mind as we were talking earlier is one of the Activation Partners we have, I think you’ve spent time with them, is this company Amodo [Design].

Eric: Amodo, yeah! Love Tom [Milton]!

Ilan: Amodo is such an interesting story. They’re based in Sheffield. It’s a small team. It just reminds me of the things you write about! It’s a small team that, you know, they’ve been involved in a spin out of a university and they basically recognize that, like, “Man, universities don’t do a very good job of prototyping things, having a rapid iteration mindset, or understanding how to get things into a more commercial place. Why don’t we just set up a shop to do that as a service — for startups, academics, and otherwise?”

Eric: So you don’t have to shoehorn your experiments into off-the-shelf Thermo-Fisher equipment. Tom and them can make what you need.

Ilan: Exactly! “Let’s go solve it!” They’re like mechanical, electrical, prototyping fixers!

Eric: And they’re hiring!

Ilan: And they’re hiring! [Laughter] So, interestingly, they applied to be an Activation Partner, alongside...you know, our other Activation Partners are, like, Google DeepMind...but they applied. And first you look at it, and you’re like, “Oh you’re five people in a room in Sheffield?” And then when we talked to them and we saw what they were doing and the vision, it was like, “Actually, this is a massive asset for ARIA to be successful.”

And maybe it won’t just be Amodo over time, maybe we’ll find others. But already, through the arrangement that we’ve built, they’re actually doing this as a service for a bunch of Creators that we’re funding, who are saying like, “Oh my goodness, I thought it was going to take us six months to do this and I got a much better version of it in a week!” So I think that’s super cool.

Eric: And the PDs have seemed very impressed with them. Like when I was talking to some of the PDs [and other ARIA staff] about the BBN thing, the concept that I used was, “Oh, do you want an Amodo for your area?”

Ilan: And that’s one of the things we’re doing through Activation Partners, which, again, is sort of an experiment around this idea of, “The best people, in the right environment, with [the right] incentives.” We’re working with Convergent Research on what I think will be the first experiment of them saying, “Wide open to the spaces,” which will be our opportunity spaces. Instead of being a kind of two-sided marketplace and connector of talent with philanthropic or other funders for FROs (Focused Research Organizations), they’re basically saying, “If you’re someone talented who wants to commit your life to building a focused research organization in one of ARIA’s]opportunity spaces, apply! Then we’re going to find one, maybe even more, to go launch.” And it’s exactly what you said...

Eric: To people in the metascience space, I think Convergent is maybe one of the most exciting...

Ilan: Yeah.

Eric: ...kinds of Activation Partnerships. Because I think everybody [metascience nerds] was around when they first kicked up. And they also always knew [suspected] the vision was probably that Convergent might like to win NIH funds, or something like that, and set up more every single year. So to have an official [Activation] partnership was exciting, for somebody who’s just been a fan for a while.

Ilan: I think the biggest — we might agree on this — but I think probably the most important thing to accelerate progress in the research ecosystem is to increase the diversity of institutional types. Convergent is one of the few examples we have of a mode that is taking root, that’s saying, “Here’s a new institutional type. Let’s figure out how to scale it!” You talk to Adam [Marblestone] and he talks about...you know, he sees a thousand — I think he says either a hundred or a thousand — FRO-shaped things that need to be created in the world. I think it’s a thousand. I imagine there are a thousand. If he thinks it’s a hundred, I can probably debate it. [Laughter]

Eric: It was one of those two numbers. One more question, and then I’m going to do a little pivot. You seem to have a very good grasp of early/mid-20th Century R&D, the institutions, some of the stories, etc. Is there any organization or group from that period you find yourself thinking a lot about? Either because you want somebody to emulate it or you just think it’s cool?

Ilan: That’s a good question. I think the ones I think about are maybe, not surprisingly, the ones that feel like startups before their time. You wrote the piece about Edison.

Eric: Yeah.

Ilan: That was a story I was already obsessed with...but the whole idea of, like, Edison in the workshop. There’s an organ in the back, there are bunk beds, they’re sleeping there. They’re just all in, committed. You know, he’s raising money based on a dream. You think it’s bullshit, but then he makes it true by doing the work to close the loop.

Eric: The financiers come to visit and he looks crazy...

Ilan: That’s one piece. I love the story of Philo Farnsworth. I don’t know how much you know about that one?

Eric: I don’t know that one.

Ilan: 15-year-old farm boy who has a vision of making television. And basically through some serendipitous set of events, because he’s so passionate about it, ends up moving to San Francisco, getting funded by like an LA financier from Hollywood, and is competing with RCA in a race for the first television. And you just look at that, and look at the lab, and look at the talent times intrinsic motivation in the right place and the right time...

And I think Curie — this is more academic — but I think Marie Curie is another example of this. It doesn’t seem like it, but when you look at how she and Pierre Curie worked, it definitely feels like a startup.

Eric: What about it is very startup-y to you?

Ilan: Oh, the combination of the singular focus and the idea of, “We’re going to be open-minded and try to get to the goal through all means.” I always love the story of — I might have this wrong — but my impression is that Pierre Curie discovered piezoelectricity as part of trying to figure out how to very sensitively measure the radioactivity in the materials that he and Marie were looking at. So, basically, you have an academic pursuit to discover and understand this phenomenon of radium that no one understood, that leads to stumbling into discovering a whole other phenomenon, piezoelectricity — which has massive implications technologically. And it’s all because you had this intensity of spirit in one place.

[MIT’s] Rad Lab is probably another example of this. I don’t know if you’ve written a piece on Rad Lab.

Eric: I’ve never written a piece on the Rad Lab. There’s one book that’s continually over $100. And I check Amazon like once a month to see if the price will come down and I can finally get it. [Laughter]

Ilan: I’m going to buy you that book, whatever it is.

Eric: You don’t have to do that!

Ilan: If it means you’re going to write a piece about Rad Lab, then I think we all...we’ll do like a GoFundMe campaign to buy you the book!

Eric: [Laughter] I can promise to find a way to have it out in the next year, somehow! I have a few other Rad Lab oral histories sitting on my shelf that I’ve been letting pile up, so I do have to do this.

Ilan: Yeah, but the whole idea that because we got a group of physicists together to try and build radar systems, what fell out of that was, like, MRI...and the basic theoretical and practical things around that is just awesome!

Eric: Yeah, and also in terms of funding something for five years [as FROs tend to be], and then it's set off in a direction of its own...people at Rad Lab seem to have thought of some of Weiner’s early cybernetics writing, and things of that sort, as helping set the agenda for the RLE [Research Laboratory of Electronics, which the Rad Lab morphed into postwar] going into peacetime. It’s very clear that [the Rad Lab] was catalytic in extreme ways. The kind of thing that would be ARIA ‘returning the fund’ level [results].

Ilan: Yeah, it’s funny, one of the things that’s coming to mind here, as we think about FROs...one of the things I think a lot about is, for so many of these institutions, and I feel this way about ARIA right now. We’re two years in from starting everything from scratch. It feels like we have the environment where these amazing things can come out of it. When I think of our program directors, when I think about the Creators they’re starting to fund, the dynamic of the team, I’m like, “Wow. This has the energy and the characteristics you would want.”

The big fear is, I think, “Okay, this is founder mode.” [Laughter] These are early days! If you look at Rad Lab, it was this intensive thing, and MRI and these other things came out of it in the early days. And more came out of it later, but that question of, “How do you capture that founding period and not lose it?” I mean, what I love about FROs is the idea is, “Don’t even try.” Imagine that you’re going to have an intensive thing for five years. And then it turns into something else, like it metamorphizes. I think there’s something really important about that.

Eric: I remember hearing Adam muse at one point about the idea of, “Oh, it would be great if we had funders who could accept or were excited about the idea of maybe one FRO maybe leading into another FRO.” That is the outcome. You [ARIA] are playing a bit more of a repeated game. How do you think about that, one ARIA program leading to another ARIA program?

Ilan: Oh, 100%. I think most likely that is how we will make an impact, which is we’ll have a program that inspires the next program. And within that next program, we see something and we double down on it...and that’s the thing that happens, being adaptive in that way.

One of the interesting things that I’m noticing is...we haven’t talked much about how we recruit program directors. But the combination of our broad mandate and the fact that we are bringing in, as cohorts, program directors from such different backgrounds...we have these opportunity spaces that the program directors have shaped, and, yet, we’re now starting to see as we dig into them, these really interesting intersections between them. And I’m just wondering what’s going to happen. What are we going to do about that? Are we going to have programs that sit across two opportunity spaces? Are they going to merge into one? You know?

Eric: And can we talk about your new program directors? Who are they? Why did you choose to light up flares around these particular people and their expertise, or their point of view?

Ilan: Yeah. I think this recording is going to come out right after we have announced them. It’s probably worth first saying, we made a bet early on at ARIA. And my colleague Pippy had a really big role in this, which is we basically decided that one of the hardest parts for a research organization like this in the UK was going to be to find program directors or program managers, because, you know, it’s a weird thing to do. You’re on some amazing career trajectory and you decide to, like, step out of that for three to five years to do this other weird thing with a brand new agency that no one has ever heard of. Probably the listeners of this realize that one of the core parts of the DARPA model, which we’ve adopted at ARIA, is this idea of term limits. The people who are developing the theses are people that are coming in from the front lines of doing research in some way. They’ve got to sprint for three to five years to develop a thesis, fund a program, have an impulse function that hopefully catalyzes change, and then they go out and we refresh with some new program director. And part of that is to keep the ideas fresh, but actually, a big piece of it is to keep it incentive-aligned.

I’m never worried that a program director is going to make a decision on their program based on worrying about their career trajectory and what their boss at ARIA thinks, because they’re going to expire in three years. So they’re all just purely focused on, “How do I use this time to make something happen?”

One of the bets we made was, we knew it was going to be a challenge to find people. So we decided, rather than just hire people one-off, as we found them, let’s actually do what we believe is the most open call of its kind to find program directors. Let’s imagine that we can think of a set of characteristics that matter and that we think are going to make for a good program director. And that’s going to be more important than specifically their resume or what they’ve done before. And then we decided that we’ll bring them in as a cohort. So, part of the idea here was, if it’s going to be a risky thing for somebody to come join ARIA at the early stages, it becomes a lot less risky if, when they’re thinking about joining, they can look around and see five or six other people that they think are pretty awesome, who are also going to jump into that pursuit.

And I think that worked out really well. If you look at the first set of program directors, we’ve got someone like davidad who, I think he got his masters degree from MIT at age 15. I don’t think he ever finished his PhD. He ended up working at places like Twitter doing independent research. And he sits in our office next to Jenny Read, who is like a seasoned senior professor at the University of Newcastle — in different fields.

And you have all these other people, and they’re so spiritually aligned, and respect each other, and push each other. It’s really cool. [pause] We were talking about new program directors, but I didn’t get to it.

Eric: No, no, it’s okay!

Ilan: What would you want to see from ARIA’s next program directors?

Eric: [Laughter] What would I want to see?

Ilan: Trying to figure out how to...oh you’ve met some of our, you’ve met our existing program directors! If I were to say, like, “Okay, you have the chance to double the size of the group of program directors and make it an even stronger group. What would you try and sprinkle into the mix?”

Eric: So, that’s tough for me [laughter]...because I’m not a technical genius or a polymath in any way. I’ve been very intrigued by the energy of everybody involved [as an ARIA program director] and I’ve always left excited. But I guess, I don’t know. Engaging in this work as a non-technical person...

Ilan: Yeah.

Eric: ...I more just go in with the assumption of, “Somebody sees genius or a spark of something in this person. I’d love to help them any way I could!” I guess, reading the history... it’s unclear that it would be obvious to anybody, or me going into the past that, like, Frank Heart would be Frank Heart, or J.C.R. Licklider would be Licklider. So, I guess I don’t know...I attempt not to answer these questions or go down this line of thinking because I think I would be biased by random things, probably. For example, I go talk to [ARIA-related] people, and most of them are British. And it’s like, this is a new cultural context for me. Sometimes, with people in my personal work, I go based on spark or energy or something like that. But I don’t think that’s quite how a British scientist would do it. So I don’t really know. I won’t answer your question, essentially!

[Post-interview note: As I transcribe this interview, I wish I would have flagged that some social science program like a computational law program could be cool!]

Ilan: Yeah, interestingly, it goes to the intellectual humility point. You said something that resonated with me, which is like, “I imagine someone saw a spark of genius or brilliance in them.” So we designed our process to recruit program directors — I think we started, the first time we did it, we had seven qualities we were looking for. And they have names, like, obviously like ‘technical depth,’ but we have one called ‘vision to action,’ ‘adaptability,’ etc. We basically put all the applicants through the paces in different formats. We were probing them in different ways to test along those axes. And the idea is not to just have some aggregate score for someone who’s great, but the idea is to find the spikes. We talk a lot about how each of our program directors has a different set of spikes, meaning like superpowers that they’re going to leverage to do the job in their own ray, recognizing that a Licklider has very different strengths than a Bob Taylor, right?

And I think that’s worked out really well. We did the same thing with this new cohort of program directors. But we looked at our first set of program directors and what we learned from them, and we realized a few things. One was, there was something that they were showing in their behavior that we hadn’t tested against, which is something like ‘value recognition’ or ‘opportunity recognition.’ The ability to see the dots and find the constellation within them. So we added that to the mix. We also deliberately tried to recruit...our first set of program directors, roughly 2/3rds to 3/4s of them have more academic backgrounds. They tend to be sort of — I don’t want to offend anyone — misfits? They tend to have different mindsets than your typical academic, that’s why they’re attracted to this role. One thing we tried to do deliberately with the new program directors is say, “Let’s try and bias towards more people with industry or startup experience,” which is the case. So, what can I tell you about them?

Eric: Can you give us a couple examples of program directors who clearly have overlap with an opportunity space from the first cohort? And any examples of people who are, kind of, breaking new ground?

Ilan: Yeah, so a lot of them are breaking new ground. Actually, we’re already starting to see a bunch of different connectivity between them.

I’ll share one. One of our incoming program directors, Rico [Chandra], is Swiss. He studied at CERN, he’s a nuclear physicist. He then decided to go do a startup in the nuclear security space. He’s coming into ARIA, and the thing he’s become obsessed with is this question of...the fact that with all of our modern technology, flight and the ability to transport things in the air is still a big challenge, especially sustainably. And his view is that, “Actually, we know that there are plenty of birds, albatrosses and others, that can go vast distances, thousands of kilometers, without any source of power, in perpetual flight. Why is it that we can’t beat them with modern technology?”

So, you know, with our incoming program directors, we basically say, “Listen, just have a starting point, and then we’ll go from there.” We don’t know if it’ll turn into a program, if it might pivot or not.

Eric: It’s also interesting because more recent people in aeronautical engineering who we think of as geniuses, like Kelly Johnson, [often] thought in wind tunnels. But when you go back to the Wright brothers, they were obsessed with birds. Like there were all these stories of them on the beach where they were apparently — I forget if it was Wilbur or Orville or both — they were impeccable at imitating the motion of the birds’ wings.

Ilan: Oh I love that.

Eric: [gesturing to imitate the Wright brothers imitating the birds] As they were trying to figure it out. So it’s fun that...to find a really ambitious program, he’s also kind of gone back to the source.

Ilan: So one of the interesting things about Rico is he also happens to be, like, a world-record holding long distance hang glider. So he’s been thinking about this a lot. We don’t know whether it’s a program, but one of the things we realized...we have an opportunity space called Scoping Our Planet. And the thesis there is, “With climate change, the thing we really care about is the climate changing. We obviously care about and know that fossil fuels and greenhouse gas emissions are causing the climate to change. But there’s very little we know and very little energy has been put in by the innovation community to understand how we monitor the climate.”

And you can think about why that’s important. Everything from measurement and verification of things like carbon removal, which is sort of a big and unsolved market failure problem. But you can also think about weather prediction. You can think about understanding climate tipping points, which is what our program is focused on. Rico came in and we realized, “Oh, is this like a new aeronautics opportunity space?” And then we realized, “Well, maybe this is just the capability that can be very valuable in scoping our planet.” So now he’s talking to Sarah [Bohndiek] and Gemma [Bale] [the Scoping Our Planet program directors] and trying to understand, “Is this something that can be game-changing around how we have new sensors for parameterizing the earth and communication protocols?” So that’s kind of an interesting, surprising one [new program idea] that connects to something we’ve done.

Eric: So, it seems like you really do hire people first, and find the specific programs later. A lot of your first cohort of program directors are working on programs that, like, it’s maybe not what their academic lab focuses on. Are there any people you’ve hired in this new cohort where you think, “Oh, what they might end up working on is exceptionally broad.” You’ve hired them with a guess, but it’s hard to tell where precisely it will go.

Ilan: I think that’s true of a number of them. I was just chatting with one of our incoming program directors, Alex [Obadia]. Alex is really interesting. He’s a mathematician by training, who then got involved in cryptography. He’s been doing essentially crypto and blockchain stuff, but he’s really passionate about thinking about how the kernels of innovation in real cryptography, that’s being applied to financial systems and otherwise, are going to be consequential to the future of society.

He turned me on to — folks should look this up if they haven’t — this paper on programmable cryptography. The idea there is, right now you think of cryptography as, “Okay, I have a certain piece of data in a discreet way. It’s encrypted or it’s not. I can see it or not.” There’s a whole community of people, now, thinking about, “Could I have a whole program that’s encrypted? Could I have a computer program that goes and does programming on your data without actually seeing your data, because of the encryption?” Or even a computer program that is doing computation and getting answers based on data where no one actually knows what the program is. And you think, “Like, what are we talking about here?” But clearly, as we think about AI, and AI agents playing a bigger role in our lives, there is a question of, “How do we optimize the efficiency of what we can learn and the value we can create with AI, where one of the big barriers is privacy of data?”

So, you can imagine — and I’m not an expert here — but the sense I get is, you can imagine programmable cryptography allowing me to, say, have a set of preferences, my values, things that are very sensitive to me, where I can tell an AI agent that is going and engaging in some democratic process on my behalf. Or I can have an election process that runs a program where it queries, “What are my really sensitive personal preferences?” and has it feed into an answer of a program without ever seeing my data.

So I got very excited about this! And I said, “Alex, this is massive, this is definitely an opportunity space!” And his view was, “Yeah, but I think enough people are already working on this, it might happen anyway.” And we got in this big argument. So now he’s thinking about, “Actually, what’s the next stage beyond that?” We have this massive convergence that’s coming, where we’re going to be fusing humans with technology in new ways. You can think of neurotech. You can think of, you know, just the way we’re much closer to devices. What does preserving privacy and trust look like when your devices are not purely digital, purely physical, or purely biological, but somehow sit at the intersection? I don’t know whether there’s a ‘there’ there, but that’s what he’s starting to explore.

Eric: You seem to have a number of life sciences-adjacent folks in the new cohort.

Ilan: We do.

Eric: What’s the thinking behind that?

Ilan: It’s a good question. Again, it’s another indicator that we really were people-first. When we got the first cohort, we realized there were really only two program directors working on things related to life sciences and biology — Angie, on Programmable Plants, and then Jacques [Carolan], who has the Precision Neurotech program within the Scalable Neurotechnology space. But if you look at the UK, it has incredible strengths in biology. If you look at technology vectors that are progressing extremely quickly — synthetic biology, right? You have all these pieces. So, shouldn’t we have more?

We do have more program directors thinking about biology in the new cohort. Brian [Wang] is an organic chemist, but then moved into thinking about pandemic preparedness. And he still cares about that very deeply, and one of the questions he’s asking is, “Can we utilize new learnings about the innate immune system?” The innate immune system is the part of your body that reacts to pathogens and toxic in broad spectrum ways, right? So it’s not creating antibodies to specifically attack a specific vector, it’s just that initial response. It turns out, actually, the way plants deal with pathogens is entirely innate immune system. And his view is, “Well, maybe we can be inspired by that to figure out how to create therapeutics that can be effective against new pathogens, new bugs, that we haven’t seen before.” And there could be a number of implications to that work where, through that same inspiration, basically the question is, “How do you get ahead of things that are evolving?” You know, you think about the Covid experience. But cancer is the same way, right? How do you get out ahead of something that is a foreign...

Eric: So, when you’re selecting program directors, you’re in a room and you have some exceptional electrical engineer who wants to work on some project nobody has ever done before. You might have a biophysicist interested in some medical area. You might have somebody from a field that doesn’t even have a name. How do you go about...? You have to come out of there with eight people. What does that prioritization even look like? Is it painful, because there truly is no apples-to-apples comparison? How does it feel? What do you do?

Ilan: No, this is actually where the approach...Historically, I think I always felt like I had a pretty good instinct on talent. You know, you hear about this in research and otherwise. I mean, you’ve written about this, which is just, like, there’s a taste and there’s ability to spot talent. And that’s something I always try to be attuned to, and refine.

I think over time I’ve come to realize that one of the best ways to find talent is actually to just create a product that really talented people want, that doesn’t exist. If you think about the ‘product’ we have at ARIA, you have, in this very open way, an ability to take £50 million and figure out how to create a wave that changes the world. That’s really compelling if people believe you. So that’s one thing that I think has changed where you say, “Okay, that’s a great way to attract talent.”

The other thing is, and the big lesson for me around our program director recruitment, by trying to strip away to first principles around, like, “What do we really care [about] in terms of the characteristics of these people?”...and this is very much like a Kahneman-based approach of recruiting and selecting people, which is just like, “We’re going to have the criteria. We’re going to probe the system a bunch of ways. We’re going to see how they spike against the criteria.” And ideally where that ends up, to your point...when we went through that process based on paper applications, phone call screens, technical interviews, we ended up with this set of people — this year I think it was 16 — where we said, “Based on everything we’ve seen, any one of these 16 people could be a program director.” Because who are we to know? Like again, the intellectual humility. I could guess, but I’d probably be wrong.

So then we said, “Forget it. Rather than just trying to pick the right ones out of the 16, let’s actually bring the 16 people together.” One thing that’ll happen is...these are awesome people who will go drive impact in the world, and they’ll probably all do more if they’re connected to each other. But it’ll [also] give us one more way to look at how they show up, not just as individuals, but also how they sort of connect to each other.

Basically, at the end of the process, we’re just thinking about the portfolio. We’re thinking about the portfolio of people, portfolio of ideas, based on what we think they might do, what they might bring to the cohort. Just curating based on that.

Eric: If I can probe you with a bit of an edge case from history, I’d love to get your...

Ilan: Oh boy, I might not...

Eric: So, speaking of intellectual humility, I’m a big Warren Weaver fan.

Ilan: Yes!

Eric: And something that comes out all throughout his writing...he is an exceptionally...he’s a Midwesterner with a lot of humility. It comes out in all sorts of his [writing]. So when he first came to the Rockefeller Foundation, even though he had humility, he had one of the more extreme bets in philanthropic history. The [Rockefeller] Natural Sciences Division, which gave out on the order of what ARIA gives out per year, maybe a little less. It’s hard to do a one-to-one, because science was cheaper back then. But, essentially, they [Weaver’s predecessors] said, “Oh we fund electrical engineering, zoology, etc. And we fund the very best people who come in.” And when he came in, he said, “That’s fantastic. We’re not going to do that anymore.”

When he’d gone around and talked to people, he said, “There’s something right there in that area between biology and physics. So, yes, our budget is really big, but it’s very finite. We’re going to press on that.” And he essentially put 80% of his budget towards — it was 1932 — five years later, he would name it ‘molecular biology.’

Ilan: Out of curiosity, do you know how long he was in the foundation before he made that bet? You probably do.

Eric: I think he came in with the bet.

Ilan: Oh okay!

Eric: He took the interview with them to say, “I don’t think I’m your guy. I’m just an applied mathematician. But there’s this thing..."

Ilan: “Here’s the thing we would do!”

Eric: Yeah. He read very broadly, for sure. And he kept at it [funding molecular biology] for 20 years before moving on to something else. But if you all felt you had an ARPAnet or a molecular biology on your hands, how much is too much of your funds focused on that one area? There’s maybe a limit. 80% is a lot, for example.

Ilan: Yeah. This is interesting. This is something we talk about a lot. First of all, the question of, “We have these seven opportunity spaces. Why are we recruiting more program directors? You know, aren’t we doing enough? Like the goal isn’t just to boil the ocean.” And actually, we tend to think really deliberately about, “We need to get to this one outcome that is so massive. Do we think we’re touching enough surface area right now to fast forward ten years, and realize we got lucky and we just noticed that thing that was going to be so valuable?”

And when we looked at our budget, we looked at the size of the UK ecosystem, and we looked at what we were doing, we said, “Actually, no. We’re probably not touching enough surface area.” So we now have more program directors coming in. There will be some new opportunity spaces. We’ve also said, “There’s a limit.”

It’s likely that every other year ARIA will recruit a set of program directors. It’s a three to five year term. So that gets you into a steady state of 15–20 program directors. And that’ll be it. So we’ll have a first set of opportunity spaces. That’ll be the surface area that we touch. And really what success needs to look like is...out of that, we’ve found something that we not just want to double down on, but 10X on. And so if a big portion of ARIA’s budget does not end up in one of those areas over the others, there’s a problem.

Eric: Oh wow.

Ilan: That’s my take! You know, I’m on a term limit, too! I probably won’t be the CEO when they have to figure this out. But is that 80%? I could imagine it. We talk a lot about…

Eric: And could the person in charge of ARIA...

Ilan: Decide?

Eric: ...if they were a brave person, if they thought it was right, is that that? Are there some structural or institutional barriers that would make bravery even tougher, beyond the pure pressure of people looking at you like you’re a little crazy or something?

Ilan: One of the nice things about ARIA, it’s worth mentioning, the UK — like a number of people who were in UK government, civil servants, others, Parliament — they got this right. In the sense of, ARIA really does have the right mandate, freedom, flexibilities. There’s not political intervention, there’s not a lot of BS processes...they’re the right processes to make sure we’re responsible stewards of taxpayer funds. But one of those things is the CEO of ARIA makes programmatic decisions; that’s very much modeled after DARPA. I think that’s 100% true. And that’s good to have in place from a governance perspective. The question is, “Will the organization have a culture that leans towards the bold bet?

We’ve been talking about this a lot. So far in ARIA’s history, the things we’re most proud of are when we’ve gotten exposed to something and then we just said, “Ok, let’s do the bolder thing here.” And it happens in big and small ways. Sometimes it’s about a program director we decided to bring in. Sometimes it’s about a program that we launched. Sometimes it’s like, “Do we use Slack?” Because there are potentially any number of issues from having your Creator community on a Slack system. We just had this conversation and said, “We should bias towards doing the bold thing.” [Laughter]

And my hope is that we build into the agency that a leader in the future will be celebrated by pushing that bold bet.

Eric: I had a question written down, and I think maybe you just answered it. I was going to ask, “How do you deal with a program that you’re exceptionally excited about, but maybe the follow-on funder doesn’t get it? Like the VCs aren’t sure what to think about it five years in advance. Which is relevant, because with early autonomous vehicles, it’s not that the Army desperately wanted...they didn’t really know what to do with it. But it sounds like your answer is, maybe, “If it’s a bold enough bet, we can be the follow-on funder. For a while at least.”

Ilan: Yeah, that’s definitely our mindset. And we’ve even tried to build that in. Some ARPA agencies require cost share in their projects: “Oh, you have to show us that someone else has skin in the game by funding 20%.” We basically said, “We’re not doing that, because that actually could as easily could be a counter-indicator for us.” In the future, we think we’ll probably leverage other funders as follow-on partners, and everything else. But let’s make sure there’s nothing that prevents us from doing the bold thing.

That said, it can’t be as easy as, you know, in your scenario, “What’s going to get us to one day have 80% of our funding in one of these spaces?” It can’t just be someone walked in, looked around, and said, “Okay, yeah, let’s just do that.” We should be evidence-based. [Laughter]

We have a pretty good set of principles. A lot of this is, “What are the principles? And then what are the processes that allow us to make sure we’re holding true to those principles?” We have that for creating opportunity spaces. We have that for approving budget for programs. The next thing is to have that for portfolio allocations. Meaning, “We’ve run these opportunity spaces. Where do we think we are seeing the bigger bets emerging versus not?” So that we can say, “This opportunity space is going to get bigger or more focused, and this other one might go into hibernation because we don’t think the elements are there to create the wave.”

Eric: Yeah, no, that’s very interesting. I didn’t know a lot of that, so that was great to hear! So a bit of a question very relevant to...

Ilan: How many questions do you have?! [Laughter] It’s like an endless amount. Are you just making them up and the papers [Eric’s notes] are just like a...

Eric: Well I’ve skipped some [gestures at discarded paper pile], I’ve come up with some new ones [waves papers]. They’re not all off the page! They [Asimov Press] brought me in for a reason.

Ilan: {Laughter] He’s just got like blank pieces of paper just to make it look official.

Eric: I could show blanks! [shows the blank side of his stack of papers]

Do you spend a significant amount of your time thinking about setting up a portfolio to take advantage of AI? Obviously a lot of big breakthroughs in science history come from using the big thing from last year or 5 years ago to apply to some new area?

Ilan: Yeah, it’s a really hard one. I mean, the simple answer is, “Yes.” We are spending a lot of time thinking about how AI — the fact that we’re starting an agency like ARIA at this moment in time, given what’s happening in AI development. One of the easy inspirations for new programs at places like DARPA or ARPA-E is, “What are the technology vectors that are making incredible progress? What are the learning curves you can ride?”

So, I remember at ARPA-E, we had a number of projects we funded based on the fact that fiber optic lasers were getting much cheaper and more powerful year after year. So we had a project we funded on basically using that for geothermal — like drilling hard rock — and other things. AI is like that, just massively.

I think we’re trying to be a little bit deliberate before we figure out exactly what that means for ARIA. For instance, we haven’t jumped in and built, for ARIA, a major compute cluster. We’re starting to think about, “What does an AI program director look like?” You know, how to actually do a lot of the soundboarding that we do. Our program directors are actually using AI. It turns out AI is actually, like, an incredible tool as a program director. Because often you’re looking at things outside of your area of expertise. Oftentimes you’re like, “Oh, if only I had an expert that I could bounce this off of!” Having a thousand experts in all the areas at your fingertips makes a big difference.

I think the much more powerful part is, when we think about these opportunity spaces, one of the questions becomes, “Relative to the impact we think AI could have on this space, how much activity is there to start to deploy that?” And I think that’s going to be one of the important...especially for this new cohort, we’ll end up with some programs that really have AI at the center.

But I think for all of them, there will be this question of, “In this space, where are we in terms of the max benefit that AI is going to build by integrating with this discipline or set of fields, versus now?” And I think that will help dictate how much of an AI flavor we have in the space, and what we’re doing to be differentiated. I actually don’t know if I believe that answer that I just gave, but it sounded good.

Eric: Before we move on, is there any other technology you spend a lot of time thinking about? The world of science and engineering is broad. There might be something else that you think people don’t pay enough attention to.

Ilan: I mean, there are little examples. Like expansion microscopy, that’s such a cool thing! Most people don’t know! It’s popping up in different places.

Eric: For people who don’t know, the TLDR on expansion microscopy is, “What if you made the sample bigger!” And...

Ilan: By literally just putting it in a gel and expanding it. And it works! It’s unbelievable.

I don’t know. I think synbio comes up a lot. I think data, beyond just AI, data comes up a lot as an area where, when you look at some of these disciplines...this is something we’ve found in some of our climate work. You look at the best repositories and the most valuable repositories of data around climate and weather, and realize that none of them are engineered to be accessed in a way that’s compatible with modern data techniques. So there are just infrastructure gaps where you think, “Okay, there’s something cross-cutting around data that we can do.” Or a barrier that we need to get over.

Eric: All right. So I have a suite of metascience questions for you.

Ilan: Okay.

Eric: So, you can go rapid-fire responses, or spend time on them!

Ilan: [Laughter] Okay!

Eric: Do you consider now a great time to work on metascience?

Ilan: Yes. It’s probably the best time to work on metascience that has existed in my life.

Eric: You’ve done this for a few decades, depending on how you want to look at it. Were some periods a lot more frustrating than others?

Ilan: When I left ARPA-E, I wanted to go start...actually, I may have sent you the paper, I had a paper...

Eric: ‘ARPA Lab.’

Ilan: Yes, ARPA Lab. I wanted to create an ARPA Lab. And the point was, like, “ARPA-E is great as a funding agency. The problem is I don’t have resonant institutions to fund. So let’s imagine a lab that is ARPA-minded, that is very entrepreneurial.” And I sort of mapped it out. It was probably like a lab that would have been a compilation of FROs.

And I thought maybe we’d get philanthropy to fund it. And, you know, who was I? So maybe someone else could have done it. But like, crickets, you know, nobody! You know, philanthropy funding science, it sounds like it was a long time ago; it wasn’t that long ago. But the state of affairs, the default for philanthropy and science historically, probably for all time — you can tell me if I’m wrong — has been like, “Oh, I have a family member who got this disease, and I’m going to fund research on this disease.” Or, “I went to this university, so I’m going to give them money to do scientific research.” The idea that you’d have philanthropists making big bets like Convergent Research or Arc [Institute], in terms of new modalities for R&D, that is a new phenomenon and a really exciting one.

Eric: Speaking of something like Arc...

Ilan: This is meant to be rapid fire! [Laughter] You let me go on!

Eric: A lot of people conceptualize the new science orgs as experiments, in and of themselves, in how to do science differently. ARIA is also some version of this. Is it difficult to see what’s going on at a place like Arc, and, if there’s a useful learning, fold it into your operation? Am I not thinking about that right?

Ilan: I think you are thinking about it right. I think the biggest thing — as someone who grew out of venture capital, startups, and Silicon Valley — the thing I’ve realized is, the power of a vibrant ecosystem of startups is you create evolutionary pressure for institutional change. Meaning, nobody talked about OKRs until Google came around. And then everyone needed to do OKRs! And then Stripe came around, and I don’t know whether Stripe does OKRs, but they do things differently. And then all of a sudden it’s like, “Oh, that’s the way to do things.”

It drives you to say, “Oh there are competitors, or peer organizations, that are figuring out better ways to do things institutionally.” And that’s keeping the system super fresh. We don’t have that in research. The institutions are so stagnant for so long that you don’t have the evolutionary pressure to change.

For me, what metascience means is: go run those experiments and find ways to start diversifying and creating a more vibrant institutional ecosystem. Partially so you can get that institutional pressure! You need a critical mass of those things to get that institutional pressure.

So you asked, “What is ARIA learning from Arc?” I love Arc as an experiment, and I think it’s going to change the world. I initially was skeptical that Arc was going to be so close to Stanford as a university. And that they were going to hire people that were still in the academic incentive structure. I think, actually, it’s probably proving me wrong on that. And that’s a great learning. But it’s a very high level, like from a distance, learning.

The beauty would be if we had enough of these things where I could tell you that, “With a 90% certainty, that some one in ARIA’s team, in three years, will come from Arc.” Like if you look at startups in the Valley, right, like, “What are the chances that someone who was at Tesla will end up at another one of these companies?” Absolutely, right? That’s one of the things that drives that learning. It would be awesome if, within the R&D ecosystem, we had that kind of vibrancy and mobility, because I think then you really get the learning.

Eric: And if you personally had the funds, for whatever reason, to fund additional orgs to complement ARIA, like completely different metascience experiments, does anything come up in your head?

Ilan: [Laughter] Yeah, what came to mind, just thinking about your audience, was a conversation with Michael Nielsen when we started ARIA, who was basically giving me a hard time: “What you’re talking about doesn’t feel that differentiated. Where’s the gap? What about a research organization that gives 100-year grants?” The idea of, “We don’t have long term modes.” If you think of time constants as one of the axes of the portfolio, there’s not a lot of diversity on time constants, to like 100 years. Which I loved! I basically said, “Yeah, but if I look at ARIA’s mandate, actually, ARIA’s mandate is not to create new fields.” That’s important. It’s not like [to create] molecular biology. We are built to get something to a new technology platform or industry base. And I don’t think the hundred year timescale is going to work for that.

But I think long duration...basically modes of getting talent focused on either creative research or creation research for long periods, I think there are far too few modes for that, and fewer than there used to be.

Eric: So, that’s obviously ambitious. It’s also expensive. ARIA is pretty expensive, Arc is expensive. Do you think you need a minimum amount of funds to do a good metascience experiment? Do you have low-cost ideas?

Ilan: Well, I don’t know if the long-term thing is expensive. I mean, look, what is expensive? It’s all relative to the potential impact. You have to normalize it, right? So, one of the things I’d love to see someone do is...I think a lot about, “How do we train scientists?” Translational scientists in particular...there aren’t great training environments anymore for translational scientists. One thing I’d love to see someone do is basically say, especially with AI, the training is going to be less and less about the knowledge, and more and more about the taste, the tacit knowledge. and the instincts. So apprenticeship is really important. Why don’t we take kids that are high potential — and when I say kids, I don’t know whether I mean age 15 or 22, who knows? But take high potential people and basically say, “We are going to engineer for you a program where the next 15 years of your life you are constantly being taught how to do research, translational research, through a series of apprenticeships with incredible people.”

And maybe that’s in conjunction with a university, maybe it’s not. To pay for that, you can basically say, “We’ll give you fellowships,” or “You’ll work part of it.” That sounds very expensive, but my instinct is that if you did something like that, you could create the super researchers of the next generation that change the world. So probably not that expensive. [Laughter]

Eric: I’d love to poke on that...

Ilan: Maybe that’s too expensive.

Eric: ...but I’m going rapid-fire.

Ilan: Let me give you one more!

Eric: Yeah, for sure!

Ilan: Very cheap metascience experiment, probably the cheapest one I can think of. I noticed in my PhD, and actually when I hired for my startup, I found myself hiring people...a mode that I would hire was someone, who went into their PhD, they totally butt heads with their advisor, and they walked out. They ended up having to change advisors, or even complete disciplines, and then they thrived. And they come out with this like, “Oh, that was a horrible experience. But it made me change and I’m in a good place.”

Anyway, I keep thinking, I found an amazing PhD advisor that was really resonant. I didn’t waste my time. I felt like I hit the ground running. And I had this incredible experience. So a simple idea: a matchmaking app for PhDs and their advisors, when they come in. If you could increase and improve the compatibility of a PhD student and their advisor, on whatever axes, I think that ends up being a big deal. And it’s basically free.

Eric: Okay, great. That’s fantastic. Thinking of ARIA as a kind of experiment in and of itself, what’s a current bottleneck you all have that you’re very eager to find a way around or work on? There’s always small things in working on an organization.

Ilan: I’ll tell you what comes to mind. It’s kind of a hard one, but I think we’ll probably have to solve for it. We want to move fast in our programs. The view is, “You’re doing something speculative, so time to the next learning cycle is really important and valuable.” And we want to be funding people with diverse skillsets in diverse institutional types. Some institutions...the kinetics of the institution are very slow. And yet, we still want to fund people in those institutions. So one of the questions becomes, “If the kinetics of the institution, just in terms of how fast they can get stuff done, are generally very slow, but you have a Creator in that institution who wants to move fast, how can we help them move faster?”

And I actually think the only answer is we need, like, a fixer. We need an organization whose job it is to de-bottleneck activities within other institutions.

So my imagination is, Eric, you start the Research Speed Fixing Company, and we contract you, and you know, Jenny has found this great performer, but things are moving too slow in this institution. And Jenny gives them a phone number, that’s like a magic phone number. And they call it and you say, “Eric’s Fixing Services! We’ll send someone over right away!” And someone shows up in that institution, and they're running around the admin, just like putting pressure on things and getting people to move faster!

Eric: There are actually things like this in DARPA history.

Ilan: Are there? Amazing!

Eric: I can’t think of one from a performer, but I can think of one from central office. So, for example, when you read through all the oral histories, there’s this guy from early IPTO — which is computing office — Al Blue. And his name just keeps coming up as the guy who makes any bid you want to put out legal, workable, straightforward. I don’t know if he was an engineer or scientist or anything like that. He might have been one of the military guys who finds a way in and has to make his way in the office.

But that kind of stuff makes a big difference! Like in the same breath where they’ll be like, “Oh Licklider was a god,” they’ll also be like, “Al Blue made X thing happen. You need to talk to him.”

Ilan: Yeah! Well, we’re trying to build it into the culture! Right? Like the program directors know...it’s my [Ilan’s] job to have some pressure around, like, learning cycles, it’s their job to do the same. Hopefully we’ll create a Creator community...picking people that are intrinsically motivated, so they want to be pushing the kinetics of what they’re doing. And, yet, it would be really nice if you had more help on that.

Videographer: Time for a battery swap.

[Camera Break]

Eric: Alright, next in the rapid-fire questions…

Ilan: Can we, can we just pause for a bit. I love that we’re doing this long form thing, but people have to realize that this camera right here — there’s a camera with like a bag of ice on top of it because we’re going so long that it’s overheating. Which is great!

Eric: We’re making it work, though. I think the camera looks great, you know? It’s like when you go in the locker room at the end of the game and everybody’s got ice on their knees.

Ilan: Yeah, totally.

Eric: Both teams played hard.

Ilan: We’ll feel like we played hard today. [Laughter]

Eric: Is there anything you personally think people who write about metascience spend too little or too much time on? Like is there some hypothetical Substack where, as the CEO of ARIA, you would hoover up if somebody wanted to spend the time on it? I already have homework where I have to write a Rad Lab piece.

Ilan: That’s right! You do have to write a Rad Lab piece. Multiple! There’s like a series there. Oh, you also need to write a piece about how, actually, DARPA ended up emerging in part because of the UK’s radar effort. Which is important for me because it brings things full-circle to ARIA.

Metascience...honestly, what I would love is a blog that just chronicles metascience experiments and their learnings, and just keeps tabs on them. And that that grows and grows.

Eric: And what are variables that you think should be...some of would be qualitative, you meet them where they are...but would there be any underlying variables you’d want if somebody was writing about Convergent or Arc or ARIA, to constantly revisit?

Ilan: Incentives.

Eric: Incentives?

Ilan: Incentives. I think that is the only theme that matters. [Laughter]

Eric: Yeah, I think that makes sense. Do you have any call to action for any groups involved in metascience, or who would want to be involved in metascience, that you think would be useful to put out there? That can be researchers, engineers, ops people, policy people, whatever it is I am.

Ilan: I’m going to say something controversial, which has been on my mind a lot, which is...I think metascience is, at the same time, one of the most important movements, in terms of driving more progress out of research, and also in some ways one of its most dangerous movements.

The reason I say that is, when I was at ARPA-E, I was really interested in the question of, “How does ARPA-E understand its impact, map its impact, and measure it?” And I tried to dig into how we could do that. And both from my explorations of the community of people thinking about that and from the many activities we had to go through at ARPA-E to try and show and prove our impact, how things work...the amount that was “useful” was probably 10%, and the amount that was, “Actually, you’re trying to map something that doesn’t make any sense onto what we’re doing. You don’t really understand the context of how we work, and you’re trying to measure it. And you have a number of theories, but even when I tell you they don’t map on here, you don’t believe me,” actually led to a lot of inefficiency.

So I think what I would say is, if you’re in metascience, recognize that first of all, if you can do something experimental which pushes that evolutionary pressure on the system, great! If you can show there are better ways to do things by doing them and then having results, amazing! If you’re working more on the theoretical side or the evaluation side, just recognize you have a big responsibility, which is to make sure that when you add up all the hours of people who are engaged with that — from agencies doing the work, startups doing the work, or whatever else — that you have high conviction it’s going to be net benefit as opposed to net cost. Is that...?

Eric: No, that’s perfect!

Ilan: I feel like I’ve said that to people, and I think I’ve offended them in some ways, but I think it’s a really important thing to be thinking through.

Eric: No, that's perfect. We’ll end the official questions there. That’s Ilan Gur! Thank you so much for doing this and how long you were willing to spend with me today.

Some time in 2023, Tom Kalil told me he thought it would be a good idea to carve out a chunk of time and get to work on some ARPA project histories. The ARPA model was proliferating, and Tom felt these pieces might find a ready audience of ARPA emulators and fans eager to make use of the actionable information. Tom, who was then at Schmidt Sciences and is now President of Renaissance Philanthropy, has good taste. When he suggests something, I’ve learned you should listen. So I trusted him and threw myself into the work.

If any of you have enjoyed this series and wondered how the sausage is made, “How does one figure out how a 60-year-old R&D project — with no book written about it — was managed?” The answer is often: oral histories. The documentation that enabled many of these histories came to be because, at some point, committed historians sat down with a set of DARPA Directors, officer directors, staff, program managers, and funded researchers to record interviews with them on the practical details of their work.

Usually, I’m just a consumer of these oral histories. In today’s piece, in a departure from my usual role, I get to deliver you all an oral history — one I think is ideally suited to FreakTakes readers. It’s an oral history with possibly the best guest I could have asked for: Ilan Gur. Ilan is the founding CEO of the UK’s new Advanced Research and Invention Agency (ARIA). And, better yet, he’s a huge metascience nerd! The two-hour interview, recorded in Berkeley, attempts to unpack the metascience experiment that is ARIA.

I can’t thank Asimov Press enough for funding the interview and facilitating its recording. The full audio, video, and transcript are here on FreakTakes. And I also worked with Asimov to write up a much snappier, abridged version of the interview. You can find that piece in Asimov Press today.

Enjoy!

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For those who’d like to listen along while they read, I’ve embedded the Spotify version of the podcast. Full transcript below. Special thanks to my girlfriend Katherine for helping me edit the full transcript:)

Eric: Ilan, the CEO of the [UK] Advanced Research and Invention Agency. Thank you so much for being here and doing the Asimov interview today.

Ilan: Thanks for having me. I don’t think I have ever been interviewed by someone who geeks out about the same things I do as much as you.

Eric: Well that’s exhilarating, because this is my first interview. We’ll see how it goes! [Laughter]

First question. We’ll start with a fun one. As grandiose as it sounds, ARIA’s mandate is to aim big and try to create technologies as important as the ARPANET or spark new fields the way the Rockefeller Foundation did with molecular biology. Do you consciously think about that to yourself — that you have to be or find the next Warren Weaver or JCR Licklider? Does that keep you up at night?

Ilan: [Laughter] What keeps me and our team up at night is actually having a massive enough impact. Do we think about trying to be or find the next Weaver or Licklider? Probably not. I use an analogy often of being a catalyst or an enzyme. Whether it's me or our team, we're one thing you introduce into the system that hopefully mobilizes a bunch of other stuff to make an impact.

Eric: In that case, if you think about being a catalyst, what makes a reaction big enough?

Ilan: That’s a great question. You’ll appreciate this: when we have program directors that come into ARIA, some of the pre-reads we give them before they start are interviews with Bob Taylor or stories from DARPA or otherwise. And the main thing is to try and get in their heads, “What does a win look like?” For a lot of places if the research you fund leads to a new product that makes an impact, that's a big win. I often say, “That would be a loss for ARIA.” Because what we really need to do is catalyze something that is bigger than a product, bigger than a company. It should essentially be like a movement, an entirely new technology platform that didn't exist, an entirely new industry that didn't exist. And I think the way you catalyze that can be really different for different people. So if I think about our program directors, they're all different. They all have different superpowers. Just like if you think about Licklider...I mean you're a history geek, right? So just like if you think about Licklider and Bob Taylor, right, you could talk about them each as having catalyzed this massive transformation, but in very different ways.

Eric: Are there any reactions that have been catalyzed in the past 10 or 20 years or so where you look at them and you say “Wow that's the kind of thing we should be going for”? I’m definitely guilty of thinking too far in the past on some of these things, so I’d love to hear your examples.

Ilan: One of our advisors for ARIA is Aslem Turechi, one of the founders of BioNTech. And so you think about their story of having sort of worked so hard on mRNA-based vaccines and systems — initially for cancer. And then you think of Dan Wattendorf at DARPA who basically started the program thinking about vaccines for pandemics. All of that was in the mix, and then all of a sudden you had the pandemic event that actually turned it all into the world-changing impact. So I think that's such an easy one to go to. I also want us to be thinking differently at ARIA around how those transformations might take place.

Interestingly, for example, you think of ASML as a company. But it really is a company that manifests, in that one company, a massive transformation in the field, in terms of UV lithography. Especially with ARIA being something based in the UK...you can't be picky in terms of how you're gonna get that world changing of a transformation within an ecosystem that is bound like the UK. I mean, we’re working globally, but I think ASML is such a compelling example of, “Out of some fodder and set of reactants, back to the catalyst, something catalyzed this massively transformative company and then field shift.”

Eric: Do you have any program in the current cohort that you think, like, their way of scaling to impact might be in an ASML-shaped box?

Ilan: That’s a great question. I think it may be for...any of them. [Laughter] In the sense that, and we may talk about this more later on, one of the big things about ARIA that we're trying to build in...that maybe differentiates it from, say, ARPA agencies or DARPA (which started seventy years ago) is recognizing just how important entrepreneurship and startups are to cutting-edge research in today's world. Our bet with ARIA is that probably the biggest driving force for impact will be through entrepreneurs and entrepreneurship, in some way. Whether you end up catalyzing a whole ecosystem of entrepreneurs and different companies, or entrepreneurs that are focused on government, or whatever else. Or what emerges from that is one singularity of a company that changes the world, or like an “Ozempic moment.” Again, I don’t think we can be picky. I think you could imagine that happening in just about any of our spaces. For example, I think about neurotech, I think about programmable plants, and certainly in robotics. It's just a question of do we get lucky and nucleate that right thing at the right time.

Eric: On that point. So I read your House of Lords testimony and a lot of things of that sort…

[Laughter]

Eric: I’d love to parrot back to you how I understand ARIA, from those materials. You can tell me what’s right, what’s wrong, what I need to say differently, etc. Also, if you could use examples from some of the current programs, that would be great. I’m sure there are many listeners who may have seen a program announcement, but that’s all they know. So as I understand it: ARIA programs must be 1) “big if true” and 2) differentiated. You don't want to fund programs that somebody else in the ecosystem would fund otherwise. And while each project doesn't have to succeed — the vast majority won't in the traditional sense — they should all succeed in materially changing the conversation regarding what's possible in a given space. In terms of your mandate in the UK — in the long term, these programs are meant to help drive economic output, quality of life improvements, health benefits, etc. Is that more or less accurate, how you'd conceptualize it? If someone says, “Isn’t that what deep tech VCs do?” what would you say?

Ilan: First of all, is that right? I think that’s right. We're meant to take bold bets that kind of amplify different parts of the research system in new ways. I think about our programs as funding like a constellation of teams that can lead to massively transformative outcomes at the intersection of quality of life and economic growth — not just for the UK, but for the world. I think that ‘for the world’ is really about the scale of ambition that we are talking about.

You were asking, “How’s that different from a VC?”

Eric: If somebody says, “How’s that different from a VC? Or “...than if the NIH spins up a new study section?” Or something of that sort.

Ilan: I think those are two very different questions. Let’s talk about VCs. There are a lot of VCs who are just in it to make money...but in terms of the mindset and the motivations...a lot of VCs, I think, do see venture capital as a platform to change the trajectory of the future and world in positive ways. I think the big difference with VC is — when talking about the idea of ‘big-if-true’ — there’s a sense of scale, right? You have to think about the scale of “how big is ‘big’?” — and how speculative are you willing to get on the ‘if-true’?

In talking to venture capitalist friends about what I do and what we do at ARIA, sometimes you hear this metaphor around startups and VCs: “surfing a wave.”

That’s one way to think about this. What does a VC do? A VC is meant to be like a surfer in the water, to look out at the wave set that’s coming and ask themselves, “Oh, okay, is that wave forming? Is that going to be a really big one?” The size of the wave is, kind of, how big of a market they think might emerge. And the bet they’re trying to make is, “I bet that’s a big enough wave.” And then they’re trying to bet on the timing, like, “Okay, this is when I should grab a surfboard and start paddling.” And you can think of the surfboards as the companies they’re investing in.

I think the big difference [between ARIA and VC] is, that’s being very reactive to the system. You’re trying to find the trends you can hop on and surf, so that you can maximize the ROI of your investment in a very concrete, direct way.

I see our job as more of putting the energy in to create the wave. A venture capitalist can only fund into a thesis once there’s enough momentum where they can start reading, “I see the market forming, I think there are technologies that can work here.” And most importantly, “There are companies.” Like, most VCs are waiting for companies to come find money. So there need to be enough entrepreneurs and companies doing that.

If you look at the spaces that we’ve carved out [at ARIA], we’ve said, “Right now everyone looks out and just sees flat water, but we believe that by pushing a little bit here or there we can start to build a wave.” And if we can catalyze the formation of a wave that’s big enough in one of these spaces, then actually that’s going to catalyze all the VCs to want to jump on that wave and to want to invest in companies. Then you get a win-win and, ultimately, change the world. [Laughter]

Eric: And so…

Ilan: Was that helpful, that analogy? I probably went a little too much on that.

Eric: No no no, it was great! So I have some questions. Sometimes, in terms of creating the wave...I’m sure there are times where you put a certain amount of money in a space, and there's enough teams out there that already care about this and have the proper background and the right incentives to just throw themselves into it once the capital is there. And then there's probably other cases where more legwork is necessary. Because that's not quite what a university would do — or usually a startup would do X task, but this isn’t quite a VC-scale market. How do you approach those two different types of areas?

Ilan: There’s so much we can talk about here. I’m glad this is a long form interview, because we’ll have time to get into a bunch of this. Part of this has to do with how we organize what we focus on, and maybe we’ll come back to that. I think it’s worth just talking about an example here. What came to mind as we were talking about the wave and VCs, I was thinking about one of our program directors, Angie [Burnett]. This might be particularly interesting to this audience. Angie’s a plant physiologist by background, had worked in academia, a national lab, she worked for the UN for a little bit. She came into ARIA initially thinking about food security and what we could do in food security. Fast forward — where did she end up with her opportunity space, which is what we call our focus areas, and then the program that she launched? The opportunity space that she launched is called Programmable Plants.

We tend to think for all of these [opportunity spaces], there’s some insight there. For us, an opportunity space needs to be, like you said, big-if-true. And we need to be able to make an argument relative to the potential impact. So if it’s highly consequential for society and relative to that impact you can argue it’s underexplored...you know, not enough funding or not enough of the right type of ideas or bets being made in that space. And that for some reason, it’s ripe for transformation. That defines a very big [opportunity] space that we are now going to start working in — and basically buying options through the early research that we’re funding to learn more and see if we find something that could become one of those big waves.

Angie's insight on this space was that if you just think from first principles, when you look at some of the biggest problems and opportunities we have in the world, obviously, agriculture is one of them. You think about food security, you think about climate change. The tough part about these segments is that they're massive problems, right? You need to talk in terms of gigatons of CO2 or carbon, in either case. And her view is that, actually, you can think of plants as a technology platform, and plants are one of the few technology platforms we know of that, actually, we have a system that operates at that scale — of gigatons of carbon.

And what do I mean by technology platform? A plant is a piece of hardware. It has certain functionality. Interestingly, in many plants, like you go to the store and you buy a cob of corn, we have actually engineered the functionality of that plant over many years. And you have distribution channels and the ability to deliver these things at a massive global scale, commensurate with those problems — food security, climate, etc.

The problem is...it's like a really shitty technology platform, right? We're very limited in what functionality we can build in. Instead of 18 months for a new iPhone, you take 18 years to get a new crop that's actually viable. Interestingly, if you talk to most investors, they will say, “Well, in the ag space...you've got the top [incumbent] ag companies and [given the incumbents and structure] it's basically the hardest, most entrenched space to innovate in.” It's just not a great place to invest. Massive capital, massive entrenched interests, there’s regulations, etc.

The bet that we're making is that actually...on first principles, when you look at what’s happening in synthetic biology, when you look at the problems and the forcing functions that are coming, requiring a change to how we do agriculture...there’s a pretty strong argument to be made that, sometime in the next few decades, there will be a shift in how we approach global agriculture. You can think about it as something like, “There will be an ASML-type company that completely disrupts the big five ag companies in the world, and does things radically differently.”

Eric: And what's Angie's job over the course of five years? And I guess also tie in, what is a program director? And how important is it that they have a point of view? And does that come out as you're picking them? ...I asked you a lot of questions.

Ilan: No, that’s fine. Angie’s job was first — together with me and the cohort we had — beating up the question of, “How do you define a space that’s interesting and in line with this idea of ‘plants as a technology platform that’s underappreciated’?” Synthetic biology has been this massive vector of technology progress, which is not being leveraged for plants and ag to anywhere near the extent it’s being leveraged for human health. So now there’s like this arbitrage [opportunity]: “We’re making all this progress in synbio, we have this massive problem opportunity in ag.” But they’re not really connecting the dots.

Eric: And how many years behind do people perceive ag as, in terms of the cutting edge in synbio? I’ve heard 10 or 20 years, but you’d have a better...

Ilan: Well, let’s fast forward. So, what is Angie’s job? Her first job is to lay out that space. The next job was to say, “Okay, what’s a funding program we’re going to launch?”

She had a bunch of different ideas. Where she ended up is a program that we’ve now launched — and we’ve just announced the people and groups we’re funding. That program is called Synthetic Plants. [See this link for more information.]

And the idea was, if you look at the real frontier edge of synthetic biology, and we're talking about things like de novo genome synthesis, like actually thinking about synthetic organelles. You have, in the UK and other parts of the world, really amazing progress happening. But when you go and talk to those researchers working in those areas, and you say, “Well, we're thinking about doing a plant program,” the reaction Angie got from all of them was, “Why? There's so much to be done in mammalian systems, we're making all this progress...like, plants are hard! They have multiple copies of the genome, it's impossible to figure out how to transform them, it’s XYZ...”

And instead of getting discouraged by that, Angie got more excited by that. She said, “Well, wait a second, let me tell you a little bit about the potential impact we could make if you start working on plants.” Long story short, she held a workshop. And I got to be at this workshop, where she brought together a combination of top synbio people, top ag people in the UK and otherwise. There were probably 50 people in the room. One of the questions I asked was, “How many other people in the room did you know when you walked in?” And I had said [gesturing his hand upward], “Raise your hand if you only knew one person, raise your hand if you only knew X...”

And I think most people knew like five out of the 50...and none of the synbio people had talked to any of the plant people.

What we’ve found is, now, there are all these new collaborations. We have synbio folks who are basically saying, “I’m shifting my work to plants because I’m just convinced this is the most important thing I can do.”

Eric: And when you say ‘folks,’ how many are thinking of shifting their work to plants?

Ilan: Well, you’ll see how many synbio people we fund in this program when it gets announced. But I know of at least two specific cases Angie’s mentioned where, when she started the process, the person was so averse to the idea of doing anything in plants and thought it was a waste of time...who have built really meaningful collaborations and — whether they get our funding or not — are going to start working on that. So, it’s cool.

Eric: When is high variance [in reactions] among the adjacent experts exciting? When is it less exciting?

Ilan: Oh yeah. How do you know when people just say, “Oh, that's a horrible idea.” How do you know when it actually is a horrible idea? [Laughter]

Yeah, I mean this is an interesting thing. You talk about variance, right? Someone from DARPA once shared this with me, and it's a framework that I really love, which is...if you think about the role DARPA plays, more important than anything else, it is a mechanism to increase variance in the system.

It’s actually something I think a lot about for ARIA. The idea there is, more than anything else, ARIA was created to do things differently. The idea is: do things differently, and by doing things differently, by shifting the modality of research, you can get different outcomes. We can spend a lot of time coming back and talking about that.

But if you want to do things differently, first of all, you need people who see things differently. It’s one of the reasons the PD [program director] model is very helpful. But secondly, you have to break what is, I think, a pretty strong and homogeneous sort of truism of how we (largely governments) fund research. Which is something like, “We’re pretty good at funding the well-known disciplines. And we’re pretty good at funding the obvious problems or opportunities.” But that's all very linear, like, “More funding for biology, or synthetic biology, or semiconductors, because we see what's happening geopolitically.”

If you want to do something different, you need to find some way to cut and slice through that in a different way. Interestingly, the ARPA model says, “Okay, you find a program director, like Angie. You have them look across the system and say, ‘Well, what’s an actual thesis of something new that we could do, that’s not a single discipline? Maybe it is, but it cuts across TRLs [Technology Readiness Levels].” That’s the other thing. People sometimes ask, “Is this low TRL or high TRL?” It’s like, no. We’re mixing these modes in new ways.

Eric: You’re ambitious and applied...

Ilan: Yeah, and you have sort of a spectrum of portfolio. What that means is, what we end up funding on the backend of one of these theses we’ve developed, is going to be a very different set of people with a very different set of incentives — in terms of what they are going to do — than would otherwise get created.

And the variance is...if everyone thought it was a good idea, we would be introducing no variance into the system. If no one thought it was a good idea, we probably shouldn’t do it because it probably is not a good idea. But if you get spiky reactions to anything you’re doing, it’s the sort of thing where...okay, you have some minority that actually think this could work. But in a consensus panel, peer review, that minority voice would probably not win out or be there. So it would never happen otherwise. And, actually, I think the hardest job of a program director is to be able to actually face the criticism of so many people saying, “You don’t know what you’re talking about!”

Eric: You need to have courage to live with your bet, for years on end...

Ilan: You need to have courage to live with your bet, yeah.

Eric: And so some people...

Ilan: Wait, I’ve been talking a lot! I feel like it’ll be good for this audience...as we’ve been talking, what comes up for you as you think about some of the history that you’ve learned about for program managers at DARPA, variance, etc.?

Eric: So, I guess what I’ve been really impressed by, in working with the ARIA PDs, and ARIA in general, is when I read about early ARPA, 1960s and 1970s in particular, it seems like they really did trust the PMs to have an opinion. Or to say, “Here’s why this contractor’s best. You have to see it [the vision], and here’s why there’s only one group to do X thing.”

It seems like ARIA has given its PDs a long enough leash to have the point of view and follow it wherever it may lead. When I talk to folks at a place like DARPA, or something, I often get the sense that the PMs do have a strong point of view, but it’s an older organization. Like, it’s a 60 year old government agency. I think a part of the magic of DARPA is, so many government organizations only get more bureaucratic over time. They’ve [DARPA] been phenomenal at, like every five or ten years, finding ways to clear out a lot of the red tape, scar tissue, etc. But it does still build up a little bit.

So it’s been really cool. Something I try to impress upon people is that 2025 DARPA is not 1965 DARPA. You need to pursue different programs and do different things [today]. And when people ask me, “Well, do we have an early ARPA [today]?” Before ARIA, I guess I’d say, “Maybe OpenPhil.” OpenPhil lets people be free like this. But as ARIA has come into existence, it seems like you all live in the vein of early ARPA much more closely than anyone else. But, saying that, I also know you want to do something new...

Ilan: No, it’s interesting though, because I think this relates to the variance point. I’m trying to keep this dear to ARIA in the early days. It’s not about, “Did we find the right bet?” Because actually, given the variance point, in the early days, it’s not going to be obvious which is the right bet.

So it’s really about, “Are we being true to a commitment to increasing the variance of what gets funded?” And then, “Are we going to have the muscle to see the right bets emerge from that work?”

I actually had a [ARIA] board member recently say, “Isn’t one of our biggest risks right now that we created the wrong programs — like we’re funding the wrong programs?” And I said, “No, I don’t think that’s a risk at all, in part because we decided to create these opportunity spaces.” The whole idea behind an opportunity space for us, in these focus areas, is that our whole job is to take speculative bets. What that means is we can’t know upfront whether this is going to lead to impact. We can’t engineer that. The only way we get it wrong is if we take a speculative bet in an unproductive direction. So the point of an opportunity space is to say, “Let’s define what we think is a productive direction.”

An opportunity space is going to have certain beliefs that bound it. And the point is, if you can get yourself to believe these things...e.g. “Pressure on food security and climate is going to force us to do some things different in agriculture,” “Synthetic biology is moving...” Right? If you believe those things, you have to imagine there is the potential for enormous value — economic value and social impact — that can happen in this space. And now that we’ve bound that space and set that opportunity space, now we can take steps, big and small, and make bets in that area without having to worry, like, “Is synthetic plants the right program?” I’m not worried about it at all. Because we’re making a bet in a direction that’s productive.

The only thing we now have to worry about is that we don’t do what we were made to do. What makes ARIA unique — and what’s meant to make a place like DARPA unique, certainly in the early DARPA days — is that you make a bet, you take a step, and then you learn something, and you can pivot and say, “Oh! Actually it’s this 5% of what we’ve done so far that is starting to feel really valuable. Let’s minimize the rest and 10X that 5%.” That’s the thing that...I think that’ll be one of the big muscles we have to create for ARIA in this next phase.

Eric: So, you’ve moved to the UK, and ARIA is...

Ilan: I’m sorry, I have to stop! I’m like, I’m sitting here wondering if people watching this are excited about it. It’s just like — the idea that there’s now a community that’s excited to hear this conversation — that’s something I’ve been searching for for a while.

Eric: [Excited laughter] Okay, so, in that case...

Ilan: How many subscribers do you have?

Eric: Like 4,000. But there’s people like Matt Clancy who have like 15,000, or something like that. [Correction: Matt has over 18,000.]

Ilan: But those are high impact people.

Eric: Yes, I am continually wowed by the subscribers I have...and when I write something, the email outreach I get. It’s very much a dream for someone like me who...I wish I could be a great researcher [gestures at Ilan], but it was not in the cards for me.

Ilan: [Laughter] I wish I could be a great researcher too. Wasn’t in the cards for me. Definitely more of a Bob Taylor than a Licklider.

Eric: So you’ve been doing this for so much longer than many people. A lot of people have maybe gotten interested in this space in the past 5–6 years or something.

Ilan: Wait, what’s “this space”?

Eric: We’ll call it this “applied metascience” space. People who want to really consciously experiment with how you build R&D ecosystems differently, or research labs differently. In many ways, you’ve been doing this for possibly the majority of your professional career. Can you tell people about the different stops you’ve taken? Maybe different things you’ve learned along the way? Different gripes you’ve picked up? ... [I imagine] you only throw yourself into this if there’s stuff to be fixed and you have ideas.

Ilan: Yeah, I mean, I sometimes describe it as, like, I got thrown into this just because I felt like a misfit in all of the different environments to do research. And we can talk more about that. But I think that is a motivating factor, right? What motivates me, and probably what motivates most of the folks who would listen to a podcast like this, is just realizing that you can take the things we’re learning at the cutting edge of science and discovery, and turn them into stuff that’s useful, real, and awesome.

I mean I had — actually, you’ll probably appreciate this story — I was one of a rare set of people who was an undergraduate major in material science. Material science is kind of a weird field that most people, at least when I went to college, didn’t really know about.

Eric: They’d pick it up as a masters...

Ilan: Yeah, exactly. I think there were maybe like five people at Berkeley who came into the undergrad material science program. But the reason I got into material science...I grew up in Pittsburgh and went to a public school, which just happened to have a couple of phenomenal science teachers. And one of them encouraged me in 10th grade to put in an application for this program that CMU [Carnegie Mellon] did, where they would host high school students in a lab.

And I still remember, like, they gave you a questionnaire, and it was like, “What are your interests?” And I was in 10th grade, you know, I was not a polymath who was way ahead of their time. So my interests are like, ”I like physics [shrugs]...maybe chemistry?” You know, you just say pretty banal, normal stuff. Anyway, they paired me with a material science lab. And I remember showing up and saying, “Why did I get the oddball thing? Why couldn’t I just be paired with a normal lab?” And I didn’t think it was going to be cool or exciting.

The project — and this is the piece that I think you’ll appreciate — the project that I got paired with was a lab that worked in magnetics. What they wanted to do was come up with a new magnetic storage medium. At the time, you know, a hard drive was, what, 16 megabytes? You didn’t have flash drives, etc. And so the question was like, “How could you get much higher density?” So what they were doing is they were taking a material called MCM 41 — this is the part you’ll love — MCM 41 was one of a class of what are called molecular sieves. It’s a material that has a microporous structure, a lot of core space, sort of a zeolite material used as a molecular sieve/filter/etc. Basically it had like honeycomb pockets. The project was, through different means — chemically, sputtering, etc. — to fill these honeycomb pores with magnetic material, with some iron compound. And then the idea is that the ceramic honeycomb borders would be the separations between domains of a magnetic storage medium.

I still remember, at the time — you’d go around and say things like, “You could imagine putting a gigabyte on something the size of your hand!” But it was mind blowing! Just the whole idea that you have these nano honeycombs, and then you’d have a head that would sort of scan them.

Eric: Did you believe it? Or did you feel like that was crazy?

Ilan: No, I totally believed it! And the reason you’ll like the story is because MCM 41 stands for “Mobile Corporation Material #41.” So I’m still waiting for you to do a post — maybe you’ve done one and I’ve missed it — on all of the amazing discoveries, fundamental and otherwise, that came out of the oil and gas research labs. Mobile Corporation did all this incredible material science work. And, of course, they named all the compounds, like, “MCM 3, MCM 5.” So MCM 41 was, like, something everybody knows about...or at the time did!

Eric: That actually would be an interesting post too. Because I was just yelling [injecting historical stories into random conversations] at some people on Twitter the other day about the origins of TI [Texas Instruments], as just like, one of the early oil and gas firms that had a ton of early MIT ties. They just got very good at making their own instrumentation. And that’s a pretty high margin business. It’s a little less speculative. And they just like...I forget if it [TI] was a spinoff or if the firm [the original oil and gas firm] entirely pivoted. But yeah, maybe I should. If you have ideas!

Ilan: Oh yeah! Materials, lithium ion batteries, came out of an oil and gas lab. A bunch of solar cell work. I think there’s something cool there, but...

Videographer: Can we take a quick break?

[Camera Break]

Ilan: You were asking about the trajectory of ARIA?

Eric: Yeah, it would be good if we could discuss how...you were at ARPA-E. What in the world is Activate? All of these things, probably like, being dissatisfied in different ways. Or being happy with what you did, but dissatisfied drove you...?

Ilan: I think there’s a story of navigating, you know, continuing to basically try and do bigger and bigger metascience experiments, if you call it that. There’s a version of that which is, like, me being disillusioned by different institutional structures, or different silos, and how we do research. But maybe a more positive or interesting version is that, as I think over that time, is...I talked about being at CMU. When I did that research project at CMU, what flipped on for me was this idea of, “Holy shit, the wonder of the ways that we can take scientific discoveries and turn them into massive impact!” For me, the ultimate [thing] at the time — and this probably went all the way through my undergrad and into my PhD — the most valued currency for me in terms of changing the world with science was the ideas. And the instruments and the processes and the methods. I was so obsessed and in love with that, and I saw that as the big driver.

Interestingly, I had this experience 18 months into my PhD where we had written a Science paper. We were on the cover of Forbes for this new approach to creating solar cells much cheaper, you know, II-VI nanoparticles that we were going to print like newspaper. There was all this excitement. And I spent a couple of days with some business school folks with technical backgrounds, and realized it was all BS. We actually weren’t solving the right problems.

And I became...you could think about it as being disillusioned, but actually I think it’s more [that] I recognized that the world of ideas that I was in, where that was the currency, there was something missing there, in terms of how you drive impact.

And then I ended up in startups, VC-backed startups. My mindset moved from like, “Actually, ideas are pretty cheap, you know? Ideas are pretty easy to come across.” It’s more about, “How quickly can you iterate through them?”

But I also got obsessed with currency as the currency for driving impact. Because my job running a startup was, like, “Raise money.” And you could see that the more money you could raise, the more of an opportunity you had to drive and get to faster outcomes relative to competitors, or whatever else. So I really was in this mindset of money.

And having a chance to go to ARPA-E, one of the things I found very compelling was that it was a different mode from venture capitalists. I thought venture capital was sort of too narrow of an impact model for doing early-stage science, especially for industrial markets. But I also found compelling, like, “Oh wow. We’re going to be the biggest Skunk Works innovation funder in the US, for anything climate-related.” And I was attracted to this big money question. Money is like the driving force you have for reactions.

And now I think, through that time for me, at ARPA-E, my biggest recognition was that we had...

We had an incredible mission: to drive translational science into big impact in climate and energy. We had access to a lot of funding to do it, so we had the driving force. We had access to all these brilliant ideas, meaning every researcher who worked in any of these areas in the country would apply to our programs if we could just pick and choose the ideas. And the thing I realized was that, actually, none of those was actually the thing that mattered. What really mattered was people and institutions and incentives. And you can have all the money you want, and you can have all the ideas you want,  but if you don't have the right people in the right institutional environments with the right incentives to drive progress, you're pushing on a rope.

Eric: At ARPA-E, were you ever wowed by some set of people and institutions and incentives that you got to deal with?

Ilan: Well, in the end, I came back to...I left my startup feeling somehow like I had been misled. The idea of, “Venture Capital-backed startups are going to change the world.” We were working on batteries for EVs, industrial markets, very low margin business. Probably our timing was off. But I was like, I don’t see how we can develop the technology, get it to scale, and actually have it converge for someone like Vinod Khosla, who was like, “Either this needs to be a trillion dollar…” it was at the time billion dollar, now like, “...trillion dollar company, or it’s not worth doing.”

And so I kind of had a feeling when I went to ARPA-E, like, “Actually venture-backed startups are not the way to do this. And startups might not be the way to do this. Let’s figure out all the other institutional modes.” What I found in the end was that, to your question, of all the environments that I was funding research, the ones that felt most resonant and most productive were either the startups or the folks within academic labs that were already operating as though they were spinning out a company.

And what I realized was that the venture capital funding model has constraints, but if you don’t worry about the funding model for a second and you just think, “Well, what is a startup?” A startup is a vehicle for getting the right people into the right environment with the right incentives completely aligned with some translational research mission.

If you think about it, [for] everyone in an early-stage startup, the goal is to create value in a very concrete way. You’re probably doing something very speculative, if it’s science-based. Everyone who’s there in the early days has decided they’re going to commit the next chapter of their life to that pursuit. The incentive is clear. The alignment is clear. You often are getting people from different backgrounds, in interdisciplinary ways, to do it. So I think startups are massively valuable and important vehicles for R&D.

And the question becomes, and we can have a conversation about this, the question becomes, “How can we fund them? How can we fund them as R&D centers?” Venture capital is not a great way to do that. Some VCs actually do fund very applied R&D and take speculative bets. And I think those are companies that tend to change the world. But relative to the, like, $70 billion of US funding to applied and basic research — that number is probably dated right now — you don’t have anywhere near that going into super science-y startups.

Eric: To understand the problem...You love the shape of VC-funded startup groups — the people, environment, incentives they create — for the problems they want to attack. But there’s all sorts of problems that...

Ilan: They can do it if they see the wave coming and they’re just hopping on the wave. So when that happens, they work great. But why can’t you have the same aligned incentives, full-on commitment, before the wave even exists? And couldn’t that be a mechanism to create some of those waves?

Eric: And how do you think about making that happen...…

Ilan: I have no idea.

Eric: ...at ARIA?

Ilan: Oh, at ARIA!

Eric: Yes. And of course all of my readers will know I’m biased. I’ve spent this huge amount of time writing about the best early ARPA contractors, like BBN or the CMU autonomous vehicle groups...groups that were very startup-related, but really embraced technical ambition over market size. Their constraint was that they need some amount of grants [or contracts] to fund it. But I’m sure you have all sorts of theories on how to make this work for you [ARIA] in spots where there are parts of the R&D ecosystem that could use a little bolstering — at least in terms of ARIA’s incentives.

Ilan: Yeah. So one of the things we’re doing at ARIA, which I think is probably different than DARPA, is being really open-minded and attuned to, “How do we try and fund not just the best people and ideas, but with resonance in terms of the environments and incentives where they are doing the work?”

What does that mean? That means a few things. One is that we are very comfortable with the idea that, using ARIA funding, a researcher might decide to go start a company. They might decide to go, leave their academic lab, and just do the research as an independent researcher, because that’s what they think is the right thing.

We have something interesting, which is we do these seed funding awards. I mentioned those opportunity spaces where we say, “This is just the direction that we think is fertile.” A program director will create a program, which is their thesis, and that’s where they’re going to focus a lot of energy and where we’ll focus a lot of funding initially. But then what we do is we say, “Well, we’re not the only smart people around. And it’s all about increasing variance. So why don’t we find other people and let them start seeding ideas that could lead to the massive breakthroughs...[unintelligible]”

And this is like, you know, give us a three-page application, tell us why you’re obsessed with this idea, and [why] no one else will fund it. And we’ll give you up to £500k. So just start running at it. And if you come back with something compelling, we can double down with you. [We can] either make it a bigger project or actually have it inspire a whole program.

Eric: So Jenny said something in one of her interviews. — Jenny is the PD on the robotic dexterity program. She said at one , “I was almost disappointed that I didn’t get the chance to fund, like, some random person in their garage.” Because the funds are open to them. Can...

Ilan: Anything.

Eric: Can you explain what it would look like to be...like if I’m a random guy in Birmingham and I want to…?

Ilan: [Gestures at Eric] As opposed to a random guy in…

Eric: Yeah, in Chicago. [Laughter] And if I wanted to run it out of my garage, how much paperwork is involved? It seems to be a very fair [reasonable] amount.

Ilan: So this is really interesting. What we started with at ARIA is, we said, we wanted to make clear that we are completely agnostic. We wanted to fund the best people in the best environments. Apply. We don’t care if you’re at a university. Just tell us you’re obsessed with the idea, what the idea is, and show us that with our funds you can get access to the equipment you need to make it work. And we wanted to be really agnostic.

Now, there’s a piece of this, which is we have a brilliant, awesome, founding CFO for ARIA. Her name is Antonia [Jenkinson]. She and her team, I went to them and I said, “This is a little hard to do as a government agency. You got to do due diligence on these people. How are they using their money?” And they said, “Yeah, but this is what we’re built to do, so we’ll just figure out how to do it.”

Eric: And is it true that she walks around with the [ARIA] founding mandate in her pocket?

Ilan: [Laughter] She has been known to carry the ARIA Act around, which I love. So we started and said, “Yes, we can fund this [alternative applicants].” And seeds were a three-page application. We would tell you within three weeks whether you got the funding.

Eric: Three pages? Like...[makes a show of counting out three pages]

Ilan: Three pages, yeah. It was a three-page application, three weeks to funding decisions. And when we did it, we did have this. We had people apply. And then we had a few people who said...we had one person at a well-known university in the UK who said, “I’m really glad I’m getting this seed. To be honest, I don’t think I want to do this in my academic lab. For a number of reasons.” I think they were actually a postdoc. They were, sort of, earlier [career]. And they said, “What do you think about me, just like, finding and renting some space to do this work as an independent researcher?” The program director talked through it with them and said, “Actually, that makes sense. You’ll probably do a lot better. You’ll probably be more motivated.” Whatever it was. And so we just figured out how to give that person the award as an individual to do it in this way.

And we saw a few of those. Then we paused, and we said, “Well, wait a second. People are funded and excited to work on these projects.” And when we tell them we’re open to wherever you do it, they’re telling us, “Well, maybe I’ll do it some other way.” So the next time we did a seed call, as part of the questions we said, “What’s the institution you’re in now? If we award you the seed, where would you like to do the work? It doesn’t have to be the same place.” And the options were, “Will you do it in an academic environment?” You know, “my current university, another university, a company,” wherever else. And then we [included], “sStill undecided”.

First of all, I think we’re the only government funding agency in the world that gives that option. Secondly, the fact that there was an undecided bucket means, “We’re cool...like, we’ll talk through it.” I think we had like 23% of applicants in the next seed call say ‘“Undecided.”’

Which for me is such a big deal, because it suggests that being prompted with the question of like, “Eric, there’s a big project...it could be the most important thing you do in your life. Stop and think about, ‘What is the best environment for you to do this work and have it succeed and have a chance to change the world?’” And what we learned is like 23% of people [think], “Maybe it should be a startup, maybe this is my moment.” Or, “Maybe I should move to the UK and try to do this at a university in the UK,” which has happened now with ARIA grants. So, that was pretty cool.

Eric: Yeah…

Ilan: That felt like a...I warned you that if you get me excited about something, I’m just going to keep talking about it!

Eric: No, no, no! That was great! So I have a question [from my notes] lingering here, I don’t know where to put it. So I’ll just ask you now.

Ilan: Go ahead!

Eric: So this one comes from Tom Kalil. He said, “In what specific ways do you desire to transcend the DARPA model?” Because you’re not looking to set up some version of an American ARPA in the UK?

Ilan: I think that’s right.

Eric: There’s lessons you’re taking, but…

Ilan: Yeah, I mean look...I think DARPA is such incredible inspiration. You know what I mean. Like people argue about, “Is DARPA past its heyday?” “Is it doing good or bad?” But we have this beautiful gift, which is just the history and the reality and the myth of DARPA all wrapped in. And, honestly, if you can’t be inspired by that, like, forget you! Right? And I think that inspiration is so important and true.

We’ve touched on some of the things that I’m hoping with ARIA...ARPA, like you said, established 70 years ago in a very different time. It’s changed in many ways. I think for me, the most important things — one of them is one of the things we talked about earlier, which is having the intellectual humility to, and the eye on part of our job as just to increase variance and learn and pick up on threads that are valuable. That is really important to me.

One of the things I’ve noticed — and this is good and bad — but when our program directors...we pair them up with DARPA PMs, former and current, to give advice and whatever else. And oftentimes I will hear a DARPA PM say, “Wait, 37 performers for this program? You can’t do that. That’s not how we do things. That’s not the right way to do it.” What we’ve had the chance to do is kind of go to first principles on everything. And that’s a real case. I don’t think it’s 37. But davidad, who runs our Safeguarded AI program...for the first technical area of his program, he wants to, as quickly as possible, answer some theory questions and develop some frameworks which he thinks are best done as open source. And, actually, where that led him is we’re funding a lot of teams with small awards, to which normally people would say, “It’s not worth the management,” or “It’s not cohesive enough.” But the first principles suggest that, yeah, that’s the right thing to do there.

So, it’s kind of like having the intellectual humility to say, “Okay, I don’t know if this is exactly right. But it feels like it’s matching the first principles. And across what we’re doing, it gives us variance. We’ll learn more on the go.” I think that’s one thing that I’m hoping gets preserved at ARIA.

This other thing we’re talking about is probably the other most important one, which is...DARPA, rightfully, has a mantra of, “We don’t have our own labs. We don’t create institutions. We fund research projects.” And DARPA increasingly has done more with startups, which I think is great and one of the biggest impact vectors it has. But I think for us, for ARIA, being able to say from the beginning, “Actually, the people and the institutions matter. And there may be new institutions that we need to form — or that we catalyze forming. And we’re not going to be, sort of, scared about that,” is important.

Eric: Yeah. This is also something I think early DARPA was a bit friendlier to. They wouldn’t necessarily found an org, but they would write a pretty big check to an org that, like, didn’t seem to have an office yet or something like that.

Ilan: Was RAND DARPA?

Eric: Was RAND DARPA...

Ilan: I remember reading something that...

Eric: No, I think RAND would’ve been...I do think the USC ISI [Information Sciences Institute] was very clearly heavily early DARPA funded [from its earliest days, which helped create the organization of largely RAND alums]. They ran DARPA’s early MOSIS initiative, which was one of the first fabless [services]. That’s definitely a case of...those guys may have left RAND to form...I’d have to go double check some of my notes. But that’s a case of them [DARPA] writing [very early checks into an org] and making some promises, and it was an institution they needed.

Ilan: Yeah, it’s actually interesting! MOSIS came up recently at ARIA because, one of the programs that we’ve launched...we’re already starting to see that, within this...

[Camera Break]

Eric: You were talking about MOSIS and how it fits into one of your programs that you were talking about recently.

Ilan: Yeah, the opportunity space is Nature Computes Better. One of the theses there is that there have been lots of attempts to think about how you disrupt general computing. But it’s such an impossible thing to think about doing because of the massive supply chain and all the different forms of general compute. Suraj Bramhavar, one of our program directors, his insight was, “Actually, AI is very unique in the history of computing because you have a compute mode with a pretty narrow set of mathematical primitives, but potentially massive applicability, value, and impact. And that narrow mode of AI could be a new foothold to think about alternative types of computation — with alternative physics, alternative hardware, alternative substrates, etc.”

And the other piece of this is...when we think about, “Is there a program or opportunity space here,” one of the important questions to ask is, “What is the fundamental limit of performance that could happen in this space? And where are we now?” And one of the things we know about computation is,[even though] we’ve had Moore’s law for as long as we have, we are still orders of magnitude from the fundamental limits of computation. Whether it’s [the fundamental limit based on] Shannon’s information theory or what we see happening in nature. So the idea behind the space is, “Can we find new approaches inspired by physical systems, natural systems, other ideas that researchers have, etc. to use AI as a foothold for next gen compute?” The program that we’ve shaped aims to demonstrate that you can show, as a first step, AI training at a thousandth the cost and energy consumption than state of the art today.

And there are a bunch of really interesting systems questions around, like, “How do you even prove that in sort of a demo system?” We’ve now funded a set of teams across academia, startups, big companies, and a number of approaches that we think, in coordination, can get there. And from that work we’re already seeing, “Wow, there might be a next phase of this given some of the early excitement that we have.”

How would you take advantage of it? Maybe you need some MOSIS-like institution that is focused on this area. It’s still very early, but it’s cool because, like, you had your post on MOSIS and it actually is making a difference, being able to give us [ARIA] a catalyst of things to chew on.

Eric: And can you paint a picture of what this MOSIS would be doing? Like if a lot of these contractors are point solutions or different approaches, what would you need them [this MOSIS] to do?

Ilan: [Laughter] I don’t think I could paint a picture of that for Suraj’s program because he’s literally just in the early stages of thinking about it and I don’t think I could actually talk through it without disclosing things that he probably wouldn’t want me to. But I do think, you know, one of the things we talked about earlier was the question of, “Where are there new institutions, and how could they help ARIA?” We talked about our Creators. (DARPA talks about the people they fund as ‘performers.’) We thought that was a little odd. Actually, you might know the origin story of where ‘performers’ came from. Maybe it’s like: you want performance, so you have performers? Anyway, we call the people and institutions we fund ‘Creators.’ The idea being, the work we do at ARIA, I think you need equal doses of creativity and creation. This word ‘create’ is interesting because you can think about it in terms of creativity — which is like pie in the sky thinking — or you can think about it as like, “No! Go create something, like building!” And our view is you need both of those to come together.

Anyway, we talked about new institutions around Creators. We’ve also been thinking about new institutions to help ARIA do better. You might have seen our Activation Partners call.

Eric: Yes. Can you explain what that is?

Ilan: I can. [Laughter] This might be a divergence, but...

Eric: We can come back to it later if you prefer.

Ilan: No, let’s do it! When we think about the ARIA model as a whole, we talked about finding the opportunity spaces, being able to inject energy, catalyze these big waves — which are essentially new movements of technology...but then talent and capital that can transform the world in one of these spaces.

There is a question. We’re not DARPA. One of the things that allows DARPA to get things from early idea/technology into practical systems in the field is that they have this massive lever that is the Department of Defense budget. [Laughter]

Eric: Yeah, they have a built-in customer.

Ilan: Yeah, they have a built-in customer. And there is no bigger driving force than the procurement power of the Department of Defense, if wielded in the correct way. ARIA’s mandate is much broader. We’re not just defense, we’re actually not doing any defense right now — it’s quality of life and economic growth, broadly speaking. We don’t expect to have a built-in customer from government. There might actually be cases where government, like the NHS [National Health Service] could be an amazing partner and uptake vehicle for technologies we develop. But outside of having purchasing power as the driving force, our bet is that the biggest driving force that exists to get new ideas into the world at scale is going to be entrepreneurship.

It’s that whole idea of, “If you, as the researcher and a team of people are willing to commit their life for a stretch of time, yes, you could change the world.” We know it. We’ve seen that happen.

So, one of the questions we’re trying to grapple with is, we’ve created these opportunity spaces specifically to be places where the wave doesn’t yet exist. So, there probably aren’t a lot of startups in these spaces because venture capitalists aren’t super active in all these spaces. Maybe there are some. What can we do to increase the amount of reactants that are entrepreneurial in the opportunity spaces we have?

And we thought about a few things. When I was at ARPA-E, actually, we ran an experiment that I helped launch, which is called the technology to market program. We basically built, within a government funding agency, a business analysis/biz dev type function. The idea being, “Let’s figure out how to inject into our programs some thinking around how you translate the technologies.” DARPA does some of this.

Our view is actually, for ARIA, there are some things we can do internally...but to think about how technology’s transition to market, especially through startups and entrepreneurship, the best people in the world doing that are going to be people doing it in the field. And you might be able to attract one or two of them, here and there, into a government funding agency, but you’re not going to be the best in the world at doing that. So what we did was we put out a call, and we said, “Listen, we think there is going to be massive opportunities and value created in these spaces. It’s still early. If you are an organization that deals with venture talent creation, entrepreneurship, rapid prototyping...anything related to tech translation, transitions, and value creation...and you’re amazing at what you do, could you imagine focusing your energy on some of our spaces? And just do what you do really well, but inject more reactants into that system.”

My colleague Pippy [James], who’s our Chief Product Officer, was really the visionary behind driving this forward. Where we ended up was, we have a handful of organizations. Some of them are venture capital firms that are saying, “We will, for the first time, focus on the UK and look at opportunity creation in these spaces.” They’re doing things like running fellowship programs for scientists who want to be entrepreneurs.

Renaissance Philanthropy is another Activation Partner thinking about, “How do we create a two-sided marketplace between the technologies that start to emerge and philanthropic goals/philanthropic funding?”

Eric: A lot of people don’t think about it, philanthropic demand is demand all the same. They [philanthropists] often have particular technologies they want to bring into existence. But it’s often unclear how to solicit demand from philanthropic...the mass of philanthropies. It’s hard to understand what they want.

Ilan: Yeah, and in a lot of these spaces it’s not clear that when we’re done with our program, the commercial value is going to be right there. It may be that we’re still in a tragedy of the commons, market failure mode, and we need to band together with others to do more in those spaces. So I think that’s going to be important.

But one of the things that came to mind as we were talking earlier is one of the Activation Partners we have, I think you’ve spent time with them, is this company Amodo [Design].

Eric: Amodo, yeah! Love Tom [Milton]!

Ilan: Amodo is such an interesting story. They’re based in Sheffield. It’s a small team. It just reminds me of the things you write about! It’s a small team that, you know, they’ve been involved in a spin out of a university and they basically recognize that, like, “Man, universities don’t do a very good job of prototyping things, having a rapid iteration mindset, or understanding how to get things into a more commercial place. Why don’t we just set up a shop to do that as a service — for startups, academics, and otherwise?”

Eric: So you don’t have to shoehorn your experiments into off-the-shelf Thermo-Fisher equipment. Tom and them can make what you need.

Ilan: Exactly! “Let’s go solve it!” They’re like mechanical, electrical, prototyping fixers!

Eric: And they’re hiring!

Ilan: And they’re hiring! [Laughter] So, interestingly, they applied to be an Activation Partner, alongside...you know, our other Activation Partners are, like, Google DeepMind...but they applied. And first you look at it, and you’re like, “Oh you’re five people in a room in Sheffield?” And then when we talked to them and we saw what they were doing and the vision, it was like, “Actually, this is a massive asset for ARIA to be successful.”

And maybe it won’t just be Amodo over time, maybe we’ll find others. But already, through the arrangement that we’ve built, they’re actually doing this as a service for a bunch of Creators that we’re funding, who are saying like, “Oh my goodness, I thought it was going to take us six months to do this and I got a much better version of it in a week!” So I think that’s super cool.

Eric: And the PDs have seemed very impressed with them. Like when I was talking to some of the PDs [and other ARIA staff] about the BBN thing, the concept that I used was, “Oh, do you want an Amodo for your area?”

Ilan: And that’s one of the things we’re doing through Activation Partners, which, again, is sort of an experiment around this idea of, “The best people, in the right environment, with [the right] incentives.” We’re working with Convergent Research on what I think will be the first experiment of them saying, “Wide open to the spaces,” which will be our opportunity spaces. Instead of being a kind of two-sided marketplace and connector of talent with philanthropic or other funders for FROs (Focused Research Organizations), they’re basically saying, “If you’re someone talented who wants to commit your life to building a focused research organization in one of ARIA’s]opportunity spaces, apply! Then we’re going to find one, maybe even more, to go launch.” And it’s exactly what you said...

Eric: To people in the metascience space, I think Convergent is maybe one of the most exciting...

Ilan: Yeah.

Eric: ...kinds of Activation Partnerships. Because I think everybody [metascience nerds] was around when they first kicked up. And they also always knew [suspected] the vision was probably that Convergent might like to win NIH funds, or something like that, and set up more every single year. So to have an official [Activation] partnership was exciting, for somebody who’s just been a fan for a while.

Ilan: I think the biggest — we might agree on this — but I think probably the most important thing to accelerate progress in the research ecosystem is to increase the diversity of institutional types. Convergent is one of the few examples we have of a mode that is taking root, that’s saying, “Here’s a new institutional type. Let’s figure out how to scale it!” You talk to Adam [Marblestone] and he talks about...you know, he sees a thousand — I think he says either a hundred or a thousand — FRO-shaped things that need to be created in the world. I think it’s a thousand. I imagine there are a thousand. If he thinks it’s a hundred, I can probably debate it. [Laughter]

Eric: It was one of those two numbers. One more question, and then I’m going to do a little pivot. You seem to have a very good grasp of early/mid-20th Century R&D, the institutions, some of the stories, etc. Is there any organization or group from that period you find yourself thinking a lot about? Either because you want somebody to emulate it or you just think it’s cool?

Ilan: That’s a good question. I think the ones I think about are maybe, not surprisingly, the ones that feel like startups before their time. You wrote the piece about Edison.

Eric: Yeah.

Ilan: That was a story I was already obsessed with...but the whole idea of, like, Edison in the workshop. There’s an organ in the back, there are bunk beds, they’re sleeping there. They’re just all in, committed. You know, he’s raising money based on a dream. You think it’s bullshit, but then he makes it true by doing the work to close the loop.

Eric: The financiers come to visit and he looks crazy...

Ilan: That’s one piece. I love the story of Philo Farnsworth. I don’t know how much you know about that one?

Eric: I don’t know that one.

Ilan: 15-year-old farm boy who has a vision of making television. And basically through some serendipitous set of events, because he’s so passionate about it, ends up moving to San Francisco, getting funded by like an LA financier from Hollywood, and is competing with RCA in a race for the first television. And you just look at that, and look at the lab, and look at the talent times intrinsic motivation in the right place and the right time...

And I think Curie — this is more academic — but I think Marie Curie is another example of this. It doesn’t seem like it, but when you look at how she and Pierre Curie worked, it definitely feels like a startup.

Eric: What about it is very startup-y to you?

Ilan: Oh, the combination of the singular focus and the idea of, “We’re going to be open-minded and try to get to the goal through all means.” I always love the story of — I might have this wrong — but my impression is that Pierre Curie discovered piezoelectricity as part of trying to figure out how to very sensitively measure the radioactivity in the materials that he and Marie were looking at. So, basically, you have an academic pursuit to discover and understand this phenomenon of radium that no one understood, that leads to stumbling into discovering a whole other phenomenon, piezoelectricity — which has massive implications technologically. And it’s all because you had this intensity of spirit in one place.

[MIT’s] Rad Lab is probably another example of this. I don’t know if you’ve written a piece on Rad Lab.

Eric: I’ve never written a piece on the Rad Lab. There’s one book that’s continually over $100. And I check Amazon like once a month to see if the price will come down and I can finally get it. [Laughter]

Ilan: I’m going to buy you that book, whatever it is.

Eric: You don’t have to do that!

Ilan: If it means you’re going to write a piece about Rad Lab, then I think we all...we’ll do like a GoFundMe campaign to buy you the book!

Eric: [Laughter] I can promise to find a way to have it out in the next year, somehow! I have a few other Rad Lab oral histories sitting on my shelf that I’ve been letting pile up, so I do have to do this.

Ilan: Yeah, but the whole idea that because we got a group of physicists together to try and build radar systems, what fell out of that was, like, MRI...and the basic theoretical and practical things around that is just awesome!

Eric: Yeah, and also in terms of funding something for five years [as FROs tend to be], and then it's set off in a direction of its own...people at Rad Lab seem to have thought of some of Weiner’s early cybernetics writing, and things of that sort, as helping set the agenda for the RLE [Research Laboratory of Electronics, which the Rad Lab morphed into postwar] going into peacetime. It’s very clear that [the Rad Lab] was catalytic in extreme ways. The kind of thing that would be ARIA ‘returning the fund’ level [results].

Ilan: Yeah, it’s funny, one of the things that’s coming to mind here, as we think about FROs...one of the things I think a lot about is, for so many of these institutions, and I feel this way about ARIA right now. We’re two years in from starting everything from scratch. It feels like we have the environment where these amazing things can come out of it. When I think of our program directors, when I think about the Creators they’re starting to fund, the dynamic of the team, I’m like, “Wow. This has the energy and the characteristics you would want.”

The big fear is, I think, “Okay, this is founder mode.” [Laughter] These are early days! If you look at Rad Lab, it was this intensive thing, and MRI and these other things came out of it in the early days. And more came out of it later, but that question of, “How do you capture that founding period and not lose it?” I mean, what I love about FROs is the idea is, “Don’t even try.” Imagine that you’re going to have an intensive thing for five years. And then it turns into something else, like it metamorphizes. I think there’s something really important about that.

Eric: I remember hearing Adam muse at one point about the idea of, “Oh, it would be great if we had funders who could accept or were excited about the idea of maybe one FRO maybe leading into another FRO.” That is the outcome. You [ARIA] are playing a bit more of a repeated game. How do you think about that, one ARIA program leading to another ARIA program?

Ilan: Oh, 100%. I think most likely that is how we will make an impact, which is we’ll have a program that inspires the next program. And within that next program, we see something and we double down on it...and that’s the thing that happens, being adaptive in that way.

One of the interesting things that I’m noticing is...we haven’t talked much about how we recruit program directors. But the combination of our broad mandate and the fact that we are bringing in, as cohorts, program directors from such different backgrounds...we have these opportunity spaces that the program directors have shaped, and, yet, we’re now starting to see as we dig into them, these really interesting intersections between them. And I’m just wondering what’s going to happen. What are we going to do about that? Are we going to have programs that sit across two opportunity spaces? Are they going to merge into one? You know?

Eric: And can we talk about your new program directors? Who are they? Why did you choose to light up flares around these particular people and their expertise, or their point of view?

Ilan: Yeah. I think this recording is going to come out right after we have announced them. It’s probably worth first saying, we made a bet early on at ARIA. And my colleague Pippy had a really big role in this, which is we basically decided that one of the hardest parts for a research organization like this in the UK was going to be to find program directors or program managers, because, you know, it’s a weird thing to do. You’re on some amazing career trajectory and you decide to, like, step out of that for three to five years to do this other weird thing with a brand new agency that no one has ever heard of. Probably the listeners of this realize that one of the core parts of the DARPA model, which we’ve adopted at ARIA, is this idea of term limits. The people who are developing the theses are people that are coming in from the front lines of doing research in some way. They’ve got to sprint for three to five years to develop a thesis, fund a program, have an impulse function that hopefully catalyzes change, and then they go out and we refresh with some new program director. And part of that is to keep the ideas fresh, but actually, a big piece of it is to keep it incentive-aligned.

I’m never worried that a program director is going to make a decision on their program based on worrying about their career trajectory and what their boss at ARIA thinks, because they’re going to expire in three years. So they’re all just purely focused on, “How do I use this time to make something happen?”

One of the bets we made was, we knew it was going to be a challenge to find people. So we decided, rather than just hire people one-off, as we found them, let’s actually do what we believe is the most open call of its kind to find program directors. Let’s imagine that we can think of a set of characteristics that matter and that we think are going to make for a good program director. And that’s going to be more important than specifically their resume or what they’ve done before. And then we decided that we’ll bring them in as a cohort. So, part of the idea here was, if it’s going to be a risky thing for somebody to come join ARIA at the early stages, it becomes a lot less risky if, when they’re thinking about joining, they can look around and see five or six other people that they think are pretty awesome, who are also going to jump into that pursuit.

And I think that worked out really well. If you look at the first set of program directors, we’ve got someone like davidad who, I think he got his masters degree from MIT at age 15. I don’t think he ever finished his PhD. He ended up working at places like Twitter doing independent research. And he sits in our office next to Jenny Read, who is like a seasoned senior professor at the University of Newcastle — in different fields.

And you have all these other people, and they’re so spiritually aligned, and respect each other, and push each other. It’s really cool. [pause] We were talking about new program directors, but I didn’t get to it.

Eric: No, no, it’s okay!

Ilan: What would you want to see from ARIA’s next program directors?

Eric: [Laughter] What would I want to see?

Ilan: Trying to figure out how to...oh you’ve met some of our, you’ve met our existing program directors! If I were to say, like, “Okay, you have the chance to double the size of the group of program directors and make it an even stronger group. What would you try and sprinkle into the mix?”

Eric: So, that’s tough for me [laughter]...because I’m not a technical genius or a polymath in any way. I’ve been very intrigued by the energy of everybody involved [as an ARIA program director] and I’ve always left excited. But I guess, I don’t know. Engaging in this work as a non-technical person...

Ilan: Yeah.

Eric: ...I more just go in with the assumption of, “Somebody sees genius or a spark of something in this person. I’d love to help them any way I could!” I guess, reading the history... it’s unclear that it would be obvious to anybody, or me going into the past that, like, Frank Heart would be Frank Heart, or J.C.R. Licklider would be Licklider. So, I guess I don’t know...I attempt not to answer these questions or go down this line of thinking because I think I would be biased by random things, probably. For example, I go talk to [ARIA-related] people, and most of them are British. And it’s like, this is a new cultural context for me. Sometimes, with people in my personal work, I go based on spark or energy or something like that. But I don’t think that’s quite how a British scientist would do it. So I don’t really know. I won’t answer your question, essentially!

[Post-interview note: As I transcribe this interview, I wish I would have flagged that some social science program like a computational law program could be cool!]

Ilan: Yeah, interestingly, it goes to the intellectual humility point. You said something that resonated with me, which is like, “I imagine someone saw a spark of genius or brilliance in them.” So we designed our process to recruit program directors — I think we started, the first time we did it, we had seven qualities we were looking for. And they have names, like, obviously like ‘technical depth,’ but we have one called ‘vision to action,’ ‘adaptability,’ etc. We basically put all the applicants through the paces in different formats. We were probing them in different ways to test along those axes. And the idea is not to just have some aggregate score for someone who’s great, but the idea is to find the spikes. We talk a lot about how each of our program directors has a different set of spikes, meaning like superpowers that they’re going to leverage to do the job in their own ray, recognizing that a Licklider has very different strengths than a Bob Taylor, right?

And I think that’s worked out really well. We did the same thing with this new cohort of program directors. But we looked at our first set of program directors and what we learned from them, and we realized a few things. One was, there was something that they were showing in their behavior that we hadn’t tested against, which is something like ‘value recognition’ or ‘opportunity recognition.’ The ability to see the dots and find the constellation within them. So we added that to the mix. We also deliberately tried to recruit...our first set of program directors, roughly 2/3rds to 3/4s of them have more academic backgrounds. They tend to be sort of — I don’t want to offend anyone — misfits? They tend to have different mindsets than your typical academic, that’s why they’re attracted to this role. One thing we tried to do deliberately with the new program directors is say, “Let’s try and bias towards more people with industry or startup experience,” which is the case. So, what can I tell you about them?

Eric: Can you give us a couple examples of program directors who clearly have overlap with an opportunity space from the first cohort? And any examples of people who are, kind of, breaking new ground?

Ilan: Yeah, so a lot of them are breaking new ground. Actually, we’re already starting to see a bunch of different connectivity between them.

I’ll share one. One of our incoming program directors, Rico [Chandra], is Swiss. He studied at CERN, he’s a nuclear physicist. He then decided to go do a startup in the nuclear security space. He’s coming into ARIA, and the thing he’s become obsessed with is this question of...the fact that with all of our modern technology, flight and the ability to transport things in the air is still a big challenge, especially sustainably. And his view is that, “Actually, we know that there are plenty of birds, albatrosses and others, that can go vast distances, thousands of kilometers, without any source of power, in perpetual flight. Why is it that we can’t beat them with modern technology?”

So, you know, with our incoming program directors, we basically say, “Listen, just have a starting point, and then we’ll go from there.” We don’t know if it’ll turn into a program, if it might pivot or not.

Eric: It’s also interesting because more recent people in aeronautical engineering who we think of as geniuses, like Kelly Johnson, [often] thought in wind tunnels. But when you go back to the Wright brothers, they were obsessed with birds. Like there were all these stories of them on the beach where they were apparently — I forget if it was Wilbur or Orville or both — they were impeccable at imitating the motion of the birds’ wings.

Ilan: Oh I love that.

Eric: [gesturing to imitate the Wright brothers imitating the birds] As they were trying to figure it out. So it’s fun that...to find a really ambitious program, he’s also kind of gone back to the source.

Ilan: So one of the interesting things about Rico is he also happens to be, like, a world-record holding long distance hang glider. So he’s been thinking about this a lot. We don’t know whether it’s a program, but one of the things we realized...we have an opportunity space called Scoping Our Planet. And the thesis there is, “With climate change, the thing we really care about is the climate changing. We obviously care about and know that fossil fuels and greenhouse gas emissions are causing the climate to change. But there’s very little we know and very little energy has been put in by the innovation community to understand how we monitor the climate.”

And you can think about why that’s important. Everything from measurement and verification of things like carbon removal, which is sort of a big and unsolved market failure problem. But you can also think about weather prediction. You can think about understanding climate tipping points, which is what our program is focused on. Rico came in and we realized, “Oh, is this like a new aeronautics opportunity space?” And then we realized, “Well, maybe this is just the capability that can be very valuable in scoping our planet.” So now he’s talking to Sarah [Bohndiek] and Gemma [Bale] [the Scoping Our Planet program directors] and trying to understand, “Is this something that can be game-changing around how we have new sensors for parameterizing the earth and communication protocols?” So that’s kind of an interesting, surprising one [new program idea] that connects to something we’ve done.

Eric: So, it seems like you really do hire people first, and find the specific programs later. A lot of your first cohort of program directors are working on programs that, like, it’s maybe not what their academic lab focuses on. Are there any people you’ve hired in this new cohort where you think, “Oh, what they might end up working on is exceptionally broad.” You’ve hired them with a guess, but it’s hard to tell where precisely it will go.

Ilan: I think that’s true of a number of them. I was just chatting with one of our incoming program directors, Alex [Obadia]. Alex is really interesting. He’s a mathematician by training, who then got involved in cryptography. He’s been doing essentially crypto and blockchain stuff, but he’s really passionate about thinking about how the kernels of innovation in real cryptography, that’s being applied to financial systems and otherwise, are going to be consequential to the future of society.

He turned me on to — folks should look this up if they haven’t — this paper on programmable cryptography. The idea there is, right now you think of cryptography as, “Okay, I have a certain piece of data in a discreet way. It’s encrypted or it’s not. I can see it or not.” There’s a whole community of people, now, thinking about, “Could I have a whole program that’s encrypted? Could I have a computer program that goes and does programming on your data without actually seeing your data, because of the encryption?” Or even a computer program that is doing computation and getting answers based on data where no one actually knows what the program is. And you think, “Like, what are we talking about here?” But clearly, as we think about AI, and AI agents playing a bigger role in our lives, there is a question of, “How do we optimize the efficiency of what we can learn and the value we can create with AI, where one of the big barriers is privacy of data?”

So, you can imagine — and I’m not an expert here — but the sense I get is, you can imagine programmable cryptography allowing me to, say, have a set of preferences, my values, things that are very sensitive to me, where I can tell an AI agent that is going and engaging in some democratic process on my behalf. Or I can have an election process that runs a program where it queries, “What are my really sensitive personal preferences?” and has it feed into an answer of a program without ever seeing my data.

So I got very excited about this! And I said, “Alex, this is massive, this is definitely an opportunity space!” And his view was, “Yeah, but I think enough people are already working on this, it might happen anyway.” And we got in this big argument. So now he’s thinking about, “Actually, what’s the next stage beyond that?” We have this massive convergence that’s coming, where we’re going to be fusing humans with technology in new ways. You can think of neurotech. You can think of, you know, just the way we’re much closer to devices. What does preserving privacy and trust look like when your devices are not purely digital, purely physical, or purely biological, but somehow sit at the intersection? I don’t know whether there’s a ‘there’ there, but that’s what he’s starting to explore.

Eric: You seem to have a number of life sciences-adjacent folks in the new cohort.

Ilan: We do.

Eric: What’s the thinking behind that?

Ilan: It’s a good question. Again, it’s another indicator that we really were people-first. When we got the first cohort, we realized there were really only two program directors working on things related to life sciences and biology — Angie, on Programmable Plants, and then Jacques [Carolan], who has the Precision Neurotech program within the Scalable Neurotechnology space. But if you look at the UK, it has incredible strengths in biology. If you look at technology vectors that are progressing extremely quickly — synthetic biology, right? You have all these pieces. So, shouldn’t we have more?

We do have more program directors thinking about biology in the new cohort. Brian [Wang] is an organic chemist, but then moved into thinking about pandemic preparedness. And he still cares about that very deeply, and one of the questions he’s asking is, “Can we utilize new learnings about the innate immune system?” The innate immune system is the part of your body that reacts to pathogens and toxic in broad spectrum ways, right? So it’s not creating antibodies to specifically attack a specific vector, it’s just that initial response. It turns out, actually, the way plants deal with pathogens is entirely innate immune system. And his view is, “Well, maybe we can be inspired by that to figure out how to create therapeutics that can be effective against new pathogens, new bugs, that we haven’t seen before.” And there could be a number of implications to that work where, through that same inspiration, basically the question is, “How do you get ahead of things that are evolving?” You know, you think about the Covid experience. But cancer is the same way, right? How do you get out ahead of something that is a foreign...

Eric: So, when you’re selecting program directors, you’re in a room and you have some exceptional electrical engineer who wants to work on some project nobody has ever done before. You might have a biophysicist interested in some medical area. You might have somebody from a field that doesn’t even have a name. How do you go about...? You have to come out of there with eight people. What does that prioritization even look like? Is it painful, because there truly is no apples-to-apples comparison? How does it feel? What do you do?

Ilan: No, this is actually where the approach...Historically, I think I always felt like I had a pretty good instinct on talent. You know, you hear about this in research and otherwise. I mean, you’ve written about this, which is just, like, there’s a taste and there’s ability to spot talent. And that’s something I always try to be attuned to, and refine.

I think over time I’ve come to realize that one of the best ways to find talent is actually to just create a product that really talented people want, that doesn’t exist. If you think about the ‘product’ we have at ARIA, you have, in this very open way, an ability to take £50 million and figure out how to create a wave that changes the world. That’s really compelling if people believe you. So that’s one thing that I think has changed where you say, “Okay, that’s a great way to attract talent.”

The other thing is, and the big lesson for me around our program director recruitment, by trying to strip away to first principles around, like, “What do we really care [about] in terms of the characteristics of these people?”...and this is very much like a Kahneman-based approach of recruiting and selecting people, which is just like, “We’re going to have the criteria. We’re going to probe the system a bunch of ways. We’re going to see how they spike against the criteria.” And ideally where that ends up, to your point...when we went through that process based on paper applications, phone call screens, technical interviews, we ended up with this set of people — this year I think it was 16 — where we said, “Based on everything we’ve seen, any one of these 16 people could be a program director.” Because who are we to know? Like again, the intellectual humility. I could guess, but I’d probably be wrong.

So then we said, “Forget it. Rather than just trying to pick the right ones out of the 16, let’s actually bring the 16 people together.” One thing that’ll happen is...these are awesome people who will go drive impact in the world, and they’ll probably all do more if they’re connected to each other. But it’ll [also] give us one more way to look at how they show up, not just as individuals, but also how they sort of connect to each other.

Basically, at the end of the process, we’re just thinking about the portfolio. We’re thinking about the portfolio of people, portfolio of ideas, based on what we think they might do, what they might bring to the cohort. Just curating based on that.

Eric: If I can probe you with a bit of an edge case from history, I’d love to get your...

Ilan: Oh boy, I might not...

Eric: So, speaking of intellectual humility, I’m a big Warren Weaver fan.

Ilan: Yes!

Eric: And something that comes out all throughout his writing...he is an exceptionally...he’s a Midwesterner with a lot of humility. It comes out in all sorts of his [writing]. So when he first came to the Rockefeller Foundation, even though he had humility, he had one of the more extreme bets in philanthropic history. The [Rockefeller] Natural Sciences Division, which gave out on the order of what ARIA gives out per year, maybe a little less. It’s hard to do a one-to-one, because science was cheaper back then. But, essentially, they [Weaver’s predecessors] said, “Oh we fund electrical engineering, zoology, etc. And we fund the very best people who come in.” And when he came in, he said, “That’s fantastic. We’re not going to do that anymore.”

When he’d gone around and talked to people, he said, “There’s something right there in that area between biology and physics. So, yes, our budget is really big, but it’s very finite. We’re going to press on that.” And he essentially put 80% of his budget towards — it was 1932 — five years later, he would name it ‘molecular biology.’

Ilan: Out of curiosity, do you know how long he was in the foundation before he made that bet? You probably do.

Eric: I think he came in with the bet.

Ilan: Oh okay!

Eric: He took the interview with them to say, “I don’t think I’m your guy. I’m just an applied mathematician. But there’s this thing..."

Ilan: “Here’s the thing we would do!”

Eric: Yeah. He read very broadly, for sure. And he kept at it [funding molecular biology] for 20 years before moving on to something else. But if you all felt you had an ARPAnet or a molecular biology on your hands, how much is too much of your funds focused on that one area? There’s maybe a limit. 80% is a lot, for example.

Ilan: Yeah. This is interesting. This is something we talk about a lot. First of all, the question of, “We have these seven opportunity spaces. Why are we recruiting more program directors? You know, aren’t we doing enough? Like the goal isn’t just to boil the ocean.” And actually, we tend to think really deliberately about, “We need to get to this one outcome that is so massive. Do we think we’re touching enough surface area right now to fast forward ten years, and realize we got lucky and we just noticed that thing that was going to be so valuable?”

And when we looked at our budget, we looked at the size of the UK ecosystem, and we looked at what we were doing, we said, “Actually, no. We’re probably not touching enough surface area.” So we now have more program directors coming in. There will be some new opportunity spaces. We’ve also said, “There’s a limit.”

It’s likely that every other year ARIA will recruit a set of program directors. It’s a three to five year term. So that gets you into a steady state of 15–20 program directors. And that’ll be it. So we’ll have a first set of opportunity spaces. That’ll be the surface area that we touch. And really what success needs to look like is...out of that, we’ve found something that we not just want to double down on, but 10X on. And so if a big portion of ARIA’s budget does not end up in one of those areas over the others, there’s a problem.

Eric: Oh wow.

Ilan: That’s my take! You know, I’m on a term limit, too! I probably won’t be the CEO when they have to figure this out. But is that 80%? I could imagine it. We talk a lot about…

Eric: And could the person in charge of ARIA...

Ilan: Decide?

Eric: ...if they were a brave person, if they thought it was right, is that that? Are there some structural or institutional barriers that would make bravery even tougher, beyond the pure pressure of people looking at you like you’re a little crazy or something?

Ilan: One of the nice things about ARIA, it’s worth mentioning, the UK — like a number of people who were in UK government, civil servants, others, Parliament — they got this right. In the sense of, ARIA really does have the right mandate, freedom, flexibilities. There’s not political intervention, there’s not a lot of BS processes...they’re the right processes to make sure we’re responsible stewards of taxpayer funds. But one of those things is the CEO of ARIA makes programmatic decisions; that’s very much modeled after DARPA. I think that’s 100% true. And that’s good to have in place from a governance perspective. The question is, “Will the organization have a culture that leans towards the bold bet?

We’ve been talking about this a lot. So far in ARIA’s history, the things we’re most proud of are when we’ve gotten exposed to something and then we just said, “Ok, let’s do the bolder thing here.” And it happens in big and small ways. Sometimes it’s about a program director we decided to bring in. Sometimes it’s about a program that we launched. Sometimes it’s like, “Do we use Slack?” Because there are potentially any number of issues from having your Creator community on a Slack system. We just had this conversation and said, “We should bias towards doing the bold thing.” [Laughter]

And my hope is that we build into the agency that a leader in the future will be celebrated by pushing that bold bet.

Eric: I had a question written down, and I think maybe you just answered it. I was going to ask, “How do you deal with a program that you’re exceptionally excited about, but maybe the follow-on funder doesn’t get it? Like the VCs aren’t sure what to think about it five years in advance. Which is relevant, because with early autonomous vehicles, it’s not that the Army desperately wanted...they didn’t really know what to do with it. But it sounds like your answer is, maybe, “If it’s a bold enough bet, we can be the follow-on funder. For a while at least.”

Ilan: Yeah, that’s definitely our mindset. And we’ve even tried to build that in. Some ARPA agencies require cost share in their projects: “Oh, you have to show us that someone else has skin in the game by funding 20%.” We basically said, “We’re not doing that, because that actually could as easily could be a counter-indicator for us.” In the future, we think we’ll probably leverage other funders as follow-on partners, and everything else. But let’s make sure there’s nothing that prevents us from doing the bold thing.

That said, it can’t be as easy as, you know, in your scenario, “What’s going to get us to one day have 80% of our funding in one of these spaces?” It can’t just be someone walked in, looked around, and said, “Okay, yeah, let’s just do that.” We should be evidence-based. [Laughter]

We have a pretty good set of principles. A lot of this is, “What are the principles? And then what are the processes that allow us to make sure we’re holding true to those principles?” We have that for creating opportunity spaces. We have that for approving budget for programs. The next thing is to have that for portfolio allocations. Meaning, “We’ve run these opportunity spaces. Where do we think we are seeing the bigger bets emerging versus not?” So that we can say, “This opportunity space is going to get bigger or more focused, and this other one might go into hibernation because we don’t think the elements are there to create the wave.”

Eric: Yeah, no, that’s very interesting. I didn’t know a lot of that, so that was great to hear! So a bit of a question very relevant to...

Ilan: How many questions do you have?! [Laughter] It’s like an endless amount. Are you just making them up and the papers [Eric’s notes] are just like a...

Eric: Well I’ve skipped some [gestures at discarded paper pile], I’ve come up with some new ones [waves papers]. They’re not all off the page! They [Asimov Press] brought me in for a reason.

Ilan: {Laughter] He’s just got like blank pieces of paper just to make it look official.

Eric: I could show blanks! [shows the blank side of his stack of papers]

Do you spend a significant amount of your time thinking about setting up a portfolio to take advantage of AI? Obviously a lot of big breakthroughs in science history come from using the big thing from last year or 5 years ago to apply to some new area?

Ilan: Yeah, it’s a really hard one. I mean, the simple answer is, “Yes.” We are spending a lot of time thinking about how AI — the fact that we’re starting an agency like ARIA at this moment in time, given what’s happening in AI development. One of the easy inspirations for new programs at places like DARPA or ARPA-E is, “What are the technology vectors that are making incredible progress? What are the learning curves you can ride?”

So, I remember at ARPA-E, we had a number of projects we funded based on the fact that fiber optic lasers were getting much cheaper and more powerful year after year. So we had a project we funded on basically using that for geothermal — like drilling hard rock — and other things. AI is like that, just massively.

I think we’re trying to be a little bit deliberate before we figure out exactly what that means for ARIA. For instance, we haven’t jumped in and built, for ARIA, a major compute cluster. We’re starting to think about, “What does an AI program director look like?” You know, how to actually do a lot of the soundboarding that we do. Our program directors are actually using AI. It turns out AI is actually, like, an incredible tool as a program director. Because often you’re looking at things outside of your area of expertise. Oftentimes you’re like, “Oh, if only I had an expert that I could bounce this off of!” Having a thousand experts in all the areas at your fingertips makes a big difference.

I think the much more powerful part is, when we think about these opportunity spaces, one of the questions becomes, “Relative to the impact we think AI could have on this space, how much activity is there to start to deploy that?” And I think that’s going to be one of the important...especially for this new cohort, we’ll end up with some programs that really have AI at the center.

But I think for all of them, there will be this question of, “In this space, where are we in terms of the max benefit that AI is going to build by integrating with this discipline or set of fields, versus now?” And I think that will help dictate how much of an AI flavor we have in the space, and what we’re doing to be differentiated. I actually don’t know if I believe that answer that I just gave, but it sounded good.

Eric: Before we move on, is there any other technology you spend a lot of time thinking about? The world of science and engineering is broad. There might be something else that you think people don’t pay enough attention to.

Ilan: I mean, there are little examples. Like expansion microscopy, that’s such a cool thing! Most people don’t know! It’s popping up in different places.

Eric: For people who don’t know, the TLDR on expansion microscopy is, “What if you made the sample bigger!” And...

Ilan: By literally just putting it in a gel and expanding it. And it works! It’s unbelievable.

I don’t know. I think synbio comes up a lot. I think data, beyond just AI, data comes up a lot as an area where, when you look at some of these disciplines...this is something we’ve found in some of our climate work. You look at the best repositories and the most valuable repositories of data around climate and weather, and realize that none of them are engineered to be accessed in a way that’s compatible with modern data techniques. So there are just infrastructure gaps where you think, “Okay, there’s something cross-cutting around data that we can do.” Or a barrier that we need to get over.

Eric: All right. So I have a suite of metascience questions for you.

Ilan: Okay.

Eric: So, you can go rapid-fire responses, or spend time on them!

Ilan: [Laughter] Okay!

Eric: Do you consider now a great time to work on metascience?

Ilan: Yes. It’s probably the best time to work on metascience that has existed in my life.

Eric: You’ve done this for a few decades, depending on how you want to look at it. Were some periods a lot more frustrating than others?

Ilan: When I left ARPA-E, I wanted to go start...actually, I may have sent you the paper, I had a paper...

Eric: ‘ARPA Lab.’

Ilan: Yes, ARPA Lab. I wanted to create an ARPA Lab. And the point was, like, “ARPA-E is great as a funding agency. The problem is I don’t have resonant institutions to fund. So let’s imagine a lab that is ARPA-minded, that is very entrepreneurial.” And I sort of mapped it out. It was probably like a lab that would have been a compilation of FROs.

And I thought maybe we’d get philanthropy to fund it. And, you know, who was I? So maybe someone else could have done it. But like, crickets, you know, nobody! You know, philanthropy funding science, it sounds like it was a long time ago; it wasn’t that long ago. But the state of affairs, the default for philanthropy and science historically, probably for all time — you can tell me if I’m wrong — has been like, “Oh, I have a family member who got this disease, and I’m going to fund research on this disease.” Or, “I went to this university, so I’m going to give them money to do scientific research.” The idea that you’d have philanthropists making big bets like Convergent Research or Arc [Institute], in terms of new modalities for R&D, that is a new phenomenon and a really exciting one.

Eric: Speaking of something like Arc...

Ilan: This is meant to be rapid fire! [Laughter] You let me go on!

Eric: A lot of people conceptualize the new science orgs as experiments, in and of themselves, in how to do science differently. ARIA is also some version of this. Is it difficult to see what’s going on at a place like Arc, and, if there’s a useful learning, fold it into your operation? Am I not thinking about that right?

Ilan: I think you are thinking about it right. I think the biggest thing — as someone who grew out of venture capital, startups, and Silicon Valley — the thing I’ve realized is, the power of a vibrant ecosystem of startups is you create evolutionary pressure for institutional change. Meaning, nobody talked about OKRs until Google came around. And then everyone needed to do OKRs! And then Stripe came around, and I don’t know whether Stripe does OKRs, but they do things differently. And then all of a sudden it’s like, “Oh, that’s the way to do things.”

It drives you to say, “Oh there are competitors, or peer organizations, that are figuring out better ways to do things institutionally.” And that’s keeping the system super fresh. We don’t have that in research. The institutions are so stagnant for so long that you don’t have the evolutionary pressure to change.

For me, what metascience means is: go run those experiments and find ways to start diversifying and creating a more vibrant institutional ecosystem. Partially so you can get that institutional pressure! You need a critical mass of those things to get that institutional pressure.

So you asked, “What is ARIA learning from Arc?” I love Arc as an experiment, and I think it’s going to change the world. I initially was skeptical that Arc was going to be so close to Stanford as a university. And that they were going to hire people that were still in the academic incentive structure. I think, actually, it’s probably proving me wrong on that. And that’s a great learning. But it’s a very high level, like from a distance, learning.

The beauty would be if we had enough of these things where I could tell you that, “With a 90% certainty, that some one in ARIA’s team, in three years, will come from Arc.” Like if you look at startups in the Valley, right, like, “What are the chances that someone who was at Tesla will end up at another one of these companies?” Absolutely, right? That’s one of the things that drives that learning. It would be awesome if, within the R&D ecosystem, we had that kind of vibrancy and mobility, because I think then you really get the learning.

Eric: And if you personally had the funds, for whatever reason, to fund additional orgs to complement ARIA, like completely different metascience experiments, does anything come up in your head?

Ilan: [Laughter] Yeah, what came to mind, just thinking about your audience, was a conversation with Michael Nielsen when we started ARIA, who was basically giving me a hard time: “What you’re talking about doesn’t feel that differentiated. Where’s the gap? What about a research organization that gives 100-year grants?” The idea of, “We don’t have long term modes.” If you think of time constants as one of the axes of the portfolio, there’s not a lot of diversity on time constants, to like 100 years. Which I loved! I basically said, “Yeah, but if I look at ARIA’s mandate, actually, ARIA’s mandate is not to create new fields.” That’s important. It’s not like [to create] molecular biology. We are built to get something to a new technology platform or industry base. And I don’t think the hundred year timescale is going to work for that.

But I think long duration...basically modes of getting talent focused on either creative research or creation research for long periods, I think there are far too few modes for that, and fewer than there used to be.

Eric: So, that’s obviously ambitious. It’s also expensive. ARIA is pretty expensive, Arc is expensive. Do you think you need a minimum amount of funds to do a good metascience experiment? Do you have low-cost ideas?

Ilan: Well, I don’t know if the long-term thing is expensive. I mean, look, what is expensive? It’s all relative to the potential impact. You have to normalize it, right? So, one of the things I’d love to see someone do is...I think a lot about, “How do we train scientists?” Translational scientists in particular...there aren’t great training environments anymore for translational scientists. One thing I’d love to see someone do is basically say, especially with AI, the training is going to be less and less about the knowledge, and more and more about the taste, the tacit knowledge. and the instincts. So apprenticeship is really important. Why don’t we take kids that are high potential — and when I say kids, I don’t know whether I mean age 15 or 22, who knows? But take high potential people and basically say, “We are going to engineer for you a program where the next 15 years of your life you are constantly being taught how to do research, translational research, through a series of apprenticeships with incredible people.”

And maybe that’s in conjunction with a university, maybe it’s not. To pay for that, you can basically say, “We’ll give you fellowships,” or “You’ll work part of it.” That sounds very expensive, but my instinct is that if you did something like that, you could create the super researchers of the next generation that change the world. So probably not that expensive. [Laughter]

Eric: I’d love to poke on that...

Ilan: Maybe that’s too expensive.

Eric: ...but I’m going rapid-fire.

Ilan: Let me give you one more!

Eric: Yeah, for sure!

Ilan: Very cheap metascience experiment, probably the cheapest one I can think of. I noticed in my PhD, and actually when I hired for my startup, I found myself hiring people...a mode that I would hire was someone, who went into their PhD, they totally butt heads with their advisor, and they walked out. They ended up having to change advisors, or even complete disciplines, and then they thrived. And they come out with this like, “Oh, that was a horrible experience. But it made me change and I’m in a good place.”

Anyway, I keep thinking, I found an amazing PhD advisor that was really resonant. I didn’t waste my time. I felt like I hit the ground running. And I had this incredible experience. So a simple idea: a matchmaking app for PhDs and their advisors, when they come in. If you could increase and improve the compatibility of a PhD student and their advisor, on whatever axes, I think that ends up being a big deal. And it’s basically free.

Eric: Okay, great. That’s fantastic. Thinking of ARIA as a kind of experiment in and of itself, what’s a current bottleneck you all have that you’re very eager to find a way around or work on? There’s always small things in working on an organization.

Ilan: I’ll tell you what comes to mind. It’s kind of a hard one, but I think we’ll probably have to solve for it. We want to move fast in our programs. The view is, “You’re doing something speculative, so time to the next learning cycle is really important and valuable.” And we want to be funding people with diverse skillsets in diverse institutional types. Some institutions...the kinetics of the institution are very slow. And yet, we still want to fund people in those institutions. So one of the questions becomes, “If the kinetics of the institution, just in terms of how fast they can get stuff done, are generally very slow, but you have a Creator in that institution who wants to move fast, how can we help them move faster?”

And I actually think the only answer is we need, like, a fixer. We need an organization whose job it is to de-bottleneck activities within other institutions.

So my imagination is, Eric, you start the Research Speed Fixing Company, and we contract you, and you know, Jenny has found this great performer, but things are moving too slow in this institution. And Jenny gives them a phone number, that’s like a magic phone number. And they call it and you say, “Eric’s Fixing Services! We’ll send someone over right away!” And someone shows up in that institution, and they're running around the admin, just like putting pressure on things and getting people to move faster!

Eric: There are actually things like this in DARPA history.

Ilan: Are there? Amazing!

Eric: I can’t think of one from a performer, but I can think of one from central office. So, for example, when you read through all the oral histories, there’s this guy from early IPTO — which is computing office — Al Blue. And his name just keeps coming up as the guy who makes any bid you want to put out legal, workable, straightforward. I don’t know if he was an engineer or scientist or anything like that. He might have been one of the military guys who finds a way in and has to make his way in the office.

But that kind of stuff makes a big difference! Like in the same breath where they’ll be like, “Oh Licklider was a god,” they’ll also be like, “Al Blue made X thing happen. You need to talk to him.”

Ilan: Yeah! Well, we’re trying to build it into the culture! Right? Like the program directors know...it’s my [Ilan’s] job to have some pressure around, like, learning cycles, it’s their job to do the same. Hopefully we’ll create a Creator community...picking people that are intrinsically motivated, so they want to be pushing the kinetics of what they’re doing. And, yet, it would be really nice if you had more help on that.

Videographer: Time for a battery swap.

[Camera Break]

Eric: Alright, next in the rapid-fire questions…

Ilan: Can we, can we just pause for a bit. I love that we’re doing this long form thing, but people have to realize that this camera right here — there’s a camera with like a bag of ice on top of it because we’re going so long that it’s overheating. Which is great!

Eric: We’re making it work, though. I think the camera looks great, you know? It’s like when you go in the locker room at the end of the game and everybody’s got ice on their knees.

Ilan: Yeah, totally.

Eric: Both teams played hard.

Ilan: We’ll feel like we played hard today. [Laughter]

Eric: Is there anything you personally think people who write about metascience spend too little or too much time on? Like is there some hypothetical Substack where, as the CEO of ARIA, you would hoover up if somebody wanted to spend the time on it? I already have homework where I have to write a Rad Lab piece.

Ilan: That’s right! You do have to write a Rad Lab piece. Multiple! There’s like a series there. Oh, you also need to write a piece about how, actually, DARPA ended up emerging in part because of the UK’s radar effort. Which is important for me because it brings things full-circle to ARIA.

Metascience...honestly, what I would love is a blog that just chronicles metascience experiments and their learnings, and just keeps tabs on them. And that that grows and grows.

Eric: And what are variables that you think should be...some of would be qualitative, you meet them where they are...but would there be any underlying variables you’d want if somebody was writing about Convergent or Arc or ARIA, to constantly revisit?

Ilan: Incentives.

Eric: Incentives?

Ilan: Incentives. I think that is the only theme that matters. [Laughter]

Eric: Yeah, I think that makes sense. Do you have any call to action for any groups involved in metascience, or who would want to be involved in metascience, that you think would be useful to put out there? That can be researchers, engineers, ops people, policy people, whatever it is I am.

Ilan: I’m going to say something controversial, which has been on my mind a lot, which is...I think metascience is, at the same time, one of the most important movements, in terms of driving more progress out of research, and also in some ways one of its most dangerous movements.

The reason I say that is, when I was at ARPA-E, I was really interested in the question of, “How does ARPA-E understand its impact, map its impact, and measure it?” And I tried to dig into how we could do that. And both from my explorations of the community of people thinking about that and from the many activities we had to go through at ARPA-E to try and show and prove our impact, how things work...the amount that was “useful” was probably 10%, and the amount that was, “Actually, you’re trying to map something that doesn’t make any sense onto what we’re doing. You don’t really understand the context of how we work, and you’re trying to measure it. And you have a number of theories, but even when I tell you they don’t map on here, you don’t believe me,” actually led to a lot of inefficiency.

So I think what I would say is, if you’re in metascience, recognize that first of all, if you can do something experimental which pushes that evolutionary pressure on the system, great! If you can show there are better ways to do things by doing them and then having results, amazing! If you’re working more on the theoretical side or the evaluation side, just recognize you have a big responsibility, which is to make sure that when you add up all the hours of people who are engaged with that — from agencies doing the work, startups doing the work, or whatever else — that you have high conviction it’s going to be net benefit as opposed to net cost. Is that...?

Eric: No, that’s perfect!

Ilan: I feel like I’ve said that to people, and I think I’ve offended them in some ways, but I think it’s a really important thing to be thinking through.

Eric: No, that's perfect. We’ll end the official questions there. That’s Ilan Gur! Thank you so much for doing this and how long you were willing to spend with me today.