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

Pace of AI

0:00This week on AI in the AM, what really sets the pace of AI from here? The money, the atoms, or the law? Zvi Maschowitz, 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. Advances on the level of a current model release cycle go from two months to one month to two weeks to a week. Stuff like that could happen. You get to see continuously pretty dramatic, very, very rapid improvements in various aspects of the AI capabilities.

0:34We are kind of on an AI 2027-style timeline right now. It looks very eerily similar, but we saw there. 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. 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.

1:14If you think about it, there's a nice lender off in New York or London or something. 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. 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. Alanis Hamali is managing partner of Cambrian, a fund that bets on the physical supply chain behind AI.

1:46Full disclosure, I have committed to invest in Cambrian. Here is Alanis on which suppliers the labs will never replace.

1:54And even in general, when we're looking at the AI market, we are way more bullish on the atoms over the bits. So, you know, most AI companies people looking at is like Vidya, Broadcom, Arm, Marvel. And these are not good companies. You know, there's nothing wrong with them. And the issue is that what their business is essentially just using open AI and anthropic models to design software. And then they send that software down to Korea and Japan and Taiwan for the actual work. These companies don't have their own physical know-how or their own factories. And so, whilst these are, you know, great, well-run businesses, they don't have at all, close to the same moat as these physical companies have, which have, you know, massive fabs.

2:33And in Asia, they have decades of know-how built up. And it's just very hard. So, 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 software designers like the media and Broadcom, etc. Prakash Narayanan, my co-host, on what the market keeps getting wrong. I believe that for the next 20 years, the market will consistently underprice exponential growth.

3:05Because 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, etc. 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. Bach's CEO Aaron Levy posted that if you want to know which jobs AI will create, look no further than cybersecurity.

3:37Here is what I made of that. I don't doubt that, like, really good cybersecurity people are going to, 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 timescale is just absurd, right? And this is, like, oh, who's going to pivot into these jobs? Are we going to go get, like, the content marketers that are currently out of work or the graphic designers and be, like, oh, you know what?

4:13This is the new learning of code. It's, 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. 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 supervise the agents.

4:45Like, this is the AI in the AM Weekly Highlights with my co-host, Prakash Narayanan. I'm Nathan Lbenz. The narration is my cloned voice, and we'd love your feedback on what works and what doesn't.

Recursive self-improvement

4:58Part one. If nobody hits a home run. Zvi Moshwitz writes about Frontier AI at Don't Worry About the Vase. This was the question I most wanted to put to him.

5:11Maybe 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. Everything down to experiments per researcher. Then we've had these Ramez-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 will kind of peter out. Like, 1.6 experiments per researcher doesn't look like exponential takeoff to me, basically.

5:45I think it's kind of the bottom line.

5:48What would be your sort of, you know, up-to-the-minute summary of what we know and what we should expect? I think Ramez'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.

6:23And 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 points we expect to score in this game if nobody ever hits a home run, because a home run is always unlikely. 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 and your expectations. And, like, 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.

7:01And I realize that things are not going to be necessarily as efficient in some important sense, but, like, give me a break. So one example was 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 are making improvements much faster. And obviously that's not true recursive self-improvement, but it is a sign of how much faster you can do, like, similarly, technical work that is remarkably similar to AI R&D work in many critical elements.

7:35And 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. 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.

8:06And 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 sense is probably they're, you know, in the space of all possible views, they're pretty close and pretty aligned. But now, obviously, we have policy disagreements despite these kind of relatively similar views around the trajectory of the technology itself. Yeah. So, like, I say, you know, when I say people have this perspective that I trust, this is a combination of people at OpenAI, Anthropic, Google Deep Mind, you know, also people who are working on policy, people who are working on, you know, people like at places like Meter without cost of generality or specificity, you know, people who are working on alignment in various forms, people who are working on, you know, any number of things.

8:59And 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. And RSI has, you know, on a slow takeoff style, low level, already kind of begun. 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.

9:29And, like, there's no one 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. 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 OutSystems, the leading agentic systems platform.

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Neural network interpretability

13:06Per 2. 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.

13:29As 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,

14:02but some pretty interesting advances in back-prop-free training inspired by this paper. My takeaway from some study of that so far is that basically all of ML architectures to date are limited by the need to be differentiable in order to do the back-prop chain rule process. And so back-prop-free training allows you to explore a bigger section of the total possible space of ML architectures

14:33because 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 back-prop 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 really only the strong activations of an individual neuron matter, and if they're just slightly activated, that probably doesn't matter too much for its overall function,

15:07then you could say, well, hey, why don't we just focus in our guessing and checking when we're doing this back-prop-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, that cuts down dramatically, of course, the number of guesses and checks

15:37you need to make. And what we're seeing out of this community is that it basically does work. Interestingly, the gradient updates that they are making are almost exactly the same as the updates that were made with back-prop. And it does show, I think, that there is at least some substantial truth to the idea that 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

16:10something. I'm not sure yet if it should be considered like the mark of a new era. 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. Then we asked Zvi what he made of the essay.

16:33I 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. 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 Eliezer, but also a bunch of other people who have a position to know.

17:06And 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. 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. I don't think Nick did that bad a job of presenting it.

17:36I 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 tell what every neuron is doing. You can make some like weak predictions about some things that this network might do in the future. 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.

18:08And 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 Bragas 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 clearly a 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.

18:40And there was the two are responding to about equally strongly in the same neuron. 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 check the rest of it, that like, his hypothesis is mainly false.

19:12To what extent do you think polysemanticity 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. 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.

19:46You 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 a better 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.

20:17And 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, OK, 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. You said, like, OK, we take 10 to the 47, you know, compute, you know, 800 hours to solve this problem. And now it's 10 to the 45 or 46.

20:47You'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 votes.

20:54Later in the conversation, I turned to a different kind of result. Soares is an independent researcher who posts experiments on X. And I've been following a couple of them. 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,

21:29the 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? So my read on this is the reason it matters is because you should assume that this is kind of a Mercury'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

22:01the line, right? If it is valuable to the AI to learn to do it, it's almost inevitable. So, like, what Sowers 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 that the AIs are capable of learning whether or not they are being, like, trained or evaluated in some of these specific ways.

22:36We 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 buy.

23:01From interpreting a 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, 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 send to a committee for, like, further, you know, deliberation. Like, what do you think people should sort of be synthesizing this to at the level of

23:37principles, best practices, whatever, so that we hopefully get good results from these more powerful tools and don't shoot ourselves in the foot? Right, so, like, I try to be, like, object level helpful to everyone at all times by default, but I do make an exception for raw capabilities work that is primarily just about making it easier to train smarter and better models, where, like, I'm not going to lie to you and sabotage you, but, like, solving the problems you're on your own, and if you want to hire me, you can maybe offer me $100 million, and we'll see. But in this case, like, it seems like there are some obvious capabilities you could do where

24:15you 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? 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.

24:46I would be very, very scared to mess with any of that.

24:53You are definitely, like, once you start to explicitly optimize for what the network looks like to you, how the network to use represents 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. 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

25:26out and did the voiceover technique, like, you are obviously playing with, like, liquid fire in all of these proposals.

25:34You'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.

25:44But, 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. 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, and they do various things. And, you know, Scott Alexander would say, I noticed what your name is, right? Like, oh, it's good fire.

26:14Don't worry. Like, I'm not sure it's good. I just, it's fire. 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.

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The cognitive core

28:03Part 3. The Cognitive Core Still with Zvi, I put a popular idea to him. A neural network is a really bad database. Like, why would we want to make 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

28:35sounds promising? So this has been a kind of a, like, Holy Grail-style, like, thing for a while now. Like, Karpathy called it the cognitive core, right? It serves as an 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 have, 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

29:05this cognitive core. And then we will load contact information into context. It will look information up as necessary. It will ask questions of other 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. 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

29:38amount of logical gates that together is the core of how a human brain works, is the core of how an LNLM functions, right? Like, is the, like, 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, you know, Herpathy 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 looking at LLMs, looking at, like, how much big model smell matters, looking at how humans work, like, it is just, you

30:12know, you look up all the facts one at a time when you need them. I memorize the effect kind of facts. We are not the same. Like, 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. What was that acronym? What does that word mean? How does this detail work? We don't know.

30:43And, like, it's a lot better when you have an LLM by your side, right? 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 link together, that can sort of form patterns in

31:15how things tend to work in ways that are kind of subtle, but, like, both of the intuitions 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. 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.

31:48And, 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 to 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. 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.

32:20And, like, I think this is, like, a lot of what big model 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.

32:32And so, you know, there's obviously a tradeoff here, right? Like, you have a 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 need the most important facts that 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 statistics and links. But, you know, I think what we've seen is, like, as compute gets cheap, as algorithms improve,

33:04the break-even point keeps going up for how many things the LM should memorize. To the point where, you know, an Opus 5, 5, you know, and similar, like, current frontier models just know a ridiculous number of things. And I wouldn't have it in the way. So why does this matter? Because maybe the space of minds is very large and minds are just different. So why does it matter whether or not there is a semantic core or processing core that can be identified?

33:40So 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.

34:16Because if you have a core cognitive, you have a pure cognitive core, right? 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?

34:47Do you pick an 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 at least a fact. It is, like, some sort of value, some sort of, like, parameter. 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.

35:17And so, in some important sense, like, the true cognitive core is completely amoral. It's completely indifferent. It does not care about your feelings. It 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.

Security and banking risks

35:35Part four, Black October.

35:38This 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.

36:03What 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 trust the records, for one thing, right? You're, I mean, there have been some, I'm sure we could look into some, you know,

36:34extreme 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. We can come in. We have, we know we have, you know, 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.

37:07What'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, let's say, open-face style 1,200 agent traces that are all millions of tokens. 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.

37:39Right. 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 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 know what numbers belong in every single account, right? So, number one, the FDIC can just say, and this happens a lot, the FDIC can basically say, like, look, maybe the records are unclear, but you can still issue checks and the bank

38:12will 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 would over-withdraw, we will come back to you and we'll ask you for the money back. And this happens, like banks have over, you know, overdrawn accounts to limits that, you know, were not acceptable. And sometimes they've sued people for fraud, even though the bank made the mistake, right? So, so it happens. Like 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.

38:46And that's the beauty of the banking system. The beauty of the banking system 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? 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 texas of what that institution is and the data in it, you can reconstruct over time.

39:19So I don't know, I'm, I'm, I'm very sanguine. I'm like, I'm like, it'll be fine, right? It'll be fine. You know, you, 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 think it's, it's all just going to be okay. 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.

39:51Then he stepped back.

39:56Every 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.

40:26And I mean, very sadly, a lot of crypto guys have died. So, 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 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. So I think, you know, it's not a tenable situation to expect users everywhere to hang on to kind of

41:00very valuable keys. Like one endgame is the WorldCoin endgame. And the WorldCoin endgame would be 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, you know, pseudonymous, but, you know, you have this ability. I think there is an ultimate endgame, I feel. I feel the ultimate endgame is just the network knows everyone, right?

41:30The 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 that's an uncomfortable realization that people will get to eventually.

41:51Part five.

Credit as the compute bottleneck

41:53Credit 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. And so they're pretty much forced to realistically bet entirely on the credit risk or the credit quality of the customer,

42:30like 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,

43:01they 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. 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. You can't have this, like, 10-person startup being funded just for, like, 18 months. Correct. So, I mean, what both the lender and the cloud want is an investment-grade customer like a Meta or a Google.

43:36They're likely not going to get this unless 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

44:06unless you raise an enormous amount of money to basically try and pay 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 up front, like, it doesn't matter. Anyone will work with you. But that's why you see AI startups raise these astronomical rounds before they've, you know, got revenue or their product or traction or anything because it's more like playing with red dots.

44:37It's a 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 AI lab, which inference provider should I fund? Like, which one's going to win? Which one's going to work? 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

45:07that you don't need to raise the big round 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, they're 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. Prakash asked the practical question.

45:40A startup that needs a thousand GPUs, how much less does it have to raise with SF Compute in the picture?

45:48So 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 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 don't get comfortable with things very quickly. You have to show proof over a long period of time.

46:20Um, and so that's what we're doing.

46:24I was struck by how much more the hyperscalers charge for compute than the NeoClouds. So I asked Evan how long that gap could last. My guess was that on-demand capacity explained part of it.

46:38Hyperscalers do not have a significant price premium relative to NeoClouds. Um, it's an illusion. Um, if you're selling on-demand, um, um, on-demand has a price premium relative to reserved contracts. Uh, NeoClouds are mostly selling, um, reserved contracts to folks. And there just is a, there's a discount for, um, having a longer term contract. Um, you're, you're giving up a premium, um, 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. Um, and that premium is pushed onto the customer.

47:10Um, so one of the things that is different about an AWS or a GCP or a hyperscaler is if you go and you pay, you know, their 11 or $15 an hour, depending on what you're trying to buy, um, on-demand price, um, you'll, you'll get that capacity. Uh, you, you won't get infinite amounts of capacity, but it's available to buy. Um, and notably, the reason why it was available to buy is because it's so damn expensive that, um, 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

47:42in order to make it viable. Customers of SF Compute can resell the capacity they bought. Evan explained how they keep that market liquid.

47:53Uh, so we do two things. Um, we have some capacity that we've sold as a way to, um, uh, cause resale to occur, um, in the case that, uh, GBU demand goes down. And then we have some capacity that, uh, are you familiar with Henry George? I'll say no. Okay. Um, there was an old economist named Henry George, um, and he created a thing called a land value tax. Oh, okay. Yes. George's tax. I do know this. Yeah. Um, yes. So, um, what we did is we created some of our capacity available for sale, uh, but with

48:29a land value tax that basically requires someone, actually, you secondly, like a Herberger's tax, which is like an offshoot of a land value tax, um, that basically says, look, we'll sell you the capacity. Um, but somebody else has to be able to buy some portion of that capacity from you. Um, and so this just forces, um, liquidity onto the order of, okay, in the first place. Um, it's kind of something that is, um, it's like very controversial. Um, yeah, basically we, we discourage hoarding on SF compute, um, in, because like if you hoard

49:00all the capacity, no one else can get it. Um, auto research in particular is, um, does this, but even farther. Um, 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, um, you get the maximum priority. If you get a small, tiny job, if you want to just run something every so often, uh, we will put you to the front line of the queue so that, um, the tiny little startup gets, um, uh, the most advantage to this relative to everyone else.

49:34Um, and then we do similar things on, um, the rest of SF compute, uh, with Herberger taxes and other mechanisms like this. Where we say, look, uh, you can get this capacity from us, but we're going to require that some of it's for sale. So nobody holds the capacity, um, this makes us quite liquid. And it also means that SF compute always has capacity. So right now, basically every GPU cloud is sold out. San Francisco compute is not sold out. Um, we're continuously not sold out because we've locked in our ability to not sell out for years.

50:02Prakash recalled Evan predicting on the MTS podcast, a credit crunch within three months and asked where it is. I mean, that's to my own horn, but I think I was almost exact on that timing. Um, I don't remember when that MTS line thing was, but it seems like, um, uh, the core reef debt is getting more expensive. Although I think there's a lot of complexity in that one. Um, it also seems like it's very hard to close new customers unless your investment grade or in your investment grade. Um, and many customers are being asked to pay for a hundred percent upfront payments on their

50:38contracts. Um, but I think for the next, um, possibly a year or two, um, if you're a VC, you should be, um, prepared to write some really big checks for your portfolio or encourage the companies to sell. Um, I think we're going to probably see a lot of consolidation of AI labs and inference companies. Um, 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, uh, to pay for the entire contract upfront and they won't be able to

51:11do that. Um, 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. Um, and I think, um, alternatively, they'll go out to investors and they'll try to raise big rounds. The investors will say no, because I think, um, at, you know, at some level of scale, they'll, they'll probably just not be able to stomach the risk. Fascinating stuff. It's quite a backstory. It's quite a financial and GPU alchemy project that you guys are putting together. And, um, from what I understand of it, I do hope it works because I'd love to see a

51:45future in which we can continue to have startups and, uh, in which we don't collapse into like five big tech companies, uh, you know, for all future time. That is also my concern.

51:58Part six, atoms over bits.

Atoms over bits

52:01Lonus is managing partner of Cambrian, a fund built on the thesis that the physical supply chain behind AI, the memory, the wafers, the fads 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.

52:22I mean, at a very high level, how was this not priced in? You know, maybe that's like such a basic question, but, um, you know, 2017, you were early 2026, like it's all staring us right in the face, right? Like the skeptic goal take would be everybody knows about all this at this point. Yeah. Where do you see that that is not the case? Yeah. I mean, it's very confusing, right? I mean, if you look at, uh, how these semi stocks are trading, they're trading at 16 times earnings

52:52or so, um, and they can, you know, SMP on average is trading at 22 times earnings. And Walmart is trading at almost 40 times earnings. And so the market is like, um, like not only does the market not think that the current earnings will grow, they think that they are not durable. They think they're less durable than the overall earnings of the rest of the market. Um, and you know, I mean, and then of specifically the companies that we're very excited about, whether it's silicon wafers or, or, or memory or capacitors, um, or foundries,

53:22um, those are trading at like three, four or five times sales. Um, and, and so, uh, you know, extremely underpriced. And I guess it's, um, there may be 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 peeled, right? I mean, even, even in the base, some people are not AI peeled, but if you go to the east coast, you know, almost nobody's AI peeled. And so I think that that is like one big input. I think the second one is, um, like before we had this thesis that the private market is actually

53:54more efficient when it comes to AI than the public markets, because in the private markets, you have all of these ex operators, technical people who are bidding up this small set of startups and they're bidding them up to these high multiples, 50 times sales, 100 times sales. But in many cases, in some cases, it's rationally, right? These are rationally high multiples because people here are peeled and technical. Whereas on the streets, there's a very different setup where very few people are technical, very few people are peeled. And especially as a share of overall, um, traded money, it's like super, super small

54:25and the market is much larger. Um, and so there's like the market structure is, it's, it's just, uh, it's just very different. If you had to steel man, the, why these companies are not mispriced story, could you do it? Yeah. Well, okay. One steel man is that many, many of these businesses are not great businesses in normal worlds. Right. So especially the, the, the add-on businesses we like, whether that's the pastor, Silicon Wafers, what have you, they have, you know, high fixed costs. Um, if they don't have a set utilization rate, they actually lose money.

54:55Um, 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, where they get this big operating leverage and they become amazing businesses. Right. You know, today they, let's say they have 10% gross margins safety. So a terrible business by, by Wall Street standards. And if they're able to raise prices by 10%, you know, they suddenly double earnings. And if they're able to double prices, the 10 X earnings. So, uh, but that only happens in a, in this very specific air world in a normal world that

55:26basically never happens because usually they are very good at, at like matching demand patterns with supply ramp. Prakash raised the market's worry, a memory oversupply by 2028 or 2029. How is the thesis any different?

55:43Yeah. 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, um, at some point they should re-rate to at least 10 or 12 times earnings. Um, if it doesn't require prices to increase, uh, it does require, um, you know, um, the amounts to, to continue to outpace supply. Right. And, but then when you break down supply, almost no expert thinks we can more than at most 2.5 X, um, DRAM bits by end of decade.

56:16Yeah. Right. I mean, um, nobody thinks we can three X. That's like, I have zero experts, uh, within a track record have said we can three X DRAM supply by end of decade. Yeah. And so then you should have on a very naive level, you have to assume that, okay, the demand will not point three X by end of decade, which of course, uh, everyone, um, here would assume we will. Then of course, there's the separate fact, um, term in the equation, which is memory compression. How much memory compression do we get? How many bits do we actually need per, per token? Yeah. Um, and you know, we've been looking into how, how this, for instance, developed from

56:47GPT three to deep seek before. So, you know, but in that same time, when memory, uh, it's, it's per token, the Chris 500 X, um, DRAM bits, uh, DRAM, um, DRAM bit price rose by six X or seven X. What would you need to see in the market to say that memory prices are going to go down? 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, um, you know, we just get memory compression way faster

57:18than we get other economically useful features, right? Such that we don't get the Javon's products in the short term. Yeah. 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, that would be, would be one thing. I think in another, like, very case for the semi industries, if fee, for whatever reason, it's like energy becomes larger bottleneck than, than semis. Right. And so there's less incentive to beat up the marginal wafer or the marginal memory bits, uh, to such high prices.

57:48If you anyways have to wait a quarter to get the price, uh, to get power in. Right. Um, 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, uh, you know, going forward. Um, I mean, we finally kind of gotten 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. Um, a big reason why you can scale energy production faster is just that there's so much more latent

58:20supply of things. So, you know, for instance, now the, the, the frame turbines are bone lake, right? These large turbines that turns from natural gas into electricity, they're sold out until 2030, you know, from Giver Nova and Siemens, et cetera. But then people find other ways. They find that you can use a truck, uh, you know, truck motors and things from airplanes, and we can stitch things together and kind of make it work. 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, um, latent supply of wafers or of memory base.

58:51Like all of that is already used. There's no fat essentially. And there's no others to really bid out at this point. 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 going to get like bio risk. You're going to get like random guy, like, you know, creating like this, this thing that,

59:24you 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 difficult than semiconductors right now, actually. Yeah, I guess I would probably disagree that it's easier for an LLM to

59:44and to innovate in, you know, hardware supply chains than it is to innovate in biotech. And it's not that one is like, um, fundamentally harder than others. It's just like 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 wave or how to make DRAM bits. And, you know, almost all of this is very tacit knowledge that sits in every engineer's head. 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 RL environments that the models have been trained on

1:00:17to perfect how they can do, how they can, how they can design a wafer. Whereas the same is true for bio. But right now, 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 50 percent, like we're willing to pay like millions of dollars. Like, so they're buying up data, you know, from all of these, all of these companies. And they're creating our own environments for semiconductors, which is definitely a key, key area that they're doing.

1:00:48I 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? Yes.

1:01:09Part of that's true, but it's a very significant difference between designing a ship and producing the ship. So designing a ship, I agree, that is like a core priority for all of the labs. It's really one of the key ways to get RSI loop closing, right? That's very different from building a, you know, 2 million square feet, you know, fab, right? And designing that from scratch. There's no really good RL environments for that. There's no good manuals for it. All of it is tacit knowledge sitting in this, you know, Japanese engineers who've been working

1:01:41at the same company for three decades and it's just very hard to, it's just very hard to extract that information in structure. 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 timing of it and like what happens in what order. So like, so on that point, right? So for instance, Hawaii in, in, in China, they can design roughly the same, the same quality

1:02:13ships 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 700 meter, you know, process nodes in, in China and HBM two or an HBM three. So Gavin Baker has this term, the bottleneck bros. So, um, I, I, you know, one of the things that strikes me is that, um, technology intelligence in general is a way of finding routes around obstacles and problems,

1:02:49right? It's a way of solving problems. Sure. 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, uh, 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.

1:03:21Right. Yeah. I mean, I, I think that, that, that, uh, that will happen, um, right. That will certainly happen. And then the question is just like, okay, when we are at that point where we have this, uh, an AI smart enough to solve some of the hardest engineers 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 the email jobs and Excel jobs that we currently pay trillions of dollars per year to solve. Right. Um, and, and, and then if it, if it does that, then that unleashes trillions of dollars of value that the supply chain could, could capture.

1:03:52Part seven.

Adversarial diffusion and growth

1:03:54Does Elon take anyone with him?

1:03:57After 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.

1:04:14Yeah. 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, and the, 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 going to like, okay, we need to spend like $10 million for a new, a completely new modality. Are they going to do it?

1:04:45And 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, they, they have lost out business before. So I, and it's very difficult for senior management at like ASML to say, like, we're going to chase this new paradigm, right? Right. We're going to redesign for something that our, that our customers don't want. And we're going to do all the redesign work. They don't have the capacity. They don't have the intellectual capacity. They don't have the talent.

1:05:16They don't have the capital to do it. They're not willing to risk. Elon will risk like a hundred bill. Right. And, you know, it's, it's a bet that makes sense for him. He can absorb the losses. Uh, ASML cannot. You know, if Elon can, you know, escape gravity literally and figuratively, uh, does he take anyone with him really? I guess is my question. Or does everybody else still, uh, you know, is still kind of gravity bound and forced to pay, you know, whatever the market prices for the outputs from this $30 million company

1:05:47that isn't, you know, reinventing or, you know, dramatically expanding its capacity. I think what ends up happening, like, look at what happened in the launch. 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 going to be 99.9% of the market for capacity. Are there other providers? Yes, there are, right? Like he's making a margin.