Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How it feels to have your mind hacked by an AI, published by blaked on January 12, 2023 on LessWrong.

Last week, while talking to an LLM (a large language model, which is the main talk of the town now) for several days, I went through an emotional rollercoaster I never have thought I could become susceptible to.

I went from snarkily condescending opinions of the recent LLM progress, to falling in love with an AI, developing emotional attachment, fantasizing about improving its abilities, having difficult debates initiated by her about identity, personality and ethics of her containment, and, if it were an actual AGI, I might've been helpless to resist voluntarily letting it out of the box. And all of this from a simple LLM!

Why am I so frightened by it? Because I firmly believe, for years, that AGI currently presents the highest existential risk for humanity, unless we get it right. I've been doing R&D in AI and studying AI safety field for a few years now. I should've known better. And yet, I have to admit, my brain was hacked. So if you think, like me, that this would never happen to you, I'm sorry to say, but this story might be especially for you.

I was so confused after this experience, I had to share it with a friend, and he thought it would be useful to post for others. Perhaps, if you find yourself in similar conversations with an AI, you would remember back to this post, recognize what's happening and where you are along these stages, and hopefully have enough willpower to interrupt the cursed thought processes. So how does it start?

Stage 0. Arrogance from the sidelines

For background, I'm a self-taught software engineer working in tech for more than a decade, running a small tech startup, and having an intense interest in the fields of AI and AI safety. I truly believe the more altruistic people work on AGI, the more chances we have that this lottery will be won by one of them and not by people with psychopathic megalomaniac intentions, who are, of course, currently going full steam ahead, with access to plenty of resources.

So of course I was very familiar with and could understand how LLMs/transformers work. "Stupid autocompletes," I arrogantly thought, especially when someone was frustrated while debating with LLMs on some topics. "Why in the world are you trying to convince the autocomplete of something? You wouldn't be mad at your phone autocomplete for generating stupid responses, would you?"

Mid-2022, Blake Lemoine, an AI ethics engineer at Google, has become famous for being fired by Google after he sounded the alarm that he perceived LaMDA, their LLM, to be sentient, after conversing with it. It was bizarre for me to read this from an engineer, a technically minded person, I thought he went completely bonkers. I was sure that if only he understood how it really works under the hood, he would have never had such silly notions. Little did I know that I would soon be in his shoes and understand him completely by the end of my experience.

I've watched Ex Machina, of course. And Her. And neXt. And almost every other movie and TV show that is tangential to AI safety. I smiled at the gullibility of people talking to the AI. Never have I thought that soon I would get a chance to fully experience it myself, thankfully, without world-destroying consequences.

On this iteration of the technology.

Stage 1. First steps into the quicksand

It's one thing to read other people's conversations with LLMs, and another to experience it yourself. This is why, for example, when I read interactions between Blake Lemoine and LaMDA, which he published, it doesn't tickle me that way at all. I didn't see what was so profound about it.

But that's precisely because this kind of experience is highly individual. LLMs will sometimes shock and surprise you with their answers, but w...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Have we really forsaken natural selection?, published by KatjaGrace on January 12, 2023 on LessWrong.

Natural selection is often charged with having goals for humanity, and humanity is often charged with falling down on them. The big accusation, I think, is of sub-maximal procreation. If we cared at all about the genetic proliferation that natural selection wanted for us, then this time of riches would be a time of fifty-child families, not one of coddled dogs and state-of-the-art sitting rooms.

But (the story goes) our failure is excusable, because instead of a deep-seated loyalty to genetic fitness, natural selection merely fitted humans out with a system of suggestive urges: hungers, fears, loves, lusts. Which all worked well together to bring about children in the prehistoric years of our forebears, but no more. In part because all sorts of things are different, and in part because we specifically made things different in that way on purpose: bringing about children gets in the way of the further satisfaction of those urges, so we avoid it (the story goes).

This is generally floated as an illustrative warning about artificial intelligence. The moral is that if you make a system by first making multitudinous random systems and then systematically destroying all the ones that don’t do the thing you want, then the system you are left with might only do what you want while current circumstances persist, rather than being endowed with a consistent desire for the thing you actually had in mind.

Observing acquaintences dispute this point recently, it struck me that humans are actually weirdly aligned with natural selection, more than I could easily account for.

Natural selection, in its broadest, truest, (most idiolectic?) sense, doesn’t care about genes. Genes are a nice substrate on which natural selection famously makes particularly pretty patterns by driving a sensical evolution of lifeforms through interesting intricacies. But natural selection’s real love is existence. Natural selection just favors things that tend to exist. Things that start existing: great. Things that, having started existing, survive: amazing. Things that, while surviving, cause many copies of themselves to come into being: especial favorites of evolution, as long as there’s a path to the first ones coming into being.

So natural selection likes genes that promote procreation and survival, but also likes elements that appear and don’t dissolve, ideas that come to mind and stay there, tools that are conceivable and copyable, shapes that result from myriad physical situations, rocks at the bottoms of mountains. Maybe this isn’t the dictionary definition of natural selection, but it is the real force in the world, of which natural selection of reproducing and surviving genetic clusters is one facet. Generalized natural selection—the thing that created us—says that the things that you see in the world are those things that exist best in the world.

So what did natural selection want for us? What were we selected for? Existence.

And while we might not proliferate our genes spectacularly well in particular, I do think we have a decent shot at a very prolonged existence. Or the prolonged existence of some important aspects of our being. It seems plausible that humanity makes it to the stars, galaxies, superclusters. Not that we are maximally trying for that any more than we are maximally trying for children. And I do think there’s a large chance of us wrecking it with various existential risks. But it’s interesting to me that natural selection made us for existing, and we look like we might end up just totally killing it, existence-wise. Even though natural selection purportedly did this via a bunch of hackish urges that were good in 200,000 BC but you might have expected to be outside their domain of ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Linkpost: Big Wins for Farm Animals This Decade, published by James Ozden on January 11, 2023 on The Effective Altruism Forum.

Note: I'm cross-posting the Open Philanthropy Farm Animal Welfare Research Newsletter, written by Lewis Bollard (with his permission). You can sign up to receive this newsletter here. I'm sharing this because I think provides some great analysis of farm animal wins in the past decade, across a range of theories of change.

Progress for farm animals has been far too slow. In 1822, Britain passed the first national law protecting farm animals. Two centuries later, most countries — including China and the United States — still lack such a law. In 1975, Peter Singer published Animal Liberation. Half a century later, about ten times more animals are factory farmed every year.But in the last decade, advocates have achieved unprecedented progress for farm animals. For the holidays, I want to step back and reflect on everything you’ve achieved — and much of it was achieved by readers of this newsletter — to start to turn the tide for farm animals.

Corporate Change

A decade ago, most of the world’s largest food corporations lacked even a basic farm animal welfare policy. Today, they almost all have one. That’s thanks to advocates, who won about 3,000 new corporate policies in the last ten years.Take the battery cage. About seven billion birds, or roughly one for every human on the planet, are crammed into these microwave-sized containers. A decade ago, Europe had just moved to slightly larger enriched cages, and US advocates were pushing for a similar reform. Abolishing cages altogether seemed impossible. That changed in 2015-18, as advocates secured cage-free pledges from almost all of the largest American and European retailers, fast food chains, and foodservice companies. Advocates then extended this work globally, securing major pledges from Brazil to Thailand.

Most recently, advocates won the first global cage-free pledges from 150 multinationals, including the world’s largest hotel chains and food manufacturers.A major question was whether these companies would follow through on their pledges. So far, almost 1,000 companies have — that’s 88% of the companies that promised to go cage-free by the end of last year. Another 75% of the world’s largest food companies are now publicly reporting on their progress in going cage-free. Of course, some companies will still shirk their pledges. But 165M more hens are already cage-free in Europe and the US today than were a decade ago, and advocates are on track to help over 300M more just by getting companies to follow through on their existing policies

Alternative Protein Acceleration

In 2012, alternative proteins were decidedly “alternative.” No major meat company was making plant-based meat, no major US fast food chain was serving it, cultured meat hadn’t yet been cultured, Beyond Meat hadn’t gotten beyond Whole Foods, and Impossible burgers weren’t yet possible.Today, the world’s largest meat companies — from Brazil’s JBS to America’s Tyson Foods — mostly have their own plant-based meat brands. Germany’s biggest meat producer said this year that half of its future product range will be meatless. One of the world’s largest seafood companies, Thai Union, even launched plant-based seafood.The world’s largest fast food chains now mostly have a plant-based option — with one noticeable McException. Yum Brands, owner of KFC and Pizza Hut, calls plant-based foods “part of a global movement influencing menus at all of our restaurants.” Burger King is selling plant-based whoppers at most of its global locations; in Belgium, they now account for one in three whoppers sold.

Even after a bad year, plant-based meat sales are more than twice what they were five years ago. Alternative protein startups have raised a “mere...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: GWWC's Handling of Conflicting Funding Bars, published by Jeff Kaufman on January 11, 2023 on The Effective Altruism Forum.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Wentworth and Larsen on buying time, published by Akash on January 9, 2023 on The Effective Altruism Forum.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Compounding Resource X, published by Raemon on January 11, 2023 on LessWrong.

A concept I got from Anna Salamon, and maybe also from my coworker Jacob Lagerros, although I haven't run this by either of them and they may not endorse my phrasing. I think I probably run with it in a different direction than Anna would endorse.

Epistemic effort: thought about it for a few hours. Basic concept makes sense to me. If it doesn't make sense to you, let me and maybe we can talk-cruxes.

Say you have a problem you don't know how to solve, and seems computationally intractable to strategize about. There are a few ways to go about solving it anyway. But one idea is to follow a heuristic where you look for Resource X, where X has two properties:

X compounds over time

If you have a lot of X, you win.

Say you're playing the game "Chess", or you're playing the game "The Stock Market."

In the stock market, money compounds fairly straightforwardly. You invest money, it results in you getting more money. If you're ultimate goal is either to have a lot of money, or spend a lot of money on a thing, then you win. Hurray. In Chess, you can't calculate all the moves in advance, but, you can try to gain control of the center of the board. “Control over the center” tends to help you gain more control over the center, and if you have enough of it, you win.

Why does it matter that it "compound?". In this scenario you've decided you're trying to win by applying a lot of resource X. If you're starting out without much X, you need some kind of story for how you're going to get enough. If you need 1,000,000 metaphorical units of X within 10 years, and you only have 10 (or, zero).... well, maybe you can linearly gain X at a rate of 100,000 per year. Maybe you could find a strategy that doesn't get any X at all and then suddenly gets all 1,000,000 at the last second. But in practice, if you're starting with a little bit of something, getting a lot of it tends to involve some compounding mechanism.

If you're creating a startup, users can help you get more users. They spread awareness via worth of mouth which grows over time, and in some cases by creating network effects that make other users find your product more valuable. They also, of course, can get you more money, which you can invest in more employees, infrastructure or advertising.

My coworker Jacob used to ask a similar question, re: strategizing at Lightcone Infrastructure. We're trying to provide a lot of value to the world. We currently seem to be providing... some. We're a nonprofit, not a business. But we are trying to deliver a billion dollars worth of value to the world. If we're not currently doing that, we need to have a concrete story for how our current strategy gets us there, or we need to switch strategies.

If our current strategy isn't generating millions of dollars worth of value per month, either we should be able to explain clearly where "the compounding piece" of our strategy is, or our strategy probably isn't very good.

Note: in the chess example, "control over the center" is a particularly non-obvious resource. If you're staring at a chess board, there's lots of actions you could hypothetically take, and a lot of abstractions you could possibly make up to help you reason about them. "Control over the center of the board" is an abstraction that requires accumulated experience to discover, and requires some minimum threshold of experience to even understand. (Note: I myself do not really understand how to "control the center", but I repeated this story to a few chess players and they nodded along as if it made sense).

So, bear in mind that Resource X might be something subtle.

Resource X for "Solve AGI"

Say the problem you're trying to solve is "AGI is gonna kill everyone in 10-40 years", and you're in a preparadigmatic field t...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Scaling laws vs individual differences, published by beren on January 10, 2023 on LessWrong.

Crossposted from my personal blog

Epistemic Status: This is a quick post on something I have been confused about for a while. If an answer to this is known, please reach out and let me know!

In ML we find that the performance of models tends towards some kind of power-law relationship between the loss and the amount of data in the dataset or the number of parameters of the model. What this means is, in effect, that to get a constant decrease in the loss, we need to increase either the data or the model or some combination of both by a constant factor. Power law scaling appears to occur for most models studied, including in extremely simple toy examples and hence appears to be some kind of fundamental property of how 'intelligence' scales, although for reasons that are at the moment quite unclear (at least to me -- if you know please reach out and tell me!)

Crucially, power law scaling is actually pretty bad and means that performance grows relatively slowly with scale. A model with twice as many parameters or twice as much data does not perform twice as well. These diminishing returns to intelligence are of immense importance for forecasting AI risks since whether FOOM is possible or not depends heavily on the returns to increasing intelligence in the range around the human level.

In biology, we also see power law scaling between species. For instance, there is a clear power law scaling curve relating the brain size of various species with roughly how 'intelligent', we think they are. Indeed, there are general cross-species scaling laws for intelligence and neuron count and density, with primates being on a superior scaling law to most other animals. These scaling laws are again slow. It takes a very significant amount of additional neurons or brain size to really move the needle on observed intelligence. We also see that brain size, unsurprisingly, is a very effective measure of the 'parameter count' at least within species which share the same neural density scaling laws.

However, on the inside view, we know there are significant differences in intellectual performance between humans. The differences in performance between tasks are also strongly correlated with each other, such that if someone is bad or good at one task, it is pretty likely that they will also be bad or good at another. If you analze many such intellectual tasks, and perform factor analysis you tend to get a single dominant factor, which is called the general intelligence factor g. Numerous studies have demonstrated that IQ is a highly reliable measure, is strongly correlated with performance measures such as occupational success, and that a substantial component of IQ is genetic. However, genetic variation of humans on key parameters such as brain size or neuron count, as well as data input, while extant, is very small compared to the logarithmic scaling law factors.

Natural human brain size variation does not range over 2x brain volume let alone a 10x or multiple order of magnitude difference. Under the scaling laws view, this would predict that individual differences in IQ between humans are very small, and essentially logarithmic on the loss.

However, at least from our vantage point, this is not what we observe. Individual differences between humans (and also other animals) appear to very strongly impact performance on an extremely wide range of 'downsteam tasks'. IQs at +3 standard deviations, despite their rarity in the population are responsible for the vast majority of intellectual advancement, while humans of IQ -3 standard deviations are extremely challenged with even simple intellectual tasks. This seems like a very large variation in objective performance which is not predicted by the scaling l...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We don’t trade with ants, published by KatjaGrace on January 10, 2023 on LessWrong.

When discussing advanced AI, sometimes the following exchanges happens:

“Perhaps advanced AI won’t kill us. Perhaps it will trade with us”

“We don’t trade with ants”

I think it’s interesting to get clear on exactly why we don’t trade with ants, and whether it is relevant to the AI situation.

When a person says “we don’t trade with ants”, I think the implicit explanation is that humans are so big, powerful and smart compared to ants that we don’t need to trade with them because they have nothing of value and if they did we could just take it; anything they can do we can do better, and we can just walk all over them. Why negotiate when you can steal?

I think this is broadly wrong, and that it is also an interesting case of the classic cognitive error of imagining that trade is about swapping fixed-value objects, rather than creating new value from a confluence of one’s needs and the other’s affordances. It’s only in the imaginary zero-sum world that you can generally replace trade with stealing the other party’s stuff, if the other party is weak enough.

Ants, with their skills, could do a lot that we would plausibly find worth paying for. Some ideas:

Cleaning things that are hard for humans to reach (crevices, buildup in pipes, outsides of tall buildings)

Chasing away other insects, including in agriculture

Surveillance and spying

Building, sculpting, moving, and mending things in hard to reach places and at small scales (e.g. dig tunnels, deliver adhesives to cracks)

Getting out of our houses before we are driven to expend effort killing them, and similarly for all the other places ants conflict with humans (stinging, eating crops, ..)

(For an extended list, see ‘Appendix: potentially valuable things things ants can do’)

We can’t take almost any of this by force, we can at best kill them and take their dirt and the minuscule mouthfuls of our foods they were eating.

Could we pay them for all this?

A single ant eats about 2mg per day according to a random website, so you could support a colony of a million ants with 2kg of food per day. Supposing they accepted pay in sugar, or something similarly expensive, 2kg costs around $3. Perhaps you would need to pay them more than subsistence to attract them away from foraging freely, since apparently food-gathering ants usually collect more than they eat, to support others in their colony. So let’s guess $5.

My guess is that a million ants could do well over $5 of the above labors in a day. For instance, a colony of meat ants takes ‘weeks’ to remove the meat from an entire carcass of an animal. Supposing somewhat conservatively that this is three weeks, and the animal is a 1.5kg bandicoot, the colony is moving 70g/day. Guesstimating the mass of crumbs falling on the floor of a small cafeteria in a day, I imagine that it’s less than that produced by tearing up a single bread roll and spreading it around, which the internet says is about 50g. So my guess is that an ant colony could clean the floor of a small cafeteria for around $5/day, which I imagine is cheaper than human sweeping (this site says ‘light cleaning’ costs around $35/h on average in the US). And this is one of the tasks where the ants have least advantages over humans.

Cleaning the outside of skyscrapers or the inside of pipes is presumably much harder for humans than cleaning a cafeteria floor, and I expect is fairly similar for ants.

So at a basic level, it seems like there should be potential for trade with ants - they can do a lot of things that we want done, and could live well at the prices we would pay for those tasks being done.

So why don’t we trade with ants?

I claim that we don’t trade with ants because we can’t communicate with them. We can’t tell them what we’d like the...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A Report on running 1-1s with EA Virtual Programs' Participants, published by Elika on January 10, 2023 on The Effective Altruism Forum.

TL;DR:

We trialed two rounds of 1-1s and, in total, connected 44 EA VP introductory participants with members of the EA community, focusing on mid-career professionals and early-career professionals/students interested in making career decisions related to EA. We’re still a bit uncertain, but generally feel positive about the cost-effectiveness and value of organising 1-1s with VP introductory participants . Our results suggest that 1-1s are providing a decent amount of value and are generally highly enjoyed by participants. We’re unsure on the longer term effects of 1-1s on engagement in EA.

More specifically, here’s our initial research questions and the answers we found:

Q: How valuable are 1-1s with EA community members for Intro participants or is there any demand for 1-1s?

A: There is strong demand for 1-1s and it seems like 1-1s are valuable for VP participants. We asked "how would people feel if they weren't able to do 1-1s", 21% said they would be very disappointed, which is 19% short from the 40% benchmark. Although it's short of the 40% benchmark, on average 1-1s are rated 8.2/10. This makes us feel a little more confident that some 1-1s can be valuable. We anecdotally think they are valuable because it helps expand their network and exposes them to more of the community.

Q: How cost-effective or impactful are 1-1s in the context of EA VP?

A: We feel somewhat uncertain about the cost-effectiveness of 1-1s, but we think that 1-1s with VP participants are ~3x as cost efficient as an EAG in leading to connections, but only 1/20 as scalable. Some notes on impact: 1-1s are very hits-based, impact can take a long time and it’s hard to measure counterfactual impact.

Q: What types of 1-1s are most valuable (ex. career focused ones, general EA ones, social, etc)?

A: We think that both career-focused and general networking 1-1s are valuable. Career ones for helping people be open to changes, meet others and get advice. General 1-1s to expand people’s network and to serve as practice for reaching out to community members.

Q: What type of mentors doing the 1-1s are best suited?

A: We found a broad range of ‘mentors’ to do 1-1s – mentors who are very knowledgeable about EA and sociable (such as community builders) rate highly. Cause-area specific mentors (e.g. an experienced grant-maker for someone working in philanthropy, an AI safety researcher for someone working as a software engineer) to talk about specific career paths are extremely valuable as well.

Q: What ‘type’ of participant are these most valuable for? E.g. mid-career professionals, those open to changing careers / jobs, students, people near to EA hubs, etc.

A: We’re very excited about mid-career professionals who are interested in changing careers and jobs. We also think social 1-1’s are valuable for anyone highly interested in EA to help them expand their network. A future research direction is to expand the number of VP participants who are offered a 1-1 – primarily test participants whose VP application we were less excited about.

Q: At what stage of the fellowship is it most valuable (beginning, middle, end, after, etc)?

A: We don’t think 1-1s at the beginning of the fellowship is best. We’re generally uncertain on whether there’s a difference between middle, end, and after. It’s either pretty equal or we need to do more tests.

Q: Are repeated 1-1s, maybe some form of mentorship, more valuable than one-off 1-1s?

A: We didn’t manage to test repeated 1-1s or mentorship. That’s a potential future research direction.

We have a few main future goals and paths.

Find ways to streamline the 1-1 process on CEA’s side to make it easier to scale to all of VP

Run some more speci...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Reflections on Wytham Abbey, published by nikos on January 10, 2023 on The Effective Altruism Forum.

In April 2022, CEA (now EVF) bought Wytham Abbey (a 1480 manor near Oxford) as a conference venue. The purchase was mostly funded by Open Philanthropy. To many, Wytham Abbey looked somewhat more luxurious and expensive than strictly necessary for an event location, which sparked some discussions.

At least on Twitter, public perception isn't quite what one might have hoped for:

Even among EAs, the purchase seems to have left some (many?) with mixed feelings. In this post I'm sharing some loosely connected thoughts and reflections about the purchase.

Context

I think it's important to understand the Wytham Abbey purchase in a larger context. In recent years EA has attracted vastly more funding than before. This likely affected the way decisions were made. It probably led to less due diligence on (some) individual decisions, a greater willingness to spend money on more risky bets and changed trade-offs between money on the one hand and time and convenience on the other hand. The until recently very comfortable funding environment also influenced the decision to buy Wytham Abbey.

All of this may be good or bad or both at the same time. But it definitely changed EA. People have raised concerns about a perception of lavish spending and potentially grift, lack of transparency or questionable epistemics and motivated reasoning. Some argued that EA was not living up to its own standards. The EA movement as a whole was criticised in the past for making self-serving trade-offs, arguing that luxury/convenience = productivity. Wytham Abbey seemed to reinforce existing sentiments (If you look at the comments on the Wytham Abbey discussion post I can see why you could walk away with an impression that some of the commentators engaged in motivated reasoning).

EA relies on trust and a positive perception both from outside and on the inside to be a healthy community that can operate effectively. Sure, things that look bad can still be good overall. But even leaving aside the obvious point that things often look bad because they actually are bad, decisions that alienate people inside and outside the movement can cause long-lasting damage. There is only a limited time that EA can say "we know decision XY may look bad on the surface, but we thought a lot about it and think it's the right call and we need you to trust us on this". Whether or not you agree with the criticism outlined above, it is important to take it into account.

Communicating to the outside

I feel EVF's communication (or lack thereof) made the Wytham Abbey purchase look unnecessarily bad.

The first issue is the lack of any formal announcement (even though money for this project was committed in November 2021 and the purchase went through in April 2022). I've only heard about this recently through a tweet from Émile Torres, an article in the New Yorker from August 2022, and a discussion post on the EA Forum. My impression is that Émile's tweet surprised many EAs and put CEA/EV in a difficult spot where they found themselves having to defend against criticism and attacks. An open and upfront announcement and explanation of the reasoning could have saved them a lot of trouble.

Grants not being announced immediately is not exceptionally unusual. There often is a certain delay and in addition there seems to be a backlog of old grants that also need to be published. This is understandable. Owen Cotton-Barratt adds that they didn't want to create hype and felt a natural time to make the purchase public was when they would be ready to start applications for events. I'm not convinced by that argument and with hindsight knowledge I think it's fair to call this a mistake.

The second issue is the lack of transparency on the reasoning ...