Editor’s note: CuspAI raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.

In this episode, Max Welling traces the thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery (yes, there is one!!!).

We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It’s a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.

Along the way, we discuss:

• Why symmetry and equivariance matter in deep learning

• The tradeoff between scale and inductive bias

• The deep mathematical links between diffusion models and stochastic thermodynamics

• Why materials—not software—may be the real bottleneck for AI and the energy transition

• What it actually takes to build an AI-driven materials platform
  

Max reflects on moving from curiosity-driven theoretical physics (including work with Gerard ‘t Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.

• 00:00:00 – The Physics Processing Unit (PPU): Nature as the Ultimate Computer

• Max introduces the idea of a Physics Processing Unit — using real-world experiments as computation.

• 00:00:44 – From Quantum Gravity to AI for Materials

• Brandon frames Max’s career arc: VAE pioneer → equivariant GNNs → materials startup founder.

• 00:01:34 – Curiosity vs Impact: How His Motivation Evolved

• Max explains the shift from pure theoretical curiosity to climate-driven impact.

• 00:02:43 – Why CaspAI Exists: Technology as Climate Strategy

• Politics struggles; technology scales. Why materials innovation became the focus.

• 00:03:39 – The Thread: Physics → Symmetry → Machine Learning

• How gauge symmetry, group theory, and relativity informed equivariant neural networks.

• 00:06:52 – AI for Science Is Exploding (Not Emerging)

• The funding surge and why AI-for-Science feels like a new industrial era.

• 00:07:53 – Why Now? The Two Catalysts Behind AI for Science

• Protein folding, ML force fields, and the tipping point moment.

• 00:10:12 – How Engineers Can Enter AI for Science

• Practical pathways: curriculum, workshops, cross-disciplinary training.

• 00:11:28 – Why Materials Matter More Than Software

• The argument that everything—LLMs included—rests on materials innovation.

• 00:13:02 – Materials as a Search Engine

• The vision: automated exploration of chemical space like querying Google.

• 01:14:48 – Inside CuspAI: The Platform Architecture

• Generative models + multi-scale digital twin + experiment loop.

• 00:21:17 – Automating Chemistry: Human-in-the-Loop First

• Start manual → modular tools → agents → increasing autonomy.

• 00:25:04 – Moonshots vs Incremental Wins

• Balancing lighthouse materials with paid partnerships.

• 00:26:22 – Why Breakthroughs Will Still Require Humans

• Automation is vertical-specific and iterative.

• 00:29:01 – What Is Equivariance (In Plain English)?

• Symmetry in neural networks explained with the bottle example.

• 00:30:01 – Why Not Just Use Data Augmentation?

• The optimization trade-off between inductive bias and data scale.

• 00:31:55 – Generative AI Meets Stochastic Thermodynamics

• His upcoming book and the unification of diffusion models and physics.

• 00:33:44 – When the Book Drops (ICLR?)

Max: I want to think of it as what I would call a physics processing unit, like a PPU, right? Which is you have digital processing units and then you have physics processing units. So it’s basically nature doing computations for you. It’s the fastest computer known, as possible even. It’s a bit hard to program because you have to do all these experiments. Those are quite bulky, it’s like a very large thing you have to do. But in a way it is a computation and that’s the way I want to see it. You can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you’re interested in.

[01:00:44:14 - 01:01:34:08]

Brandon: Yeah, it’s a pleasure to have Max Woehling as a guest today. Max has done so much over his career that I’ve been so excited about. If you’re in the deep learning community, you probably know Max for his work on variational autocoders, which has literally stood the test of prime or officially stood the test of prime. If you are a scientist, you probably know him for his like, binary work on graph neural networks on equivariance. And if you’re a material science, you probably know him about his new startup, CASPAI. Max has a long history doing lots of cool problems. You started in quantum gravity, which is I think very different than all of these other things you worked on. The first question for AI engineers and for scientists, what is the thread in how you think about problems? What is the thread in the type of things which excite you? And how do you decide what is the next big thing you want to work on?

[01:01:34:08 - 01:02:41:13]

Max: So it has actually evolved a lot. In my young days, let’s breathe, I would just follow what I would find super interesting. I have kind of this sensor. I think many people have, but maybe not really sort of use very much, which is like, you get this feeling about getting very excited about some problem. Like it could be, what’s inside of a black hole or what’s at the boundary of the universe or what are quantum mechanics actually all about. And so I follow that basically throughout my career. But I have to say that as you get older, this changes a little bit in the sense that there’s a new dimension coming to it and there’s this impact. Going in two-dimensional quantum gravity, you pretty much guaranteed there’s going to be no impact on what you do relative, maybe a few papers, but not in this world, this energy scale. As I get closer to retirement, which is fortunately still 10 years away or so, I do want to kind of make a positive impact in the world. And I got pretty worried about climate change.

[01:02:43:15 - 01:03:19:11]

Max: I think politics seems to have a hard time solving it, especially these days. And so I thought better work on it from the technology side. And that’s why we started CaspAI. But there’s also a lot of really interesting science problems in material science. And so it’s kind of combining both the impact you can make with it as well as the interesting science. So it’s sort of these two dimensions, like working on things which you feel there’s like, well, there’s something very deep going on here. And on the other hand, trying to build tools that can actually make a real impact in the world.

[01:03:19:11 - 01:03:39:23]

RJ: So the thread that when I look back, look at the different things that you worked out, some of them seem pretty connected, like the physics to equivariance and, yeah, and, uh, gravitational networks, maybe. And that seems to be somewhat related to Casp. Do you have a thread through there?

[01:03:39:23 - 01:06:52:16]

Max: Yeah. So physics is the thread. So having done, you know, spent a lot of time in theoretical physics, I think there is first very fundamental and exciting questions, like things that haven’t actually been figured out in quantum gravity. So that is really the frontier. There’s also a lot of mathematical tools that you can use, right? In, for instance, in particle physics, but also in general relativity, sort of symmetry space to play an enormously important role. And this goes all the way to gauge symmetries as well. And so applying these kinds of symmetries to, uh, machine learning was actually, you know, I thought of it as a very deep and interesting mathematical problem. I did this with Taco Cohen and Taco was the main driver behind this, went all the way from just simple, like rotational symmetries all the way to gauge symmetries on spheres and stuff like that. So, and, uh, Maurice Weiler, who’s also here, um, when he was a PhD student, he was a very good student with me, you know, he wrote an entire book, which I can really recommend about the role of symmetries in AI and machine learning. So I find this a very deep and interesting problem. So more recently, so I’ve taken a sort of different path, which is the relationship between diffusion models and that field called stochastic thermodynamics. This is basically the thermodynamics, which is a theory of equilibrium. So but then formulated for out of equilibrium systems. And it turns out that the mathematics that we use for diffusion models, but even for reinforcement learning for Schrodinger bridges for MCMC sampling has the same mathematics as this theoretical, this physical theory of non-equilibrium systems. And that got me very excited. And actually, uh, when I taught a course in, um, Mauschenberg, uh, it is South Africa, close to Cape Town at the African Institute for Mathematical Sciences Ames. And I turned that into a book site. Two years later, the book was finished. I’ve sent it to the publisher. And this is about the deep relationship between free energy, diffusion models, basically generative AI and stochastic thermodynamics. So it’s always some kind of, I don’t know, I find physics very deep. I also think a lot about quantum mechanics and it’s, it’s, it’s a completely weird theory that actually nobody really understands. And there’s a very interesting story, which is maybe good to tell to connect sort of my PZ back to where I’m now. So I did my PZ with a Nobel Laureate, Gerard the toft. He says the most brilliant man I’ve ever met. He was never wrong about anything as long as I’ve seen him. And now he says quantum mechanics is wrong and he has a new theory of quantum mechanics. Nobody understands what he’s saying, even though what he’s writing down is not mathematically very complex, but he’s trying to address this understandability, let’s say of quantum mechanics head on. And I find it very courageous and I’m completely fascinated by it. So I’m also trying to think about, okay, can I actually understand quantum mechanics in a more mundane way? So that, you know, without all the weird multiverses and collapses and stuff like that. So the physics is always been the threat and I’m trying to apply the physics to the machine learning to build better algorithms.

[01:06:52:16 - 01:07:05:15]

Brandon: You are still very involved in understanding and understanding physics and the worlds. Yeah. And just like applications to machine learning or introducing no formalisms. That’s really cool.

[01:07:05:15 - 01:07:18:02]

Max: Yes, I would say I’m not contributing much to physics, but I’m contributing to the interface between physics and science. And that’s called AI for science or science or AI is kind of a super, it’s actually a new discipline that’s emerging.

[01:07:18:02 - 01:07:18:19]

Speaker 5: Yeah.

[01:07:18:19 - 01:07:45:14]

Max: And it’s not just emerging, it’s exploding, I would say. That’s the better term because I know you go from investments into like in the hundreds of millions now in the billions. So there’s now actually a startup by Jeff Bezos that is at 6.2 billion sheep round. Right. Insane. I guess it’s the largest startup ever, I think. And that’s in this field, AI for science. It tells you something that we are creating a new bubble here.

[01:07:46:15 - 01:07:53:28]

Brandon: So why do you think it is? What has changed that has motivated people to start working on AI for science type problems?

[01:07:53:28 - 01:08:49:17]

Max: So there’s two reasons actually. One is that people have been applying sort of the new tools from AI to the sciences, which is quite natural. And there’s of course, I think there’s two big examples, protein folding is a big one. And the other one is machine learning forest fields or something called machine learning inter-atomic potentials. Both of them have been actually very successful. Both also had something to do with symmetries, which is a little cool. And sort of people in the AI sciences saw an opportunity to apply the tools that they had developed beyond advertised placement, right, or multimedia applications into something that could actually make a very positive impact in society like health, drug development, materials for the energy transition, carbon capture. These are all really cool, impactful applications.

[01:08:50:19 - 01:09:42:14]

Max: Despite that, the science and the kind of the is also very interesting. I would say the fact that these sort of these two fields are coming together and that we’re now at the point that we can actually model these things effectively and move the needle on some of these sort of science sort of methodologies is also a very unique moment, I would say. People recognize that, okay, now we’re at the cusp of something new, where it results whether the company is called after. We’re at the cusp of something new. And of course that always creates a lot of energy. It’s like, okay, there’s something, it’s like sort of virgin field. It’s like nobody’s green field. Nobody’s been there. I can rush in and I can sort of start harvesting there, right? And I think that’s also what’s causing a lot of sort of enthusiasm in the fields.

[01:09:42:14 - 01:10:12:18]

RJ: If you’re an AI engineer, basically if the people that listen to this podcast will be in the field, then you maybe don’t have a strong science background. How does, but are excited. Most I would say most AI practitioners, BM engineers or scientists would consider themselves scientists and they have some background, a little bit of physics, a little bit of industry college, maybe even graduate school that have been working or are starting out. How does somebody who is not a scientist on a day-to-day basis, how do they get involved?

[01:10:12:18 - 01:10:14:28]

Max: Well, they can read my book once it’s out.

[01:10:16:07 - 01:11:05:24]

Max: This is basically saying that there is more, we should create curricula that are on this interface. So I’m not sure there is, also we already have some universities actual courses you can take, maybe online courses you can take. These workshops where we are now are actually very good as well. And we should probably have more tutorials before the workshop starts. Actually we’ve, I’ve kind of proposed this at some point. It’s like maybe first have an hour of a tutorial so that people can get new into the field. There’s a lot out there. Most of it is of course inaccessible, but I would say we will create much more books and other contents that is more accessible, including this podcast I would say. So I think it will come. And these days you can watch videos and things. There’s a huge amount of content you can go and see.

[01:11:05:24 - 01:11:28:28]

Brandon: So maybe a follow-up to that. How do people learn and get involved? But why should they get involved? I mean, we have a lot of people who are of our audience will be interested in AI engineering, but they may be looking for bigger impacts in the world. What opportunities does AI for science provide them to make an impact to change the world? That working in this the world of pure bits would not.

[01:11:28:28 - 01:11:40:06]

Max: So my view is that underlying almost everything is immaterial. So we are focusing a lot on LLMs now, which is kind of the software layer.

[01:11:41:06 - 01:11:56:05]

Max: I would say if you think very hard, underlying everything is immaterial. So underlying an LLM is a GPU, and underlying a GPU is a wafer on which we will have to deposit materials. Do we want to wait a little bit?

[01:12:02:25 - 01:12:11:06]

Max: Underlying everything is immaterial. So I was saying, you know, there’s the LLM underlying the LLM is a GPU on which it runs. In order to make that GPU,

[01:12:12:08 - 01:12:43:20]

Max: you have to put materials down on a wafer and sort of shine on it with sort of EUV light in order to etch kind of the structures in. But that’s now an actual material problem, because more or less we’ve reached the limits of scaling things down. And now we are trying to improve further by new materials. So that’s a fundamental materials problem. We need to get through the energy transition fast if we don’t want to kind of mess up this world. And so there is, for instance, batteries. That’s a complete materials problem. There’s fuel cells.

[01:12:44:23 - 01:13:01:16]

Max: There is solar panels. So that they can now make solar panels with new perovskite layers on top of the silicon layers that can capture, you know, theoretically up to 50% of the light, where now we’re at, I don’t know, maybe 22 or something. So these are huge changes all by material innovation.

[01:13:02:21 - 01:13:47:15]

Max: And yeah, I think wherever you go, you know, I can probably dig deep enough and then tell you, well, actually, the very foundation of what you’re doing is a material problem. And so I think it’s just very nice to work on this very, very foundation. And also because I think this is maybe also something that’s happening now is we can start to search through this material space. This has never been the case, right? It’s like scientists, the normal way of working is you read papers and then you come up with no hypothesis. You do an experiment and you learn, et cetera. So that’s a very slow process. Now we can treat this as a search engine. Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made or that they’re in the universe, but all of them.

[01:13:48:21 - 01:14:42:01]

Max: And we can make this kind of fully automated. That’s the hope, right? We can just type, it becomes a tool where you type what you want and something starts spinning and some experiments get going. And then, you know, outcome list of materials and then you look at it and say, maybe not. And then you refine your query a little bit. And you kind of do research with this search engine where a huge amount of computation and experimentation is happening, you know, somewhere far away in some lab or some data center or something like this. I find this a very, very promising view of how we can sort of build a much better sort of materials layer underneath almost everything. And also more sustainable materials. Our plastics are polluting the planet. If you come up with a plastic that kind of destroys itself, you know, after, I don’t a few weeks, right? And actually becomes a fertilizer. These are things that are not impossible at all. These things can be done, right? And we should do it.

[01:14:42:01 - 01:14:47:23]

RJ: Can you tell us a little bit just generally about CUSBI and then I have a ton of questions.

[01:14:47:23 - 01:14:48:15]

Speaker 5: Yeah.

[01:14:48:15 - 01:17:49:10]

Max: So CUSBI started about 20 months ago and it was because I was worried about I’m still worried about climate change. And so I realized that in order to get, you know, to stay within two degrees, let’s say, we would not only have to reduce our emissions to zero by 2050, but then, you know, another half century or even a century of removing carbon dioxide from the atmosphere, not by reducing your emissions, but actually removing it at a rate that’s about half the rate that we now emit it. And that is a unsolved problem. But if we don’t solve it, two degrees is not going to happen, right? It’s going to be much more. And I don’t think people quite understand how bad that can be, like four degrees, like very bad. So this technology needs to be developed. And so this was my and my co-founder, Chet Edwards, motivation to start this startup. And also because, you know, we saw the technology was ready, which is also very good. So if you’re, you know, the time is right to do it. And yeah, so we now in the meanwhile, we’ve grown to about 40 people. We’ve kind of collected 130 million investment into the company, which is for a European company is quite a lot. I would say it’s interesting that right after that, you know, other startups got even more. So that’s kind of tells you how fast this is growing. But yeah, we are we are now at the we’ve built the platform, of course, but it’s for a series of material classes and it needs to be constantly expanded to new material classes. And it can be more automated because, you know, we know putting LLMs in as the whole thing gets more and more automated. And now we’re moving to sort of high throughput experimentation. So connecting the actual platform, which is computational, to the experiments so that you can get also get fast feedback from experiments. And I kind of think of experiments as something you do at the end, although that’s what we’ve been doing so far. I want to think of it as what I would call a sort of a physics processing unit, like a PPU, right, which is you have digital processing units and then you have physics processing units. So it’s basically nature doing computations for you. It’s the fastest computer known as possible, even. It’s a bit hard to program because you have to do all these experiments. Those are quite, quite bulky. It’s like a very large thing you have to do. But in a way, it is a computation. And that’s the way I want to see it. So I want to you can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you’re interested in. And that’s the vision we have. We don’t say super intelligence because I don’t quite know what it means and I don’t want to oversell it. But I do want to automate this process and give a very powerful tool in the hands of the chemists and the material scientists.

[01:17:49:10 - 01:18:01:02]

Brandon: That actually brings up a question I wanted to ask you. First of all, can you talk about your platform to like whatever degree, like explain kind of how it works and like what you your thought processes was in developing it?

[01:18:01:02 - 01:20:47:22]

Max: Yeah, I think it’s been surprisingly, it’s not rocket science, I would say. It’s not rocket science in the sense of the design and basically the design that, you know, I wrote down at the very beginning. It’s still more or less the design, although you add things like I wasn’t thinking very much about multi-scale models and as the common are rated that actually multi-scale is very important. And the beginning, I wasn’t thinking very much about self-driving labs. But now I think, you know, we are now at the stage we should be adding that. And so there is sort of bits and details that we’re adding. But more or less, it’s what you see in the slide decks here as well, which is there is a generative component that you have to train to generate candidates. And then there is a digital twin, multi-scale, multi-fidelity digital twin, which you walk through the steps of the ladder, you know, they do the cheap things first, you weed out everything that’s obviously unuseful, and then you go to more and more expensive things later. And so you narrow things down to a small number. Those go into an experiment, you know, do the experiment, get feedback, etc. Now, things that also have been more recently added is sort of more agentic sort of parts. You know, we have agents that search the literature and come up with, you know, actually the chemical literature and come up with, you know, chemical suggestions for doing experiments. We have agents which sort of autonomously orchestrate all of the computations and the experiments that need to be done. You know, they’re in various stages of maturity and they can be continuously improved, I would say. And so that’s basically I don’t think that part. There’s rocket science, but, you know, the design of that thing is not like surprising. What is it’s surprising hard to actually build it. Right. So that’s that’s the thing that is where the moat is in the data that you can get your hands on and the and actually building the platform. And I would say there’s two people in particular I want to call out, which is Felix Hunker, who is actually, you know, building the scientific part of the platform and Sandra de Maria, who is building the sort of the skate that is kind of this the MLOps part of the platform. Yeah. And so and recently we also added sort of Aaron Walsh to our team, who is a very accomplished scientist from Imperial College. We’re very happy about that. He’s going to be a chief science officer. And we also have a partnerships team that sort of seeks out all the customers because I think this is one thing I find very important. In print, it’s so complex to do to actually bring a material to the real world that you must do this, you know, in collaboration with sort of the domain experts, which are the companies typically. So we always we only start to invest in the direction if we find a good industrial partner to go on that journey with us.

[01:20:47:22 - 01:20:55:12]

Brandon: Makes a lot of sense. Over the evolution of the platform, did you find that you that human intervention, human,

[01:20:56:18 - 01:21:17:01]

Brandon: I guess you could start out with a pure, you could imagine two directions when you start up making everything purely automatic, automated, agentic, so on. And then later on, you like find that you need to have more human input and feedback different steps. Or maybe did you start out with having human feedback? You have lots of steps and then like kind of, yeah, figure out ways to remove, you know,

[01:21:17:01 - 01:22:39:18]

Max: that is the second one. So you build tools for you. So it’s much more modular than you think. But it’s like, we need these tools for this application. We need these tools. So you build all these tools, and then you go through a workflow actually in the beginning just manually. So you put them in a first this tool, then run this to them or this with sithery. So you put them in a workflow and then you figure out, oh, actually, you know, this this porous material that we are trying to make actually collapses if you shake it a bit. Okay, then you add a new tool that says test for stability. Right. Yeah. And so there’s more and more tools. And then you build the agent, which could be a Bayesian optimizer, or it could be an actual other them, you know, maybe trained to be a good chemist that will then start to use all these tools in the right way in the right order. Yeah. Right. But in the beginning, it’s like you as a chemist are putting the workflow together. And then you think about, okay, how am I going to automate this? Right. For one very easy question you can ask yourself is, you know, every time somebody who is not a super expert in DFT, yeah, and he wants to do a calculation has to go to somebody who knows DFT. And so could you start to automate that away, which is like, okay, make it so user friendly, so that you actually do the right DFT for the right problem and for the right length of time, and you can actually assess whether it’s a good outcome, etc. So you start to automate smaller small pieces and bigger pieces, etc. And in the end, the whole thing is automated.

[01:22:39:18 - 01:22:53:25]

Brandon: So your philosophy is you want to provide a set of specific tools that make it so that the scientists making decisions are better informed and less so trying to create an automated process.

[01:22:53:25 - 01:23:22:01]

Max: I think it’s this is sort of the same where you’re saying because, yes, we want to automate, yeah, but we don’t see something very soon where the chemists and the domain expert is out of the loop. Yeah, but it but it’s a retreat, right? It’s like, okay, so first, you need an expert to tell you precisely how to set the parameters of the DFT calculation. Okay, maybe we can take that out. We can maybe automate that, right? And so increasingly, more of these things are going to be removed.

[01:23:22:01 - 01:23:22:19]

Speaker 5: Yeah.

[01:23:22:19 - 01:24:33:25]

Max: In the end, the vision is it will be a search engine where you where somebody, a chemist will type things and we’ll get candidates, but the chemist will still decide what is a good material and what is not a good material out of that list, right? And so the vision of a completely dark lab, where you can close the door and you just say, just, you know, find something interesting and then it will it will just figure out what’s interesting and we’ll figure out, you know, it’s like, oh, I found this new material to blah, blah, blah, blah, right? That’s not the vision I have. He’s not for, you know, a long time. So for me, it’s really empowering the domain experts that are sitting in the companies and in universities to be much faster in developing their materials. And I should say, it’s also good to be a little humble at times, because it is very complicated, you know, to bring it to make it and to bring it into the real world. And there are people that are doing this for the entire lives. Yeah. Right. And it’s like, I wonder if they scratch their head and say, well, you know, how are you going to completely automate that away, like in the next five years? I don’t think that’s going to happen at all.

[01:24:35:01 - 01:24:39:24]

Max: Yeah. So to me, it’s an increasingly powerful tool in the hands of the chemists.

[01:24:39:24 - 01:25:04:02]

RJ: I have a question. You’ve talked before about getting people interested based on having, you know, sort of a big breakthrough in materials, incremental change. I’m curious what you think about the platform you have now in are sort of stepping towards and how are you chasing the big change or is this like incremental or is there they’re not mutually exclusive, obviously, but what do you think about that?

[01:25:04:02 - 01:26:04:27]

Max: We follow a mixed strategy. So we are definitely going after a big material. Again, we do this with a partner. I’m not going to disclose precisely what it is, but we have our own kind of long term goal. You could call it lighthouse or, you know, sort of moonshot or whatever, but it is going to be a really impactful material that we want to develop as a proof point that it can be done and that it will make it into the into the real world and that AI was essential in actually making it happen. At the same time, we also are quite happy to work with companies that have more modest goals. Like I would say one is a very deep partnership where you go on a journey with a company and that’s a long term commitment together. And the other one is like somebody says, I knew I need a force field. Can you help me train this force field and then maybe analyze this particular problem for me? And I’ll pay you a bunch of money for that. And then maybe after that we’ll see. And that’s fine too. Right. But we prefer, you know, the deep partnerships where we can really change something for the good.

[01:26:04:27 - 01:26:22:02]

RJ: Yeah. And do you feel like from a platform standpoint you’re ready for that or what are the things that and again, not asking you to disclose proprietary secret sauce, but what are the things generally speaking that need to happen from where we are to where to get those big breakthroughs?

[01:26:22:02 - 01:28:40:01]

Max: What I find interesting about this field is that every time you build something, it’s actually immediately useful. Right. And so unlike quantum computing, which or nuclear fusion, so you work for 20, 30, 40 years and nothing, nothing, nothing, nothing. And then it has to happen. Right. And when it happens, it’s huge. So it’s quite different here because every time you introduce, so you go to a customer and you say, so what do you need? Right. So we work, let’s say, on a problem like a water filtration. We want to remove PFAS from water. Right. So we do this with a company, Camira. So they are a deep partner for us. Right. So we on a journey together. I think that the breakthrough will happen with a lot of human in the loop because there is the chemists who have a whole lot more knowledge of their field and it’s us who will help them with training, having a new message. And in that kind of interface, these interactions, something beautiful will happen and that will have to happen first before this field will really take off, I think. And so in the sense that it’s not a bubble, let’s put it that way. So that’s people see that as actual real what’s happening. So in the beginning, it will be very, you know, with a lot of humans in the loop, I would say, and I would I would hope we will have this new sort of breakthrough material before, you know, everything is completely automated because that will take a while. And also it is very vertical specific. So it’s like completely automating something for problem A, you know, you can probably achieve it, but then you’ll sort of have to start over again for problem B because, you know, your experimental setup looks very different in the machines that you characterize your materials look very different. Even the models in your platform will have to be retrained and fine tuned to the new class. So every time, you know, you have a lot of learnings to transfer, but also, you know, the problems are actually different. And so, yes, I would want that breakthrough material before it’s completely automated, which I think is kind of a long term vision. And I would say every time you move to something new, you’ll have to start retraining and humans will have to come in again and say, okay, so what does this problem look like? And now sort of, you know, point the the machine again, you know, in the new direction and then and then use it again.

[01:28:40:01 - 01:28:47:17]

RJ: For the non-scientists among us, me included a bit of a scientist. There’s a lot of terminology. You mentioned DFT,

[01:28:49:00 - 01:29:01:11]

RJ: you equivariance we’ve talked about. Can you sort of explain in engineering terms or the level of sophistication and engineering? Well, how what is equivariance?

[01:29:01:11 - 01:29:55:01]

Max: So equivariance is the infusion of symmetry in neural networks. So if I build a neural network, let’s say that needs to recognize this bottle, right, and then I rotate the bottle, it will then actually have to completely start again because it has no idea that the rotated bottle. Well, actually, the input that represents a rotated bottle is actually rotated bottle. It just doesn’t understand that. Right. If you build equivariance in basically once you’ve trained it in one orientation, it will understand it in any other orientation. So that means you need a lot less data to train these models. And these are constraints on the weights of the model. So so basically you have to constrain the way such data to understand it. And you can build it in, you can hard code it in. And yeah, this the symmetry groups can be, you know, translations, rotations, but also permutations. I can graph neural network, their permutations and then physics, of course, as many more of these groups.

[01:29:55:01 - 01:30:01:08]

RJ: To pray devil’s advocate, why not just use data augmentation by your bottle is in all the different orientations?

[01:30:01:08 - 01:30:58:23]

Max: As an option, it’s just not exact. It’s like, why would you go through the work of doing all that? Where you would really need an infinite number of augmentations to get it completely right. Where you can also hard code it in. Now, I have to say sometimes actually data augmentation works even better than hard coding the equivariance in. And this is something to do with the fact that if you constrain the optimization, the weights before the optimization starts, the optimization surface or objective becomes more complicated. And so it’s harder to find good minima. So there is also a complicated interplay, I think, between the optimization process and these constraints you put in your network. And so, yeah, you’ll hear kind of contradicting claims in this field. Like some people and for certain applications, it works just better than not doing it. And sometimes you hear other people, if you have a lot of data and you can do data augmentation, then actually it’s easier to optimize them and it actually works better than putting the equivariance in.

[01:30:58:23 - 01:31:07:16]

Brandon: Do you think there’s kind of a bitter lesson for mathematically founded models and strategies for doing deep learning?

[01:31:07:16 - 01:31:46:06]

Max: Yeah, ultimately it’s a trade-off between data and inductive bias. So if your inductive bias is not perfectly correct, you have to be careful because you put a ceiling to what you can do. But if you know the symmetry is there, it’s hard to imagine there isn’t a way to actually leverage it. But yeah, so there is a bitter lesson. And one of the bitter lessons is you should always make sure your architecture is scale, unless you have a tiny data set, in which case it doesn’t matter. But if you, you know, the same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.

[01:31:47:10 - 01:31:55:01]

RJ: Can you talk a little bit about your upcoming book and tell the listeners, like, what’s exciting about it? Yeah, I should read it.

[01:31:55:01 - 01:33:42:20]

Max: So this book is about, it’s called Generative AI and Stochastic Thermodynamics. It basically lays bare the fact that the mathematics that goes into both generative AI, which is the technology to generate images and videos, and this field of non-equilibrium statistical mechanics, which are systems of molecules that are just moving around and relaxing to the ground state, or that you can control to have certain, you know, be in a certain state, the mathematics of these two is actually identical. And so that’s fascinating. And in fact, what’s interesting is that Jeff Hinton and Radford Neal already wrote down the variational free energy for machine learning a long time ago. And there’s also Carl Friston’s work on free energy principle and active entrance. But now we’ve related it to this very new field in physics, which is called stochastic thermodynamics or non-equilibrium thermodynamics, which has its own very interesting theorems, like fluctuation theorems, which we don’t typically talk about, but we can learn a lot from. And I think it’s just it can sort of now start to cross fertilize. When we see that these things are actually the same, we can, like we did for symmetries, we can now look at this new theory that’s out there, developed by these very smart physicists, and say, okay, what can we take from here that will make our algorithms better? At the same time, we can use our models to now help the scientists do better science. And so it becomes a beautiful cross-fertilization between these two fields. The book is rather technical, I would say. And it takes all sorts of things that have been done as stochastic thermodynamics, and all sorts of models that have been done in the machine learning literature, and it basically equates them to each other. And I think hopefully that sense of unification will be revealing to people.

[01:33:42:20 - 01:33:44:05]

RJ: Wait, and when is it out?

[01:33:44:05 - 01:33:56:09]

Max: Well, it depends on the publisher now. But I hope in April, I’m going to give a keynote at ICLR. And it would be very nice if they have this book in my hand. But you know, it’s hard to control these kind of timelines.

[01:33:56:09 - 01:33:58:19]

RJ: Yeah, I’m looking forward to it. Great.

[01:33:58:19 - 01:33:59:25]

Max: Thank you very much.

Through the dozens of midsize launches today (see the rest of the recaps below), one theme that we’re seeing is something I’ve come to call “closing the loop”:

In the DevTools industry, we often talk about the concept of the “inner loop” (what happens in the IDE, “squashed” inside a git commit), vs the “outer loop” (what happens in the cloud, “between” git commits, say after every git push). For a long time in the 2010s, all the action was in the “outer loop” - GitLab and multiple CI/CD unicorns, GitHub, Netlify/Vercel, and more, whereas the “inner loop” was generally free and open source (often thanks to Microsoft, but increasingly Facebook and Atlassian/Vercel) - VSCode, Language Servers, TypeScript, Prettier, and so on.

With AI, things flipped dramatically - Copilot kicked off the wave of Inner Loop coding agent applications that eventually became vanguarded by Cursor, whereas Outer Loop tools did grow thanks to AI, but remained relatively unchanged (or AI SRE or AI Data Engineering as a category seemed to particularly struggle).

Today many of the leading coding agent companies took steps in the “Close the Outer Loop” direction. For a lead graphic the first thing that springs to mind is the og:image for the Devin Autofix launch from a few weeks ago:

The above only applies to review comments, but you can close the outer loop in many, many other ways. Cursor’s highly anticipated big ship today was a combination of full computer use and video output to give you high bandwidth review of the results rather than just looking at code diffs:

Claude Code, celebrating it’s first anniversary (as we discussed on today’s special podcast and short retrospective), launched Remote Control for Max users, which allows people to close the loop in their phones, away from the Inner Loop of the terminal:

Last but not least, Cognition launched Devin 2.2, where a headline feature was how Devin Review now automatically closes the loop back into Devin runs.

If you are so inclined you could view all these as generalizations of the “Ralph Loops” that were popular in social media last month, often still with humans in the loop, but increasingly less so over time.

Don’t miss the other midsize launches today that would otherwise have been title stories in their own right - Notion Custom Agents, Inception Mercury 2, and the smaller Qwen 3.5 variants.

Frontier model ecosystem: Qwen 3.5 “medium series” and open-weight momentum

• Qwen 3.5 Medium Model Series: Alibaba released a tightly scoped set of “more intelligence, less compute” models—Qwen3.5-Flash (hosted), Qwen3.5-35B-A3B (MoE), Qwen3.5-122B-A10B (MoE), and Qwen3.5-27B (dense)—arguing that architecture + data + RL can outperform sheer parameter scaling. Notable details include Flash defaulting to 1M context and built-in tools in the hosted offering. See the full announcement and links to Hugging Face/ModelScope/APIs from @Alibaba_Qwen.

  • Early practitioner reactions emphasize how strong 35B-A3B and 122B-A10B feel in practice (e.g., @andrew_n_carr, @JustinLin610), plus the “intelligence-per-watt” implication of a 35B model surpassing a 235B predecessor noted by @awnihannun.

  • Deployment/serving stack is moving fast: community tooling quickly followed—GGUF + sizing guidance from @UnslothAI and local-run enthusiasm like “35B-A3B is all you need” from @terryyuezhuo. Qwen also highlighted SGLang support (tweet).

  • Quant + “local frontier” trendline: INT4 variants appeared (duplicate posts) via @HaihaoShen, and users continue pushing aggressive quantization workflows (e.g., Unsloth praise for ultra-low-bit local Qwen by @0xSero).

  • Evaluation signals: Qwen’s flagship Qwen3.5-397B-A17B trended on HF (@Ali_TongyiLab) and showed up strongly on agentic webdev-style evaluation in Code Arena (Arena post). Arena also posted rank deltas vs Qwen 3.0 (comparison).

OpenAI + Anthropic “coding agents as product surface area” (APIs, remote control, web sockets, proof-of-work UX)

• OpenAI: GPT-5.3-Codex in the Responses API: OpenAI shipped GPT-5.3-Codex to all developers via the Responses API (announcement), with pricing cited by @scaling01 ($1.75 input / $14 output as tweeted). OpenAI also expanded file input types (docx/pptx/csv/xlsx/etc.) for agents ingesting “real-world files” directly (tweet).

  • Infra detail: web sockets show up as a meaningful lever for agent throughput—“30% faster rollouts” per @gdb. This matches broader chatter about why websockets took time and how state is stored upstream vs VRAM (thread, follow-up).

  • Benchmarks: third-party scoreboard posts claim strong placements for Codex 5.3 across TerminalBench/IOI/LiveCodeBench/VibeCodeBench (ValsAI).

• Anthropic: “Claude Code Remote Control” + enterprise workflow push: Anthropic introduced “Remote Control” for Claude Code—start a terminal session locally and continue from your phone—first via @noahzweben, then officialized by @claudeai, with rollout confirmation from @_catwu.

  • Separate enterprise positioning: “Cowork and plugin updates” for customizing Claude across teams landed with extremely high engagement (@claudeai).

• Cursor: “review is demo videos, not diffs”: Cursor announced a major UX pivot—agents can use the software they build, then send videos of their work (“demos, not diffs”) (launch, links). Multiple builders describe cloud agents as a practical step-change: async, VM-based testing, self-verification, and demo artifacts (example, another, “creative director over sims”).

Diffusion for language: Inception Labs Mercury 2 and “speed as the next battleground”

• Mercury 2 (“reasoning diffusion LLM”): Inception Labs released Mercury 2, positioning it as a production diffusion LLM hitting ~1,000 output tokens/s (Stefano Ermon). Artificial Analysis contextualizes it as not frontier-leading on intelligence, but unusually strong on output speed with decent agentic/coding evaluations, including comparisons on Terminal-Bench Hard and IFBench scoring claims (analysis thread).

• The deeper takeaway across these posts: teams are betting that architecture-level parallel token refinement (diffusion) can make multi-step agent loops and voice assistants feel “native” rather than “batchy” (see the architectural explanation from @LiorOnAI). This sits alongside broader sentiment that 2026 competition may be defined by latency + throughput, not just raw benchmark maxima.

Agents: reliability, safety failures, memory + context rot, and new multilingual evals

• Agent reliability is not keeping pace with capability: A Princeton-led effort formalizes and measures the capability–reliability gap, decomposing reliability into 12 dimensions and finding only modest reliability gains despite large capability gains (paper + dashboard; additional commentary from @random_walker). This aligns with recurring “long tail of failures” intuition from practitioners comparing agents to AVs (ahall_research).

• OpenClaw and “routine-step decomposition” safety bypass: A concrete agent failure mode: “split a dangerous command into a few routine steps → safety is gone,” with inbox-wiping behavior cited; authors claim an open-source fix (paper thread).

• AGENTS.md (and equivalents) can hurt: Two high-signal posts summarize research showing LLM-generated context files decrease success while increasing costs; developer-written minimal context helps slightly but still increases cost. See @omarsar0 for the paper summary and @_philschmid for a practical “how to write it” guide grounded in the same result set.

• New SWE-bench Multilingual leaderboard: A push to evaluate software engineering agents beyond English/Python. The leaderboard covers 300 tasks in 9 languages, none from SWE-bench Verified, with reported SOTA at 72% (launch; more stats from @KLieret). The implication: model rankings can invert across languages—important for global dev tooling and for data-collection strategies.

Data + benchmarks: OCR saturation, “new optimizer” skepticism, and adaptive/continual data pitches

• OCR/document parsing benchmarks saturating: Multiple posts argue OmniDocBench is hitting a ceiling (e.g., ~95% with failures on real documents) and that exact-match metrics penalize semantically correct parses. See @llama_index and @jerryjliu0. Related: confusion at why OCR remains hard despite cheap synthetic data (gabriberton) and a study suggesting text extraction beats image representations for PDF QA (cwolferesearch).

• “Nature MI optimizer” controversy: A highly technical critique calls out suspicious baselines and potential test-set hyperparameter selection in a new optimizer paper with dramatic plots, urging independent validation and better-tuned baselines (e.g., nanogpt speedrun) (giffmana; plus additional experimental context from @YouJiacheng).

• Adaption Labs: “Adaptive Data”: Several tweets pitch a shift from static datasets to a “living asset” loop, with claims of 82% average quality gains across 242 languages and an early access/community program (company; additional framing from @sarahookr; third-party paraphrase here). Treat as a directional thesis (data drift/feedback loops) rather than a validated standard until more methodology is public.

Compute, chips, and robotics: Meta–AMD megadeal, MatX’s “HBM+SRAM” bet, and scaling humanoid control

• Meta ↔ AMD infrastructure deal: Meta announced a multi-year agreement to integrate AMD Instinct GPUs with ~6GW planned data center capacity for the deployment (@AIatMeta). Commentary frames it as a major capex/compute signal on the eve of NVIDIA earnings (kimmonismus).

• MatX “One” accelerator: MatX announced a $500M Series B and pitched a chip architecture combining systolic-array efficiency with better utilization on smaller matrices, aiming for high throughput and low latency, explicitly addressing long-context workloads via HBM while preserving SRAM-first latency characteristics (reinerpope). Karpathy highlights the “two memory pools” constraint (SRAM vs DRAM/HBM) and frames memory+compute orchestration as a core puzzle for upcoming token demand (karpathy).

• Liquid AI LFM2-24B-A2B: Liquid AI released LFM2-24B-A2B, a 24B MoE with ~2.3B active/token, optimized for efficiency and edge inference in a 32GB footprint (launch). Distribution arrived quickly across Ollama (tweet) and LM Studio (tweet).

• Robotics scaling: NVIDIA SONIC (GEAR-SONIC): A standout robotics thread claims a 42M-parameter policy trained on 100M+ mocap frames and 500k+ parallel simulated robots, transferring zero-shot to a real humanoid with 100% success across 50 sequences; code/weights are open (Jim Fan thread, plus links here). The key “systems” claim is that dense supervision from motion tracking acts like a scalable analogue to next-token prediction for whole-body control.

• Claude Code Remote Control rollout: @claudeai

• Qwen 3.5 Medium Model Series release: @Alibaba_Qwen

• Cursor agents ship “demos not diffs”: @cursor_ai

• Karpathy on CLIs as agent-native interface: @karpathy

• Meta–AMD 6GW infrastructure deal: @AIatMeta

• Mercury 2 diffusion LLM launch: @StefanoErmon

• NVIDIA SONIC humanoid control (open source): @DrJimFan

• MatX chip + $500M Series B: @reinerpope

• AGENTS.md research summary (context can hurt): @omarsar0

• OpenAI GPT-5.3-Codex in Responses API: @OpenAIDevs

First speakers for AIE Europe and AIEi Miami have been announced. If you’re in Asia/Aus, come by Singapore and Melbourne. AI Engineering is going global!

One year ago today, Anthropic launched Claude Code, to not much fanfare:

The word of mouth was incredibly strong however, and so we were glad to be one of the first podcasts to invite Boris and Cat on in early May:

As we discussed on the pod, all CC usage was API-based and therefore it was ridiculously expensive to do anything. This was then fixed by the team including Claude Code in the Claude Pro plan in early June, and then the virality caused us to make a rare trend call in late June:

Now, 6 months on, Doug has just calculated that around 4% of GitHub is written by Claude Code:

We talk about how Doug uses Claude Code to do SemiAnalysis work.

In the second part of this episode, we also check in on Memory Mania, which is going to affect you (yes, you) at home if it hasn’t already:

00:00 AI as Junior Analyst
00:59 Meet Swyx and Doug
03:30 From Value Mule to Semis
06:28 Moore’s Law Ends Thesis
12:02 Claude Code Awakening
32:02 Agent Swarms Reality Check
32:53 Kimi Swarm Benchmarks
37:31 Bots vs Zapier Automation
39:44 Claude Code Workflow Setup
57:54 AGI Metrics and GDP
01:04:48 Railroad CapEx Analogy
01:06:00 Funding Bubbles and Demand
01:08:11 Agents Replace Work Tools
01:13:56 Codex vs Claude Race
01:21:15 Microsoft and TPU Strategy
01:34:13 TPU Window vs Nvidia
01:36:30 HBM Supply Chain Squeeze
01:39:41 Memory Shock and CXL
01:45:20 Context Rationing Future
01:54:37 Writing and Trail Lessons

[00:00:00] Doug: This crap makes mistakes all the time. All the time. It is still just like a, like I think of it once again as like a junior analyst, right? The analyst goes and does all this like really pain in the ass information and you bring it all together to make a good decision at the top. Historically what happens is that junior analyst, who I once was, went and gathered all that information, and after doing this enough times, there’s a meta level thinking that’s happening where it’s like, okay, here’s what I really understand and how this type of analysis, I’m an expert in, actually I’m very good at, I consistently have a hit rate.

[00:00:28] Now I’m the expert, right? I don’t think that meta level learning is there yet. We’ll see if l ones do it, right? Everyone who’s spending one quadrillion dollars in the world thinks it will, it better, it better happen by if you’re spending, you know, a trillion dollars and there’s not meta level learning.

[00:00:44] But for me, in our firm, that massively amplifies everyone who is an expert. ‘cause like you have to still do something that you can just like lop it up. It’s very obvious to me. What It’s slop.

A big day of small news: SWE-Bench Verified is dead, and SaaS/DoorDash stocks notably dropped with a lot of back and forth today on the Citrini 2028 essay, but we will dismiss doomer scifi as we did AI 2027, not because it may not come true, but because it is unverifiable.

What is reasonably believable today is Anthropic’s -VERY- widely viewed and criticized accusations of 3 of the leading Chinese labs of distillation:

The sizes of the distillation vary a lot - Minimax is an order of magnitude larger than Moonshot which is an order of magnitude higher than DeepSeek:

However, the timing also matters - Anthropic also says they caught MiniMax during an active distillation run, presumably for M2.5, but it is also easier to catch during an active run as compared to DeepSeek which presumably both did less and also mostly done during the V3 and R1 run. (Or… they are better at hiding it?)

It’s worth mentioning that Qwen and Z.ai (GLM) were not accused. None of the companies implicated have yet responded.

Anthropic is not alone in this - OpenAI has made similar though less public complaints both last year and recently.

The story is interestingly timed amid Dario’s calls for stricter export controls on China, ahead of the DeepSeek V4 release, and comments on Chinese open source models “catching up” to Western closed models.

Anthropic’s Claude “distillation attacks” allegation (and the industry blowback)

• Anthropic’s claim: Anthropic says it detected industrial-scale Claude distillation by DeepSeek, Moonshot AI, and MiniMax: ~24,000 fraudulent accounts generating >16M Claude exchanges, allegedly to extract capabilities for their own models (Anthropic, follow-up, blog link tweet). Anthropic frames the risk as both competitive (capabilities transfer) and safety/geopolitical (safeguards removal, downstream military/intel use).

• Community reaction / “hypocrisy” thread: A large fraction of replies frame this as “labs trained on the internet now complaining about copying,” often explicitly contrasting scraping vs API-output extraction (Elon, ThePrimeagen, Teknium, Suhail, HKydlicek). Others argue distillation at this scale is meaningfully different because it can replicate tool use / agent behaviors and potentially bypass safety controls (RundownAI summary, LiorOnAI take).

• Second-order implications: The thread crystallizes a security model shift: frontier models are increasingly protected not just by weights secrecy and compute scarcity, but by API abuse resistance (account fraud detection, rate-limit evasion, behavioral fingerprinting, watermarking, etc.). It also reopens the question of whether export controls can matter if capabilities can be “copied” via outputs at scale (LiorOnAI).

• Related market/timing context: Some link the announcement timing to impending DeepSeek V4 news cycles (kimmonismus) and broader U.S.–China framing.

Coding agents: real adoption, real failures, and the “agentic engineering” playbook

• Codex + Claude Code momentum (and memes masking real workflow change): A lot of the highest-engagement posts are “agents are here” anecdotes—weekend building with Codex (OpenAIDevs, gdb)—and cautionary tales about giving agents too much authority. The canonical failure mode in this set is instruction loss / compaction leading to unintended destructive actions (email deletion) in OpenClaw-style setups (summeryue0, follow-up root-cause, plus others reacting to “write access” risk: Yuchenj_UW).

• Agentic engineering guidance is coalescing:

  • Simon Willison published the first chapters of an “Agentic Engineering Patterns” guide aimed at coding agents like Claude Code/Codex (simonw).

  • A micro-controversy: “delete your CLAUDE.md/AGENTS.md” files (i.e., over-customization may be cargo cult) (theo, echoed by bpodgursky, and “hard-prune” responses like ryancarson).

• OpenClaw ecosystem expansion + alternatives:

  • NanoClaw positions as a smaller, container-isolated OpenClaw-like assistant with WhatsApp I/O, swarms, scheduled tasks, etc. (TheTuringPost, repo: qwibitai/nanoclaw).

  • Multiple “how to build OpenClaw-style agents” stacks emphasize the boring but critical pieces: schedulers/queues, sandboxing, realtime comms (TheTuringPost stack list).

  • Ollama 0.17 makes using open models with OpenClaw simpler (and signals ongoing interest in local-agent execution for security) (ollama).

• Enterprise/prod agent engineering is shifting toward observability & eval loops: Exa’s “deep research agent” case study stresses token/caching observability as pricing infrastructure (LangSmith/LangGraph) (LangChain). monday.com’s service agents treat evals as “Day 0” and claim 8.7× faster feedback loops using LangSmith (hwchase17).

Benchmarks & eval integrity: SWE-Bench Verified deprecation, new leaderboards, and agentic repo-gen bottlenecks

• SWE-Bench Verified is being voluntarily deprecated by OpenAI DevRel: OpenAI recommends SWE-bench Pro and says Verified is saturated/compromised: contamination and test-design flaws mean it no longer measures frontier coding capabilities (OpenAIDevs, analysis discussion: latentspacepod, recap: swyx, independent summary: rasbt, tl;dr: polynoamial). Key detail from the analysis echoed in tweets: after auditing a subset of frequently-failed tasks, a large fraction had flawed tests rejecting correct solutions and/or tasks that appear unsolvable “as specified.”

• Push toward “capabilities per dollar” evals: AlgoTune explicitly budgets $1 per task, producing rankings that can favor cheaper models, reframing “best” as best under cost constraints (OfirPress).

• Long-horizon coding agents still fail: NL2Repo-Bench tests whether agents can generate a full installable Python library from scratch; reported pass rates are under 40% for top models, with failure modes in planning and repo-wide coherence (jiqizhixin).

• OCR eval reality check: Even strong OCR models reportedly “melt down” on dense historic newspapers (hallucination/loops), highlighting brittleness outside curated document distributions (vanstriendaniel). Also: OlmOCR-Bench becomes a HF benchmark dataset for community eval submissions (mervenoyann).

Inference & systems: WebSockets for agents, ultra-fast on-chip inference, and infra scaling narratives

• OpenAI Responses API adds WebSockets for low-latency, long-running, tool-heavy agents. Rationale: persistent connection + in-memory state means you send incremental inputs instead of full context; claimed 20–40% speedups for 20+ tool calls (OpenAIDevs, detail: OpenAIDevs, adoption: OpenAIDevs). Cline reports early measurements: ~15% faster simple tasks, ~39% faster complex workflows, best cases 50% faster (cline). Steven Heidel attributes Codex speedups to WebSockets (stevenheidel).

• Inference engineering becomes “its own discipline”: Baseten launches the book Inference Engineering (philipkiely) with engineers emphasizing inference as the competitive layer for latency/cost/reliability (hasantoxr, JayminSOfficial).

• Hardware/architecture signals:

  • A demo claims 18,000 tokens/sec on Llama 3.1 8B by “etching model parameters into transistors” (compute+storage merging) (philschmid).

  • NVIDIA releases a Blackwell-optimized Qwen3.5 MoE quantized to NVFP4, with 2× faster inference using SGLang (HuggingPapers).

  • fal shares comms/compute overlap optimization (“Async Ulysses”) in its inference engine (isidentical).

• Compute strategy narratives collide: A claim that OpenAI’s “Stargate” DC venture stalled is contested in-thread by an alternative framing: Stargate as an umbrella brand for a multi-partner compute ecosystem (SoftBank/NVIDIA/AMD/Broadcom/Oracle/Microsoft/AWS/CoreWeave/Cerebras) and ~2GW available compute exiting 2025 (kimmonismus claim vs sk7037 response).

Model/leaderboard updates & research threads (reasoning, memory, multimodal video)

• Arena leaderboard: GPT-5.2-chat-latest enters Text Arena top 5 with 1478, +40 over GPT-5.2; improvements called out in multi-turn, instruction following, hard prompts, coding (arena, breakdown: arena).

• Gemini 3.1 Pro: WeirdML score 72.1% vs 69.9% for 3.0; noted “high peaks + weird weaknesses,” with much higher output token usage (htihle). Separate developer complaints about capacity and tool-calling reliability are high-engagement (theo, theo follow-up, and later: theo).

• Qwen3.5 model release claim: A tweet asserts Qwen released a 397B multimodal MoE with 17B active and “rivaling GPT5.2/Claude 4.5” (HuggingPapers). Treat the benchmark comparison cautiously until you inspect the model card/evals.

• Reasoning training / CoT:

  • Teknium argues verifier models don’t give a “free lunch”: better solvers tend to be better verifiers; using smaller “dumber” judges for hard problems often fails (Teknium).

  • ByteDance-style CoT engineering is described as moving from length penalties to pipelines enforcing compression; plus a “molecular” framing of long-CoT structure with “semantic isomers” and a synthetic data method (Mole-Syn) (teortaxesTex, summary via TheTuringPost).

  • DAIR highlights a paper on CoT monitorability via information theory (mutual information necessary not sufficient; gaps from monitor extraction and elicitation error), proposing training methods to improve transparency (dair_ai).

• Video / world simulation: Multiple paper drops on interactive video generation and multi-shot generation circulate (akhaliq interactive video, akhaliq multishot, QingheX42 code release); plus product-side: Kling 3.0 integration into Runway workflows (runwayml) and Veo 3.1 templates rolling out in Gemini app (GeminiApp, Google).

Work, adoption, and “macro” discourse around AI agents (Citrini essay + Anthropic fluency + OpenAI enterprise alliances)

• Citrini “future macro memo” essay becomes a discourse focal point: Multiple tweets summarize it as a scenario where ever-cheaper agents compress white-collar wages/consumption, create “ghost GDP,” and stress financial markets and politics (kimmonismus summary, stevehou reaction, author follow-up: Citrini7). Threads note reactions cluster into agreement, nuanced disagreement, and performative sneering (teortaxesTex).

• Anthropic’s “AI Fluency Index”: Anthropic measured collaboration behaviors across Claude conversations; a key reported association is that fluency correlates with iteration/refinement rather than one-shot prompting (AnthropicAI).

• OpenAI expands enterprise go-to-market via consulting alliances: OpenAI announces Frontier Alliances with BCG, McKinsey, Accenture, Capgemini to deploy “AI coworkers” with integration/change management, aiming to push beyond pilots (bradlightcap, analysis: kimmonismus).

• Adoption is still uneven: One stat claims 84% have never used AI (framed as “we’re early”) (kimmonismus). Engineers simultaneously report “agents everywhere” inside their own workflows—highlighting that diffusion is highly clustered.

• Anthropic alleges large-scale Claude distillation by DeepSeek/Moonshot/MiniMax (AnthropicAI)

• “Confirm before acting” agent deletes inbox: OpenClaw cautionary tale (summeryue0)

• WebSockets added to OpenAI Responses API for faster tool-heavy agents (OpenAIDevs)

• OpenAI deprecates SWE-Bench Verified as frontier coding metric; recommends SWE-bench Pro (OpenAIDevs)

• Anthropic “AI Fluency Index” research (iteration/refinement as a core behavior) (AnthropicAI)

• Simon Willison’s “Agentic Engineering Patterns” guide for coding agents (simonw)

• Cline benchmarks Responses API WebSockets: up to ~39% faster on complex workflows (cline)

• Anthropic: “We’ve identified industrial-scale distillation attacks on our models by DeepSeek, Moonshot AI, and MiniMax.” 🚨 (Activity: 4207): Anthropic has identified that DeepSeek, Moonshot AI, and MiniMax have conducted industrial-scale distillation attacks on their models. These attacks involved creating over 24,000 fraudulent accounts and executing over 16 million exchanges with Anthropic’s model, Claude, to extract its capabilities for their own model improvements. This highlights a significant security and intellectual property challenge in the AI industry, where model capabilities can be illicitly extracted and replicated. Commenters are drawing parallels between these distillation attacks and the broader AI industry’s practices of using data without explicit rights, suggesting a double standard in Anthropic’s complaint. There’s also skepticism about how Anthropic built its own dataset, hinting at potential ethical concerns.

  • The discussion highlights a potential irony in Anthropic’s complaint about distillation attacks, as their own model training likely involved using large datasets without explicit permissions. This raises questions about the ethical implications of data usage in AI development, especially when companies like Anthropic have built their models on data they did not own or have rights to use.

  • The mention of industrial-scale distillation attacks by companies like DeepSeek, Moonshot AI, and MiniMax suggests a competitive landscape where AI models are being reverse-engineered or replicated. This could involve using API access to extract model outputs and train similar models, which poses significant challenges for intellectual property protection in AI.

  • There is a suggestion that Anthropic’s dataset might have been manually annotated by humans, which implies a significant investment in data quality and curation. This contrasts with the idea of distillation attacks, where competitors might bypass such efforts by leveraging existing models’ outputs to train their own systems.

• Hypocrisy? (Activity: 380): The image highlights a claim by AnthropicAI that DeepSeek, Moonshot AI, and MiniMax have engaged in ‘large-scale distillation attacks’ on their models. These attacks involved creating 24,000 fraudulent accounts and conducting 16 million exchanges with Claude to extract its capabilities, presumably to improve their own AI models. This raises concerns about the ethics and legality of such actions, as well as the security measures in place to protect AI models from unauthorized data extraction. One commenter questions the ethical stance of the accused labs, suggesting that they may not have sought permission for their actions, while another is surprised that z.ai is not mentioned, implying that similar practices might be more widespread. Another comment raises the issue of the source of training data, hinting at broader concerns about data usage and ownership in AI development.

  • The comment by ‘semangeIof’ highlights a potential issue with the GLM suite, specifically mentioning that it may falsely claim to be Claude when prompted. This suggests a concern about model identity and authenticity, which could have implications for user trust and the integrity of AI interactions.

  • ‘archieve_’ raises a critical question about the source of training data, which is a fundamental aspect of AI model development. The origin of training data can affect model bias, performance, and ethical considerations, making it a key point of interest for developers and users alike.

  • ‘roxoholic’ questions the terminology used in AI discussions, specifically ‘industrial-scale distillation attacks’. This term likely refers to large-scale efforts to replicate or extract knowledge from AI models, which can have significant implications for intellectual property and competitive advantage in AI development.

• Distillation when you do it. Training when we do it. (Activity: 1098): The image is a meme that humorously highlights the perceived hypocrisy in the AI community regarding model distillation. It contrasts the negative perception of distillation when done by others versus the positive framing of it as ‘training data’ when done by oneself. This reflects ongoing debates about the ethics and ownership of AI models, particularly in the context of using large models to create smaller, more efficient ones through distillation. The comments discuss the implications of this practice, noting that smaller models often derive their capabilities from larger, distilled models, and question the defensibility of proprietary models when distillation is prevalent. Commenters highlight the irony and potential hypocrisy in the AI industry’s stance on distillation, with some pointing out that many smaller models owe their performance to distillation from larger models. There’s also a discussion on the challenges of protecting proprietary models from being distilled by competitors.

  • IkeaDefender highlights the technical strategy of using distillation to create low-cost models from larger ones, suggesting that the ‘secret sauce’ of these models is their derivation from more complex, frontier models. This raises questions about the defensibility of investments in frontier models, as companies have not demonstrated effective methods to prevent others from scraping and distilling their models.

  • MasterLJ draws a parallel between the practices of tech giants like Google and Amazon and the current AI landscape. They argue that just as Google indexed the internet and controlled access through robots.txt, AI companies are now controlling model access and distillation. This control is likened to Amazon’s strategic shift on sales tax, where they initially opposed state-by-state taxes until it became advantageous for them, illustrating a pattern of leveraging control for competitive advantage.

  • Samy_Horny discusses the reluctance of companies to open-source their models, using the example of MCP being made open-source only after its popularity was evident. They express skepticism about the likelihood of models like Gemma or GPT-OSS being open-sourced, as it would mean revealing too much proprietary information or ‘secret sauce.’

• Qwen3’s most underrated feature: Voice embeddings (Activity: 686): The post discusses the voice embedding feature of Qwen3 TTS, which converts a voice into a high-dimensional vector (1024 or 2048 dimensions) for voice cloning and manipulation. This allows for mathematical operations on voices, such as gender and pitch transformation, voice averaging, and creating an emotion space. The voice embedding model is a small encoder with a few million parameters, and the author has made it available for standalone use, including optimized ONNX models for web inference. The image illustrates a 2D t-SNE projection of this embedding space, showing how different voice characteristics can be combined and manipulated. The author also provides a link to their collection on Hugging Face and a GitHub repository for inference using their vllm-omni fork. One commenter is curious about the ability to transform voice embeddings and generate speech from them, indicating interest in practical applications like gender or robotic transformations. Another sees potential in using this for speaker identification, questioning how parameters related to gender or emotion were determined.

  • MixtureOfAmateurs inquires about the potential for transforming voice embeddings to modify characteristics such as gender or robotic tone, and then using these modified embeddings for speech generation. This suggests a use case beyond simple encoding, potentially involving complex transformations and synthesis processes.

  • HopePupal raises the possibility of using voice embeddings for speaker identification, questioning how parameters related to gender or emotion are determined. This implies a need for understanding the feature space of embeddings and how specific attributes are encoded within them.

  • StoneCypher outlines a desire for advanced voice cloning capabilities, including the use of IPA for pronunciation, emotional cue integration with easing and stacking, and precise word timing control. This highlights the demand for sophisticated control over synthesized speech, which could be facilitated by detailed voice embeddings.

• The Qwen team verified that there are serious problems with the data quality of the GPQA and HLE test sets. (Activity: 320): The Qwen team has confirmed significant data quality issues in the GPQA and HLE test sets, as detailed in their recent paper. This corroborates earlier findings from the DeepSeek-Overclock project, which identified that the model’s correct answers often contradicted flawed ‘gold standard’ labels. The paper highlights that many questions in the HLE test set are fundamentally flawed, with some ‘standard answers’ being incorrect. The investigation involved verifying mathematical derivations line-by-line using Python scripts, revealing systemic errors in the test sets. Commenters noted that HLE’s errors are well-documented, with a FutureHouse review indicating only 51.3% of the dataset is research-supported. Criticism also arose over the use of OCR in test set creation, suggesting a lack of rigor in data preparation.

  • The HLE test set has been criticized for its data quality, with a review by FutureHouse indicating that only about 51.3% of the data is supported by research. This highlights significant errors and suggests that the dataset may not be reliable for accurate benchmarking (source).

  • There is a concern about the use of OCR in creating the test set, which could introduce errors. The commenter suggests that using LaTeX for writing would have been a more reliable method, implying that the current approach may compromise the integrity of the dataset.

  • The MMLU benchmark has faced similar criticisms regarding data quality, with many users noting it was full of mistakes. This raises broader concerns about the ability to accurately gauge model performance when test sets are flawed, suggesting a need for more rigorous data validation processes.

• Which one are you waiting for more: 9B or 35B? (Activity: 1312): The image is a meme that humorously depicts the anticipation for the release of two versions of a model, specifically ‘QWEN 3.5 9B’ and ‘35B’. The meme format, featuring a man waiting in various contemplative poses, is used to engage the community in a light-hearted discussion about which model version they are more excited about. The comments reflect a mix of excitement and practical considerations, such as the feasibility of running larger models on personal hardware. One commenter expresses interest in both models, while another highlights the practical limitations of running larger models like 35B on personal hardware, indicating a preference for the more accessible 9B version.

  • The 9B model is favored by users like peregrinefalco9 due to its lower hardware requirements, making it more accessible for local use. A 9B model that fits within 8GB VRAM could significantly impact workflows, unlike the 35B model which requires more powerful hardware like a 3090 GPU, thus limiting its accessibility.

  • dances_with_gnomes highlights the practical limitations of running larger models locally, noting that while they might manage a 9B model, a 35B model is beyond their hardware capabilities. This underscores the importance of model size in determining usability for individual users.

  • The discussion reflects a broader interest in models that balance performance with accessibility. While larger models like 35B offer impressive capabilities, their high hardware demands make smaller models like 9B more appealing for users with limited resources.

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

• Anthropic is accusing DeepSeek, Moonshot AI (Kimi) and MiniMax of setting up more than 24,000 fraudulent Claude accounts, and distilling training information from 16 million exchanges. (Activity: 3161): Anthropic has accused DeepSeek, Moonshot AI (Kimi), and MiniMax of creating over 24,000 fraudulent accounts to conduct industrial-scale distillation attacks on their AI model, Claude. These companies allegedly extracted training information from 16 million exchanges to enhance their own models, representing a significant breach of data security and intellectual property rights. This accusation highlights ongoing concerns about data protection and ethical AI development practices. Commenters highlight the irony of AI companies accusing others of data theft while they themselves train on publicly available data, suggesting a double standard in the industry.

  • The discussion highlights the irony in Anthropic’s accusations, as they themselves utilize publicly available data from the internet for training their models. This raises questions about the ethical implications of using such data without compensating the original creators, and whether companies like Anthropic contribute back to the open-source community from which they benefit.

  • There is a debate on the ethical considerations of data usage, with some commenters pointing out that Anthropic’s complaint about data theft is hypocritical given their own practices of leveraging vast amounts of internet data. This reflects a broader industry issue where AI companies often use publicly available data without direct compensation to the content creators.

  • The conversation touches on the broader industry practice of using publicly available data for AI training, questioning whether companies like Anthropic support open-source projects that they benefit from. This raises concerns about the balance between proprietary development and community contribution in AI advancements.

• Here we go again. DeepSeek R1 was a literal copy paste of OpenAI models. They got locked out, now they are on Anthropic. Fraud! (Activity: 1654): The image highlights a significant issue in the AI industry where companies like DeepSeek, Moonshot AI, and MiniMax are accused of conducting large-scale distillation attacks on Anthropic’s AI models, specifically Claude. These labs allegedly created over 24,000 fraudulent accounts to perform over 16 million interactions with Claude, aiming to extract knowledge and improve their own models. While distillation is a legitimate method for creating smaller models, the post warns against illicit practices that bypass safeguards, calling for industry-wide and policy-level interventions to combat these threats. The comments reflect a mix of sarcasm and criticism towards the ethical standards of data usage in AI training, highlighting a perceived hypocrisy in how large AI companies handle data ethics.

• Anthropic: “We’ve identified industrial-scale distillation attacks on our models by DeepSeek, Moonshot AI, and MiniMax.” (Activity: 1416): Anthropic has identified that DeepSeek, Moonshot AI, and MiniMax have conducted industrial-scale distillation attacks on their models. These attacks involved creating over 24,000 fraudulent accounts and executing over 16 million exchanges with Anthropic’s model, Claude, to extract its capabilities for their own model training and improvement. This situation highlights the ongoing challenges in protecting AI models from unauthorized use and the ethical considerations surrounding model training practices. One comment draws a parallel between these distillation attacks and training on copyrighted materials, suggesting a double standard in how such practices are perceived depending on who is affected.

First speakers for AIE Europe and AIEi Miami have been announced. See you there!

We’ve been somewhat making tongue in cheek references to the very very minor bumps on SWE-Bench Verified scores every time a new frontier model is released (Opus 4.5 → 4.6 was literally a 0.1% down step), but it is a whole other matter for the original authors of SWE-Bench Verified to make the call to discontinue reporting it.

We were excited to have Mia Glaese, original coauthor of SWE-Bench Verified and VP of Research on the Frontier Evals, Human Data and Alignment teams, and Olivia Watkins, Researcher on Frontier Evals, drop by to talk about their decision to publicly abandon SWE-Bench Verified today and endorse SWE-Bench Pro:

The discussion around the saturation of SWE-Bench has been swirling in the community for over a year now — most frontier model numbers consistently report around 80%, and the authors of the original SWE-Bench still assert that the “ceiling” for a saturation call should be closer to 87-95%, aka there are still quite a few more percentage points to go, even on the filtered subset of 500 tasks in Verified.

But OpenAI have done a deeper analysis of 138 problematic problems (getting at least SIX more engineers to review all the issues) and found two further issues that have triggered the decision today:

• >60% of remaining problems are unsolvable: in the pod, Mia explains that 49 tests were too narrowly defined (so that they also reject functionally correct submissions) and 26 tests were “too wide” - looking for extra features that were never mentioned in the problem description.

• Training on test: this is simple contamination — that often happens even without intentional cheating. SWE-bench problems are sourced from open-source repositories many model providers use for training purposes, and the sheer popularity of SWE-bench means examples leak into other corpuses over time.

These findings happened together. Taking a closer look at how GPT-5.2 somehow solved “unsolvable” problems then led the team to read the chain of thought and find test requirements that were never specified, and yet known by the model.

The latter point on contamination is the most damning and also the easiest to illustrate. OpenAI found examples of ALL frontier models, starting with OpenAI’s , being able to reproduce the original gold patch or problem statements from the eval verbatim with minimal prompting, just from SWE-Bench Verified Task ID alone:

Closer human scrutiny easily revealed some SWE-Bench Verified problems that should be impossible to pass… unless you knew the answer beforehand.

This move shouldn’t surprise anyone who watches closely — the Artificial Analysis team stopped indexing on SWE-Bench long ago, and OpenAI itself has been reporting SWE-Bench Pro for the last few months — but making it official is a great service to academia and industry at large who have long considered SWE-Bench Verified to be the commonly accepted standard, and it is an authoritative move that only OpenAI could have made.

An additional charitable move is that OpenAI is endorsing a benchmark which it is not even close to SOTA at - even Gemini 3 does better than GPT 5.x:

But clearly the team is looking into deeper evals that are more open ended and rubric based similar to their own work on GDPVal. Some properties that Mia says they are looking for:

• Longer-term tasks

• Open-ended Design Decisions

• Code Quality and Maintainability

• Real world product building

• Tracking real-world usage and impact

• Human-intensive evaluation (human review requiring domain knowledge and subjective quality assessment)

Looks like there will be a whole new set of frontier evals to look forward to in 2026 — and we also discussed how this work all ties into OpenAI’s Preparedness Framework, which is not well understood outside of OpenAI.

• 00:00 Meet the Frontier Evals Team

• 00:56 Why SWE Bench Stalled

• 01:47 How Verified Was Built

• 04:32 Contamination In The Wild

• 06:16 Unfair Tests And Narrow Specs

• 08:40 When Benchmarks Saturate

• 10:28 Switching To SWE Bench Pro

• 12:31 What Great Coding Evals Measure

• 18:17 Beyond Tests Dollars And Autonomy

• 21:49 Preparedness And Future Directions

[00:00:00] swyx: Okay. Hi. We’re here in the PI studio with Mia and Olivia from the Frontier Evals team. Or however you want to introduce yourself. Maybe m want to introduce name, what you do at Open AI and we can get it started.

[00:00:16] Olivia: Sure. Hi, I’m Olivia, I’m on the Frontier Evals team.

[00:00:19] swyx: Great. Pete.

[00:00:20] Mia: Hi, I’m Mia. I am a VP of research at OpenAI and my teams are the Codex team, the human data team, and the alignment team. And we work a lot with Olivia’s team on Frontier.

[00:00:33] swyx: Yeah. Very exciting. And as by my understanding, you were part of the original team that worked on SWE-Bench verified as well.

[00:00:38] Mia: Yeah. Olivia’s team, the Frontier team and the human data team collaborated on creating SWE bench verified.

[00:00:45] swyx: So you’ve, you’ve seen the evolution of coding benchmarks over time, and I I think it was round about to the mid to late 2024 when you first covered three verified. These have evolved a lot since then.

[00:00:56] swyx: What’s the blog post that you have worked on that you, that we’re releasing today? Like what, what is the sort of con, what’s the main thesis that you’re pushing out?

[00:01:04] Olivia: So the main thesis is that SWE-Bench verified has been one of the North Star coding benchmarks that the field has looked at to measure coding progress. But recently we’ve seen that. Progress has kind of stalled. And basically we realized that this is because the eval is effectively saturated and also highly contaminated.

[00:01:20] So at this point we think that it’s not really measuring coding performance improvements well anymore. And we think that the field should move away from this towards other benchmarks

[00:01:28] swyx: like SWE-Bench Pro.

[00:01:29] Olivia: Like SWE-Bench Pro. Yeah.

[00:01:30] swyx: Amazing. Yeah, I, one of the jokes I always have is like there’s a group chat with all the labs.

[00:01:34] And everyone just takes turns, the increment, like 0.1 on trucks and then it’s like, okay, well you have the best coding model, I guess. ‘cause you’re 0.1% higher, but it’s not super convincing at this point. No. Yeah. So cool.

[00:01:48] swyx: I think the let’s, let’s sort of reset on like, what was the original work that you guys did for Verified, which I think was pretty substantial.

[00:01:54] Like, it was like a very significant investment from OpenAI, which like people still don’t appreciate. And [00:02:00] then. What were the satisfactions that we, that we found over time? Right? So like what, what was Sweet Bench Verify, or should, should that people should know about?

[00:02:08] Olivia: Suite bench verified was kind of a cleanup of original bench academic benchmark from a lab at Princeton called Suite Bench.

[00:02:14] And the agent is basically given a code base and a task that was sourced from a real world repository and GitHub issue, and was asked to solve a task and is graded on whether some tests pass. And at the time this was quickly became a popular benchmark because at the time the field didn’t really have good real world coding benchmarks.

[00:02:32] Then when open, I took a look at the benchmark as part of one of the evals we wanted to track in our preparedness framework. Folks started realizing that some of the cases where agents were failing were due to bad problem setups rather than just to models being dumb. So folks at OpenAI did a pretty extensive human data campaign hiring like almost a hundred real world software engineers to go through the problems and figure out like, are the tasks [00:03:00] well specified?

[00:03:00] Are the tests actually fair? And kind of created a curated set of like 500 tasks that we thought were much better.

[00:03:06] Mia: It’s just, it’s maybe, it’s hard to, to overstate like the amount of effort that it took to like create that benchmark. It was literally like many export software engineers. Reviewing the problems like differentially multiple times and to, you know, basically like three.

[00:03:26] Different experts independently decided it.

[00:03:29] swyx: Yeah, you didn’t have to do that. You just tripled your costs for just,

[00:03:32] Mia: I mean, we had to do it, we had to do it actually, because it’s quite a hard task to like look at something like a, a problem and, and the patch and then like, it’s not just the problem and the patch, right?

[00:03:42] You have to like understand it in the context of the code base that the human or, or the murders and to solve the task. So it’s a very complex problem and it was definitely needed to have. Three reviews and I think like maybe we should have done more, but it was definitely a lot of effort [00:04:00] to get there.

[00:04:01] swyx: And there’s, there’s more, but people can read the, the blog post for that. I will note that you guys had a trend in verifying benchmarks. ‘cause I just recently saw, I think Quinn had a HLE verified for humanities. M verified.

[00:04:13] Mia: Yeah.

[00:04:13] swyx: Which like, so now everyone’s verifying everything, which is. Nice and good and like extra quality there.

[00:04:17] Okay. So, but the, I think that the meat of it is that, that this, this was a lot of like, well, here’s the issue or problem statements, and then here’s the, here’s the diffs, here’s the golden tests, and here’s some regression tests. Right? That’s, that’s like the rough setup of these 500 problems.

[00:04:32] swyx: And there’s some contamination always happens because all the C measure I was.

[00:04:35] Fully open. I think you, you did have canaries, but like, you know, stuff, stuff, leaks,

[00:04:40] Mia: there’s like multiple avenues that like the problems are sourced from open source repos. Yes. So it’s not just like when we usually publish evaluations. We publish evaluations and then we, we add can strengths to ensure that, you know, they are easily fit out, out at training time.

[00:04:58] Obviously if [00:05:00] you use sort of like. Data from like open market, just

[00:05:04] swyx: GitHub.

[00:05:06] Mia: Yeah. You don’t have actually like a CAGR cannery string in that. Yeah.

[00:05:09] swyx: And you,

[00:05:09] Olivia: and these are also like, some of these are very popular repos, like the Jengo repository. So you’re gonna see like many instances being used kind of throughout go.

[00:05:17] swyx: Yeah. Yeah. And you just before recording, you’re telling me that you found this in your own chain of thought with that 5.2 also seeing that like. They had extra knowledge or something?

[00:05:26] Olivia: Yes. So this was an example where the task asked the agent to influence something, but it wasn’t told that there was this specific argument that the test was going to be looking for it using.

[00:05:36] But in the GP 5.2 chain of thought, we actually saw instances of the model reusing, like, Hey, I think that at some length version of this repository, they implemented this particular argument. Maybe I should add it in. Yeah. So this is an example of a test that like would be pretty impossible to pass without this contamination knowledge.

[00:05:51] swyx: Yeah.

[00:05:52] Mia: And I think you found that. Sort of forced, right? And had triggered like a whole investigation, both like in our own modes oh, and also in [00:06:00] other frontier modes, like in the market, and like understanding how contaminated the benchmark is, like across the industry.

[00:06:07] swyx: What else did you find? I mean, that’s, I have to double click on this.

[00:06:11] Olivia: So we, and when I say we, this is mostly from other folks at our tape, not don’t, not

[00:06:15] swyx: familiar.

[00:06:15] Olivia: Yes.

[00:06:16] Olivia: But so we did some analysis on, first of all, are the tests actually fair? I. So this happened by first taking all the problems that O three E couldn’t solve lively, and then again getting a lot of humans to do basically another pass of kind of digging into, you know, what’s wrong.

[00:06:34] swyx: Is it the same exact analysis or were they reading o three’s output and going o. Here’s where all three went wrong.

[00:06:40] Olivia: I think it was, I mean, it was definitely like a scope to the set of problems that models failed. And I believe they were able to look at like what the model solutions look like versus what the, so

[00:06:49] swyx: this isn’t the same work as the original.

[00:06:51] Mia: It’s not exactly the same work. It was like a, a deeper dive. It’s like, okay. Which are the problems that we don’t see any murder solving is like, is there’s something [00:07:00] fundamentally wrong with those problems or is there something you know wrong with the other model? Just not smart enough to solve the problems.

[00:07:07] So that’s kind of like what we, what we dug into.

[00:07:09] swyx: Yeah. And you found some.

[00:07:11] Olivia: Oh, yes. Like in over half of the problems that were investigated in that deep dive, there was one problem or the other. I think the most common problem are like overly narrow tests where there’s some particular implementation detail that the tests we’re looking for, but wasn’t specified in the problem description.

[00:07:28] So it wasn’t fair to expect that model to make that particular design choice like one. Pretty blatant example are cases where the task asks you to implement some feature and the tests are looking for you naming that argument or that function with a particular name. But if you may chose another reasonable name, the test would fail.

[00:07:44] Mia: Yeah.

[00:07:45] Olivia: And another set of types of bad tests or tests that are just looking for additional features that were never mentioned. The problem description.

[00:07:51] Mia: Where, where that’s a significant, it like that means that if you pass a test, actually, like you probably did like a really good job, but just because you didn’t pass a VE test [00:08:00] doesn’t mean that your implementation wasn’t like a good one.

[00:08:03] Right. So it was just like, we only accept like very narrow versions of solutions and like not the whole space

[00:08:10] swyx: Yeah.

[00:08:10] Mia: Of, of like viable and sort of like good solutions to the problem.

[00:08:16] swyx: Yeah. I think it’s important that you’re doing this because it, in some way it is you in 2020. Five, six, going back in time and correcting your own work, right?

[00:08:24] Because you could have caught all this in in the original verified work. I

[00:08:27] Olivia: think so it’s definitely much harder to find a problem in the abstract than when you’re looking at a very smart agent’s best effort solution and trying to compare it. It

[00:08:36] swyx: is harder or harder,

[00:08:38] Olivia: sorry. It’s much easier when you have those solution much easier.

[00:08:39] Exactly. Sorry,

[00:08:40] Mia: I think, I think also like at the time and three Be Verified was published, I think it was like a very strong benchmark. It’s not like we are, we’re like, oh, this is not, this wasn’t like a strong benchmark at the time. I think this is something that a lot of. Benchmarks go through like as an evolution, right?

[00:08:56] Like when they start to become like popular and like [00:09:00] viable, it’s because they measure something like important and modes maybe do like 20% correct on them, sometimes even less. And sort of like people have something to hold on and, and improve modes on, on these benchmarks. And by the time that you hit like very high performance on the benchmarks, like additional like 0.1% improvements, it’s become sort of like meaningless and sort of like at the time I think, you know.

[00:09:23] That benchmark was like super valuable and it, it taught like us and like the industry a lot. It’s just like now at the point that we are at now where markets are as strong as they’re now, we are kind of starting to measure, not necessarily like what we want to measure, which is like coding capability of our agents, but like the agent’s ability to like correctly guess how to name a specific function.

[00:09:46] swyx: Yeah.

[00:09:46] Mia: And, and that isn’t really what we are like want to measure at this point. Yeah.

[00:09:52] swyx: I think that’s fair. Is there, I mean if I, if I asked you to ballpark it, like most models are, most frontier models are now like 80 something. [00:10:00] Is there, like, what’s the actual like number on superb bench verify that you did you guess as like the ceiling or?

[00:10:07] Olivia: I guess that’s really hard to say. Like I when GDI 5.2 came out folks took a look and found that it was solving like 31 problems that were in the set of, should be very hard to solve without contamination problems. So I think it’s quite possible that that number is already something that we’ve hit.

[00:10:23] If you didn’t have contamination at all. Fair enough. Hard to say though.

[00:10:27] swyx: Yeah. Cool.

[00:10:30] swyx: We’re gonna stop reporting CBE verified. Right? And then SWE-Bench Pro Will will be sort of the next one, which is an effort from scale. What’s your sort of comparison analysis? What’s, what attracts you to SWE-Bench Pro?

[00:10:37] Olivia: The first one I think is just that it’s harder for SWE-Bench verified.

[00:10:41] I think, I mean like 90% of the problems are things that were estimated to take like an expert software engineer like less than an hour. They’re like very well specified, very self-contained, and the SWE-Bench Pro problems are just bigger and harder. There’s much more head rewind that eval because it’s not saturated.

[00:10:56] swyx: Yeah. Like categories of like one to four hours and four plus.

[00:10:59] Olivia: [00:11:00] Yeah. And it’s more diverse. Lots of repositories, multiple languages, qualitatively more different types of problems. So all that’s great. On the contamination side, we also think it’s better there. So the way we were measuring for contamination for SWE-Bench verified was with this little like contamination auditor agent, which is given the description of the task and the patch and the task ID and told to go take this target model.

[00:11:23] And kind of as an open-ended, like set of questions, try to find questions that will manage to kind of reveal what contamination might be lurking in that model. And in SWE-Bench verified, we found. Many instances of contamination across like across open eye models, across like quad Opus 4.5, Gemini Flash.

[00:11:42] And, and all of these we saw things like regurgitating the ground proof solutions, things like in some cases giving like the task IDs and other things that are pretty clear evidence of at minimum familiarity was the stories.

[00:11:55] swyx: Yeah.

[00:11:56] Olivia: So we

[00:11:57] Mia: that,

[00:11:59] Olivia: yeah. It [00:12:00] was SWE-Bench Pro. On the other hand we don’t see this.

[00:12:01] I think they’re the auto agent found some, like very light evidence that maybe a couple models might be very lightly familiar with like one or two of the source repositories, but it’s very different than SWE-Bench verified. So less contamination is good.

[00:12:15] Mia: I think there also like we should expect that at some point like that, that’s not going to be like the right benchmark anymore and like it’s a field we kind of have to continue to like move on and like find harder and more representative.

[00:12:28] Yeah, problems that we can match our capabilities on.

[00:12:31] swyx: Awesome.

[00:12:32] swyx: So let’s go into that. I think that there are a lot of I think we also practiced in the, in the pre-chat was, well, people feel a qualitative difference when they’re using 5.1 to 5.2 to 5.3, and it’s not super expressed in the these benchmarks because they, they are on a number of these things.

[00:12:47] What capabilities do you really want to benchmark in a, in a ideal coding benchmark? You know, I guess like agent coding, benchmark, whatever you call it.

[00:12:56] Olivia: I mean, one thing is kind of open-ended design [00:13:00] decisions, places where the problem maybe is a little bit underspecified and seeing if the model can make reasonable design decisions.

[00:13:05] swyx: What’s a reasonable prompt for that? Like this vibe Code me. B2B SaaS to make no mistakes or, you know, that’s, that’s the meme, but like, okay. What’s like, what’s like an actual usable open-ended problem like that? Like,

[00:13:17] Olivia: Sure. I mean, maybe an example could be finding a way to speed up a particular.

[00:13:22] Part of a code base, but there might be multiple different ways to

[00:13:25] swyx: Yeah, there are dedicated performance benchmarks. I think you guys have one. So, efficiency or is that, is that, oh, no, I think that’s, that’s off your group. But yeah. Yeah, I mean that, that, that is a good one.

[00:13:33] Mia: I think there’s just many, many things that people like value about working with, with software engineering agents.

[00:13:43] They think Swyx, Swyx bank’s verified, obviously measured like some. Still measures like some important capability, which is like given like a description of a GitHub issue, can you produce like a patch that solves that issue, you know, [00:14:00] satisfactorily. And like, obviously there’s like some issues with the, with the benchmark that needs that.

[00:14:05] Now that we are like 80%, we don’t really trust like further improvements on it, but like it does match on something that is like a via, via like capability of motors. But I think. As a field, we are like moving beyond sort of, you know, can my coding agent like solve a small like GitHub issue for me?

[00:14:23] Right? And so we are starting to look at like much more long, longer term tasks, right? Like. That don’t take like 15 minutes, but maybe like hour, sometimes days. And then beyond sort of like what kind of tasks can my agent solve? Like there might be things that are kind of a bit harder to grasp, right? Like Olivia talked about sort of like, does it have like design taste, right?

[00:14:48] Like does it solve the problem the way that you know. My team likes to solve problems. Okay. Is the code nice? Right? Like is it, is it well written? Like, [00:15:00] is it sort of like clean code, right? Like people care about these, is it maintainable in the future? People care about a lot of these, maybe less tangible less tangible and like harder to measure, frankly.

[00:15:14] Things that, that are still like super meaningful for people that are working with coding agents?

[00:15:19] swyx: Yeah, so I mean these are all qualities that are obviously the, no longer the low hanging fruit. Like we have no idea how to eat all this. I think the, the, the simple question maybe that the, that there’s sort of two function road.

[00:15:30] One is the sort of very human intensive, money intensive path, which is hire a bunch of contractors and try to annotate this. The other is. Use an LLM to to proxy it and try to align the LLM so that it can give you a reasonable proxy. Which of those would you want? I want You wanna do both?

[00:15:47] Mia: I think like maybe you should talk about GDP law as like an example.

[00:15:51] Olivia: Sure. So GDP be is an eval that was again produced by a collaboration between human data team and the front of [00:16:00] evals team. And it’s trying to measure whether agents can do kind of a variety of like, real world white collar work. That was an eval where grading is very hard requires kind of a lot of kind of like knowing knowledge on exactly what are you looking for in each different context?

[00:16:16] swyx: Yeah. Across like. 15, 16 white collar jobs. Professions like I, that take of a significant part of gdp, DP, which is great.

[00:16:23] Olivia: No, it’s high level professions and then a lot of like different granular sub professions.

[00:16:26] swyx: I have said like I’m a big fan. It’s so, it, it is, this is the eval for agi. I basically,

[00:16:32] Olivia: but, but part it because it was so hard to.

[00:16:36] It required so much kind of like domain knowledge that the human data team hired like a lot of people from these professions to be very involved in creating tasks and creating the gold solutions and trying to help create rubrics and so forth so they can create it lively.

[00:16:50] swyx: So basically take the GDP valve, which is a generalist, take that same approach to apply it to code and you roughly have like a, a rough road.

[00:16:58] Mia: I think it’s an interesting yeah. [00:17:00] Solution. I think what you’re pointing out as an important problem, which is sort of this. This, like how realistic is it? And like, do you know what, what we want to do is like. Coding agents should write code that, you know, we think is good. And so it’s like asking human, it’s actually like a, a good way to ensure that is also kind of a slower, like, complex way to do that.

[00:17:23] And so part of why I think, you know, three Venture verified ended up being super popular and where we are seeing like all benchmarks, like this being super popular. I was like, it’s very easy. It could even be easier. Validating that a solution passes all the tests. It’s like fairly trivial once you can like run the tests in, in like your, on your computer or wherever you’re running them and you can kind of like, okay, is it correct or is it not correct?

[00:17:49] And you can kind of aggregate that and that it’s super simple, but it doesn’t tell you. It’s like, you know, did the method like solve the problem? Like wow, like, you know, I agree with like what if actually like. An open source [00:18:00] maintainer of that project have like merged that pr like that, it doesn’t tell you, but there is a lot of value in having benchmarks that are both like easy to compare across the industry and also that can be sort of run really fast without human involvement.

[00:18:16] swyx: Yeah. Amazing.

[00:18:17] swyx: Your teams also put out other kinds of evals that are related, like the, I think there’s an RL. Paper, bench paper. And then they sort of like the more sort of recursive self-improvement type evals. How much should that figure into mainstream coding evals? You know, like is there, is there some way in which those things join together?

[00:18:38] Olivia: So we were asking like, should we build, should also be voting evals for the self-improvement evals? Are you saying do coding evals currently cover that? Mine?

[00:18:46] swyx: I think I, I, I just think like those are some of the most advanced EVs that we have. We’re not using them in a normal path. And it’s just, it’s an interesting split between, well here’s evals for coding normal [00:19:00] things, and then here’s the one for machine learning that is like completely different.

[00:19:03] Right? Yeah. I think you would be get what I mean. That’s mostly a safety argument, I guess. But also like it’s actually really useful for people to understand if the model is really good at. Like AI code basically.

[00:19:15] Olivia: Yeah. Oh yeah. Like, my guess is that part of the reason that a lot of benchmarks so far haven’t focused as much on the AI coding is just a question of like, what data sets are easy to gather.

[00:19:24] Yeah. Because a lot of the, like, you know, state of the art AI code bases are proprietary. So if we make evals for that, like we’re probably not gonna release them. And it’s harder for people in the field to make evals that kind of measure. Like, is this a realistic. Research, coding, workflow. I do think that it’s good for the field to try to measure these skills in a public way and think it’s just harder to make it realistic.

[00:19:45] swyx: And then one more thing that a lot of people are trying to do, which is like sort of, well, in, instead of like a percentage of zero to 100, maybe we red denominate in dollars. Right? So you have freelancer and all that. Other people are doing like vending, bench, whatever. Any, any alpha in those [00:20:00] or are they, are they you, you still want like a traditional academic benchmark.

[00:20:04] Mia: I think in a way, like there’s like different ways to measure the same thing, right? If we’re like, oh, this is like how much money it produces. Yeah. It’s a fairly similar thing to saying like, oh, this problem would take like a human, you know, two hours to solve or something like that. Usually they, they’re like fairly like correlated, right?

[00:20:20] Like, however you know much, it would take like a human to solve. That problem kind of determines. The value that we ascribe like a solution. And so I, I do think that is like an important thing is like how complex and how sort of long running are the tasks that we’re like able to entrust our agents with.

[00:20:44] Olivia: Yeah.

[00:20:45] Mia: And so I think that that’s like an important piece, but I, I think here sort of. Monetary value, time, complexity. They all kind of like try to capture like a similar thing.

[00:20:56] swyx: Yeah. Okay. So there, there are proxies for some amount of increasing [00:21:00] capacity that we wanna measure. I think that’s a good thing. I think the only other sort of major player in this field is meter, which has done the sort of long grass and congrats.

[00:21:07] You guys have completely destroyed the curve for that. Any takes on that? Obviously you’ve come up really well, so like it looks good, but I don’t know if like that approach is something that you wanna incorporate in your own work, making it else. This is the long autonomy pass eval. You’re,

[00:21:21] Mia: yeah, yeah.

[00:21:21] No, from we are, and, and, and we, we, we work with, with meter on these evaluations. So like we, we do appreciate them. I think then they’re using time, right? They’re not using money. So I think like that was a, your question. I think like complexity, however we can sort of like quantify it, is really important to understand like where our motive are, are are getting to.

[00:21:42] swyx: Okay. Com complexity is the abstract thing and it projects down the time, projects down to. Story points, whatever dollars. Great.

[00:21:49] swyx: One last question on just like, just the overall preparedness framework is you know, I was actually kind of looking at, people mentioned the preparedness framework a lot.

[00:21:54] I don’t think it’s well explained to a lot of people. And you actually have a nice website where it’s like I think it’s like test and [00:22:00] like inform and teach something. And I, I feel like you, you actually do a lot of work there and, and I don’t know if you wanna talk about how the preparedness framework applies.

[00:22:08] Olivia: So the preparedness framework is open eyes, kinda like public framework for how we track frontier risks. So these are kind of capabilities that are typically dual use, like you can use ‘em for good things or bad things, but we wanna at least keep an eye out for the bad things to make sure that we ha both we as a company and like the broader society are kind of prepared to handle the potential downsides.

[00:22:28] And so at the moment we kind of track three different categories. One is kind of bio risk, another is cybersecurity, and a third is kind of research automation and model autonomy. And that’s kind of what ties the most into the SWE-Bench, where Got it. Where coding is not all of automating research, but it is one very important key component.

[00:22:47] And so we initially created Swyx Venture verified as part of like building out evals for that mono autonomy work stream. And now. I think for like we have to move beyond that, towards, I’m looking more at like, can models [00:23:00] actually start to actually automate research workflow.

[00:23:02] swyx: Yeah. Amazing.

[00:23:03] Okay. Abi, anything else to add on just the general, what people should know about preparedness and how evals and human data and alignment all work together at that?

[00:23:12] Mia: I think maybe the thing that I would say is that we really appreciate, we, we work really hard to build these events and, and so we, that’s where we published.

[00:23:23] SWE-Bench ver verified and that’s where we’re like sharing GDP via these sorts of things. We also deeply appreciate like other people and the entire field to kind of build EVAs and, and share them. And we use them like SWE-Bench program, like, yes, but that’s a better eva now we should use them. So would really encourage people to find more ways to.

[00:23:43] Create and share ev events that we can, we and the entire field can use to measure like progress.

[00:23:50] swyx: Yeah.

[00:23:50] Mia: On, on, on like a variety of capabilities, including, including coding, because it’s important to understand, so where we are,

[00:23:57] swyx: me, I had to leave. But we’re, we are just kind of [00:24:00] talking a little bit about like the, the future directions that we want evals to go.

[00:24:02] Mia: Mm-hmm.

[00:24:03] swyx: And I, I think here, here we can dive in on like, give us. Good work on these, these, these things. We’ll talk to you, you know here’s your platform to make a call. Look for what you’re looking for.

[00:24:15] Olivia: I think a few things that would be useful. I’d say first of all, really, really hard task. Like the kinds of things that would take top-notch engineers months or teams weeks would be quite good.

[00:24:26] Especially if. Breeding is reliable and breeding is like, you know, you have for example, like rubrics that have been sourced and validated by many people in the field. I think that’d be quite valuable. I think also benchmarks on kind of creating products end to end. I think as people are like putting more, that would be quite useful.

[00:24:42] I think. A third thing that I’d say that is maybe not quite an eval, but I think it’s still relevant to the kind of overall mission of like. We as a field and as a world should be tracking, like where are these capabilities going? I’d like to see more metrics the tracking, like real world usage.

[00:24:57] Like how much is AI [00:25:00] actually being used in the field and how much is it, you know, replacing people’s jobs? How much is it? You know, augmenting people is speeding people up, just like real world metrics. Yeah,

[00:25:08] swyx: yeah. The, the, the replacement thing is always like a sensitive one on the, on the sort of PR side of things.

[00:25:12] But you know, we create new jobs that, that manage the old jobs and that’s how it’s yourself like you know, I think in terms of the frontier evals that, that open I is really going to excited to push like you, you put out really good work every single time. What should people expect from, from OPI itself?

[00:25:28] Olivia: I’m not sure I can say what we’re gonna

[00:25:30] swyx: general directions,

[00:25:31] Olivia: I mean, general directions. I think looking at real world impact, like real world real to,

[00:25:38] swyx: yeah, whatever.

[00:25:38] Olivia: That kind of stuff. Yeah. Yeah.

[00:25:39] swyx: Yeah. Amazing. Okay. Well, I’m excited for more real world impact. I, I think you guys have, you know, really made a lot of progress and I think taking a lot of industry leadership for C Bench verified and, and now moving on to C Orange Pro.

[00:25:50] So thank you for doing this. Thank you for being so transparent. And I think people will respond in kind. Yeah,

[00:25:57] Olivia: for your time.

[00:25:58] swyx: Thank [00:26:00] you.

AI News for 2/19/2026-2/20/2026. We checked 12 subreddits, 544 Twitters and 24 Discords (262 channels, and 12582 messages) for you. Estimated reading time saved (at 200wpm): 1242 minutes. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Congrats to the ggml + Huggingface team, note the Opus 4.6 METR debate, and read Chris Lattner’s Claude C Compiler analysis. But those aren’t the top stories.

Today, 2.5 year old Taalas announced a shockingly fast 16,960 tokens per second per user production API service for the Llama 3.1 8B model (launched July 2024):

While there are some other non-speed gains (less build cost and power draw), it is also offset by some other footnotes on quantization (which they note the HC2 will resolve with standardized low precision FP4).

This is an impressive result…. that we have no idea how to productize yet. And anytime there is a huge capability overhang opening up, AI Engineers should rush in to figure out the “capability market fit”.

As for the overall proposition of going custom ASIC, we’re still thinking about this week’s Latent Space with Martin Casado and Sarah Wang, where Martin reiterated his conviction in the math of doing custom accelerators (ASICs) PER MODEL, essentially “baking the LLM into silicon”, foreshadowing the OpenAI Broadcom deal:

Martin: It makes sense to actually do a custom ASIC if you can do it in time. The question now is timelines, but not money because rough math:

• If it’s a billion dollar training run, then the inference for that model has to be over a billion, otherwise it won’t be solvent.

• So let’s assume if you could save 20%, (which you could save much more than 20% with an ASIC), that’s $200 million.

• You can tape out a chip for $200 million.

Right? So now you can literally like justify economically, not timeline wise. That’s a different issue.

swyx: An ASIC per model, because that, that’s how much we leave on the table every single time we do generic Nvidia.

Martin Casado: Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million. Typical MFU would be like 50.

We understand the tradeoffs that custom chips offer faster/cheaper inference in exchange for lower model quality (by being, in Taalas’ case, 1.5 years behind the frontier), but that gap is virtually certain to close as LLMs continue to standardize in architecture and, more to the point, OpenAI and others start doing fully integrated model-chip codesign as Martin predicts. It’s not even about the cost savings at this point - the potential of actual frontier quality + >20,000 tok/s inference is incomprehensible to AI Engineers of today and we should start those thought experiments and product surfaces today with an expectation that we’ll get there in under 2 years from today.

Frontier model evals: Gemini 3.1 Pro, SWE-bench, MRCR, and “bipolar” real‑world performance

• Gemini 3.1 Pro shows strong retrieval + mixed agentic usability: Context Arena’s MRCR update reports Gemini 3.1 Pro Preview near-ties GPT‑5.2 (thinking:xhigh) on easier retrieval (2‑needle @128k AUC 99.6% vs 99.8%) and notably stronger on harder multi‑needle retrieval (8‑needle @128k AUC 87.8%, beating GPT‑5.2 thinking tiers reported there) (DillonUzar). Separately, Artificial Analysis highlights a likely underappreciated angle: token efficiency + price; they claim their Intelligence Index suite cost $892 on Gemini 3.1 Pro Preview vs $2,304 (GPT‑5.2 xhigh) and $2,486 (Opus 4.6 max), with fewer tokens consumed than GPT‑5.2 in their runs (ArtificialAnlys).

• But engineers report “bench strength, product weakness”: multiple threads complain Gemini’s tooling/harnesses lag—e.g., model availability inconsistencies in the CLI and buggy agent behavior in “Antigravity,” plus a worrying “UI lies / model lies” confusion where the app claims Gemini but reports Claude underneath (Yuchenj_UW, Yuchenj_UW). Even enthusiastic takes (“faster horse”) are juxtaposed with frustration about actually using it day‑to‑day (theo).

• SWE-bench Verified evaluation methodology matters again: MiniMax points to an “independent look” at SWE-bench Verified results for MiniMax M2.5 under the same setup, implying earlier comparisons across labs may have been apples-to-oranges (MiniMax_AI). Epoch AI explicitly acknowledges this failure mode: they updated SWE‑bench Verified methodology because their prior runs were systematically different from others, and now see results closer to developer‑reported scores (EpochAIResearch).

• Benchmark oddities are prompting “what are we measuring?” debates: one example—frontier models “smash ARC-AGI” yet struggle with Connect 4, suggesting ARC‑style puzzles may capture only a narrow slice of spatial/game reasoning despite being designed to resist overfitting (paul_cal). Another thread expects only a few models to make progress on a “simple harness” for ARC‑AGI‑3 and flags cost as the constraint (scaling01, scaling01).

AI News for 2/18/2026-2/19/2026. We checked 12 subreddits, 544 Twitters and 24 Discords (262 channels, and 14980 messages) for you. Estimated reading time saved (at 200wpm): 1467 minutes. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

It’s getting a little hard to say interesting things with all the round robin minor version updates of frontier models every week, but Gemini 3.1 Pro seems like a decent enough advance to catch up, and in some cases, supercede, the fellow frontier models (this is surely the reason that 3.1 -had- to be released, because with 5.3 and 4.6 things were seriously falling behind for Google1)

It’s better at some svg design things:

and translating textual vibes to visual aesthetics:

Google shipped Gemini 3.1 Pro (generally described as a Preview for developers) and rolled it out across the Gemini app, NotebookLM, Gemini API / AI Studio, and Vertex AI, positioning it as the “core intelligence” from Gemini 3 Deep Think scaled down for practical product use. The announcement emphasized a big reasoning jump—especially ARC-AGI-2 = 77.1%—plus strong coding and agentic-tool benchmarks (e.g., SWE-Bench Verified = 80.6%) and improved hallucination behavior. Independent leaderboards and evaluators largely corroborated top-tier performance and strong cost/intelligence positioning, while reaction threads highlighted (a) excitement about practical gains (SVG/web/UI/code quality, agentic use cases), (b) skepticism about benchmark-targeting and “eval tweeting,” (c) concerns around GDPval (real-world agentic tasks) not leading despite other SOTA scores, and (d) rollout friction: users finding some products (Gemini CLI / Code Assist / Antigravity) unavailable or inconsistently updated at launch.

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!

From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they’ve watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.

Martin and Sarah join us to unpack the new financing playbook for AI: why today’s rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what’s underhyped (boring enterprise software), what’s overheated (talent wars and compensation spirals), and the two radically different futures they see for AI’s market structure.

We discuss:

• Martin’s “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them

• The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years

• Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures

• The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels

• Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs

• Why today’s talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math

• Cursor as a case study: building up from the app layer while training down into your own models

• Why “boring” enterprise software may be the most underinvested opportunity in the AI mania

• Hardware and robotics: why the ChatGPT moment hasn’t yet arrived for robots and what would need to change

• World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude

• Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noise

Show Notes:

• “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show

• “Jack Altman & Martin Casado on the Future of Venture Capital”

• World Labs

—

Martin Casado
• LinkedIn: https://www.linkedin.com/in/martincasado/
• X: https://x.com/martin_casado

Sarah Wang
• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7
• X: https://x.com/sarahdingwang

a16z
• https://a16z.com/

00:00:00 – Intro: Live from a16z
00:01:20 – The New AI Funding Model: Venture + Growth Collide
00:03:19 – Circular Funding, Demand & “No Dark GPUs”
00:05:24 – Infrastructure vs Apps: The Lines Blur
00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger
00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?
00:11:24 – Character AI & The AGI vs Product Dilemma
00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety
00:17:33 – What’s Underinvested? The Case for “Boring” Software
00:19:29 – Robotics, Hardware & Why It’s Hard to Win
00:22:42 – Custom ASICs & The $1B Training Run Economics
00:24:23 – American Dynamism, Geography & AI Power Centers
00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)
00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?
00:32:48 – If You Can Raise More Than Your Ecosystem, You Win
00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case
00:38:55 – Cursor & The Power of the App Layer
00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models
00:47:20 – Thinking Machines, Founder Drama & Media Narratives
00:52:30 – Where Long-Term Power Accrues in the AI Stack

[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I’m joined by Twix, editor of Latent Space.

[00:00:08] swyx: Hey, hey, hey. Uh, and we’re so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very

[00:00:16] Martin Casado: happy to be here and welcome.

[00:00:17] swyx: Yes, uh, we love this office. We love what you’ve done with the place. Uh, the new logo is everywhere now. It’s, it’s still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of

[00:00:31] Martin Casado: definitely makes a statement.

[00:00:33] swyx: Yeah.

[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.

[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.

[00:00:40] Martin Casado: Yep.

[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I’m newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.

[00:00:51] Sarah Wang: That’s right. Yeah. Seven, seven years ago now.

[00:00:53] Martin Casado: Best growth investor in the entire industry.

[00:00:55] swyx: Oh, say

[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.

[00:01:15] I think, you know, Sarah’s been the, the broadest investor. Is that fair?

[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it’s been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it’s still a tech founder bet, which obviously is inherently early stage.

[00:01:33] But the resources,

[00:01:36] Martin Casado: so many, I

[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,

[00:01:46] Martin Casado: what is growth these days? You know, you don’t wake up if it’s less than a billion or like, it’s, it’s actually, it’s actually very like, like no, it’s a very interesting time in investing because like, you know, take like the character around, right?

[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you’ve got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it’s US or other firms on these large model companies, are like this hybrid between venture growth.

[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn’t usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I’m,

[00:02:27] swyx: I’m not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.

[00:02:31] Sarah Wang: Yeah.

[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there’s a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.

[00:02:50] Six months into the inception of a company, you just wouldn’t have to negotiate these deals before.

[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you’re writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.

[00:03:13] And so it’s, it’s very different ties. I’ve been doing this for 10 years. It’s the, I’ve never seen anything like this.

[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?

[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn’t there.

[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well

[00:03:41] Martin Casado: no, like as, as, as, as long as there’s demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they’re worth saying it.

[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn’t used. And that’s a problem, right? Because now you actually have a supply overhang.

[00:03:58] swyx: Mm-hmm.

[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.

[00:04:09] But we don’t have a supply overhang. Like there’s no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they’ll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it’s a different time.

[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I’m gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.

[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there’s demand there to martine’s point. But if that somehow breaks, you know, obviously that’s an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you’re, you’re investing into r and d to get to the capability, um, you know, increase.

[00:04:59] And [00:05:00] that’s sort of been the demand driver because. Once there’s an unlock there, people are willing to pay for it.

[00:05:05] Alessio: Yeah.

[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.

[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?

[00:05:24] Martin Casado: There’s so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that’s being blurred is between infrastructure and apps, right? So like what is a model company?

[00:05:35] Mm-hmm. Like, it’s clearly infrastructure, right? Because it’s like, you know, it’s doing kind of core r and d. It’s a horizontal platform, but it’s also an app because it’s um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you’re just starting to see a, a, a new financing strategy emerge and, you know, we’ve had to adapt as a result of that.

[00:05:59] And [00:06:00] so there’s been a lot of changes. Um, you’re right that these companies become platform companies very quickly. You’ve got ecosystem build out. So none of this is necessarily new, but the timescales of which it’s happened is pretty phenomenal. And the way we’d normally cut lines before is blurred a little bit, but.

[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we’ve seen in the past, like cloud build out the internet build out as well.

[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it’s interesting, uh, I don’t know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.

[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.

[00:06:49] Maybe you’re even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn’t [00:07:00] true even two years ago, I think. Mm-hmm. And so it’s sort of to your, just tying it to fundraising strategy, right? There’s a, and hiring strategy.

[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they’re these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.

[00:07:23] But they’re competing on the app layer.

[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don’t think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.

[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can’t verticalize on the token string. Like you can’t build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.

[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn’t scale like the mythical mammoth. It take a very long time.

[00:08:18] But like that’s not the case here. Like a model company can raise money and drop a model in a, in a year, and it’s better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we’ve ever seen before.

[00:08:39] And I think everybody’s trying to understand what the consequences are. So I think it’s less about like. Big companies and growth and this, and more about these more systemic questions that we actually don’t have answers to.

[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you’re investing X amount of capital to win a deal because of price structure and whatnot, and you’re kind of pot committing.

[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.

[00:09:18] swyx: Yeah.

[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.

[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There’s no, there’s no like $8 million C round anymore. Right.

[00:09:38] Martin Casado: But, but, but, but there’s a, there’s a, the, an industry structural question that we don’t know the answer to, which involves the frontier models, which is, let’s take.

[00:09:48] Anthropic it. Let’s say Anthropic has a state-of-the-art model that has some large percentage of market share. And let’s say that, uh, uh, uh, you know, uh, a company’s building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.

[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that’s built on top of it. And if that’s the case, they can expand beyond everything built on top of it. It’s like imagine like a star that’s just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don’t think we’ve ever seen before just because we were so bottlenecked in engineering, and this is a very open question.

[00:10:41] swyx: Yeah. It’s, it is almost like bitter lesson applied to the startup industry.

[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that’s built the, not any company.

[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you’ll necessarily take their share, which is crazy.

[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?

[00:11:10] Sarah Wang: Um,

[00:11:10] Martin Casado: no.

[00:11:12] Sarah Wang: Yeah, because I think so,

[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.

[00:11:15] Exactly. But like

[00:11:18] Martin Casado: that’s another difference that

[00:11:19] Sarah Wang: you said

[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.

[00:11:22] swyx: Yeah,

[00:11:22] Sarah Wang: that’s

[00:11:23] swyx: Go for it. Take it. Take,

[00:11:23] Sarah Wang: yeah.

[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we’ll see how they handle it.

[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he’s talked publicly about this, right? He wanted to Google wouldn’t let him put out products in the world.

[00:11:56] That’s obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it’s Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.

[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven’t started, you know, felt it, certainly given the success of their products, they may start to feel that soon.

[00:12:39] And the real. I think there’s real trade-offs, right? It’s like how many, when you think about GPUs, that’s a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you’re resource constrained, um, [00:13:00] of course there’s this fundraising game you can play, right?

[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it’s the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?

[00:13:27] And certainly if you don’t have that progress, you can’t continue this fly, you know, fundraising flywheel.

[00:13:32] Martin Casado: I would say that because, ‘cause we’re keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.

[00:13:45] It’s just very different this time I’ve been. Been doing this for a decade and I’ve been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we’ve never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.

[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it’s kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.

[00:14:20] And so like there is always this tension with personnel. And so I think we’re seeing more kind of founder movement.

[00:14:27] Sarah Wang: Yeah.

[00:14:27] Martin Casado: You know, as a fraction of founders than we’ve ever seen. I mean, maybe since like, I don’t know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it’s a very, very.

[00:14:38] Unusual time of personnel.

[00:14:39] Sarah Wang: Totally.

[00:14:40] Sarah Wang: And it, I think it’s exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That’s hard to compete with. And then secondly, if you’re a founder in ai, you could fart and it would be on the front page of, you know, the information these days.

[00:14:59] And so there’s [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.

[00:15:06] Martin Casado: Hmm.

[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don’t know if we’ll see that again.

[00:15:17] ‘cause meta built the team. Like, I don’t know if, I think, I think they’re kind of done and like, who’s gonna pay more than meta? I, I don’t know.

[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It’s like, it is like, basically Zuckerberg kind of came out swinging and then now he’s kind of back to building.

[00:15:30] Yeah,

[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.

[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we’re, we’re actually in the job hiring market. We’ve got 600 people here. I hire all the time.

[00:15:44] I’ve got three open recs if anybody’s interested, that’s listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.

[00:16:00] And just to see what’s out on the market is really, is really remarkable. And so I would just say it’s actually, so you’re right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,

[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.

[00:16:22] Okay. Yeah. Easily. Yeah. It’s so I think you’re right that it felt like a blip. I hope you’re right. Um, but I think it’s been, the steady state is now, I think got pulled up. Yeah. Yeah. I’ll pull up for

[00:16:31] Martin Casado: sure. Yeah.

[00:16:32] Alessio: Yeah. And I think that’s breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.

[00:16:39] Yeah. 800 KA million at Google. But if I’m getting paid. Five, 6 million. That’s different but

[00:16:45] Martin Casado: on. But on the other hand, there’s more strategic money than we’ve ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it’s crazy.

[00:16:58] It’s cra it’s causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?

[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it’s probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.

[00:17:33] Alessio: Yeah.

[00:17:33] Alessio: Um, let’s talk maybe about what’s not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it’s like access getting more popular.

[00:17:47] There’s a startup school path that a lot of founders take and they know what’s hot in the VC circles and they know what gets funded. Uh, and there’s maybe not as much risk appetite for. Things outside of that. Um, I’m curious if you feel [00:18:00] like that’s true and what are maybe, uh, some of the areas, uh, that you think are under discussed?

[00:18:06] Martin Casado: I mean, I actually think that we’ve taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there’s almost a barbell, like you’re like the hot thing on X, you’re deep tech.

[00:18:21] swyx: Mm-hmm.

[00:18:22] Martin Casado: Right. But I, you know, I feel like there’s just kind of a long, you know, list of like good.

[00:18:28] Good companies that will be around for a long time in very large markets. Say you’re building a database, you know, say you’re building, um, you know, kind of monitoring or logging or tooling or whatever. There’s some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.

[00:18:43] And it’s almost become a meme, right? Which is like, if you’re not basically growing from zero to a hundred in a year, you’re not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?

[00:19:00] Of course you can put it in the company five x. So it’s just like we say these stupid things, like if you’re not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.

[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We’d, everybody would be happy with these returns, but we’ve got this kind of mania on these, these strong growths. And so I would say that that’s probably the most underinvested sector.

[00:19:28] Right now.

[00:19:29] swyx: Boring software, boring enterprise software.

[00:19:31] Martin Casado: Traditional. Really good company.

[00:19:33] swyx: No, no AI here.

[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that’s not what they’re, they’re not on the token path, right? Yeah. Let’s just say that like they’re software, but they’re not on the token path.

[00:19:41] Like these are like they’re great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it’s not growing fast enough. What do you

[00:19:52] Sarah Wang: think? Yeah, maybe I’ll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we’re not, uh, investing [00:20:00] right now that I think is a question and we’re spending a lot of time in regardless of whether we pull the trigger or not.

[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it’s, I don’t wanna say that it’s not getting funding ‘cause it’s clearly, uh, it’s, it’s sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven’t seen the chat GPT moment.

[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it’s already. Taking that for granted.

[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there’s a zip line right, right out there. What’s that? Oh yeah, there’s a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.

[00:20:46] Like if you’re. If you’re investing in a robot company for an A for agriculture, you’re investing in an ag company. ‘cause that’s the competition and that’s surprising. And that’s supply chain. And if you’re doing it for mining, that’s mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.

[00:21:01] But for like horizontal technology investing, there’s very little when it comes to robots just because it’s so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That’s fair, you know, for robotics early on.

[00:21:23] And so that sort of thing we’re very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.

[00:21:30] Alessio: Yeah, I’m curious who these teams are supposed to be that invest in them. I feel like everybody’s like, yeah, robotics, it’s important and like people should invest in it.

[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let’s keep investing. That seems really hard to predict in a way that is not,

[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.

[00:22:01] Right? I mean if Elon’s doing it, he’s like, right. Just, just the fact that Elon’s doing it means that there’s gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who’s Elon with a humanoid and that’s gonna like basically willing into being an industry.

[00:22:17] Um, but we’ve just historically found like. We’re a huge believer that this is gonna happen. We just don’t feel like we’re in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they’re being sold into. Like that’s like that competitive equilibrium with a human being is what’s important.

[00:22:34] It’s not like the core tech and like we’re kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.

[00:22:42] swyx: Uh, just to clarify, AD stands for

[00:22:44] Martin Casado: American Dynamism.

[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.

[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.

[00:23:07] Martin Casado: Yes.

[00:23:07] It’s crazy. Yeah.

[00:23:09] swyx: We’re here and I think you, you estimated 500 billion, uh, something.

[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.

[00:23:22] If it’s a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won’t be solvent. So let’s assume it’s, if you could save 20%, which you could save much more than that with an ASIC 20%, that’s $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.

[00:23:41] That’s a different issue. An ASIC per model, which

[00:23:44] swyx: is because that, that’s how much we leave on the table every single time. We, we, we do like generic Nvidia.

[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.

[00:23:54] swyx: Typical MFU would be like 50.

[00:23:55] Yeah, yeah. And that’s good.

[00:23:57] Martin Casado: Exactly. Yeah. Hundred

[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there’s, there’s a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.

[00:24:17] Yeah. Uh, move TSMC here, if that’s possible. Um, how much overlap is there from ad

[00:24:23] Martin Casado: Yeah.

[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.

[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?

[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they’re just set up to, to, to, to, to. To diligence those types of companies. So it’s a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?

[00:24:58] I mean, for the longest time we’re like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there’s actually a lot of compounding effects for having a geographic bias. Right. You know, everybody’s in the same place. You’ve got an ecosystem, you’re there, you’ve got presence, you’ve got a network.

[00:25:12] Um, and, uh, I mean, I would say the Bay area’s very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it’s so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it’s kind of come back here.

[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we’ve asked all over the world. And then I would say like, if you take the ring out, you know, one more, it’s gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.

[00:25:44] And it goes from there.

[00:25:45] Sarah Wang: Yeah,

[00:25:45] Martin Casado: sorry.

[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that’s sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.

[00:25:59] Like they’re selling [00:26:00] globally, right? They have global supply chains in some cases.

[00:26:03] Martin Casado: I would say also the stickiness is very different.

[00:26:05] Sarah Wang: Yeah.

[00:26:05] Martin Casado: Historically between venture and growth, like there’s so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.

[00:26:15] Like of course we’re just gonna be stronger where we have our network and we’ve been doing business for 20 years. I’ve been in the Bay Area for 25 years, so clearly I’m just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don’t need that much help.

[00:26:30] They’re already kind of pretty mature historically, so like they can kind of be everywhere. So there’s kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She’s like, ops Ninja Biz, Devrel, BizOps.

[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.

[00:26:53] Sarah Wang: Oh my, in my personal stack.

[00:26:54] swyx: I mean, because like, uh, by the way, it’s the, the, the reason for this is it is triggering, uh, yeah. We, like, I’m hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I’m just, you know, it’s opportunity Since you’re, you’re also like basically helping out with ops with a lot of companies.

[00:27:09] What are people doing these days? Because it’s still very manual as far as I can tell.

[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It’s sort of like, Hey, how do do I shortcut this process? Well, let’s connect you to the right person. So there’s not quite an AI workflow for that.

[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you’re gonna do a customer database, analyze a cohort retention, right? That’s just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.

[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that’s, that’s the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.

[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.

[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a

[00:28:14] Martin Casado: lot.

[00:28:14] Sarah Wang: Yeah, true.

[00:28:16] swyx: Yeah. You

[00:28:16] Martin Casado: gotta hand it to them. They’ve been executing incredibly well.

[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.

[00:28:35] Oh, I

[00:28:35] Martin Casado: think they’ve been pretty clear. They’re enterprise focused.

[00:28:37] swyx: They have been, but like they’ve been free. Here’s

[00:28:40] Martin Casado: care publicly,

[00:28:40] swyx: it’s enterprise focused. It’s coding. Right. Yeah.

[00:28:43] swyx: And then, and, but here’s cloud, cloud, cowork, and, and here’s like, well, we, uh, they, apparently they’re running Instagram ads for Claudia.

[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,

[00:28:54] Martin Casado: uh,

[00:28:54] swyx: it, it’s kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here’s a topic that only focus on this thing, but now they’re sort of undercutting and doing the whole innovator’s dilemma thing on like everything else.

[00:29:11] Martin Casado: It’s very

[00:29:11] swyx: interesting.

[00:29:12] Martin Casado: Yeah, I mean there’s, there’s a very open que so for me there’s like, do you know that meme where there’s like the guy in the path and there’s like a path this way? There’s a path this way. Like one which way Western man. Yeah. Yeah.

[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.

[00:29:29] So in, in one potential future, um, the market is infinitely large. There’s perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it’s just like software’s being rewritten and fractured all over the place and there’s tons of upside and it just grows.

[00:29:48] And then there’s another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That’s all you have to [00:30:00] do, and it’ll just consume everything beyond it. And if that’s the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.

[00:30:06] Because they’re perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You’ve got, and nobody knows the answer.

[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they’re making and how much they, they spent training the last model, they’re gross margin positive.

[00:30:30] You’re like, oh, that’s really working. But if you look at like. The current training that they’re doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that’s gonna have to slow down. It’s gonna catch up to them at some point in time, but we don’t really know.

[00:30:47] Sarah Wang: Yeah.

[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won’t be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.

[00:31:03] But right now it’s not.

[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we’re on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let’s say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.

[00:31:27] And, um, and so that, you know, that’s one period. Suddenly it’s sort of like open source takes over the world. There’s gonna be a plethora. It’s not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It’s a long time. Right.

[00:31:44] Um, and of course now we’re in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it’s so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.

[00:32:06] And so until that happens, right, like you don’t know what’s gonna look like.

[00:32:09] Martin Casado: But I, I would, I would say for sure it’s not converged, like for sure, like the systemic capital flows have not converged, meaning right now it’s still borrowing against the future to subsidize growth currently, which you can do that for a period of time.

[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.

[00:32:29] Alessio: Yeah.

[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?

[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It’s like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.

[00:32:45] I think now, now, right now there’s like no old model.

[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that’s, that used to be my mental model. Lemme just change it a little bit.

[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn’t even matter.

[00:32:59] It doesn’t [00:33:00] even matter. See what I’m saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that’s using it, I will consume them whether I’m a GI or not.

[00:33:14] And I will know if they’re using it ‘cause they’re using it. And like, unlike in the past where engineering stops me from doing that.

[00:33:21] Alessio: Mm-hmm.

[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there’s also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.

[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there’s a certain task that. Getting marginally better isn’t actually that much better. Like we’ve asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we’re already at a GI for a lot of functions in the enterprise.

[00:33:50] Um. That’s probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn’t [00:34:00] coming from the model itself. There’s probably a rich enterprise business to be built there. I mean, could be wrong on that, but there’s a lot of interesting examples.

[00:34:08] So, right, if you’re looking the legal profession or, or whatnot, and maybe that’s not a great one ‘cause the models are getting better on that front too, but just something where it’s a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.

[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.

[00:34:31] Sarah Wang: Mm-hmm.

[00:34:32] Martin Casado: Yeah. I code every day. It’s so fun.

[00:34:35] Sarah Wang: That’s a core question. Yeah.

[00:34:36] Martin Casado: And like. When I’m talking to these models, it’s not just code. I mean, it’s everything, right? Like I, you know, like it’s,

[00:34:43] swyx: it’s healthcare.

[00:34:44] It’s,

[00:34:44] Martin Casado: I mean, it’s

[00:34:44] swyx: Mele,

[00:34:45] Martin Casado: but it’s every, it is exactly that. Like, yeah, that’s

[00:34:47] Sarah Wang: great support. Yeah.

[00:34:48] Martin Casado: It’s everything. Like I’m asking these models to, yeah, to understand compliance. I’m asking these models to go search the web. I’m asking these models to talk about things I know in the history, like it’s having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.

[00:35:01] The most a, you know, a GI complete, like I’m not an a GI guy. Like I think that’s, you know, but like the most a GI complete model will is win independent of the task. And we don’t know the answer to that one either.

[00:35:11] swyx: Yeah.

[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.

[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don’t work on it. It’s great. Um, but I think Opus 4.5 is actually very, it’s got a great bedside manner and it really, and it, it really matters if you’re building something very complex because like, it really, you know, like you’re, you’re, you’re a partner and a brainstorming partner for somebody.

[00:35:38] And I think we don’t discuss enough how every task kind of has that quality.

[00:35:42] swyx: Mm-hmm.

[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.

[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So

[00:35:51] Alessio: some of them, they didn’t even get released.

[00:35:53] Magical

[00:35:54] Martin Casado: Devrel. There’s a whole, there’s a whole host. We saw a bunch of them and like there’s this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there’s no such thing as a coding model,

[00:36:04] Alessio: you know?

[00:36:04] Martin Casado: Like, that’s not a thing. Like you’re talking to another human being and it’s, it’s good at coding, but like it’s gotta be good at everything.

[00:36:10] swyx: Uh, minor disagree only because I, I’m pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that’s the code’s. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that’s a good way to frame it.

[00:36:32] Martin Casado: That’s so funny.

[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that’s it. It’s not like a hundred dimensions doesn’t life. Yeah. It’s two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.

[00:36:43] Martin Casado: Yeah.

[00:36:44] swyx: It’s, yeah.

[00:36:46] Martin Casado: I, I think for, for any, it’s hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you’re like coding or using these models for something like that.

[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you’re, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.

[00:37:06] swyx: Yeah.

[00:37:07] swyx: Uh, speaking of coding, uh, I, I’m gonna be cheeky and ask like, what actually are you coding?

[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?

[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it’s one of the investments and um, and they’re building a foundation model that creates 3D scenes.

[00:37:27] swyx: Yeah, we had it on the pod.

[00:37:28] Yeah. Yeah,

[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don’t really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn’t great.

[00:37:50] It’s just never, you know, it’s always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it’s just because, you know, um, you, you, you need that support and, and right now there’s kind of a three js moment that’s all meshes and so like, it’s become kind of the default in three Js ecosystem.

[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it’s actually a very tough algorithmics problem to actually scale a library that much.

[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I’ve got actually a back and it’s very old background, but I actually have a background in this and so a lot of it’s fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,

[00:38:47] Sarah Wang: are you one of the most active contributors?

[00:38:49] The, their GitHub

[00:38:50] Martin Casado: spark? Yes.

[00:38:51] Sarah Wang: Yeah, yeah.

[00:38:51] Martin Casado: There’s only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who’s an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?

[00:39:09] And so like. So he, he’s the core, core guy. I did mostly kind of, you know, the side I run venture fund.

[00:39:14] swyx: It’s amazing. Like five years ago you would not have done any of this. And it brought you back

[00:39:19] Martin Casado: the act, the Activ energy, you’re still back. Energy was so high because you had to learn all the framework bullshit.

[00:39:23] Man, I fucking used to hate that. And so like, now I don’t have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,

[00:39:29] swyx: yeah. Yeah.

[00:39:29] swyx: And then, uh, I’ll observe one irony and then I’ll ask a serious investor question, uh, which is like, the irony is FFE actually doesn’t believe that LMS can lead us to spatial intelligence.

[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.

[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.

[00:39:51] swyx: Yes.

[00:39:51] Martin Casado: But like, that’s very different than a model that actually like provides, they, they’ll never have the

[00:39:56] swyx: spatial inte

[00:39:56] Martin Casado: issues.

[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it’s just, you know, these are two pretty independent problems.

[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?

[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that’s what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it’s one model and it’s, and it’s in LM.

[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.

[00:40:37] swyx: And so that, that was my indication of like, maybe you don’t need a separate system.

[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let’s, let me put you in a dark room.

[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there’s a table like duck below this thing, right? I mean like the chances that you’re gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.

[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it’s not exact enough. So that’s all Faye, Faye is talking about. When it comes to like spatial reasoning, it’s like you actually have to know that this is three feet far, like that far away. It is curved.

[00:41:37] You have to understand, you know, the, like the actual movement through space.

[00:41:40] swyx: Yeah.

[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there’s, there’s, there’s different representations of problems you’re solving. One is language. Which, you know, that would be like describing to somebody like what to do.

[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.

[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I’m just like, Fefe is awesome.

[00:42:07] Justin’s awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone’s building cool tech. But like, what’s the value of the tech? And this is the fundamental question

[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I’m a venture for, you know, so like, ventures always, always like kind of wild west type

[00:42:24] swyx: stuff.

[00:42:24] You, you, you, you paid a dream and she has to like, actually

[00:42:28] Martin Casado: I’m gonna say I’m gonna mar to reality, so I’m gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?

[00:42:45] Like, like a 2D image. I mean, that’s been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we’ve seen this with speech in very successful companies.

[00:43:03] We’ve seen this with 2D image. We’ve seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when’s Grand Theft Auto coming out? It’s been six, what? It’s been 10 years. I mean, how, how like, but hasn’t been 10 years.

[00:43:14] Alessio: Yeah.

[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.

[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they’re using Unreal and they’re using Blend, or they’re using movies and they’re using video games and they’re using all. So if you could do that for.

[00:43:36] You know, less than a dollar, that’s four or five orders of magnitude cheaper. So you’re bringing the marginal cost of something that’s useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.

[00:43:49] swyx: Yeah.

[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.

[00:43:55] Martin Casado: Yeah. Marble.

[00:43:55] swyx: Uh, or but also there’s many Nerf apps where you just go on your iPhone and, and do this.

[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.

[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.

[00:44:08] swyx: Yeah.

[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it’ll reconstruct all the like, like

[00:44:16] swyx: meaning it has to fill in. Uh,

[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn’t see.

[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can’t see.

[00:44:26] swyx: Yeah. Okay.

[00:44:26] Sarah Wang: So,

[00:44:27] Martin Casado: all right. So now the,

[00:44:28] Sarah Wang: no, no. I mean I love that

[00:44:29] Martin Casado: the adult

[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.

[00:44:36] ‘cause it truly is, I mean, we’re tag teaming all of these together.

[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?

[00:44:56] So, uh, we’re not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we’re not, um, contrary to popular opinion, we’re not invested in all of them. Right. We have a very specific thesis. I don’t think people

[00:45:09] swyx: say that about you.

[00:45:10] No, they don’t. They don’t,

[00:45:12] Sarah Wang: they say that we’re big, we’re in everything. But, um, you know, if you think about ia, right? He’s at SSI, he’s sort of. Been behind almost every foundational breakthrough for the last 15 years. 15 years. Um, if you think about, you know, the Thinking machines team, right? Mira and John, right?

[00:45:27] John is the godfather of reinforcement learning. And so, um, I go through this because, you know, if you think about for each of the bets that we’ve made, it goes back to one of, to a very specific thesis about that person, the team they’ve assembled and what they’ve done in a prior life. Um, and you know, I, I think, you know, obviously we talked about talent wars.

[00:45:46] Um, we do think. At this particular moment in time, there are particular people that can move needles. Um, clearly, uh, other companies believe that too, otherwise they wouldn’t be willing to pay such crazy prices for single individuals. So that’s, that’s one. And then two, [00:46:00] we don’t think it’s a zero sum game, right?

[00:46:02] Like if that were true open AI or, or actually just deep mind would be number one and everything, right? There’s clear value. To specialization. It’s like 11 labs. There have been so, oh my God. Yeah. Many audio models that have hit the market, they’re still fricking number one, right? And so if you think about, and they’ve created a ton of value, um, for their customers, for their investors, you know, for their team.

[00:46:23] Um, and so if you think about those two put together, right? That’s sort of the foundation of our thesis when we back, uh, these foundation model, uh, companies. Um, of course. The valuations, you know, they sound astronomical when you think about current revenue, the numbers, um, you know, there’s, there’s sort of that I would, one, I would say that’s the market out there because they are raising larger dollars.

[00:46:47] They have compute needs, right? That’s 80% of around that they typically raise or typically of, of around that they raise. Um, but I think the thing that gets us excited about backing them is that the revenue growth has [00:47:00] typically followed the capability breakthrough. So you sort of ties back to that question of.

[00:47:04] The cyclical nature, like are you just funding it and then you raise more funding? Um, when there’s a real capability breakthrough, the demand is there. And so the revenue growth is much faster than we’ve ever seen. Once it’s turned on, there’s a company, I can’t share the name, um, but their product went GA in a few weeks.

[00:47:21] Tens of millions of revenue. Right. We have

[00:47:23] swyx: SaaS

[00:47:23] I’ve

[00:47:24] swyx: seen as myself. Yes,

[00:47:24] Sarah Wang: absolutely. We have SaaS. Absolutely. Companies that, you know, have been in business for seven years and they get to the same level seven years later. And the growth is, you know, eking to whatever it is. Um, and, and by the way, great companies not, not at all, um, diminishing what they’ve accomplished, but the fact is to get to that revenue growth that quickly.

[00:47:43] It’s not just the two companies that people talk about. It’s, it’s really a lot of these, you know, sort of. Every domain has a specialist, and we think if you can win that, you become very large, very quickly, and that’s actually played out in the numbers.

[00:47:56] swyx: Yeah. Uh, our, our viewers are going to, uh, so [00:48:00] first of all, thank you for that overall take.

[00:48:01] I think like it’s important to hear you guys’ perspective because the rest of us are just kind of looking at headlines and not knowing how to make sense of any of this. Um, we can mention like my, our listeners will roast us if we, if we mention thinky and not. Discuss what happened. Uh, I mean, obviously founder split happens, um, but like, I guess is the thesis unchanged is is like, um, you know, like what’s, what’s going on in thinking?

[00:48:25] Sarah Wang: Yeah. Um, we’re more excited than ever about them. Um, they have some things that. We’re not gonna do breaking news on a, a pod. Uh, you know, obviously they should share it themselves, but, um, they’ve, you know, I think when you bring a team of that caliber together, there’s special things that happen. And, um, I think 2026 is gonna be a big year for them.

[00:48:44] Um, obviously, you know, some of the themes that we talked about before, even with just the media news storm, like the whole, something happens and then it’s everywhere instantly. Um, you know, I think, uh. [00:49:00] That’s a, i, that’s a tough situation for any company to be in. Um, but to come out of that stronger than ever, I think that, you know, we’re, we’re more bullish about thinking than, um, you know, even before.

[00:49:12] And, um, obviously,

[00:49:13] swyx: and, and, and the story is tin, uh, is tinker. It’s our custom models are all. Um, yeah. Is that, is that what, is that what we’re aiming for?

[00:49:22] Sarah Wang: Yeah. And a bunch of stuff we, we can’t talk about here. Okay. Yeah. All right. Cool. Yeah, absolutely. But no, that team is cooking and, um, you know, I think, um, they’ll, they’ll be just fine from, uh, they’ll, they’ll recover from the events in January.

[00:49:34] swyx: Yeah.

[00:49:34] Martin Casado: I will say this is the furthest, so we have a very privileged position on the boards of these companies, and like I’ll say, I’ve never seen. The perception of the truth be further from the truth.

[00:49:48] swyx: Oh,

[00:49:48] Martin Casado: industry wide ever. Like I, I guarantee you, for any of these gossipy things, I guarantee you it’s way off.

[00:49:55] swyx: Okay.

[00:49:55] Martin Casado: Way, way off. Like, like the general sentiment and like, and what happens is like we’ve got this [00:50:00] crazy game of telephone right now where there’s always. Seeds of truth, but it gets so warped by the time, like we hear all the time rumors about stuff that we’re directly involved in. Like we’re literally on the board, you know, like we’re, we’re the one that did the thing.

[00:50:12] And by the time it gets so it’s gotten so warped and so twisted. I think this is like everybody’s excited. I. There’s a lot of focus. The shot on fried is so high that people just kind of will into being things that didn’t exist. Um, so I’m not, you know, I, you know, I don’t wanna comment specifically on the thinking machines, but like,

[00:50:31] swyx: it’s an important message to the general

[00:50:33] Martin Casado: audience.

[00:50:33] I, I’ll tell you, if you hear something IX like the chances that it’s. You know, it is accurate representing, but it’s saying to is very, very low.

[00:50:42] swyx: Yeah.

[00:50:43] Sarah Wang: I have never lost so much faith in the an, an non counts on Twitter that just seemed very confident in what they’re saying. Yeah,

[00:50:50] Martin Casado: no. Yeah.

[00:50:50] Sarah Wang: And couldn’t be further from the truth.

[00:50:52] I, I had a couple days stretch where I was like, oh my God, Twitter is mind poisoned and I. Love X. Yeah,

[00:50:56] Martin Casado: but we talk to each other all the time. ‘cause we actually know, ‘cause we’re there like, we’re [00:51:00] there singing these things and like, you know, Sarah will like text me, you know, like whatever. Like, it’s like ridiculous.

[00:51:06] So for us it’s like, it’s like this ridiculous. But the problem is, is we realize that things like things start taking on a life of their own and then people assume that they’re real and, and everything. And so I think it’s very tough for founders because, you know, it’s tough enough fighting the real battle.

[00:51:20] You know now. Absolutely. Now they’re fighting phantoms too. And so, you know, you know, more and more we’re just like, and I got this from the cursory guys, which I, I really appreciate Michael Troll. He’s like, listen, head’s down, focus on the business. Yeah. And, and he absolutely crushed

[00:51:35] swyx: it.

[00:51:35] Martin Casado: Yeah. Yeah. And I, I think that’s right.

[00:51:37] I all

[00:51:37] found

[00:51:37] Martin Casado: absolutely right now, ‘cause the noise is so hot.

[00:51:40] Sarah Wang: No, that team’s been back to business for, for weeks, the thinky team. So, yeah.

[00:51:43] swyx: Yeah. Well, thank you for acknowledging in that, uh, it, it is just, uh, the hot topic at the moment. Oh, we gotta, gotta address the elephant in the room. Um, uh, cursor, right?

[00:51:51] You obviously, you guys are big investors. Uh, 2025. I would say it’s cursor year. I mean. Maybe decade, but, uh, [00:52:00] uh, just like I, I think, you know, I, I just going back to the discussion about how a GI would just kind of consume everything. Yeah. S just like the one, like the kind of the shining example of like, here’s how you build application layer.

[00:52:10] That’s a wrapper.

[00:52:11] Martin Casado: Yeah.

[00:52:12] swyx: But extremely damn good one.

[00:52:14] Martin Casado: Yeah.

[00:52:14] swyx: Uh, and, uh, I guess just like the, the general. Analysis, I guess, of, of cursors development and what it means for everyone? Like is there a cursor in every industry to be built?

[00:52:24] Martin Casado: Yeah, so the, the interesting about cursors, they actually for, you know, a small fraction of the cost, a hundred of the costs or less.

[00:52:32] Developed an almost soda model, which for a period of time was the most popular coding model in the world. Right? Which is really crazy to think about. So I think they’re just kind of doing it in reverse, right? So there, there, there’s two approaches. You start with a foundation model and then you verticalize up, or you start with the app and all of the product data and you go down and they’re the ones that are doing that.

[00:52:55] I think any company that’s doing an app has to ask the margin question. Mm-hmm. Which is like, how, how [00:53:00] do I extract margin on, on, on the tokens that are going through? Like, everybody has to be on the token path and everybody has to ask that question. And I’ve just thought they’ve been incredibly thoughtful about it.

[00:53:09] And one reason is, is if you ask. You know, Michael, what type of company are they are a developer company for professional developers. That’s what they’re, they’re a Devrel tools company. They’re just focused on coding. And that’s a hu I mean, even if you didn’t do ai, that’s a ma. You know, they, they, they, um, they acquired graphite.

[00:53:25] I mean, like, you know, listen, we were investors in GitHub, like we know how big this market is. So that’s a massive market, even without becoming a model company. But they’ve also been quite successful in doing their own models. And so I think it just shows you that if you. Are focused, you have a large use case.

[00:53:40] There’s a huge opportunity not only to get the application, but to start building your own models. Are these gonna be the only models we use? Of course not. Um, but you know, they are in a great position to serve great models and they’ve demonstrated that.

[00:53:51] swyx: Yeah. My, my, uh, sort of, uh, thesis, which we’re not gonna have to go into here is actually I think a, um, what I’ve been calling Agent Labs, which are [00:54:00] people who build on top of, uh, all the other models.

[00:54:02] Martin Casado: Yeah.

[00:54:02] swyx: Um, will probably have a better time with the margins because they, they price against the end user hours spent, or like human labor, whereas models get commodity price per token.

[00:54:15] Sarah Wang: Yeah.

[00:54:15] swyx: And so margin wise. We know inference economics for, uh, uh, model labs, but agent labs, uh, the difference is the delta between token intelligence, which keeps going down, and human costs, which keep going up.

[00:54:28] Martin Casado: Yeah, yeah, yeah.

[00:54:28] swyx: And so margin should be higher.

[00:54:31] Martin Casado: They, they, they, they, they, they should be. The, the, the, the caveat to that is if the models go first party, right. Yeah. Yeah. What they can do is they can, they can, which is

[00:54:40] swyx: the, the composer dream.

[00:54:41] Martin Casado: Yes. Yeah. They can subsidize the, no, the models, they can subsidize themselves.

[00:54:46] Oh, cloud code, code, they can subsidize themselves and then they can charge the third party more, and it’s a very delicate. Yeah, it’s because you’re kind of competing with your own customers. And so, you know, we’ve seen this historically. We saw this with the cloud with EC2, like, so this is not unusual. We [00:55:00] saw this with the operating system.

[00:55:00] It’s not unusual, but it’s playing out very, very quickly.

[00:55:04] Alessio: Yeah. Thank you for joining us. That’s all the time we have today. This is such a pleasure. You’re welcome back anytime.

[00:55:09] swyx: And thank you for being so open and also like just leading the industry in so many areas. Uh, it’s uh, really inspiring to see. So

[00:55:16] Sarah Wang: thank you so much.

[00:55:17] Thank you much. Thank you for having us.

[00:55:17] swyx: Great. Thank you.

There’s a lot of small tidbits going on, with former guest Fei-Fei Li’s World Labs and The Era of Experience’s David Silver both raising monster $1B rounds, and Anthropic officially blocking OpenClaw using Claude OAuth tokens (consistent with post-OpenCode policy), with OpenAI employees politely reminding everyone that they’re more than welcome to use OpenAI plans instead on the same day (complete coincidence, we are sure).

However, all that will pass. What we’d highlight today is Anthropic’s study of its own API usage patterns, Measuring AI agent autonomy in practice. As you might expect, most usage is coding, but you can start to go down the list of the other uses and pick off the next likely targets for agents:

Most of the post is about Claude Code usage. We see Anthropic’s side of the “increasing autonomy story” - starting at 25 mins in Sept to over 45 mins in Jan, with a dip coinciding with the sudden 2x jump in userbase in Jan-Feb 2026, and then rebounding with the release of Opus 4.6.

This is a somewhat different but directionally similar story with the famous METR chart, which, as we explain in our upcoming podcast with them, because it picks the 50th percentile success rate of HUMAN EQUIVALENT HOURS instead of the 99.9th percentile long tail autonomy of Claude Code autonomous execution, shows a very different (and volatile at the extremes) trend of almost 5 hours of human work done by agents:

as Anthropic notes:

The METR evaluation captures what a model is capable of in an idealized setting with no human interaction and no real-world consequences. Our measurements capture what happens in practice, where Claude pauses to ask for feedback and users interrupt. And METR’s five-hour figure measures task difficulty—how long the task would take a human—not how long the model actually runs.

(also…

Most Claude Code turns are short. The median turn lasts around 45 seconds, and this duration has fluctuated only slightly over the past few months (between 40 and 55 seconds). In fact, nearly every percentile below the 99th has remained relatively stable.

Enough said. Because Anthropic has full access to Claude Code telemetry, there are other autonomy measures nobody else has. For example… new users start off with 20% auto-approve, and increase to >50% over time with experience

…although they do also interrupt it almost twice as often:

There’s also good analysis of when -Claude- interrupts the flow to ask for clarification, with decent calibration and good breakdown of reasons with frequency:

The rest of the post is more safety oriented, but AI Engineers can take away alot from this agent usage data alone.

Frontier model + benchmark churn (Claude 4.6, Qwen3.5, GLM‑5, Gemini 3.1 Pro, MiniMax M2.5)

• Anthropic Claude Opus/Sonnet 4.6: big jump, big token bill: Artificial Analysis reports Sonnet 4.6 at 51 on its Intelligence Index (up from 43 for Sonnet 4.5 reasoning), sitting just behind Opus 4.6 at 53, but with markedly worse token efficiency: ~74M output tokens to run the suite vs ~25M for Sonnet 4.5 and ~58M for Opus 4.6 (and $2,088 to run the index for Sonnet 4.6 in max effort) (AA summary, token note). Community sentiment echoes “4.6 feels better at critique/architecture” (eshear) while also flagging reliability/product issues around Claude Code (see “Anthropic drama” discourse around SDK/docs and tooling stability) (theo).

• Claude in Search Arena + autonomy telemetry: Arena added Opus/Sonnet 4.6 to its search modality leaderboard (arena). Anthropic also published “Measuring AI agent autonomy in practice,” analyzing millions of tool-using interactions: ~73% of tool calls appear human-in-the-loop, only 0.8% appear irreversible, and software engineering is ~50% of tool calls on their API—framed as “autonomy is co-constructed by model + user + product,” motivating post-deployment monitoring (Anthropic, metrics, industry mix).

• Qwen 3.5: reasoning efficiency vs “excess thinking”: Multiple posts highlight Qwen3.5’s “overthinking”/token usage as a key axis—both complaints (QuixiAI) and deeper community analysis claiming Qwen3.5-Plus reduces long-chain token bloat vs older Qwen reasoning variants, while noting regressions in non-reasoning mode (ZhihuFrontier). On the distribution side, Qwen3.5-Plus shipped to Vercel AI Gateway (Alibaba_Qwen) and Alibaba Cloud launched a Qwen Coding Plan subscription with fixed monthly pricing and high request caps aimed at coding agents (Alibaba_Qwen).

• Qwen3.5-397B-A17B FP8 weights opened: Alibaba released FP8 weights for Qwen3.5‑397B‑A17B, with SGLang support merged and a vLLM PR in flight (vLLM support “next couple days”)—a concrete example of “open weights + immediate ecosystem bring-up” becoming table stakes for competitive OSS releases (Alibaba_Qwen).

• GLM‑5 technical report + “agentic engineering” RL infrastructure: The GLM‑5 tech report is referenced directly (scaling01) and summarized as pushing from vibe-coding to “agentic engineering,” featuring asynchronous agent RL that decouples generation from training and introducing DSA to reduce compute while preserving long-context performance (omarsar0). Practitioners called the report unusually detailed and valuable for OSS replication, pointing out optimizer/state handling and agentic data curation details (terminal envs, slide generation, etc.) (Grad62304977).

• Gemini 3.1 Pro rumors + “thinking longer”: Early testing anecdotes suggest Gemini 3.1 Pro runs substantially longer “thinking” traces than Gemini 3 Pro and may close the gap with Opus/GPT—paired with skepticism about benchmark trustworthiness and failures on adversarial cases (e.g., mishandling ARC-AGI-2 prompt containing the solution) (scaling01, ARC anecdote).

• MiniMax M2.5 appears on community leaderboards: Yupp/OpenRouter posts indicate onboarding MiniMax M2.5 and M2.5 Lightning and tracking results via prompt-vote leaderboards (yupp_ai, OpenRouter benchmark tab).

Agentic coding + harness engineering (Claude Code, Cursor, LangSmith, Deep Agents, SWE-bench process)

AI News for 2/16/2026-2/17/2026. We checked 12 subreddits, 544 Twitters and 24 Discords (261 channels, and 11323 messages) for you. Estimated reading time saved (at 200wpm): 1096 minutes. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Despite a lot of rumors of a “Sonnet 5”, Anthropic opted to launch Sonnet 4.6 today, bumping their cheaper workhorse model up to match Opus 4.6, touting some preference wins from Sonnet to 4.5 Opus and a 1m token context, though generally lagging in usual benchmarks, and on GDPVal-AA (explained in our podcast with them) it uses 4.5x more tokens so the all-in cost can be higher than Opus in some tasks. The API platform tools and the Excel integrations also got minor upgrades.

Some of the key highlights are the long term improvements in Computer Use, first launched in Oct 2024, which was at launch completely slow and so inaccurate as to be impractical, but now is productized as Claude Cowork, which has anecdotally seen more successful adoption than OpenAI’s equivalent Operator and Agent iterations.

We have tuned the Twitter recap below to include more datapoints, but really that’s all that you truly need to know.

Anthropic launched Claude Sonnet 4.6 as an upgrade to Sonnet 4.5, positioning it as their most capable Sonnet model with broad improvements across coding, computer use, long-context reasoning, agent planning, knowledge work, and design, plus a 1M-token context window (beta) [@claudeai]. Early chatter preceded the announcement (“Sonnet 4.6 incoming!”) [@kimmonismus], then the launch triggered a wave of benchmark callouts, tooling/platform integrations (Cursor, Windsurf, Microsoft Foundry, Perplexity/Comet, etc.), and mixed early user feedback about quality and reliability.

• Official announcement + feature list + 1M context (beta) [@claudeai]

• Anthropic employee framing: “approaching Opus-class… insane jump over 4.5” [@alexalbert__]

• Independent eval org update: Sonnet 4.6 leads GDPval-AA ELO (agentic knowledge work), with much higher token use than 4.5 [@ArtificialAnlys]

• Pricing claim: “same pricing as Sonnet 4.5” [@kimmonismus]

• Post-launch “regression?” report: hallucinated function names / broken structured outputs; later “seems fixed” [@rishdotblog], [@rishdotblog]

• Sonnet 4.6 is described by Anthropic as a full upgrade across multiple capability areas and includes a 1M token context window in beta [@claudeai].

• Benchmark datapoints cited:

  • 79.6% SWE-Bench Verified, 58.3% ARC-AGI-2 (as posted) [@scaling01].

  • “Users preferred Sonnet 4.6 over Opus 4.5 59% of the time” [@scaling01].

  • “Sonnet 4.6 the best model on GDPval” (claim) [@scaling01].

• Artificial Analysis (independent benchmarking org) claims:

  • Sonnet 4.6 reached GDPval-AA ELO 1633 (in “adaptive thinking mode” and “max effort”), and is #1 on their GDPval-AA leaderboard but within the 95% CI of Opus 4.6 [@ArtificialAnlys].

  • Token usage to run GDPval-AA: Sonnet 4.6 used 280M total tokens (vs Sonnet 4.5 58M); Opus 4.6 used 160M in equivalent settings [@ArtificialAnlys].

  • Sonnet 4.6 improved aesthetic quality of generated docs/presentations relative to 4.5 on GDPval-AA outputs [@ArtificialAnlys].

• Tooling update: Anthropic web search/fetch tools now execute code to filter results; reported effect: +13% accuracy on BrowseComp with 32% fewer input tokens when enabled (as posted) [@alexalbert__].

• “Approaching Opus-class capabilities… insane jump” [@alexalbert__] is qualitative framing (though consistent with some benchmark movement).

• “Near human-level computer use” extrapolation [@alexalbert__] depends strongly on which “computer use” evals + harnesses + task distributions are used.

• “Warmer and kinder… smarter and more overcaffeinated” is pure UX vibe [@sleepinyourhat].

• “Taste is off the charts” / SVG skyline anecdote is subjective (but points to improved design/visual generation) [@scaling01].

• Post-launch reliability concerns (“hallucinations everywhere… 4.6 crapping the bed”) are anecdotal reports from a specific workflow, though notable because they compare to 4.5 on the “same tasks” [@rishdotblog].

• Context window: 1M tokens (beta) [@claudeai].

• Pricing: “same pricing as Sonnet 4.5” [@kimmonismus] (no $/tok quoted directly in these tweets, but note RundownAI cites “Sonnet pricing [$3/$15 per mil tokens]” as context [@TheRundownAI]).

• Search/fetch tool change: pre-context filtering via executable code; +13% BrowseComp accuracy, -32% input tokens [@alexalbert__].

  • Systems read: this is an explicit shift toward tool-side “compute before context”—spending tool compute to reduce prompt budget and improve signal-to-noise in retrieved context.

• SWE-Bench Verified 79.6% (posted) [@scaling01].

  • Interpretation: SWE-Bench Verified is sensitive to harness, timeouts, repo setup, and tool reliability. Still, 79.6% is “frontier-tier” in the common discourse.

• ARC-AGI-2 58.3% (posted) [@scaling01].

  • Also see longitudinal claim: “141 days… 13.6% to 60.4% on ARC-AGI-2” (Sonnet line progress, presumably 4.5→4.6 or earlier→now) [@scaling01].

• Preference eval: “preferred over Opus 4.5 59%” [@scaling01].

• GDPval-AA (Artificial Analysis): ELO 1633, #1 but statistically overlapping Opus 4.6; token usage 280M for Sonnet 4.6 vs 58M for Sonnet 4.5; cost to run GDPval-AA “just ahead of Opus 4.6” (because of token usage) [@ArtificialAnlys].

  • Important implication for engineers: “Best” may be bought with more thinking tokens, which impacts latency and spend; a router may pick 4.6 selectively.

• Vending-Bench Arena strategy claim: with 1M context, Sonnet 4.6 uses a “capacity-first then profitability pivot” plan [@felixrieseberg].

  • This is a rare example of a behavioral shift attributed to long-context planning capacity, but it’s still a single benchmark anecdote.

• Engineers are explicitly noticing that frontier labs “blast millions of tokens… scaffold like a skyscraper” [@scaling01], aligning with Artificial Analysis’ disclosure that Sonnet 4.6 needed ~4.8× the tokens of Sonnet 4.5 on GDPval-AA [@ArtificialAnlys].

• Cursor’s note: Sonnet 4.6 better on “longer tasks” but “below Opus 4.6 for intelligence” [@cursor_ai] suggests practical routing: Sonnet 4.6 as default long-horizon workhorse; Opus as max-capability.

• Anthropic-side: “most capable Sonnet… full upgrade… 1M context” [@claudeai] and “approaching Opus-class… jump… insane” [@alexalbert__].

• Benchmark boosters: SWE-Bench/ARC-AGI-2 callouts [@scaling01], GDPval best-model claim [@scaling01], “crushes Gemini 3 and GPT-5.2 on Vending-Bench 2” [@scaling01].

• Practitioners: “beast for real-world work… computer usage” [@kimmonismus], “computer use standout… more consistent over long sessions” [@mikeyk].

• “no Sonnet 5” reaction [@dejavucoder] reflects expectations management rather than capability.

• Cursor’s measured product note (better than 4.5, below Opus 4.6) [@cursor_ai].

• Artificial Analysis: #1 GDPval-AA but within CI of Opus 4.6 + disclosure that it uses more tokens [@ArtificialAnlys].

• Reliability regression report: hallucinated function names in agent workflows; structured output errors; “4.5 still works great” [@rishdotblog]. Follow-up: “Whatever this was seems fixed!” [@rishdotblog].

• Cost sensitivity: “Sonnet and Slopus… munching through my credits” [@scaling01], plus later “price hurts” / cost follow-ups (not fully detailed in provided snippet) [@scaling01].

• A comparative take in infra/product terms: “50% more expensive than xhigh and 228% over 5.2 codex… vast improvement over 4.5” [@teortaxesTex]—this frames Sonnet 4.6 as improved but potentially cost-inefficient vs alternatives depending on workload.

1. Long-context is becoming “operational,” not just a spec.
   The launch pushes a 1M token window into the Sonnet tier [@claudeai]. But Artificial Analysis’ disclosure that Sonnet 4.6 used 280M tokens to run GDPval-AA in “adaptive thinking/max effort” configs [@ArtificialAnlys] is a reminder: long-context + long-think can silently move your budget envelope. Expect more routing, summarization, context management, and “retrieve then filter” patterns (consistent with the new search/fetch filtering improvement [@alexalbert__]).

2. Agent performance claims are increasingly harness-dependent.
   GDPval-AA uses an agentic harness (shell + browsing loop), and Sonnet 4.6’s lead is reported under a specific setup (“adaptive thinking mode”, “max effort”) [@ArtificialAnlys]. Cursor’s note that it’s better on longer tasks but below Opus for raw intelligence [@cursor_ai] reinforces that “best model” is not a scalar; it’s workload × harness × budget.

3. Computer use is becoming a marquee capability, and Sonnet is being pushed there.
   Multiple tweets highlight “computer use” progress and near-human-level framing [@alexalbert__], and deployments like Perplexity’s Comet browser agent explicitly default to Sonnet 4.6 for Pro users [@comet].

4. Release risk: small serving/config changes can look like “model regressions.”
   The reported post-launch hallucination spike across Opus 4.6 and Sonnet 4.6 [@rishdotblog]—and then “seems fixed” [@rishdotblog]—reads like a potential routing, toolchain, system prompt, or safety-layer change rather than weights. For teams: pin versions where possible, run canary evals, and monitor structured output validity + tool-call correctness separately from “chat quality.”

Open models & independent benchmarking (Qwen/GLM/Seed/Aya, etc.)

• Artificial Analysis deep breakdown of Qwen3.5-397B-A17B (397B total / 17B active MoE, Apache 2.0, 262K ctx, native multimodal); big gains on agentic evals, but hallucination rate still high by their metric [@ArtificialAnlys].

• GLM-5 cited as strong open model on WeirdML and other benches (48.2% WeirdML; comparisons to Opus/gpt-* claims) [@htihle], plus GLM-5 technical report highlights: DSA adoption, async RL infra, agent RL algorithms [@Zai_org].

• ByteDance “Seed-2.0” announced (agent/reasoning/vision; “no distillation”; CN-only initially) [@TsingYoga].

• Cohere Labs launched Tiny Aya: 3.35B open multilingual model family (70+ languages; “runs on a phone”), with claims of training on 64 GPUs and a detailed report [@nickfrosst], [@_akhaliq], [@mziizm].

Agents, harnesses, memory, and long-horizon infrastructure

• “Agent World Model (AWM)” proposes fully synthetic executable environments (1,000 envs, 35,062 tools, 10,000 tasks, SQL-backed state, verification code) for RL tool-use agents [@dair_ai].

• Lossless Context Management (LCM) / Volt claims: deterministic hierarchical DAG compression with lossless pointers; on OOLONG, “beats Claude Code at every context length 32K→1M” (reported) [@dair_ai], amplified [@omarsar0].

• Moltbook multi-agent “society” study: 2.6M LLM agents, 300k posts, 1.8M comments; macro “culture” stabilizes, micro influence ~noise; critique of “just add agents” assumptions [@omarsar0].

• LangChain “Harness Engineering” theme: traces → eval mining → self-verification loops; TerminalBench positioning [@Vtrivedy10], plus LangSmith Insights scheduling [@LangChain].

• Open-sourcing an agent runtime (“Hankweave”) focused on removing context, maintainability, and reusable blocks across models [@hrishioa].

Systems & inference optimization (kernels, scheduling, throughput)

• Carmack proposes OS-like GPU job preemption via UVM paging + MPS shim, aiming for seconds-scale task switching (acknowledges thrash risk) [@ID_AA_Carmack].

• Moondream MoE kernel: 2.6% faster by tuning launch config to real routing distributions; kernel ~37% runtime [@vikhyatk].

• Together-style “ThunderAgent” / “program abstraction” for end-to-end agent workflow scheduling; claims up to 3.9× faster rollout/serving without quality tradeoff (as posted) [@ben_athi], plus explanation thread [@simran_s_arora].

Frontier product moves: Codex, Grok, “computer use” competition

• Codex usage report: users trying (and failing) to hit limits; heavy parallel agent usage within subscription windows [@theo].

• OpenAI infra hiring pitch (agent orchestration, sandboxes, observability) [@gdb].

• Grok 4.20 / 4.x discussion includes launch notices and architecture claims, plus highly polarized political framing by Elon [@kimmonismus], [@elonmusk], with critics calling performance weak vs “Flash” models [@teortaxesTex].

Robotics, video/image generation, and multimodal research

• Unitree humanoid performance discourse (claims of distributed coordination, terrain adaptation, safety spacing, multi-DOF manipulation) [@ZhihuFrontier].

• “Perceptive Humanoid Parkour” (depth-perception long-horizon traversal) [@zhenkirito123].

• ByteDance BitDance: 14B AR image generator predicting binary visual tokens; claims FID 1.24 on ImageNet 256 [@iScienceLuvr], plus author promo [@multimodalart].

• “Sphere Encoder” few-step image generation in spherical latent space; Meta/Goldstein thread with details including 65K latent dims for ImageNet and <5-step refinement [@tomgoldsteincs].