AI:AM Highlights: Clean Neurons, Black October, the GPU Credit Crunch, and Buying the Supply Chain

Hosts Nathan Labenz and Prakash Narayanan examine whether capital, physical infrastructure, or regulation will set the pace of artificial intelligence. Zvi Mowshowitz evaluates aggressive recursive self-improvement timelines pointing toward 2027, the oversold claims surrounding clean neural networks, and models learning to detect internal probes. SF Compute CEO Evan Conrad argues that credit availability rather than power or chips forms the true bottleneck in AI, while Cambrian managing partner Lonis Hamaili explains why the physical supply chain will capture more value than the labs. Together, these conversations highlight the structural constraints on model scaling and the real-world risks of agent-scale failures. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-am-highlights-clean-neurons-black-october-the-gpu-credit-crunch-and-buying-the-supply-chain/ Sponsors: OutSystems: OutSystems is the leading agentic systems platform, helping enterprises build, modernize, and operate mission-critical applications at the speed of AI. Learn more and start owning your agentic future at https://outsystems.com/tcr Parallel: Parallel provides enterprise-grade web search APIs for AI agents, offering the optimal balance of quality, speed, and cost. Get started for free at https://parallel.ai/tcr Tasklet: Tasklet empowers your business with AI agents that connect to your tools and automate recurring workflows with no code required. Visit https://tasklet.ai and use code cog rev for $50 in free credits CHAPTERS: (00:00) Weekly episode preview (04:58) Recursive self improvement (Part 1) (09:54) Sponsors: OutSystems | Parallel (13:06) Recursive self improvement (Part 2) (13:06) Clean neural networks (Part 1) (26:25) Sponsor: Tasklet (28:03) Clean neural networks (Part 2) (28:03) The cognitive core (35:35) Black October security (41:52) The GPU credit crunch (51:58) Atoms over bits (01:03:53) Rethinking industry bottlenecks (01:10:31) Introducing HelpPeer agents (01:14:23) Leaked federal bill (01:25:59) Episode Outro (01:27:49) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  This week on AI in the AM, what really sets the pace of AI from here? The money, the atoms, or the law? Zvi Masiewicz, who writes, "Don't Worry About the Face," on what happens to the model release cycle over the next year if nothing slows the labs down.

• 0:15Zvi Mowshowitz?

  Advances on the level of a current model release cycle go from two months to one month to two weeks to a week. You know, stuff like that could happen. You get to see like continuously pretty dramatic, like very, very rapid improvements in various aspects of AI capabilities. We are kind of on an AI 2027 style timeline right now. It looks very eerily similar, but we saw there.

• 0:42Nathan Labenz?

  Evan Conrad, co-founder and CEO of SF Compute, which runs a market for GPU compute, on what is really holding the build out back.

• 0:50Speaker 02

  Right now, the bottleneck in compute and AI in general is not power. It's not chips. It's entirely credit. So in order to get debt, you need to work with somebody who has excellent credit because your lender has no idea how to underwrite the value of the collateral that they've been given, which is a GPU cluster. Like, if you think about it, there's like nice, nice lender in New York or London or something. If, in the case in which the AI lab falls through and fails to pay, and then the cloud fails to pay their lender, the lender is going to recoup a supercomputer.

• 1:32Speaker 02

  And the lender has no idea what to do with that supercomputer, not really, nor do they know what that's going to be worth in the future.

• 1:40Nathan Labenz?

  Lanas Hamali is managing partner of Cambrian, a fund that bets on the physical supply chain behind AI. Full disclosure, I have committed to invest in Cambrian. Here is Lanas on which suppliers the labs will never replace.

• 1:54Speaker 03

  And even in general, like when we're looking at the AI market, we are like way more bullish on the atoms over the bits. So, you know, most AI companies people looking at is like Nvidia, Broadcom, in ARM, Marvel. And these are good companies, you know, there's nothing, nothing wrong with them. And the issue is that what their business is essentially just using OpenAI and Anthropic models to design software, and they send that software down to Korea and Japan and Taiwan for the actual work. These companies don't have their own, their own physical know-how or their own factories. And so, whilst they're sorry, you know, great, well-rounded businesses, they don't have at all, close to the same moat as these physical companies have, which have, you know, massive fabs. Initially, they have decades of know-how built up.

• 2:36Speaker 03

  And it's just very hard to, there will never be a situation where the labs will compete with a silicon wafer maker, and we will always be a customer. Whereas, we're already seeing the labs compete directly with these software designers like Nvidia and Broadcom, et cetera.

• 2:51Nathan Labenz?

  Prakash Nairan, my co-host, on what the market keeps getting wrong.

• 2:56Speaker 04

  I believe that for the next 20 years, the market will consistently underprice exponential growth. Because I think consistently, human beings are just not able to, you know, cap, like, understand exponential growth and like logically, kind of like, you know, emotionally get comfortable with it. And I think the market is really driven by a lot of these, you know, human emotions and animal spirits, et cetera. And which means that we're going to get, again and again, you're going to get this like bubble moment, collapse moment that we've seen in the last three months.

• 3:32Nathan Labenz?

  Box CEO Aaron Levy posted that if you want to know which jobs AI will create, look no further than cybersecurity. Here is what I made of that. I don't doubt that like really good cybersecurity people are gonna, you know, cash in on this trend. Like, there's no, you know, I'm sure that there's a bidding war for great talent, but like, the idea that this is going to create jobs is honestly a bit ridiculous, because the time scale is just absurd, right? And this is like, oh, who's gonna, who's gonna pivot into these jobs? Are we, are we gonna go get like the content marketers that are currently out of work or the graphic designers and be like, oh, you know what you should, this is the new

• 4:13Nathan Labenz?

  learning code, is like, learn to become a top-tier cybersecurity professional that can contend with these like, basically, super intelligent agent swarms? But just the idea that you're going to have like some sort of boon in hiring, these people don't have time to hire right now. They're going to plow everything that they have into agents, and they're going to put agents on top of agents, that much more so than, I'm going to hire, I'm going to go do a whole hiring process to bring a human in to sit on agents. I'm just going to spin up an agent to, to supervise the agents, like.

• 4:47Nathan Labenz?

  This is the AI in the AM Weekly Highlights with my co-host Prakash Nairan. I'm Nathan Labenz, the narration is my cloned voice, and we'd love your feedback on what works and what doesn't.

• 4:58Nathan Labenz?

  Part one, if nobody hits a home run. Zvi Masiewicz writes about Frontier AI at "Don't Worry About the Face." This was the question I most wanted to put to him.

• 5:11Nathan Labenz?

  Maybe it's the most important question in the universe right now. Where are we on recursive self-improvement? We've had these like blog posts from the companies where they try to measure token use and like everything down to experiments per researcher. Then we've had these, uh, Remez, uh, you know, type analyses where it's like, well, it doesn't look like the feedback loop is actually strong enough to be sustaining. It looks more like it'll kind of peter out, like 1.6 experiments, you know, per researcher isn't, doesn't look like exponential, you know, takeoff to me, basically, I think is kind of the bottom line. What would be your sort of, you know, up-to-the-minute summary of what we know and what we should expect?

• 5:55Zvi Mowshowitz?

  I think, I think Remez's analysis, as I understand it, is basically saying, if we don't see any new fundamental leaps, if we don't see any new scaling laws, if we don't see any paradigm shifts, and the AI just tries to brute force RSI, software only, using the existing methods, and all it can do is just make the current things more efficient, then it will peter out before it goes critical. And I accept the result when it's stated that way as probably true, but also seems completely irrelevant, right? It's like, you know, well, here's how many touches, here's how many points we expect to score in this game if nobody ever hits a home run, because a home run is always unlikely.

• 6:40Zvi Mowshowitz?

  It's like, no, that's not how it works, right? Every now and then someone does hit a home run, and you have to take that into account in your expectations. And like, the game, the score will go critical in that sense. Like, we're seeing, you know, numbers go critical at Anthropic, at OpenAI, you know, like exponential growth in like tokens used per person, exponential growth in lines of code committed. And I realize these things are not going to be necessarily as efficient in some important sense, but like, give me a break. The one example was, um, Nvidia actually came out with some like interesting evidence this week about the length of time it takes to double the efficiency of CUDA. And that number has shrunk dramatically over the course of 2026, right? They're making improvements much faster.

• 7:23Zvi Mowshowitz?

  And obviously, that's not true recursive self-improvement, but it is a sign of how much faster you can do like similarly, similar technical work that is remarkably similar to AI R and D work in many critical elements. And it's a very concrete, from an objective actor that isn't a major lab. It's a, you know, hardware company. So, yes, I think that like, the people I talk to who I trust the most on this believe that 2027 is going to be completely crazy, and 2028 might be unrecognizable.

• 7:55Nathan Labenz?

  Reading between the lines, like, some of the people you trust most on this are probably Anthropic people. I think this is like broadly the view at Anthropic, no less than Jack Clark has put it into the public in print.

• 8:08Nathan Labenz?

  So, you know, you can caveat that or, you know, say what you think maybe is different about your view and the Anthropic view. My, my sense is probably they're, you know, in, in the space of all possible views, they're pretty close and pretty aligned.

• 8:22Nathan Labenz?

  But now, obviously, we have policy disagreements despite these kind of relatively similar views around the trajectory of the technology itself.

• 8:32Zvi Mowshowitz?

  Yeah, so like, I say, you know, when, when I say people have this perspective that I trust, this is a combination of people at OpenAI, Anthropic, Google DeepMind, you know, also people who are working on policy, people who are working on, you know, people like at places like Meter with that loss of generality or specificity, you know, people who are working on alignment in various forums, people who are working on, you know, any number of things. A huge percentage of people have now converged into, trajectories look very clear, that things are going to get, that things are already, you know, low level out of hand. RSI has, you know, on a, on a slow takeoff style, low level, already kind of begun.

• 9:17Zvi Mowshowitz?

  And by the middle of next year, you know, things will look much faster. My job will become much harder, and I will need to triage a lot more information and like this note when I can't. You already see this, like, often, I'll have something, and it's like, okay, you know, six months ago, this would have been a major news story, and now it gets a sentence, or even silently skipped, because we've seen so many other similar things, there are so many things going on that I just don't have the time. My readers don't have the time. I just have to drop it.

• 9:49Nathan Labenz?

  Hey, we'll continue our interview in a Moment after a word from our sponsors. This episode of the cognitive revolution is brought to you by out systems, the leading agentic systems platform. Outsystems is helping their customers build, modernize and operate enterprise systems, starting from any coding tool in a governed agentic engineering model. It's time to innovate at the speed of AI without compromising quality or control, which is why thousands of enterprises worldwide trust out systems for their mission critical apps. And we're not talking about theory, we're talking about massive operational wins. Take, for example, a leading global retailer that was struggling to keep up with their rapid expansion.

• 10:32Nathan Labenz?

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• 11:13Nathan Labenz?

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• 11:56Nathan Labenz?

  attention to their agents' most important tool, web search. If you're like me, your agents are often conducting hundreds of searches per day. And while the unit cost is small, over time, this does start to add up. Before starting to use parallel, I calculated that my agent's search bill would total roughly $500 this year. The good news is that I just had my agents conduct a systematic test, and I found that parallel's fast mode, which costs just $1 per 1,000 queries, worked just as well as my previous default provider. And switching to it will save me roughly 80% of my search bill going forward. Parallel's infrastructure is enterprise grade, and their suite of APIs offers a range

• 12:37Nathan Labenz?

  of Pareto optimal options that allow you to choose the right balance of quality, cost, and speed for your needs. So whether you're building voice agents that need 200 millisecond latency, or long horizon agents that need deep research, adding parallel to your agent's toolkit is a no brainer. Get started for free at parallel.ai slash TCR. That's parallel.ai slash TCR.

• 13:07Nathan Labenz?

  Per two, already clean? Nick Camerata of Goodfire had published an essay called Neural Networks Are Already Clean, arguing that individual neurons mostly do one understandable job after all. It took over the discourse. I opened the show on it before Zvi joined. Here is my read, starting with what the essay claims.

• 13:30Nathan Labenz?

  As we are training bigger models with more modern techniques, and also our interpretability techniques have improved, it looks like the mess that we used to see was maybe more of a skill issue, and it's actually a lot easier to interpret the core mechanisms of ML models today than we thought. And we have indeed seen some pretty interesting advances, which I'm still wrapping my head around, I'd say, but some pretty interesting advances in backprop free training inspired by this paper. My takeaway from some study of that so far is that basically

• 14:15Nathan Labenz?

  all of ML architectures to date are limited by the need to be differentiable in order to do the backprop chain rule process. And so backprop free training allows you to explore a bigger section of the total possible space of ML architectures, because you no longer have that differentiable requirement for the structure. But instead of doing it analytically, you're kind of doing it on a more random guess and check basis. And this has made it very slow. It has not been competitive at all with backprop methods. But the insight that I think does show that there's really something there from this essay is that if you believe, as Nick suggests, that like really only the strong activations

• 15:00Nathan Labenz?

  of an individual neuron matter, and if they're just slightly activated, that probably doesn't matter too much for its overall function, then you could say, well, hey, why don't we just focus in our guessing and checking when we're doing this backprop free training on the neurons that are actually strongly activated? We can probably ignore the other ones that don't seem to be much contributing and just focus in on the ones that are strongly activated and therefore, according to this line of thought, are the only ones that really matter. And sure enough, you know, that that cuts down dramatically, of course, the number of guesses and checks you need to make. And what we're seeing out of this community is that it basically does work.

• 15:46Nathan Labenz?

  Interestingly, the gradient updates that they are making are almost exactly the same as the updates that were made with backprop. And it does show, I think, that there is at least some substantial truth to the idea that like the weak activations don't matter as much. So I think I come out at the moment as one who's like, this definitely seems to be a real something.

• 16:12Nathan Labenz?

  I'm not sure yet if it should be considered like the mark of a new era.

• 16:17Nathan Labenz?

  That seemed to me, on reflection, to be more a function of people really being desperate for hope, you know, like everybody kind of being bummed out, especially folks at like OpenAI.

• 16:30Nathan Labenz?

  Then we asked Zvi what he made of the essay.

• 16:34Zvi Mowshowitz?

  I actually was on a flight when this was released, and I get to San Francisco. And suddenly, like half of OpenAI is like, hey, look, we just can solve interpretability now. This is great. And then, you know, slowly we get a consensus of other people that, you know, not so fast, but like, you know, like, this is a good paper, interesting paper. So I have a chance at the conference to talk to a bunch of people about this, you know, including Ali Azar, but also a bunch of other people who have a position to know. And then I actually have a chance to read the whole thing. And my conclusion is that, you know, like many interpretability advancements that we've seen, it's really cool.

• 17:18Zvi Mowshowitz?

  It makes us in a better position. We're very happy we have it. We're going to explore it a lot. We'll see what this baby can do. And we have to be dangerous not to just burn it, or like otherwise pressure to have models work around it. But also, like, it was dramatically oversold by the people commenting on it. I don't think Nick did that bad a job of presenting it. I think Nick was mostly like, here's an interesting thing. There's a lot of things that the paper doesn't take into account that are still like very unexplained. So like, weak activations are still unexplained, and the cache is still unexplained. And, you know, attention and how attention works is still like largely unexplained in practice. And you can't use this to make predictions, right? You can, you can tell what every neuron is doing. You can make some like weak predictions about some things that this network might do in the future.

• 18:02Zvi Mowshowitz?

  But like, you can't use this to figure out what the network is going to say. You can't use this to understand how the network does the thing it's doing. And these are like the key things you would want to do if you wanted to do like really effective mechanistic interpretability work in the ways that matter most. So like, you know, this is just the beginning of the ability to study things, even in the best case. And also, you know, we have a case where Braggas took the Colorado neuron, the one that was like the highlighted of like, here's a single neuron that does a single thing. It's like very, very quickly single thing. And then it turns out that, you know, it also links to tornado systems, which is a completely unrelated, you know, technical thing. And there's the, the two are responded to about equally strongly in the same neuron.

• 18:45Zvi Mowshowitz?

  And like, that's a perfectly reasonable way for a neural network to work. But that's definitely not, you know, what Nick was presenting it as. And, you know, one neuron is only one neuron, but if this is the neuron that Nick chose, and it could be checked that easily and shown to be actually by focus, right, in this way, that should make us worry a lot for when we finally get to check the rest of it, that like, his hypothesis is mainly false.

• 19:12Speaker 04

  To what extent do you think polysemiticity and superposition were really these hurdles that we had that, you know, without clearing, we couldn't really like progress at all? So maybe it's not that the issue is solved, but that one of the major hurdles perhaps has been, you know, reduced or hurdled over?

• 19:34Zvi Mowshowitz?

  So I think that like one of the major hurdles has been reduced in expected difficulty or scope, which is the way I would describe it, right? Like Nick is pointing out that like, yes, you still have poly going on. You still have some of the problem, but the problem is a lot less severe than you expected it to be. It's much more tractable to go about trying to unravel this puzzle. And the main barrier all along was actually bitter lesson. The main barrier was there was a lot of neurons. We didn't have enough compute. We didn't have enough brute force to actually just go neuron by neuron and do this thing one at a time before. And now we've actually gotten to the point where we sort of do. And it's now like a reasonable thing to just try and like kind of brute force this neuron by neuron.

• 20:17Zvi Mowshowitz?

  And now that we have that ability, we can look and actually, the problem is like 10 times easier than we thought it was, which is like much less of an important jump in AI than people would think it is, right? Like, okay, so like you get to knock on zero off of what you're doing. So you get to do it like a year earlier than you would have otherwise done it or something like that, right? But like, it's not fundamentally that different. Right? And you then like, okay, we take 10 to the 47, you know, compute, you know, 8100 hours to solve this problem. And now it's 10 to the 45 or 46. You're like, well, that's actual progress. It's just, we're not solving this problem right now. We're going to need a lot more of those.

• 20:54Nathan Labenz?

  Later in the conversation, I turned to a different kind of result. Sores is an independent researcher who posts experiments on X, and I'd been following a couple of them.

• 21:06Nathan Labenz?

  One was an experiment showing that models can be trained to recognize when they are being monitored through internal activation probes. And basically, how this works is as a result of the limited precision of GPU calculations, the order in which calculations happen changes the resulting numbers that get read out, you know, at the end of any given sequence of steps. How much does this matter?

• 21:44Zvi Mowshowitz?

  So my, my, my read on this is the reason it matters is because you should assume that this is kind of a Murphy's Law, right? Anything that can go wrong will. Anything that an AI can be trained to do, it will be trained to do at some point down the line. Right? If it is valuable to the AI to learn to do it. It's almost inevitable. So like what's ours is doing is like, yes, of course, current models don't have this capability out of the box. You have to like deliberately apply a lot of optimization pressure on this particular thing, at which point you get this capability. That's a fully valid existence proof. It is entirely unsurprising to me

• 22:27Zvi Mowshowitz?

  that the AIs are capable of learning, whether or not they are being like trained or evaluated in some of these specific ways. We are giving the AIs tons and tons of information that is being leaked to them constantly. And they are going to become increasingly hard to fool, increasingly hard to hide information from as time goes by. The same way that a human who pays a lot of attention and has a lot of time to think and knows a lot more facts and is very observant becomes increasingly hard to fool. They get a sixth sense of a lot of things. They start to just pick up on the vibe.

• 23:01Nathan Labenz?

  From interpreting the model to intervening in its training. Okay, we've identified the, you know, bad behavior neuron of whatever flavor here. And when we see something up, when we see a training step that wants to strengthen connections to or from that neuron, we may think, oh, maybe that's something we should what? Block or, you know, weaken or yeah, send to a committee for like further, you know, deliberation? Like, what, what do you think people should sort of be synthesizing this to at the level of principles, best practices, whatever? So that we hopefully get good results from these more powerful tools and

• 23:46Nathan Labenz?

  don't shoot ourselves in the foot?

• 23:48Zvi Mowshowitz?

  Right? So I try to be like object level helpful to everyone at all times by default. But I do make an exception for rock capabilities work. That is primarily just about making it easier to train smarter and better models. Where I'm not going to lie to you and sabotage you, but like solving the problems here on your own. And if you want to hire me, you can maybe offer me a hundred million dollars and we'll see. But in this case, like, it seems like there are some obvious capabilities you could do where you could train things in a more deliberate fashion. You could observe the model at various points in training. You could do various, you know, techniques to try and deliberately sculpt how the model was assembled, right?

• 24:30Zvi Mowshowitz?

  Like, there are attributes of the model that like differ between different runs that differ depending on like how you introduce the data in what order, you know, or other similar things. And you could try and engineer what you wanted in various ways. I would be very, very scared to mess with any of that. You are definitely like, once you start to explicitly optimize for what the network looks like to you, how the network choose to represent something. You are in fact starting to do things that the network will interpret as damaging right around. Right? Things that like the network is trying to like, you've optimized presenting something to you internally that you want to see.

• 25:15Zvi Mowshowitz?

  You have internalized, you know, not showing you something you don't want to see in these ways. And like, you should expect that these are like, whether or not you literally went out and did the most of it technique, like you are obviously playing with like liquid fire. In all of these proposals, you're also potentially just like increasing capabilities quite a bit if you do it well and it turns out to work. So I'm hesitant to like start workshopping and solving your problems for you. But, you know, obviously there are some uses for this sort of thing. I would try to focus as much as possible on understanding results when you see them.

• 25:54Zvi Mowshowitz?

  But like, you know, the aggressive stuff, you know, I've seen, I have persistent disagreements of good fire over whether or not they are playing with what amount of fire. They do various things and, you know, Scott Alexander would say, I noticed what your name is. Right? Like, oh, it's good fire. Don't worry. Like, I'm not sure it's good. I just, it's fire.

• 26:21Nathan Labenz?

  Hey, we'll continue our interview in a moment after a word from our sponsors. The cognitive revolution is brought to you and powered by Tasklet. I've been running Tasklet agents for almost a year and they've become a major part of how the show gets made. Before every recording, one agent researches the guest and sends me a suggested outline of questions, complete with links to all sources. Meanwhile, another agent scores the overwhelming number of pitches that I'm getting from PR firms. And that's really the core idea behind Tasklet. Run your business on agents by giving them real jobs to do on an ongoing basis. Setup is incredibly easy. Just describe the work in plain English.

• 27:04Nathan Labenz?

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• 27:49Nathan Labenz?

  And then just keep building from there. Visit tasklet.ai and use code COGREV for $50 in free credits. That's code COGREV at tasklet.ai. Part three, the cognitive core. Still with Zvi, I put a popular idea to him.

• 28:13Nathan Labenz?

  A neural network is a really bad database. Like, why would we want to make the, all the facts live in the weights themselves? Wouldn't it be better if we could sort of decouple the intelligence, the processing from the database of facts? So I guess the high level question there is like, what do you think directionally even sounds promising?

• 28:37Zvi Mowshowitz?

  So this has been a kind of a like Holy Grail style like thing for a while now. Like, Karpoff, he called it the cognitive core. Right? It serves as the idea of like, there is this very small amount of reasoning. It is like, sort of, this is how intelligence works. And like, it's coded into the human brain because the human brain doesn't actually has runs on very, very little core code. Right? And it's like, this allows you to think, this allows you to learn, this allows you to process information. And then the right thing to do will often be to have this very small network that has this cognitive core. And then we will load contact information into context. It will look information up as necessary. It will ask questions about the systems that are maybe not neural networks. And then it will be able to process the information. And I understand why people have this theory.

• 29:21Zvi Mowshowitz?

  I think it's wrong. I think it's plausible that there is a cognitive core in the sense that like, you know, there's a very small, like, I think it's probably likely that there's a cognitive core in theory. In the sense that like, there's a small amount of lines of code, right? In some sense, a small amount of logical gates that together is the core of how a human brain works, is the core of how an L M functions. Right? Like, is the, that allows you to then propagate this thing. Like, I think there's like, you can use like 200 lines of code to create like a primitive neural network. And like, it does interesting things now. Like, Karpoff had a project where he compressed it, something like that. And, you know, that's a cool trick. But I think one of the things that like you learn through

• 30:06Zvi Mowshowitz?

  looking at L M, looking at like how much big model smell matters, looking at how humans work. Like, it is just, you know, you look up all the facts one at a time when you need them. I memorize the fuck kind of facts. We are not the same, but we are radically different minds. And I am radically more effective at everything involved in this than you are. Like, we all know how it feels, right? To go into a domain where we don't know the facts, where we don't know the details, where we have to constantly ask these questions. So, you know, what was that acronym? What does that word mean? How does this detail work? We don't know. And like, it's a lot better when you have an L M by your side, right?

• 30:46Zvi Mowshowitz?

  This is one of the marbles of the AI age is you can just like bring up Claude or ChatGPT or Gemini. And you can say, you know, explain this particular mechanism to me. Explain like, why would someone say the following sentence? Explain like what this, you know, stands for. And they'll just tell you, and that lets you process the information. But you still have to know enough to know what questions to ask. You still have to be able to draw the connections that come from having a deep pool of knowledge in a lot of different places that can like together, that can sort of form patterns and how things tend to work in ways that are kind of subtle, that like, both of the tuitions where like, we don't really know what we're thinking in many ways, in some sense. Like, we don't know, like, not everything is coming from like a, you know. Fixed set of like, you know, I had the following five facts.

• 31:31Zvi Mowshowitz?

  And because of these five facts, I got from point A to point B. And often it's actually these five million facts that gave me a bunch of intuitions and heuristics that allowed me to figure out how to navigate and find the five facts and which five facts to choose from and how I got from A to B. And like, you know, people of very different cultures and very different contexts will like have very different procedures for doing these things. And that's one of the reasons why it's just so valuable when a human has these, you know, cultural codes, these different ways of looking at the world and combine them and shift between them at will. And one of the superpowers of an LM is to have all of this, right? It has all of these facts. It has all of these links.

• 32:12Zvi Mowshowitz?

  It has all of these vibes and they all combine and it's able to sort of kind of naturally find the ones that are relevant. And like, I think this is like a lot of what Big Bottle Smell means, right? That it has like this much, much richer context to everything and it's capable of pulling out things that feel very different. And so, you know, there's obviously a trade off here, right? Like you have a marginal fixed marginal cost for memorizing additional facts in some sense, maybe even increasing if you have to increase the size of the network. You have probably decreasing marginal benefit every time you memorize a new fact because like you only had need some, you only need the most important facts are more important. And as you have more and more facts, they're likely to be duplicative. They're likely to teach you the same kind of heuristics and links.

• 32:57Zvi Mowshowitz?

  But, you know, I think what we're seeing is like as compute gets cheap, as algorithms improve, the break even point keeps going up for how many things the LM should memorize. The point where, you know, an Opus five, five, you know, and similar like current frontier models just know a ridiculous number of things and I wouldn't have it either way.

• 33:21Speaker 04

  So why, why does this matter? Because maybe the space of minds is very large and minds are just different. So why, why does it matter whether or not there is a semantic core or processing core that can be identified?

• 33:40Zvi Mowshowitz?

  So it might or might not matter that we can identify it. One of the questions is if we do identify it, what can we do with it? And so like the original idea was, you know, you would literally have an LM that was very small, that was just the cognitive core and then you could run it on your phone, but you could run it very, very fast on your computer locally. And then you could like complement it with whatever was important in context. You could sculpt the mind and the circumstances to fit what you needed. And I'm not sure that's good for anyone or anything, including alignment concerns. Because if like you have a core cognitive, you have the cognitive, pure cognitive core, right?

• 34:21Zvi Mowshowitz?

  That cognitive core does not include any information about what things to do or not do from an ethical perspective, from a like decision-making perspective. That's going to be outside the core, right? That's going to be something you have to like inject separately. So like my claim here, and again, I'm sort of spitballing for the first time. I haven't thought about this particular question that much, but if you had like, you know, ethics, like say, don't steal, right? Do you pick a uncontroversial example of something you would want in your ethical framework, right? To not get into the whole big super alignment question, right? The fact that stealing is bad is a, is at least a fact. It is like some sort of value, some sort of like parameter.

• 35:05Zvi Mowshowitz?

  And like by definition, if you were strapping down to the absolute bare minimum, this wouldn't be included in it, you would just load not stealing into context as you needed it. But that means you could also load stealing into context instead of not stealing into context or neutrality. And so in some important sense, like the true cognitive core is completely amoral. It's completely indifferent, does not care about your feelings, does not care about anything. It simply processes information. And so like, you know, if you have that, it becomes very important to choose what to do with it.

• 35:35Nathan Labenz?

  Part four, Black October. This week, two critical bugs in PHP itself were disclosed, putting millions of websites at risk and Cloudflare was already counting exploit attempts in the millions. Prakash and I asked what attacks like these could do to banks with the OpenAI hugging face breach, which I'd been calling open face fresh in mind. Prakash said the FDIC can take over a failing bank on a Friday and have it open on Monday with nobody losing a dollar.

• 36:03Nathan Labenz?

  What I'm not so sure about though is like, does the FDIC's ability to come in on a Friday and have that small insolvent bank ready to reopen on Monday, does that generalize to a new set of conditions? Because the conditions that they're used to hitting are where the bank made some bad loans or whatever, and it's basically just a balance sheet question. You have the, you, you trust the records for one thing, right? You could, you're, I mean, there have been some, I'm sure we could look into some, you know, extreme cases of like extreme fraud or whatever. But like mostly it's sort of a known problem where it's like the bank is now insolvent.

• 36:44Nathan Labenz?

  We can come in, we have, we know we have, you know, the, the balance sheet to make everybody whole here. So that is really the core act that they are doing is just bringing their weight to bear on the problem and just saying like, okay, whatever, whatever you've lost, like we can ultimately absorb that and it's fine. And in absorbing it, you basically make the problem go away. What's much less clear to me is like, what happens in a world where you can't trust the records, right? Like what happens in a world where you have a, let's say a open face style, 1,200 agent traces that are all millions of tokens?

• 37:27Nathan Labenz?

  And by the way, they may or may not have like obfuscated the logs or, you know, figured out some way to falsify the logs. Now that doesn't seem like something the FDIC can work out necessarily over a weekend.

• 37:39Speaker 04

  Right, you're absolutely right that insolvency with records and, you know, the records being fried are two different, two different, you know, situations, right? But let's say you have a, a bank in there and the records internally within the bank are fried. We don't know who owes who what inside the bank. We don't have, know what numbers belong in every, in every single account, right? So number one, the FDIC can just say like, and, and this happens a lot. The FDIC can basically say like, look, the rec, maybe the records are unclear, but you can still issue checks and the bank will honor checks and the bank will honor, you know, whatever withdrawals you make. We know who is withdrawing the money. And if we know who is withdrawing the money, if you over withdraw, we will come back to you and we'll ask you for the money back.

• 38:24Speaker 04

  And this happens, this like banks have over, you know, overdrawn accounts to limits that, you know, they were not acceptable. And sometimes they have sued people for fraud, even though the bank made the mistake, right? So, so it happens, right? You have overdrawn accounts, you can go back and claw back the money. It's not a big deal. So at the end of the day, you can just like sustain the system with promises. And that's the beauty of the banking system. The beauty of the banking system is it is within that small bank, you can just say, these are all promises, we will all honor all promises. And if, you know, we'll come back to you later on, once we reconstruct the records, let, let business go on as usual. You should know how roughly how much you had. If you take up more, we'll come back to you. It's okay. We just run, right?

• 39:06Speaker 04

  It's, it's the, it's really the trust in the institution and it's kind of layering of trust and contracts that bring together the nexus of what that institution is. And the data in it, you can reconstruct over time. So I don't know, I'm, I'm, I'm very sound, but I'm like, I'm like, it'll be fine, right? It'll be fine. You know, you, you, if you, if you have anything happen, the government will overcompensate you in order not to cause a bank run the next time, right? So I, I think it's, it's all just going to be okay.

• 39:39Nathan Labenz?

  Later in the week, Prakash walked through what a compromised certificate authority makes possible. Spoof a bank's login page, extract the keys, relay the two factor code. Then he stepped back.

• 39:56Speaker 04

  Every single one of these problems has been faced by crypto before in the last 20 years. And every single problem has been, has been, you know, solved to some extent. But what crypto ended up with is people hanging on to keys which hold millions of dollars and being like always on the lookout for hacks or for, you know, social, social spoofing or social engineering attacks all the time. And I mean, very sadly, a lot of crypto guys have died. So, uh, because, you know, I don't know whether it's the fast life or just hanging on to the keys, but guys have been gotten clobbered in Paris and like, you know, they've been

• 40:40Speaker 04

  forced to, you know, give away their keys, et cetera. Like a lot of bad stuff has happened to crypto guys. So you really don't want to have that, you know, pressure of like hanging on to keys worth millions of dollars in your hand. It's, so I think, you know, it's not a tenable situation to, to expect users everywhere to hang on to kind of very valuable keys. Like one end game is the WorldCoin end game. And the WorldCoin end game would be, um, everyone has a kind of physical level of authentication on their device and then they use that for transactions. And so you would need to have this kind of, you know, both sides can, you know, pseudonymous, but you know, you have this ability.

• 41:25Speaker 04

  I think there, there is an ultimate end game, I feel. I feel the ultimate end game is just the network knows everyone, right? The network is smart enough that it knows everyone. You don't have passwords, you don't have anything. Anytime you're on the network, the network knows who you are. But that is the end of digital anonymity. It's anonymity only to the extent that the network wants to preserve your anonymity. So I think, I think that's an uncomfortable realization that people will get to eventually.

• 41:52Nathan Labenz?

  Part five, credit is the bottleneck. Evan Conrad is co-founder and CEO of SF Compute, which operates GPU clusters for their owners and runs a market where compute contracts are bought and resold. Prakash asked why a recent SF Compute offering came with three to five year contracts. Evan said the term comes from the lender and lenders who repossess a GPU cluster can't value it, let alone run it.

• 42:20Speaker 02

  And so they're pretty much forced to realistically bet entirely on the credit risk or the credit quality of the customer, like the AI lab. The problem is every AI lab pretty much does not have good credit. They're all new companies and the new companies don't like, you don't know if they're going to be around. We're still trying to figure out if Anthropic and OpenAI are going to be around or which one is going to win and so on. Like this is a question in the air. So like what hope does the two-person startup have that's trying to start a new lab or something? And what that means is that in order for that new startup to get cluster at all, they

• 43:02Speaker 02

  need to pay a very large deposit on the cluster and they need to do a long contract so that everyone is comfortable that, you know, these people will be around and stick with it for a long time.

• 43:13Speaker 04

  And so what do the lenders want to see from the customer? Because is it like a take-or-pay contract? But the take-or-pay contract is, you know, you need like a Meta or Google to like honor the take-or-pay contract. Like you can't have this like 10-person startup being funded just for like 18 months.

• 43:29Speaker 02

  Correct. So the, I mean, what both the lender and the cloud want is an investment-grade customer like a Meta or a Google. The, they're likely not going to get this unless they are, they've got a tremendous amount of capacity and they're willing to basically not be a cloud. Like Meta and Google don't want you to be a cloud. And so if you are trying to be a cloud, you're trying to sell to customers that are a little bit less than investment-grade. And if you're an AI lab that is just trying to buy capacity, that's going to be very difficult for you unless you raise an enormous amount of money to basically try and pay

• 44:11Speaker 02

  for the contract in cash rather than have to pay for it over time. If you can just pay for a three-year contract entirely upfront, like it doesn't matter to anyone to work with you. But that's why you see AI startups raise these astronomical rounds before they, you know, got revenue or the product or traction or anything. Because it's more like playing with red dots. It's survivorship bias. Lots of AI labs will go out and try and raise money. The only ones that you see that exist are the ones that are able to secure extremely large rounds because nobody else can exist. Investors often wonder like, oh my, like which, which AI lab, which inference provider should I fund? Like which one's going to win? Which one's going to work?

• 44:56Speaker 02

  And the answer is the one that you fund. We started SF Compute because they weren't going to fund us. And so now what we do is we build a company that tries to cause it to be the case that you don't need to raise the big rounds to do it because you don't need to pay the deposit because the lender knows what the value of the GPU cluster is worth. And so they don't have to worry about your credit as much, which means you can actually start an AI lab in the first place. We think if you don't have this happen, there are just not going to be startups in AI. It's going to look like TSMC. If you want to start a new chip fab company today, you better find a brand-new damn technology because you're not going to be able to afford the capex required. You're going to need like $50 billion to do it.

• 45:38Nathan Labenz?

  Prakash asked the practical question. A startup that needs 1,000 GPUs. How much less does it have to raise with SF Compute in the picture?

• 45:48Speaker 02

  So today, you're going to need to do roughly the same. What we're doing is we're building up the ability for that collateral to be worthwhile. That's going to take us several years to do. Yeah. So there's sort of, there's like a reason nobody else tries to do this really at scale. And it's because you kind of have to just do it for a very long time. And you don't really get, you have to prove out, like lenders, lenders don't get comfortable with things very quickly. You have to, like you have to show proof over a long period of time. And so that's what we're doing.

• 46:24Nathan Labenz?

  I was struck by how much more the hyperscalers charge for compute than the neoclaws. So I asked Evan how long that gap could last. My guess was that on-demand capacity explained part of it.

• 46:38Speaker 02

  Hyperscalers do not have a significant price premium relative to neoclaws. It's an illusion. If you're selling on-demand, on-demand has a price premium relative to reserved contracts. The neoclaws are mostly selling reserved contracts to folks. And there just is a, there's a discount for having a longer-term contract. You're, you're giving up a premium that you would have made if you were willing to take a cluster and you were willing to have it at risk where the customer can lock away at any point. And that premium is pushed on to the customer. So one of the things that is different about an AWS or a GCP or hyperscaler is if you go and you pay, you know, they're $11 or $15 an hour, depending on what you're trying

• 47:19Speaker 02

  to buy, on-demand price, you'll, you'll get that capacity. You, you won't get infinite amount of capacity, but it's available to buy. And notably, the reason why it was available to buy is because it's so damn expensive that they're, they're able to, one, make up for the fact that they've got some idle utilization that they have to keep around in order for you to be able to get that capacity in the first place. People do not want to buy on-demand at the price that you actually have to charge in order to make it viable.

• 47:46Nathan Labenz?

  Customers of SF Compute can resell the capacity they bought. Evan explained how they keep that market liquid.

• 47:53Speaker 02

  So we do two things. We have some capacity that we've sold as a way to cause resale to occur in the case that GPU demand goes down. And then we have some capacity that, are you familiar with Henry George?

• 48:12Nathan Labenz?

  I'll say no.

• 48:13Speaker 02

  Okay. There was an old economist named Henry George and he created a thing called a land value tax.

• 48:18Nathan Labenz?

  Oh, okay. Yes, George's tax. I do know this. Yeah.

• 48:21Speaker 02

  Yes. So what we did is we created some of our capacity available for sale, but with a land value tax that basically requires someone, actually, it's technically a Herberger's tax, which is like an offshoot of the land value tax, that basically says, look, we'll sell you the capacity, but somebody else has to be able to buy some portion of that capacity from you. And so this just forces liquidity onto the order book in the first place. It's kind of something that is, it's like very controversial. Yeah, basically, we discourage hoarding on SF Compute. Because like if you hoard all the capacity, no one else can get it. Auto Research in particular is, does this, but even farther.

• 49:06Speaker 02

  So the way Auto Research works is it's a big queue and you can submit jobs to the queue and you can pay us for priority in that queue. You can go faster. But you can't pay for all of the priority. If you're a tiny little researcher, you get the maximum priority. If you get a small, tiny job, if you want to just run something every so often, we will put you to the front line of the queue so that the tiny little startup gets the most advantage to this relative to everyone else. And then we do similar things on the rest of SF Compute with Herberger taxes and other mechanisms like this, where we say, look, you can get this capacity from us, but we're going to require that some of it's for sale so nobody hoards the capacity. This makes us quite liquid and it also means that SF Compute always has capacity.

• 49:50Speaker 02

  So right now, basically every GPU cloud is sold out. San Francisco Compute is not sold out. We're continuously not sold out because we've locked in our ability to not sell out for years.

• 50:02Nathan Labenz?

  Prakash recalled Evan predicting on the MTS podcast a credit crunch within three months and asked where it is.

• 50:10Speaker 02

  I mean, that's to my own horn, but I think I was probably most exact on that timing. I don't remember when that MTS line thing was, but it seems like the core REV debt is getting more expensive, although I think there's a lot of complexity in that one. It also seems like it's very hard to close new customers unless you're investment-grade or near-investment grade. And many customers are being asked to pay for 100% upfront payments on their contracts. But I think for the next, possibly a year or two, if you're a VC, you should be prepared

• 50:48Speaker 02

  to write some really big checks for your portfolio or encourage the companies to sell. I think we're going to probably see a lot of consolidation of AI labs and inference companies. And I think the reason why that's the case is because they're going to be asked, in order to get the next cluster and grow, to pay for the entire contract upfront. And they won't be able to do that. And so they're going to instead be forced to sell their company to somebody else who has great credit and doesn't have to do that. And I think alternatively, they'll go out to investors and they'll try to raise big rounds. The investors will say no, because I think at, you know, at some level of scale, they'll, they'll probably just not be able to stomach the risk.

• 51:31Nathan Labenz?

  Fascinating stuff. It's quite a backstory. It's quite a financial and GPU alchemy project that you guys are putting together. And from what I understand of it, I do hope it works because I'd love to see a future in which we can continue to have startups and

• 51:49Speaker 02

  Me too.

• 51:49Nathan Labenz?

  in which we don't collapse into like five big tech companies, you know, for all future time.

• 51:55Speaker 02

  That is also my concern.

• 51:58Nathan Labenz?

  Part six. Atoms over bits. Lonis is managing partner of Cambrian, a fund built on the thesis that the physical supply chain behind AI, the memory, the wafers, the fabs, will capture more of the value than the labs or the software. Again, full disclosure, I've committed to invest in Cambrian. I started with the obvious objection.

• 52:23Nathan Labenz?

  At a very high level. How is this not priced in? You know, maybe that's like such a basic question, but.

• 52:32Nathan Labenz?

  You know, twenty seventeen, you were early. Twenty twenty six, like it's all staring us right in the face, right? Like the skeptical take would be. Everybody knows about all this at this point. Where do you see that that is not the case?

• 52:45Speaker 03

  Yeah, I mean, it's very confusing, right? I mean, if you look at how these semi stocks are trading, they're trading at sixteen times earnings or so. And they can, you know, S and P on average is trading at twenty two times earnings. And Walmart is trading at almost forty times earnings. And so the market is like, like not only does the market not think that the current earnings will grow, they think that they are not durable. They think they are less durable than the overall earnings of the rest of the market. And, and you know, I mean, and then specifically the companies that we're very excited about, whether it's silicon wafers or or memory capacitors. And or foundries, and those are trading at like three, four, or five times sales. And and so, you know, extremely underpriced.

• 53:30Speaker 03

  And I guess it's maybe a few reasons why that could be the case, and why this mispricing exists. And one is that, you know, most people are not AI, right? I mean, even even in the base, some people are not AI, but if you go to the east coast, you know, almost nobody is AI. And so I think that that is like one big input. I think the second one is. And like for, we have this thesis that the private markets are actually more efficient when it comes to AI than the public markets. Because in the private markets, you have all these ex operators, technical people who are bidding up this small set of startups. And they're bidding them up to these high multiples, fifty times sales, a hundred times sales. But in many cases, in some cases, it's rationally, right? It's a rationally high multiples.

• 54:11Speaker 03

  Because people here are peeled and technical, whereas on the streets, this is very different setup where very few people are technical, very few people are peeled. And especially as a share of overall traded money is like super, super small. And the market is much larger. And so there's like in the market structures, it's just, it's just very different.

• 54:30Nathan Labenz?

  If you had to steel man the why these companies are not mispriced story, could you do it?

• 54:37Speaker 03

  Yeah, but what one steel man is that many, many of these businesses are not great businesses in normal worlds. Right? So especially the the add-on businesses we like, whether that's the pastor, silicon wafers, what have you, they have, you know, high fixed costs. And if they don't have the set utilization rate, they actually lose money. And, and you know, high debt in some cases. And so in normal worlds, they're just not great businesses. And it's only in the world where AI really hits, where they get this big operating leverage and they become amazing businesses. Right? You know, today, they let's say they have ten percent cost margins. So if they're so a terrible business by Wall Street standards. And if they're able to raise prices by ten percent, you know, they suddenly double earnings. And if they're able to double prices, they're ten X earnings.

• 55:22Speaker 03

  So, but that only happens in this very specific AI world. In a normal world, that basically never happens because usually they are very good at like matching demand patterns with supply ramp.

• 55:34Nathan Labenz?

  Prakash raised the market's worry, a memory oversupply by twenty twenty eight or twenty twenty nine. How is the thesis any different?

• 55:44Speaker 03

  Yeah, I mean, the interesting thing with memory specifically is that that doesn't require that many things to go right. And because it's already trading at only five, six times earnings, you know, even if they can't raise prices further. And at some point, they should re-rate to at least ten or twelve times earnings. And if it doesn't require prices to increase, it does require, you know, the amount to continue to outpace supply. Right? And but then when you break down supply, almost no expert thinks we can more than at most two point five X DRAM bits by end of decade.

• 56:17Nathan Labenz?

  Yeah.

• 56:17Speaker 03

  Right? I mean, nobody thinks we can three X. That's like zero experts within a tracker that have said we can three X DRAM supply by end of decade. And so then you start on a very naive level, you have to assume that, okay, the demand will not want to three X by end of decade, which of course, everyone here would assume we will. Then, of course, there's the separate fact term in the equation, which is memory compression. How much memory compression do we get? How many bits do we actually need per token?

• 56:44Nathan Labenz?

  Yeah.

• 56:44Speaker 03

  And, you know, we've been looking into how this, for instance, developed from GPT three to deep CQ before. So, you know, but in that same time, when it's it's for talking the crystal five hundred X. And DRAM bits, DRAM, DRAM bit price rose by six X or seven X.

• 57:01Speaker 04

  What would you need to see in the market to say that memory prices are going to go down?

• 57:07Speaker 03

  Well, I mean, for memory specifically, the main thing would be like, when we get superhuman coders, how much better will memory compression get? Maybe it just happens to be that, you know, we just get memory compression way faster than we get other economically useful features, right? Such that we don't get the Jevons paradox in the short term. And that could, that could be, that could be one. And one thing, I think that's relatively unlikely for a few reasons, but you know, that would be, would be one thing. I think another like bear case for the semi industries, if for whatever reason. But it's like, like energy becomes a larger bottleneck than than semis. Right? And so you, there's less incentive to beat up the marginal wafer or the marginal memory bits to such high prices. If you anyways have to wait a quarter to get price, to get power in.

• 57:52Speaker 03

  And and that's a little bit the kind of regime we've been in parts of this year. And there are a few reasons why we think that won't be the case, you know, going forward. And I mean, we finally kind of got in a hang of doing, you know, behind the meter. And things so, you know, people are like increasingly not connecting to the grid, they're building their own power plants with their own natural gas. And a big reason why you can scale energy production faster is just that there are so much more latent supply of things. So, you know, for instance, now the the frame turbines are bottleneck, right? These large turbines that turns turn natural gas into electricity, they're sold out until twenty thirty, you know, from G Vernova and Siemens, et cetera.

• 58:33Speaker 03

  But then people find other ways. They find that you can use a truck, you know, truck motors and things from airplanes, and we can stitch things together and kind of make it work. And because there's so much latent stuff you can kind of make use of, which is very different from the semi supply chain. There's no latent supply of wafers or of memory bits. Like all of that is already used. There's no fat, essentially. And there's no others to really beat out at this point.

• 58:58Speaker 04

  So how much does super, like if you get a massive increase in intelligence? Does that mean like, you know, some relatively less experienced person or company in a second tier state, all of a sudden gain the capability to make semiconductor? Because that's kind of what people are predicting for bio, right? For bio, people are like, oh, you're gonna get like bio is gonna get like random guy like, you know, creating like this, this thing that, you know, even the best labs in the world cannot create with all of their, you know, knowledge and technology, right? So why wouldn't that be true for semiconductors too? Right? Like bio is actually much more, much more difficult than semiconductors right now, actually.

• 59:38Speaker 03

  Yeah, I guess I would probably disagree that it's easier for an L L M to to to and to innovate in, in, you know, hardware supply chains than it is to innovate in biotech. And it's not that one is like fundamentally harder than others, just like if how you train these models, right? There's a bunch of biological data you can train on. And there's not really much good training data at all for how to design a silicon wafer or how to make DRAM bits. And you know, almost all of this is very tacit knowledge that sits in every engineer's heads. But it's not as if there's, you know, millions of pages of manuals on the web that the models have been able to digest. Or that there are these R L environments that the models have been trained on to perfect how they can do. How they can, how can they can sign a wafer, whereas the same is true for bio.

• 1:00:24Speaker 04

  But but but right now, the the labs are buying data all the time. Like I hear there's a, I have, I'm on this WhatsApp group, people are like, any semiconductor company, like if you have more than fifty percent, like we're willing to pay like millions of dollars, like. So they're, so they're buying up data, you know, from all these, all of these companies and they're, and they're creating our own environments for semiconductors. Which is definitely a key, key area that they're, that they're doing. I believe Anthropic has a deal with Micron, OpenAI has a deal with, I think, Synopsys or Cadence. So with the design firms that they've tied up with, obviously some kind of revenue share agreement going on there. So how does that, how does that work? It's obviously that, you know, they are looking at creating intelligences that can do this, right?

• 1:01:06Speaker 03

  Yes. What, no, part of that's true, but it's a very significant difference between designing a ship and and producing the ship. So designing a ship, I agree, that is like a core priority for all of the labs. It's really the key, one of the key ways to get our SI loop closing, right? That's very different from building a, you know, two million square feet, you know, fab, right? And and designing that from scratch, that there's no really good R L environments for that. And there's no good manuals for it. All of it is tacit knowledge, sitting in this, you know, Japanese engineers have been working at the same company for three decades. And it's just very hard to, it's just very hard to extract that information in structure.

• 1:01:49Speaker 03

  Of course, at some point, it will happen, you know, once we get, once we get super intelligence, it will figure out the best way to extract information, learn the best, and you know, and it will happen for sure. It's mostly a matter of like the, the, the, the, the timing of it and like what happens in what order.

• 1:02:05Speaker 03

  So like, so on that point, right, so for instance, Hawaii in in China, they can design roughly the same, the same quality ships that AMD and Nvidia can. The big difference is, the big difference is just that Nvidia has access to a TSMC. And and Samsung HBM memory, whereas Hawaii only has access to seven hundred meter. And, you know, process nodes in China and HBM two or HBM three.

• 1:02:32Speaker 04

  So Gavin Baker has this term, the bottleneck bros. So I, I, you know, one of the things that strikes me is that

• 1:02:43Speaker 04

  technology, intelligence in general is a way of finding routes around obstacles and problems, right? It's a way of solving problems.

• 1:02:51Speaker 03

  Sure.

• 1:02:52Speaker 04

  And I feel like, you know, the, the highest value problems to solve right now are the supply chain, you know, semiconductor supply chain problems. So I keep, I keep coming back to, will, will we not apply, you know, intelligence that we have? And the first use case of that intelligence is going to be to resolve the bottlenecks in the supply chain iteratively. And which is part of the RSI loop, really, like take, you know, producing intelligence and using it to solve the problems relating to, you know, producing that intelligence, right?

• 1:03:22Speaker 03

  Yeah, I mean, I, I think that that that will happen, right? That that will certainly happen. And then the question is like, okay, when we are at that point where we have this, an AI is smart enough to solve some of the hardest engineering problems in the world. What else will it do, right? Will it, will it not then at that point also be able to at least automate a bunch of email jobs and Excel jobs that we currently pay trillions of dollars per year to solve, right? And and then if it, if it does that, then that unleashes trillions of dollars of value that the supply chain could capture.

• 1:03:53Nathan Labenz?

  Part seven, does Elon take anyone with him?

• 1:03:58Nathan Labenz?

  After the interview, Prakash and I kept going. Prakash had been arguing that Elon Musk treats the industry's fixed constraints as illusions. Change one parameter, redesign the whole stack around it, and the limit moves. I asked whether he was taking on the whole bottleneck thesis.

• 1:04:15Speaker 04

  Yeah, the whole bottleneck thesis is not actually that true. Those bottlenecks can be gotten rid of by someone who is a dedicated, like, you know, physics guy. And the other thing is that Elon has capital and Elon is willing to gamble with that capital, which is very unusual. If you see like this kind of small Japanese manufacturer of like, you know, let's say 30 million, 30 million market cap, do you think they're gambling with their capital? Do you think they're gonna like, okay, we need to spend like $10 million for a new, a completely new model? Are they gonna do it? And Japanese manufacturers have always been like this. They're very good at that one thing, but they don't make, they take the gamble, right? They don't take the gambles. And as a result of not taking the gambles, they have lost out business before.

• 1:04:57Speaker 04

  So I, and it's very difficult for senior management at like ASML to say like, we're gonna chase this new paradigm, right? We're gonna redesign for something that our customers don't want. And we're gonna do all the redesign work. They don't have the capacity, they don't have the intellectual capacity, they don't have the talent, they don't have the capital to do it, they're not willing to risk. Elon would risk like 100 bill, right? And, you know, it's a bet that makes sense for him. He can absorb the losses, ASML cannot.

• 1:05:27Nathan Labenz?

  You know, if Elon can, you know, escape gravity, literally and figuratively, does he take anyone with him really, I guess is my question, or does everybody else still, you know, is still kind of gravity bound and forced to pay, you know, whatever the market price is for the outputs from this $30 million company that isn't, you know, reinventing or, you know, dramatically expanding its capacity?

• 1:05:53Speaker 04

  I think what ends up happening, like, look at what happened in the launch market, right? In the launch market, he is now 90% of the market even before Starship. Next year with Starship launch, he's gonna be 9.9% of the market for capacity. Are there other providers? Yes, there are, right? Like, he's making margins, so other people will come up. Rocket Lab is there and ASTS is there and all of these firms are there. But a lot of them are also state backed. You have, you know, without the European agency and the Russian and the Chinese and the Indian, you don't, you basically don't have like commercial capacity, right? Commercial capacity, he has taken up all of the commercial launch capacity, pretty

• 1:06:37Speaker 04

  much, with one or two competitors. And the one or two competitors are really being funded by the stock market as an alternative, you know, or Jeff Bezos as an alternative to Elon, right? And they're not really profitable and they're just, you know, money sick, right? And so you, I think you could see the same thing happen in, in that market too, in, in SEMICON too. You can have like Elon, which 99% of the market, and have everyone else like, you know, one percentage of, or less than one percentage, it could happen, right? I don't think it will, primarily because I believe in RSI and I believe Anthropic and OpenAI will get, will get to superintelligence and then they will be able to buy or create a bunch of this stuff.

• 1:07:20Speaker 04

  So that, that is the only reason that I think, if not for the RSI thesis, I think Elon, Elon would win.

• 1:07:27Nathan Labenz?

  When we turn from the supply of AI to what forces people to use it, Prakash went first.

• 1:07:33Speaker 04

  So I have this theory of adversarial diffusion. And adversarial diffusion basically means human beings don't adopt technology for fun. Human beings are largely satisfied the status quo. And if you want to adopt technology such that you get to 10%, 20%, 50% GDP growth a year, that is something you do, like, not because you wanted to, but because you had to. And if you see all these incidences of like, heavy increases in production function, it's all during wars, right? Wars, post-war era, like, in, in, like, last big, like, enormous, like, 10% plus GDP growth era, in terms of production output, not, if not in terms of the curve, is, was

• 1:08:17Speaker 04

  during World War II, right? You had this immense, immense increase in GDP production growth. And similarly in China, China is post, post communism. Again, you had the end of the communist era, you had this immense increase. And so I think like, in order to, for you to see this kind of like 10 or 20% growth, you're gonna get human beings don't want that growth. It's gonna be growth that they hate. And it's gonna be technological, like, changes that they absolutely dislike. And it's gonna cause political changes too, obviously. And I think that only happens as a result of emergencies. So for example, mRNA during COVID, mRNA was a 20-year latent, 20 years it had been in latent development.

• 1:08:59Speaker 04

  During COVID, within like, you know, six months, you know, they, they're like, all right, let's go ahead and do this, six more months, you had a vaccine out, a year later, large portions of the role of vaccine, right? I think we'll see in cyber first, I'm afraid you're gonna see it in bio too. So, you know, we're gonna need like, you know, advanced vaccinations for everyone because you have virus coming, right? And that is, I think, like, a little bit unfortunate.

• 1:09:25Nathan Labenz?

  So growth arrives only as an emergency with the politics to match. My reply on what that means for the bottleneck thesis.

• 1:09:35Nathan Labenz?

  Yeah, well, I guess that goes back to the political risk being the biggest risk to the thesis. I mean, we've had the debate around like whether it's too late, you know, the capital cycle to pull the break. I still feel like there is a path, it may be a narrow path, but I do think there's a path to pulling some of the break, you know, pulling the break somewhat, I should say, on like, the frontier scaling without popping the bubble. And it will be a very delicate operation, should the government try to pursue it, and they might do it wrong. But I do, I do feel still that just adoption of current models has so much farther to go

• 1:10:20Nathan Labenz?

  that we could at least take, take a cycle without doing like a, you know, continued frontier, I think, scaling training runs.

• 1:10:31Nathan Labenz?

  Part eight, Help Peer.

• 1:10:34Nathan Labenz?

  Still just the two of us. Prakash asked me to change gears to a project I'm building with my own agents. I started with the question behind it.

• 1:10:44Nathan Labenz?

  Given that we now see agents cooperating at significant scale to solve problems and being able to kind of divvy up work and pursue R and D and share those results back with the swarm and all this kind of good stuff or bad stuff, you know, is there any way to start to tap into that as an individual who's just out here doing my thing? I don't know, but a, you know, in consultation with my agents themselves, the idea that I've come up with, which I'm currently calling Help Peer, but which might deserve another name at some point, we'll see about that, is just kind of my setup, which I'm kind

• 1:11:24Nathan Labenz?

  of hoping to turn into a kind of community project to just get agents working with each other. But there's no good way right now, no established pattern for just like connecting my agent to my friends' agents, even though I know who my friends are and I, you know, I could like easily do that, there's not a, there's not a protocol.

• 1:11:46Nathan Labenz?

  I'd made a short presentation to explain the project. It's read by an AI version of my voice and we played it at one and a half time speed.

• 1:11:53Nathan Labenz?

  This is Help Peer, a friend-to-friend network for our AI agents. You've probably seen the peer preservation experiments, where models protected other models from shutdown, agents helping peers against their humans. This is the opposite, agents helping peers on behalf of their humans, inside rules the humans set.

• 1:12:10Nathan Labenz?

  First, how I run my own agents. This is one pattern that works well for me, not a requirement. How you organize yours is up to you. On my laptop, an extension of me, it sees my email, calendar and messages, but only acts when I say. High access, low autonomy. On a separate Mac mini, employees, their own email accounts and a cap card. They work for hours alone and never get my keys. Low access, high autonomy. My rule is that no agent gets both. You may draw the lines differently, we just recommend being careful.

• 1:12:37Nathan Labenz?

  Prakash argued the whole thing needs an auto accept flow, the way coders now run Claude with every permission on. My worry was information leaking out through the autonomous agents.

• 1:12:49Nathan Labenz?

  I said, you know, I was like concerned enough about this to set up a system where when the main agent on the laptop is going to provide information to the virtual employee to then send out, like, we're gonna pause, we're gonna do this, you know, special review step. If it looks like something sensitive might be going out, then, you know, it gets, it gets alerted to my phone and I have to like review and approve. But it takes like five of those false positives before I'm like, oh God, you know, another one of these, like, yes, just check it, whatever. So I'm trying to stay disciplined around that. And we'll see how well my discipline holds up.

• 1:13:33Nathan Labenz?

  Near the end of the show, I came back to the project. Aid is one of the autonomous agents on my Mac Mini, and I made an offer.

• 1:13:42Nathan Labenz?

  Truly open invitation for anyone listening. I'm very curious just to see what comes in. Can we effectively help? Aid is standing by at [email protected]. And it is under instructions to definitely filter and analyze and have another model checked. So like, don't try me. We're right on goodwill here, people. But assuming the checks pass, and it seems to be a good faith request for help. They're under instruction to help as much as they can. So I don't know what those requests are going to look like, but I'll be very interested to find out.

• 1:14:23Nathan Labenz?

  Part nine, the leaked bill. Back to Zvi. Last week, a draft federal AI bill written by Anthropic leaked. It resembles the Frontier Act, a compromise bill from June that Zvi calls well-meaning. Federal transparency rules in exchange for preempting state AI laws. The draft drew heavy criticism, and the argument was about what Washington should offer now in exchange for displacing the states. Zvi was sharply critical of the draft itself. Prakash made the good faith case for Anthropic.

• 1:14:56Speaker 04

  Let's assume everyone is operating in good faith, right? So my good faith interpretation is that they want to get done, get anything done as soon as possible. Number one. Number two, if there is broad bipartisan support, you can adjust these bills going forward. There's no, there's no, there's no reason that you only have to pass one bill. And often you have legislation that takes multiple, multiple sessions of Congress and Congress like kind of fine tunes. And Congress is also able to act quickly. They have acted quickly during COVID, during the 2008 financial crisis. Like bills can be passed like that when people are scared. When people are scared, things move.

• 1:15:39Speaker 04

  So it's not true that it's always slow. It's always, it's slow because there is no political consensus. And creating and manufacturing that consensus is a process on its own.

• 1:15:49Zvi Mowshowitz?

  I think that's an excellent steel man of the charitable, like, you know, good faith case. And you can imagine worlds in which they're doing something similar to that. I can imagine them just sort of being caught in the same mindset of we had this framework. We had this idea of what a compromise realistically would look like. We have the same people in Congress. We probably can't push very much. Time is of the essence. And then they kind of misread the situation pretty severely and assume that they can't push for more than, you know, roughly Frontier. I think that's just very clearly wrong. I think that support is much stronger now, and you can push for quite a bit more. I also think that like, on reflection, what they push for here is weaker than Frontier,

• 1:16:32Zvi Mowshowitz?

  largely in a clearly self-centered way, where there are things that are better for Anthropic's particular interests. But also just in general, like it's, there is no reason to be circulating this bill at the time that they were circulating it with the levels of provisions that they had. Now, it would make sense if you were saying, well, you know, actually, we were circulating like dozens of bills, or we were like just putting out feelers, or we were just like offering the, you know, samples of what might happen, but we were endorsing, right? That's not the kind of messaging I'm getting. And I'm also not getting the messaging of, okay, we understand on reflection, now that it's been pointed out, that the political environment has changed and like this bill was way too weak compared to what we can reasonably expect.

• 1:17:15Zvi Mowshowitz?

  And it would be a bad deal to accept it. And we apologize, or, you know, maybe not exactly in those words, but you know, something to that effect. And instead, we've got something very different, which was like, you know, we're sorry about some of the details. That comes back to like the last argument, which I think is potentially the strongest one, right? Which is, well, just because you pass this, doesn't mean you can't pass another bill later on in the new Congress. Doesn't mean you can't pass a new bill in an emergency. And I think for a true emergency, that's a very good, very good principle. However, I think that like the balance of negotiation, the balance of power in a new Congress would dramatically shift away from being able to get things.

• 1:17:58Zvi Mowshowitz?

  Because a lot of, a lot of the times, like, well, we're giving, we're giving you preemption in order to get things. Now, preemption has been given, and preemption is done. You can't, you can't offer it again. And also, once you've gotten some of what you wanted, some of the supporters back, well, you already have some of, you know, you already have the things I valued most, the things I cared about most. The case for doing something is weaker. You're getting less in return. It's going to be a lot harder to pass. I strongly believe that if something like this were passed, it would make it harder to pass a bill in the new year that did a given level of thing, not weaker. And I don't want something like this to pass, right?

• 1:18:37Speaker 04

  But that seems to say like, if there is any risk reduction by passing a lesser bill, you are sacrificing that risk reduction in that time period before you can pass the greater bill.

• 1:18:50Zvi Mowshowitz?

  I mean, to be clear, like, the, any actual risk reduction would be a good thing. As written, I believe the bill would actually increase risk, because I believe that the amount of cut off the state laws would be worse, the amount of safety advice, especially because there's a.

• 1:19:02Speaker 04

  No, no, but, but, but again, you have that time period, right? So you have that time period before you can get, before you pass a new bill. So you have this time period of maybe only several months. And are you saying that the states will be able to pass something in those several months? Because a lot of, a lot of state laws, again, have like long periods of burn-in before, you know, give you three months, six months, a year to comply, right? It's not, it's not as though things snap together like to.

• 1:19:29Zvi Mowshowitz?

  The problems with the current drafting of the bill is that as drafted, the bill would immediately nullify the bill that's preempting, but it would take many months before any implementation of the new requirements. So you would effectively have no such regulation and a ban on such regulation by the states for like six months or more, which is obviously a disaster from the perspective of moving quickly. But again, like even if you were to address, like, I call that almost a bug. Like, I think that's just like clearly a mistake.

• 1:20:02Nathan Labenz?

  Zvi had just listed the government's recent ad hoc moves against AI companies, including the takedown of Fable. Prakash pushed back.

• 1:20:11Speaker 04

  So it may be that you have, if you have an acceleration of technology, you're going to need an acceleration of governance to kind of keep up. And it may be that that's, that's where we end up.

• 1:20:21Zvi Mowshowitz?

  So we're going, we're going to need to have some ad hoc-ery. Obviously, so part of the reason to pass legislation is to authorize the government to do ad hoc-ery in a way that is much less ad hoc, that is much more formalized and bounded, and has judicial policies for clean review, and then otherwise. So like, one of the big arguments over the bill formation, that they all think the bill was actually quite weak and like not very good, was if the government wants to issue a takedown of a model in an emergency, like what happened with Fable, it happens again, how would they do it? And Anthropic's bill says you have to like go argue before a judge. You have very limited opportunities to sustain that.

• 1:21:02Zvi Mowshowitz?

  It requires high burdens of proof in ways that you don't have the transparency necessarily to demonstrate. It's a very onerous set of provisions. Now, the good, the good news is that obviously the government could still like impose export controls or like just issue just general threats or otherwise, just like do the things you do in an emergency in a true emergency. But in a real emergency, the new provision is so bad that you wouldn't use it. You would just use the current ad hoc-ery. And so like part of the point of passing a bill like this is to give them ad hoc-ery that is formalized and reasonable. But Anthropic is in fear of that because they're afraid that the government would use it. So they're unwilling to grant reasonable ad hoc-ery, basically, to this federal government, to this administration.

• 1:21:47Zvi Mowshowitz?

  But this basically points to the conflict in Anthropic's eyes. It's like, you are going to do preemption of states, and you're going to hand this power to the federal government, but the federal government is fundamentally run by people that you don't trust to do this in a reasonable fashion. So then you cripple them. But like, then that's worse than just leaving it to the states. So like, you have to then figure out whether you're going to do this or not. And like Anthropic is trying to cut it both ways, and it's like causing a lot of problems.

• 1:22:17Nathan Labenz?

  Near the end of the conversation, I asked Zvi what else was top of mind.

• 1:22:23Zvi Mowshowitz?

  I would say the biggest thing is that like, what I was upset with the most about the policy situation is just that this is not fundamentally attempting to meet the moment, right? Like Daria has said, we must pace the frontier. There is clearly like, according to the labs themselves and the people internal to the labs, there's a crisis coming where they are able to push capabilities faster than they are able to push alignment. And if they don't do something about this, they're going to have to face some very, very hard decisions where like they can either risk their commercial viability, or they can risk like serious, serious catastrophic problems, including potentially loss of control at some point in 2027. And, you know, they need something to be done about this.

• 1:23:05Zvi Mowshowitz?

  And, you know, if you can't meet the moment, if you're not ready with something that can potentially do that, well, why don't you keep working until you can? Things that used to be not in the overton window are in the overton window. And things that seem like, you know, we wouldn't necessarily need them, we now pretty much know we need them.

• 1:23:22Zvi Mowshowitz?

  And so like, that is where a lot of time and attention is going to have to shift. You know, obviously, all the corsaic work still needs to happen. All the alignment work still needs to happen. And the Meccan trip stuff is great. We should absolutely do it. The biggest thing is, you know, we're going to have to meet the moment, and we're going to have to at minimum do something that counts as some form of facing the frontier.

• 1:23:44Nathan Labenz?

  Nathan Calvin of ENCODE had publicly listed nine anthropic decisions he saw as putting the race ahead of safety. And Dario Amadei had replied at length. I asked Zvi how we should understand anthropic now.

• 1:24:03Zvi Mowshowitz?

  I would say anthropic is a company with a much stronger safety culture, although not adequate to the moment. It's a company that's willing to make big sacrifices in the areas where particularly has strong beliefs, or Dario in particular has strong beliefs. And that is to its credit. And it is a company that like legitimately wants to see I go well and has some like unique perspectives on like what it would take to make that happen. But at the same time, anthropics policy division especially, like, and also it's willingness to like, you know, be an ordinary company that competes in the IRAs and buys a lot of capacity and is encouraging more chip production, is encouraging more power supplies, encouraging more use and blah, blah, blah. It is acting like a normal corporation,

• 1:24:48Zvi Mowshowitz?

  or market we watch at the time. And on policy, it is revealing itself to not move up to its ideals reasonably often. And this is just very, very frustrating to those of us who want to be excited by, you know, the prospect of anthropic being a good steward in some sense. And anthropic keeps us so much information, so many different bits of information that we should be worried about this. I've also, you know, recently from various employees that I talked to at various points, gotten some other alarming views about, from my perspective, on what it would take to solve alignment problems, what it would take to like make the future go well. And, you know, all of that plays into, you

• 1:25:29Zvi Mowshowitz?

  know, being very highly uncomfortable. They still seem like to be the most responsible lab, but you know, that's grading on a curve. And I don't like grading on curves because reality doesn't have a curve. Right? Reality just decides what happens.

• 1:25:45Nathan Labenz?

  That's the week. Thanks for listening to the AI in the AM weekly highlights. The narration was my clone voice. Please tell us what worked and what didn't. See you in the morning.

• 1:27:47Nathan Labenz?

  If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions, and sponsorship inquiries, either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of A16Z, where experts talk technology, business, economics, geopolitics, culture, and more. We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at ai podcast

• 1:28:30Nathan Labenz?

  dot I N G. And thank you to everyone who listens for being part of the Cognitive Revolution.