In this episode of AI + a16z, Bowen Peng and Jeffrey Quesnelle of Nous Research join a16z General Partner Anjney Midha to discuss their mission to keep open source AI research alive and activate the community of independent builders. The focus is on a recent project called DisTrO, which demonstrates it's possible to train AI models across the public internet much faster than previously thought possible. However, Nous is behind a number of other successful open source AI projects, including the popular Hermes family of "neutral" and guardrail-free language models.

Here's an excerpt of Jeffrey explaining how DisTrO was inspired by the possibility that major open source AI providers could turn their efforts back inward:

"What if we don't get Llama 4? That's like an actual existential threat because the closed providers will continue to get better and we would be dead in the water, in a sense. 

"So we asked, 'Is there any real reason we can't make Llama 4 ourselves?' And there is a real reason, which is that we don't have 20,000 H100s. . . . God willing and the creek don't rise, maybe we will one day, but we don't have that right now. 

"So we said, 'But what do we have?' We have a giant activated community who's passionate about wanting to do this and would be willing to contribute their GPUs, their power, to it, if only they could . . . but we don't have the ability to activate that willingness into actual action. . . . The only way people are connected is over the internet, and so anything that isn't sharing over the internet is not gonna work. 

"And so that was the initial premise: What if we don't get Llama 4? And then, what do we have that we could use to create Llama 4? And,  if we can't, what are the technical problems that, if only we slayed that one technical problem, the dam of our community can now flow and actually solve the problem?"

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DisTrO paper

Nous Research

Nous Research GitHub

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In this episode of AI + a16z, Ambience cofounder and chief scientist Nikhil Buduma joins Derrick Harris to discuss the nuances of using AI models to build vertical applications (including in his space, health care), and why industry acumen is at least as important as technical expertise. Nikhil also shares his experience of having a first-row seat to key advances in AI — including the transformer architecture — which not only allowed his company to be an early adopter, but also gave him insight into the types of problems that AI could solve in the future.

Here's an excerpt of Nikhil explaining the importance of understanding your buyer:

"If you believe that the most valuable companies are going to fall out of some level of vertical integration between the app layer and the model layer, [that] this next generation of incredibly valuable companies is going to be built by founders who've spent years just obsessively becoming experts in an industry, I would recommend that someone actually know how to map out the most valuable use cases and have a clear story for how those use cases have synergistic, compounding value when you solve those problems increasingly in concert together. 

"I think the founding team is going to have to have the right ML chops to actually build out the right live learning loops, build out the ML ops loops to measure and to close the gap on model quality for those use cases. [But] the model is actually just one part of solving the problem. 

"You actually need to be thoughtful about the product, the design, the delivery competencies to make sure that what you build is integrated with the right sources of the enterprise data that fits into the right workflows in the right way. And you're going to have to invest heavily in the change management to make sure that customers realize the full value of what they're buying from you. That's all actually way more important than people realize."

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Fundamentals of Deep Learning

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In this episode of AI + a16z, a16z General Partner Jennifer Li joins MotherDuck Cofounder and CEO Jordan Tigani to discuss DuckDB's spiking popularity as the era of big data wanes, as well as the applicability of SQL-based systems for AI workloads and the prospect of text-to-SQL for analyzing data.

Here's an excerpt of Jordan discussing an early win when it comes to applying generative AI to data analysis:

"Everybody forgets syntax for various SQL calls. And it's just like  in coding. So there's some people that memorize . . . all of the code base, and so they don't need auto-complete. They don't need any copilot. . . . They don't need an ID; they can just type in Notepad. But for the rest of us, I think these tools are super useful. And I think we have seen that these tools have already changed how people are interacting with their data, how they're writing their SQL queries.

"One of the things that we've done . . .  is we focused on improving the experience of writing queries. Something we found is actually really useful is when somebody runs a query and there's an error, we basically feed the line of the error into GPT 4 and ask it to fix it. And it turns out to be really good. 

". . . It's a great way of letting you stay in the flow of writing your queries and having true interactivity."

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Small Data SF conference

DuckDB

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In this episode of the AI + a16z podcast, Black Forest Labs founders Robin Rombach, Andreas Blattmann, and Patrick Esser sit down with a16z general partner Anjney Midha to discuss their journey from PhD researchers to Stability AI, and now to launching their own company building state-of-the-art image and video models. They also delve into the topic of openness in AI, explaining the benefits of releasing open models and sharing research findings with the field.

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Flux

Keep the code to AI open, say two entrepreneurs

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In this episode, a16z General Partner Vijay Pande walks us through the past two decades of applying software engineering to the life sciences — from the Folding@Home project that he launched, through AlphaFold and more. He also discusses the major opportunities for AI to transform medicine and health care, as well as some pitfalls that founders in that space need to watch out for.

Here's an excerpt of Vijay discussing how AlphaFold and other projects revolutionized biology research not just because of their algorithms, but because of how they introduced software engineering into the field:

"I think the key thing about AlphaFold that really got people excited was not just the AI part, because people have been using machine learning. And so that part was there. I think it was how fast, at least to me, an engineering approach could make a big jump in this field. Because this was a field largely addressed by academics, and academics would have a lab of maybe 20 [or] 30 people — some of the bigger ones, maybe slightly bigger. And of that, these are graduate students working on their PhDs. It's very different than having a team of professional programmers and engineers going after the problem. 

"And so that jump in team ability, plus the technology, I think was very critical for the jump in results. And also, finally, I think having a company like Google say, 'You know, this is a problem we're excited about and we're interested in,' and that AI and biology is something that is an area of great interest to them . . . was a huge flag to plant."

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a16z Bio + Health

Folding@Home

AlphaFold

Raising Health podcast

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In this episode of the AI + a16z podcast, a16z General Partner Anjney Midha speaks with PromptFoo founder and CEO Ian Webster about the importance of red-teaming for AI safety and security, and how bringing those capabilities to more organizations will lead to safer, more predictable generative AI applications. They also delve into lessons they learned about this during their time together as early large language model adopters at Discord, and why attempts to regulate AI should focus on applications and use cases rather than models themselves.

Here's an excerpt of Ian laying out his take on AI governance:

"The reason why I think that the future of AI safety is open source is that I think there's been a lot of high-level discussion about what AI safety is, and some of the existential threats, and all of these scenarios. But what I'm really hoping to do is focus the conversation on the here and now. Like, what are the harms and the safety and security issues that we see in the wild right now with AI? And the reality is that there's a very large set of practical security considerations that we should be thinking about. 

"And the reason why I think that open source is really important here is because you have the large AI labs, which have the resources to employ specialized red teams and start to find these problems, but there are only, let's say, five big AI labs that are doing this. And the rest of us are left in the dark. So I think that it's not acceptable to just have safety in the domain of the foundation model labs, because I don't think that's an effective way to solve the real problems that we see today.

"So my stance here is that we really need open source solutions that are available to all developers and all companies and enterprises to identify and eliminate a lot of these real safety issues."

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Securing the Black Box: OpenAI, Anthropic, and GDM Discuss

Security Founders Talk Shop About Generative AI

California's Senate Bill 1047: What You Need to Know

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In this episode of the AI + a16z podcast, Command Zero cofounder and CTO Dean de Beer joins a16z's Joel de la Garza and Derrick Harris to discuss the benefits of training large language models on security data, as well as the myriad factors product teams need to consider when building on LLMs. 

Here's an excerpt of Dean discussing the challenges and concerns around scaling up LLMs:

"Scaling out infrastructure has a lot of limitations: the APIs you're using, tokens, inbound and outbound, the cost associated with that — the nuances of the models, if you will. And not all models are created equal, and they oftentimes are very good for specific use cases and they might not be appropriate for your use case, which is why we tend to use a lot of different models for our use cases . . .

"So your use cases will heavily determine the models that you're going to use. Very quickly, you'll find that you'll be spending more time on the adjacent technologies or infrastructure. So, memory management for models. How do you go beyond the context window for a model? How do you maintain the context of the data, when given back to the model? How do you do entity extraction so that the model understands that there are certain entities that it needs to prioritize when looking at new data? How do you leverage semantic search as something to augment the capabilities of the model and the data that you're ingesting? 

"That's where we have found that we spend a lot more of our time today than on the models themselves. We have found a good combination of models that run our use cases; we augment them with those adjacent technologies."

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The Cuckoo's Egg

1995 Citigroup hack

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In this episode of the AI + a16z podcast, Anyscale cofounder and CEO Robert Nishihara joins a16z's Jennifer Li and Derrick Harris to discuss the challenges of training and running AI models at scale; how a focus on video models — and the huge amount of data involved — will change generative AI models and infrastructure; and the unique experience of launching a company out of the UC-Berkeley Sky Computing Lab (the successor to RISElab and AMPLab).

Here's a sample of the discussion, where Robert explains how generative AI has turbocharged the appetite for AI capabilities within enterprise customers:

"Two years ago, we would talk to companies, prospective customers, and AI just wasn't a priority. It certainly wasn't a company-level priority in the way that it is today. And generative AI is the reason a lot of companies now reach out to us . . . because they know that succeeding with AI is essential for their businesses, it's essential for their competitive advantage.

"And time to market matters for them. They don't want to spend a year hiring an AI infrastructure team, building up a 20-person team to build all of the internal infrastructure, just to be able to start to use generative AI. That's something they want to do today."

At another point in the discussion, he notes on this same topic:

"One dimension where we try to go really deep is on the developer experience and just enabling developers to be more productive. This is a complaint we hear all the time with machine learning teams or infrastructure teams: They'll say that they hired all these machine learning people, but then the machine learning people are spending all of their time managing clusters or working on the infrastructure. Or they'll say that it takes 6 weeks or 12 weeks to get a model to transition from development to production . . . Or moving from a laptop to the cloud, and to go from single machine to scaling — these are expensive handoffs often involve rewriting a bunch of code."

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Anyscale

Sky Computing Lab

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In this archive episode from 2015, a16z's Sonal Chokshi, Frank Chen, and Steven Sinofsky discuss DeepMind's breakthrough AlphaGo system, which mastered the ancient Chinese game Go and introduced the public to reinforcement learning.

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In this episode of the AI + a16z podcast, Luma Chief Scientist Jiaming Song joins a16z General Partner Anjney MIdha to discuss Jiaming's esteemed career in video models, culminating thus far in Luma's recently released Dream Machine 3D model that shows abilities to reason about the world across a variety of aspects. Jiaming covers the history of image and video models,  shares his vision for the future of multimodal models, and explains why he thinks Dream Machine demonstrates its emergent reasoning capabilities. In short: Because it was trained on a volume of high-quality video data that, if measured in relation to language data, would amount to hundreds of trillions of tokens.

Here's a sample of the discussion, where Jiaming explains the "bitter lesson" as applied to training generative models, and in the process sums up a big component of why Dream Machine can do what it does by using context-rich video data:

"For a lot of the problems related to artificial intelligence, it is often more productive in the long run to use methods that are simpler but use more compute, [rather] than trying to develop priors, and then trying to leverage the priors so that you can use less compute.

"Cases in this question first happened in language, where people were initially working on language understanding, trying to use grammar or semantic parsing, these kinds of techniques. But eventually these tasks began to be replaced by large language models. And a similar case is happening in the vision domain, as well . . . and now people have been using deep learning features for almost all the tasks. This is a clear demonstration of how using more compute and having less priors is good.

"But how does it work with language? Language by itself is also a human construct. Of course, it is a very good and highly compressed kind of knowledge, but it's definitely a lot less data than what humans take in day to day from the real world . . . 

"[And] it is a vastly smaller data set size than visual signals. And we are already almost exhausting the . . . high-quality language sources that we have in the world. The speed at which humans can produce language is definitely not enough to keep up with the demands of the scaling laws. So even if we have a world where we can scale up the compute infrastructure for that, we don't really have the infrastructure to scale up the data efforts . . .

"Even though people would argue that the emergence of large language models is already evidence of the scaling law . . . against the rule-based methods in language understanding, we are arguing that language by itself is also a prior in the face of more of the richer data signal that is happening in the physical world."

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Dream Machine

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The bitter lesson

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