Dylan Patel – Two labs will soon control most of the world's workforce

Okay, I'm back with Dylan Patel, founder of SemiAnalysis. Our version of a family Thanksgiving dinner is a regular yearly podcast. We are not actually related.

Don't tell the people this.

It'll destroy the myth. Walk me through, basically where the world economy is headed is more and more becoming a function of where lab economics are headed, where the compute market is headed, et cetera. So I want to understand where the crazy future ends up within a few years. But let's start with just where we are today. So walk me through lab compute and lab revenue right now and maybe projecting out a year or two.

Yeah, so when we go back to last year, even at the end of the year, most of GDP growth in America was just AI infrastructure. And as we look towards this year, about a third of the compute coming online is for the labs, for OpenAI and Anthropic. Now it may be built by others and then rented to them, but At the end customer, it's them. As we go forward into the future, the numbers for computer ballooning, we're at a little bit over $1 trillion of CapEx this year. As we go out into '28, it's going to be more than $2 trillion. The labs are also taking an increasing percentage of this. And so ultimately, you've got a very interesting situation where the labs are going from companies that spend tens of billions of dollars a year to hundreds of billions of dollars a year to forecasting to spend trillions of dollars a year even towards the end of the decade. And this is at least some of the contracts they've begun signing with their partners. And so this requires a big reshaping of what happens with their economics, right? You know, so up until now, they have been companies that mostly lost money. Anthropic started turning a profit in Q2. It's believed at some point in Q3, OpenAI could potentially start turning a profit even with the big rise of codecs and 5.6 and all this. But if we go back a year ago, everything that they— all the money they had was venture-funded losses, right? If we go back to even the beginning of this year, it was venture-funded losses. They've now turned the corner and are actually starting to profit. Now, that doesn't mean they're not taking in new capital. The new capital is still coming in to accelerate the growth further. But ultimately, there's more and more of their business is being funded off of their own revenue rather than capital injections into them. Over the last year and a half, their margins have really skyrocketed. The base cost of compute tends to be around $10 or $13 or $15 million per megawatt. The most interesting aspect about what's happening now is before, again, they were generating, if they served a model, right? GPT-4 being served on NVIDIA Hopper GPUs was generating negative gross margin. for OpenAI. But now when OpenAI serves GPT-5.6 or Anthropic serves Opus 5 or Mythos, Fable 5, their revenue generation has passed well beyond the sort of incremental $10, $15 million per megawatt. In the case of Anthropic, the revenue has gone as high as $50 million per megawatt. And what that now enables them to do is, hey, if I spend $10 on inference capacity, I actually generate $50 of revenue, and then I can turn around and incrementally spend all of that profit on training.

One thing I'm very interested in understanding is how you see the centralization of compute happening at the labs, or the relative ratio of compute that goes to the world versus goes to the labs. Where if you say right now a third of marginal compute is going to the labs, by when is it over half of the incremental compute in the world is going to the labs? And by what point do the labs have basically Vast majority of the world's compute.

Yeah. So earlier this year, the beginning of this year, Anthropic OpenAI started at 2 for OpenAI and less than 2 for Anthropic. End of this year, they're both above 5. So they've 3, 4x compute as a whole. When you look at the incremental compute added, that's about 30% of the compute added this year. And as we step forward to next year, given what's already been signed and penned and inked, you've got something even more dramatic, right? You've got Anthropic, OpenAI are taking as much as 40 to 50% of compute next year. And this centralization doesn't look like it's slowing down or stopping. In fact, it looks like it's only accelerating.

Right.

Now, who's building that compute for them will change. Next year, a big new entrant is, for example, SpaceX is building a ton of compute, and they're actively going to lease quite a bit of it. to Anthropic and OpenAI most likely, 'cause they're the ones who can, who have the marginal capability to pay the highest price. In addition, OpenAI and Anthropic are also starting to build their own compute. OpenAI with their own chips, Anthropic with TPUs that they're purchasing from Google and deploying with FluidStack. And so when you ask, hey, when does half of the world's incremental new compute go to just OpenAI and Anthropic? I mean, it's really by the end of next year, it's already half of the incremental compute is going to Anthropic and OpenAI.

Because, Compute is growing so fast, incremental compute is going to be basically most of compute. So it's very soon, you're saying maybe within a year and a half or 2 years, that most of the world's compute is owned by 2 labs, or at least is serving the demand from 2 labs. How long do you think? So there's this trend where maybe world compute in gigawatts doubles every year, but the compute at the frontier labs triples every single year. But if you keep the current trend going, it goes from 2 at the beginning of this year to close to 6 at the end of this year. Just multiplying out by 3, 18 by the end of 2027, 54 by the end of 2028. Are you like, okay, at that point they simply can't continue tripling given the amount of world compute? Or how, how do you see the world compute situation over the next few years?

Yeah, so if the incremental compute adds this year are 30 gigawatts, next year 50 gigawatts, and the year after that 70, roughly, you end up with this really interesting phenomenon, which is, okay, well, a new watt deployed this year is significantly more efficient than the watts deployed 2 years ago. So actually, you know, a humongous percentage of the world's compute was deployed this year.

Yeah.

Even though it didn't double the number of watts deployed, I'm deploying GB300s and TPUv7s and Trainium 3s, which are way, way, way more efficient, you know, 3x, 5x more performance per watt than the prior generation chips. And so ultimately you've got a huge ladder here. So if Anthropic and OpenAI take on, you know, 45% of compute next year, You've got them in, let's say, December '27, they have taken on half of the world's incremental new compute, but that half of the world's new incremental compute is actually at a higher performance than everything else before it.

Right.

So you've got another multiplier on that. So by the time you're in like towards the end of 2028, if this trend continues, which I see nothing that's stopping it, you've got them just controlling most of the usable, you know, FLOPS in the world.

The thing I'm confused about is why you think we only add 80 gigawatts in 2028 if we enter a world in which the price, the value of compute increases so much.

That's the upper bound, by the way. That's the, that's the like, I'm so fucking bullish.

Right. Okay, so let's, let's do some chain of thought here. So when I interviewed you a few months ago, you said in order to make a gigawatt of, I think, Vera Rubins, you need, what's it like, You need 55,000 N3 wafers, 6K N5 wafers, and 170K DRAM wafers. I don't know those numbers.

I'm gonna troll you, but the way you said wafers was so fucking idiot.

By the way, when we first moved to the US, I had the VEW thing pretty bad, and I was a vegetarian.

Vegetarian. I remember you told me about this.

In Nerd Dakota, I was in elementary school, and I'd be like, can I get a wedgie? Can I get some wedgies? Anyways, so that's for 1 gigawatt, right?

Yeah.

Now, I had an LLM run your wafer fab equipment model and figure out how much tooling, how much the tooling costs to produce a gigawatt of compute basically every single year. And it said like $3 to $4 billion. Now, suppose you add in clean rooms and shell and everything else at the fab, So $6 billion of like fab CapEx produces every single year a gigawatt, and a gigawatt produces right now $100 billion of revenue. But also that $6 billion in CapEx is producing a gigawatt every single year, and that gigawatt is producing $100 billion every single year. So even over the course of 5 years, so, you know, the first gigawatt has generated 5 years of profits, the second gigawatt that the fab has produced has generated 4 years of profits, and so on. $6 billion of CapEx at the fab level will have generated over $1 trillion of end AI revenue.

Yeah, there's a lot of OpEx along the way.

Of course there is.

There's a lot of other CapEx like the data center, the power.

And you need to pay like, you know, the OpenAI for the R&D.

Installation. There's a lot of different people who need money here. So, but yeah, it's gonna be huge.

But take away half of it for all these middlemen. That still means there's 100x discrepancy between fab CapEx and end revenue generated. More than that, actually, really, but we're just being very conservative. And as a result, this is capitalism, right? Like, you would imagine that people are gonna figure out, like, we're gonna be— you have this huge discrepancy where you can turn $1 into $100, and they're not gonna figure out a way to make more mirrors?

I mean, they are, right? Just these mirrors take some time to bake, right?

But the emergency is so big, we're like, Anthropic and OpenAI are like, we could make a trillion dollars right now, but we're just bottlenecked on the mirrors that go into the ASML machines. Okay, how can we make more mirrors if we spend $100 billion on this, right? That's the situation we're going to be in pretty soon. And I'm just like, we're not going to be able to solve that supply constraint? That just seems quite hard to imagine.

No, there's definitely— you've seen people do funny arbitrages here where they buy turbines and then they try and resell them, because the value of a turbine is way more because it's the the thing bottlenecking your data center. I think if anyone had $400 million and the ability to convince ASML to sell them an EUV tool, they should totally just go buy one and wait and then sell it for north of $1 billion. But ultimately, yes, capitalism will cause these things to expand, but it's a whip, right? It takes a long time for the whip signal to get to the tail end of that. And so the supply chain doesn't react immediately. In fact, You go to talk to someone at Carl Zeiss, they're like, yeah, yeah, yeah, we need to make 100 EUV tools by the end of the decade. I think when we first had our— when we had our episode earlier this year, they didn't even think they needed to make that many, enough mirrors to make 100 EUV tools a year. And so now they're like, sort of, they're like, okay, we need to do that. But in reality, you know, because of all the economics of what's going on, it should be even more. But it takes so long to—

Suppose that every single company in the firm— sorry, in the stack got private equity'd. Like, somebody came in who was super AGI-pilled and was like, we're going to maximize production. What do you think the physical constraints on making more things would be? Because the reason I ask is we're pretty soon going to be in a world where the lab revenue or just AI cash flows, because obviously the accelerators also have these huge cash flows, will be so big that you can just fund extreme expansion of all this production from cash flows themselves.

Yeah, I do agree generally. There's obviously some physical constraints. The way the supply chain is expanding currently, the 100 is roughly still the right number.

For 2030?

100 ASML tools for 2030. But if you said, Carl Zeiss, here's $10 billion, please fucking just expand production, that would change things. And you would have to do this with every company in the supply chain. I don't think it'll happen this year. I don't think it'll happen next year. I don't think it'll happen the year after because the world is capital constrained.

But in a world where, say, the top labs are generating, let's say even combined, $1 trillion in revenue next year. They're not able to take 10%—

I don't think they're going to do that, but—

Yeah, or hundreds of billions at least, right? It just seems like they realize where the world is headed. I feel like they could just make—

So the thing is, the labs can spend hundreds of billions. They're going to generate hundreds of billions of revenue next year, but ultimately CapEx next year is like $2 trillion. So you've got this big mismatch, right? The wafer fabrication equipment supply chain will do something on the order of $200 billion. The data center market supply chain will do even more. The accelerator supply chain will do even more. The energy supply chain will do a number. You sum all this up, it's going to be well north of $2 trillion of CapEx. So the labs have not yet gotten to the point where their cash flows can fund this stuff.

Of course. Yeah. I mean, obviously they will never get to that point because they want to keep Yeah, you want to reinvest. You want to make your CapEx higher than your returns. But the key question I really want to understand is if the current trends continue, it would be north of 50 gigawatts per lab by the end of 2028. So between them, they'd have 100 gigawatts. Those gigawatts, as you're saying, drive manyfold more throughput or more performance by 2028 than they are now, right? Because the hardware's gotten better. So not only have flops per watt increased, but also The hardware gets better at working with AI workloads. Okay, so 100 gigawatts for the labs end of 2028. How much is like world compute?

I think that may be a little difficult given 2028, you start to have, they've taken 70, 80% of incremental compute, and I'm not sure what happens to markets then, right? You know, how much does the price of compute skyrocket for them to actually be able to buy 70, 80% of compute? is, you know, Google or Meta or Amazon willing to sell even that much? Also, one caveat when we're sort of talking about these gigawatt numbers is, you know, when Amazon is serving Bedrock Anthropic models, that counts as Anthropic compute in sort of our worldview because it is effectively at the end of the day counted as revenue for Anthropic, even though like there's a revenue share and credit back and all that.

Yeah.

But ultimately in 2028, it's, it's, you know, if they get to 100 gigawatts combined, They have done really disruptive things to the market because anyone can make money off of $10 to $15 million per megawatt compute today. You literally, like, I kid you not, it's not that hard. Go get a GB300 rack, go download the KIMI weights, go download VLM or SG Lang, set it up. You know, Codex and Fable can actually help you do this. It's pretty simple. I mean, it's not like it's, you know, it's not trivial, but it's not like rocket science and go put it on OpenRouter. It's very simple. And you'll start generating more revenue than you're paying for the compute. And so this has sort of already led to this compute pricing, $10 to $15 million per megawatt, start to inflect up. And to get to that 100 gigawatts in 2028, you have to believe that the labs can outpay for compute because anyone can make money at $10 to $15. Does compute now get to $25 million a megawatt?

Yeah.

Does it get to $40 million a megawatt?

But as you're saying, it's already the case that the labs are generating way more revenue per megawatt than everybody else. If they stay as far ahead as they are currently, you would expect that to be the continuing case. If there's some kind of recursive self-improvement where the AI labs are relatively uplifted, where they have models internally they're not releasing externally that are helping them make the next model better, you'd expect that to be even more the case. And aren't you already seeing this where SpaceX or whoever's slightly further behind will just sell compute to the highest bidder if they can't internally monetize it as well as the labs? I feel like it's continue expecting them to be able to gobble up, like bid for larger and larger shares of the compute market.

I think that is my worldview that they will continue to gobble up more of the compute, but ultimately they can't do it at current pricing or anywhere close to it.

Sure, sure.

They do have to start paying $25, $30, $50 million a megawatt to really gobble up 70% of the world's compute in 2028.

Yeah.

To get to that 100 gigawatts by 2028, which is a very sort of aggressive goal. The other aspect of this that's really challenging is we've already seen a huge slowdown for the AI labs, right? This regulation that they advocate for is actually slowing down the labs a lot more than it slows down the open-source Chinese language models. OpenAI not releasing Astra, OpenAI stopping training for 2 weeks, Anthropic not releasing what their safety assessment said is Model 2, which is widely believed to be the next version of Mythos. They're clearly not releasing their best models, and in which case their revenue per megawatt stalls, or even can start to decline again because other models are competitive again. So it's not that they're falling behind, it's just that they're not releasing their best stuff.

Right.

What if there is some regulatory impact that prevents them from releasing their best models? Now their revenue per megawatt does not climb as fast, then their ability to buy that incremental compute for a higher price than everyone else starts to diminish, and then maybe they can't get to that 100 gigawatts is sort of the— in a world where safety doesn't matter, I do believe that's exactly what happens, right? They can start generating $100 million per megawatt or more, and they can pay $50 million a megawatt, and no one else has any logical reason to do anything with their compute besides say, please, Dario, take everything off of my hands. But there are, you know, forces at play which we cannot describe that would potentially slow this down.

Yeah, yeah, yeah. I mean, I think a good intuition pump is just What if the AI models were literally as good as a fully automated software engineer? They're not currently there yet, right? I think they're far from just being able to fully automate the job of a full white-collar worker. But white-collar workers earn 6 figures or north of that a year. And if you have a gigawatt that can sustain a population of, say, a million white-collar workers, let's say roughly, right? That's like, you could then off the back of that, that would be $100 billion. That's actually surprisingly low.

Yeah, $100K per person, million population.

Yeah. Yeah, yeah, yeah. I don't know, but it'd be many hundreds of billions of dollars if you get full AGI per gigawatt.

I think the other aspect of this is, and we've continued to see this, most of the value capture is not happening, right? Most of the value that these models generate does not get given to OpenAI and Anthropic. Thankfully, so far it is mostly just being given to the users. Yeah, right. Jane Street, with their exclusive contract with OpenAI for GPT-5.6 Ultra Fast mode, or Jane Street, where they're like one of Anthropic's biggest customers, is generating way, way, way, way more value out of the tokens they're paying for than Anthropic is generating in terms of profit, right? Because they get to, you know, make money off of the market. Or Meta, who at one point was, you know, rumored to be as much as 10% of Anthropic's business, they're generating way more efficiencies by optimizing their ad algorithms or what have you and getting engagement time 5% longer and all these things. They're making way more money off of using these models than Anthropic. And so ultimately, and that's what's required. So sure, if you had a million new software engineers, the cost per software engineer would also fall.

One thing I'm confused about is, does the market come into equilibrium? And if it comes into equilibrium, would you just expect the price of compute to equal whatever Anthropic and OpenAI can generate from it, or be very close to it with a small amount of markup for Anthropic and OpenAI? Right now, it's really weird that there is a 4x or more difference between what compute sells for and how much money Anthropic can make from it. And in a world where the revenue per gigawatt continues to increase, If Anthropic's ability to monetize a gigawatt doubles or triples or something, it'd be weird if then the gap continued to increase. And so Anthropic, just by having some software, having some weights, can take something that costs them $10 and then turn it into $100.

Yeah, so there's a bit of a— this is always a fun question, right? Which is, where does the value go in AI? AI's generating all this value. You've got the end user, which I think we all agree is generating more value than anyone else, hence they're paying a lot for these models. But then you have, you know, the app layer. Well, so far the app layer has generated very little value. Then you've got the model layer, which again, up until a year ago was generating negative gross margins and is now generating massive positive gross margins.

Yeah.

And looks like it's on the path to generating, you know, $100 million per megawatt. So turning, you know, $10, $15 into $100, as you said. But if we go back again a year ago, the hardware supply chain was generating all this gross margin while literally everyone else was losing money on it. OpenAI and Anthropic were just plowing VC money in, and as were many other startups. And many of these hyperscalers are building infrastructure without knowing if there was going to be a payoff. So ultimately, you had this negative value being created on the model layer almost, if you will, because they were selling the tokens for less than it cost them on the infra side. And all the value is being created, used at the chip, the fab. Initially in 2023, the memory guys were making no money. off of, you know, HBM or memory for AI, even though theoretically the value they were delivering was humongous. Now you've got— well, actually, TSMC makes way less value than the memory guys. Is that actually how much— you know, they're capturing less value, even though— so the value capture has shifted around a lot, which is very fun for people tracking the market or participating in the market, like Jane Street as an example.

They're a sponsor, but you don't have to plug them that hard.

So, you know, what happens, you know, going forward? Does Anthropic and OpenAI, you know, they've slowly started to balloon in value capture. Do they balloon and take all the value capture? Well, that was a thought. And then Elon showed, actually, no, I can sell my compute for $25 million a megawatt or $40 million a megawatt to Anthropic and Google. Even if it's a short-term thing, I've sold it for this price and I'll recoup my entire CapEx in a year.

So what's your prediction of how much the relevant Toronto compute, like B300s or whatever, that sold for $40B a gigawatt, that SpaceX sold for $40B a gigawatt to Google, what does that sell for at the end of next year?

I think most compute will still continue to transact at sub-$20 billion a gigawatt.

Even at the end of next year?

Because all of it has to be financed. For compute that you can build without financing, right, if Meta can build compute, Microsoft, Amazon, SpaceX can build compute without finding a customer, just saying, fuck it, I'm gonna build this compute, and then turn around and wait till it's already built. They now control what's going on. So most compute is contracted well before it's built.

Yeah, yeah.

And so this is sort of what Elon took advantage of in the market is he actually had all this compute and he was like, hey, Anthropic, I know you're making like $60+ billion per gigawatt. Why don't you just buy my stuff for a crazy amount of money? And obviously, it's not like Elon decided this or Anthropic decided this. It's sort of, The market's figured itself out. Other people, you know, you go to a random cloud, they're like, okay, I'm gonna build a gigawatt of compute or 100 megawatts of compute. I'm going to spend the CapEx. I need to turn around and find a customer. If I wanna find a customer, I need to find the capital. Who's gonna gimme the capital? And the customer, the customer has to sign a deal. And then I take the customer's commitment to the credit markets and I raise the capital. And so there's this sort of like completely different power structure where, Meta, who is effectively hoarding compute, them and SpaceX are plausibly the like number 3. And the only plausible number 3 is because they're hoarding all this compute. They're using their balance sheets and capabilities to build compute, to build compute without an end customer that's monetizing at a huge degree. And they have an actual balance sheet so they can go to the credit market and being like, hey guys, I have, you know, you build a gigawatt, you can make, you know, your margin, not a crazy margin, but you can make a good margin. And I now have all this compute. And now Meta and SpaceX have this optionality of looking around and being like, is my internal use case gonna make me more money, or should I go out there and sell it to Anthropic, OpenAI at crazy margins?

Yeah.

So now we've sort of entered a regime where SpaceX and Meta are saying, actually, I'm gonna build the compute and I can start to rent it out for not $13, I can sell it for $25, $50, and more.

So as I've been doing video essays and other formats, I've been looking to hire a new editor for the podcast, but actively searching for editors has been quite time-consuming because the vast majority majority of candidates don't fit the profile that I'm looking for. So I created a recruiter in GrokBot to see if it would help. I gave it a huge context dump where I monologued basically everything that I wanted, and then it spun up 4 other bots to narrow in on different parts of the search. One went through the last year of my email for relevant inbound, one searched my X feed and DMs, one went through the end credits on various documentaries I liked, and the last one looked for editors who work for some of the YouTubers that I follow. Grokbot then took all these different candidates that these subagents had found, it filtered them against my criteria, and then delivered for me a final shortlist to review. To be honest, the first batch had a few good candidates I wanted to see, but mainly a bunch of duds. But after I gave Grokbot some more feedback about what it was missing, it came back with a new list of candidates that I'm actually extremely excited about. I ended up saving this whole workflow as a routine, so every week now Grokbot checks my inbound email and X DMs for promising new candidates to potentially interview. If you want to try GrokBot yourself, go to x.ai/bot. What do you think their revenue per gigawatt is by the end of 2027? Like for Anthropic or OpenAI by the end of 2027?

I think it's highly dependent on who has the best model, if they're allowed to keep releasing their best models, but I don't see why it wouldn't be $50+ million a megawatt.

By the end of '27.

Oh, by the end of '27?

Yeah.

That's where it gets more challenging, but I think it could get to higher than that, like $70, $80 million a megawatt. blended across a company, if not higher.

Yeah.

And so I think if that's the case, right, then what happens to the price of compute? Well, if I'm Anthropic, incremental compute is worth it. Maybe I spend $40 million a megawatt on SpaceX compute. And if I'm SpaceX, I look to the supply chain, I'm like, well, I've struck this deal with Jensen where he's now all of a sudden using Twitter. And Elon's saying they're exclusive to Nvidia, but Why doesn't Jensen raise his prices? And then, you know, SK Hynix and Micron and Samsung looked at Nvidia and were like, well, why don't they raise their price? So I think the value capture, there's a bullwhip effect here, right? Where just because someone has risen the prices doesn't mean the entire supply chain rebalances immediately.

Yeah.

But over time, the supply chain will rebalance and things will cost more and more. And, you know, to get that incremental capacity, you sort of have to, right? So TSMC raising prices very slowly, but memory companies raising prices very quickly. You know, substrate companies raising prices very quickly, different parts of the supply chain raise. You know, Elon wouldn't have sold if it was $15, but he's selling because it's $25+. So obviously he rose his prices really quickly.

Yeah, I'm sort of surprised you think like revenue per gigawatt doesn't increase way more than even like $100 per gigawatt by the end of next year.

When does RSI happen? When does it take off, right?

I think— Even if RSI doesn't happen, the current rate of progress continues. If you just look at how much progress have you made in, let's say, the last A year and a half. Like, what was the model from a year and a half ago? Let's take like Claude 3.5.

My problem with this is the best model that exists in the world was trained in February. Okay.

You're saying maybe we just won't be allowed to release the best model.

And OpenAI says they're not training models for 2 weeks, man. What the hell?

Yeah. I mean, there's another, there's one thing like internally, are they getting enough use for it such that they'll like bid up the price of compute? Or another is like, does AI progress as a whole slow down because of regulation?

Yeah, but they're not even allowed to use this like new model inter— like Astra's not widely deployed internally even.

But still, I don't know, just like, if you have a model that is, what was a model released, let's say the beginning of last year, like GPT—

4.0? Is that 4.0?

Yeah, that's like, you're talking about a 4.0 to Fable size or Mythos 2 size leap by this point. Again, by the end of 2027.

Yeah, but Mythos 2 is not out.

Yeah, or even Mythos, right? That leap again.

Even Mythos is not allowed to be out, right? They've neutered it.

Yeah, yeah, yeah.

We can't use it to optimize inference performance. We can't use it to optimize all sorts of things. Right.

Yeah, maybe Maybe there's like some slowdown in AI progress or the deployment of AI that means that the revenue per gigawatt can be lower. But that's the only way I could see it being only $100 per megawatt by the end of next year.

Yeah, I mean, as long as the model gets better, the value generated out of it gets better. Obviously, who captures the value is still up for debate, but ultimately everyone's going to raise their prices because they can. And it's super inflationary, especially if the method of regulation is— right now, so far, it's just don't release the models. But more and more, the method of regulation is New York's banning data centers, Texas is holding moratoriums, Ohio's saying you have to, or at least trying to say you have to pay everyone's property tax in a certain radius. These sorts of things are going to decrease supply and increase cost, and that's going to get passed on as well. So you start to end up in a spot where progress does slow, at least in the external sense.

Yeah.

Even if the models internally keep getting better and better, I see no reason why, again, in a takeoff scenario, why would Anthropic not have their best model 6 months ahead of what is externally available because of safety and regulation, but also, you know, the competitive advantage. And then that 6-month difference, if progress accelerates, is actually a bigger differential. So, so that's the thing that would cap revenue per megawatt gains to a much lower growth than we've seen in the first half this year.

Here's something I'm very interested in. As these companies go public and they're accountable to investors and Let's say end of next year they have, I don't know, close to 20 gigawatts. So 10% of the compute, 2 gigawatts. Let's say they want to go from 60% compute to training to 70% compute to training, and their investors are like, well, if you're able to generate $100 billion per gigawatt, you're basically saying no to $200 billion of revenue in order to increase your training compute. And so investors are like, what the fuck? You're already spending so much on training. Why are you spending even more on training? As a public company, what do you think would happen if they're just like, no, we will keep increasing the share of compute we spend on training to offset the increase in revenue that each gigawatt of compute is giving us?

Yeah, so this is sort of what I personally believe, that the labs are going to allocate less and less compute to inference over time, which I think is very non-consensus. Everyone's sort of The standard belief of most people is, oh, most compute will go to inference. Most of it will go to forward passes for training, not maybe necessarily revenue-generating inference. But ultimately you end up with, if they're generating $30, $40 million per megawatt today, you allocate 40% to inference. If you now get to generating $60, $70 million per megawatt, do you still allocate 40% to inference and generate all this profit? and then do dividends and share buybacks, or do you go build AGI?

Yeah.

And I think the obvious answer from Anthropic and OpenAI, and not just at the executive level, but also their board, is go build AGI, 'cause it's way more profitable.

Right.

And so ultimately, you're going to see them ratchet up their percentage of compute dedicated to training.

While each increment of compute is getting more and more Profit generating if they had dedicated it to inference.

Right. And so the whole point is, well, okay, if I'm selling tokens, is OpenAI releasing ultra-fast mode for just external, or are they doing it internally too? And it turns out, no, actually I'm going to allocate it to internal and external because my internal value that I'm generating from super-fast AI or the best AI model is way more than what someone externally is. And so ultimately, Sure, I could generate $100 million per megawatt, but if I turn that towards AI research, what is the incremental progress that I get? And then what does that do towards my future earnings potential, the discounted cash flows of whatever the hell I've done, right? And so, you know, they're not going through that calculation, but ultimately it makes more sense to dedicate more and more compute internally. And the only reason to, you know, have inference compute be so large is so you can grow your training fleet.

Right, right, right. I think this is an interesting economics question that I feel like we can have the models digest of what would have to be true about a world where they reduce fraction of compute spent on inference.

I think they have been over the last 3 months already.

Interesting.

I think parts of this year they were increasing fraction of compute. So let's just take month by month. You would agree that every month Anthropic has added more compute than the prior month. There might be some noise when they sign a SpaceX deal or whatever, but in general, The amount of compute is a curve up. And so in January they added less compute than December, and yet their revenue adds skyrocketed and then they've sort of plateaued. They're only adding, they're not adding $25 billion of ARR every month now. And so that means the marginal megawatt they're getting is going higher percentage to R&D than it is inference.

Interesting. Yeah.

And so they are factually increasing their compute towards R&D today.

Yeah, yeah, yeah.

Yeah, I think this is self-evident if you look at what they're doing enough. Yeah.

So if I look at the numbers you said of how fast world compute grows, here's some things I want to understand. So it seems like if I added the numbers you just said, it would be over 200 gigawatts of world compute by the end of 2028, right?

Yeah, globally.

Okay. And how fast can that continue growing, like global AI compute after 2028?

Yeah, so 30 this year, 50 next year, 70 in '28. '29 should be like on the order of 90 to 100.

Like then just 100 more every single year or something?

I think the slope can continue to go upwards. I mean, it's hard to predict anything more than 4 years out given who knows what's, you know, are we in RSI regime or, you know, when is the world economy growing at 10% a year? 'Cause if you're at 100+ gigawatts a year, you're at absurd revenue GDP growth.

If you think there's 200 gigawatts globally in 2028, how much is in China by that point? And how does Chinese compute continue increasing through this whole trend? Because if the RSI stuff kicks off in the West before China has a large amount of compute, maybe we're living in a different world than when it doesn't.

Yeah, so China today, so if we sort of level set back to 2022, The US was adding about 45 to 50% of the world's compute. China was adding about 30 to 35% of the world's compute and the rest being taken up by the rest of the world. Since 2022, we've had big regulations against China and a dramatic increase in America. So today, 70% of watts are being deployed in America. And China is really a very small number. It's sub-10% of watts being deployed. for data center AI compute is in China. And as we step forward, they're still at a very small number. Their domestic production is quite small. Their purchasing from Nvidia is still quite small. And a lot of that ends up in other places as well, right? Malaysia or what have you. So ultimately, China domestically still continues to have sub-10% of incremental new compute. So in 2028, it might start to inflect up, I think. But it's pretty easy to say China will have like 30 gigawatts of AI compute or less.

By 2028?

Yeah, in 2028.

Okay. And then how fast does their hockey stick go up?

I do think in 2028 they have a big uplift in what compute they're able to deploy. 2026, they're still mostly relying on a lot of the smuggled chips, you know, you know, a lot of the chips that TSMC made for companies that they thought weren't Huawei but ended up being Huawei, or a lot of HBM that Samsung is shipping, you know, sort of. But in '27, fabs start to go up. In '28 especially, fabs start to go up from SMIC and CXMT and such, where domestic production is actually reaching many millions of units a year. And now they're incrementally adding, you know, 5, 10 gigawatts in just 2028 of domestically produced chips.

Right.

Those chips are definitely worse than the chips that Nvidia will have in '28, or Google will have in '28, or OpenAI will have in 2028.

So even the gigawatt number overstates things, you're saying? It's like 30 gigawatts, but it's really much worse chips. But then how does it— if you think the world is going to add 100 gigawatts the following year or something, I know you said you can't really say that far out. How much is China able to add the subsequent year? Basically, I want to know, do they just hockey stick at the point at which they are able to start shipping large amounts of Do you think China is going to be able to catch up in terms of compute, or is it still going to be less than US plus allies?

There's a lot left to whether or not the US passes the MATCH Act, whether or not tools continue to get export controlled, how fast China can build their new equipment that they're starting to be able to produce domestically. But ultimately, China is definitely going to hockey stick. China's really good at is scaling manufacturing really, really quickly. And, you know, I imagine, you know, China will start to be able to extract more and more purchasing of even foreign chips into domestic China, or at least close the gap in what the US is allowing, you know, Nvidia to sell them or what have you.

But do you think China could do adding 50 gigawatts by 2029, marginal incremental gigawatts in 2029?

I think that's completely reasonable. And part of that could also be purchased from foreign. But yeah, I think it's completely reasonable that China in 2029 can do 50 gigs. But if most of those are domestic chips, there is some factor there where that 50 gigawatts is really worth as much as 20 gigawatts in America or from American chips.

Right, right, right. So yeah, you're actually projecting a world where maybe the leading lab in 2028 Has more compute that China will have in like all of China will have in 29 or even 30.

If you if you weighed gigawatts by their quality, implying that there's nothing done to slow down the U.S. labs.

Yeah, that's right.

Clearly, the government is starting and politicians are starting to do that.

Yeah, yeah, yeah.

Whereas China is not going to slow down AI. In fact, the only thing they're going to do is accelerate it.

So honestly, I when I interviewed Jensen and asked about export controls. I am a libertarian person, and I'm like, I wasn't like genuinely sure what I thought about this issue. I was steelmanning what I what is like the opposite view that he has, and because I think it's important to hash out ideas. But I'm like, yeah, maybe there's a world where if we just cooperated with China, it would be better for us, especially since they control so much of the supply chain and the other things that will be needed for robotics and other things. But I didn't realize the computer situation was as fucked as you're saying. Like actually, the export controls do seem to have like really If they ship the amount that you're saying, that's a huge difference. By the time we have automated coder and even getting into like automated researcher, China is like way far behind on the compute stock. And so if that ends up being the case, that would have worked. I think that's actually a notable success.

I would say the only caveat there is some of it is export controls, but some of it is also just financial systems, right? American financial systems are more willing to YOLO into startups than Chinese financial systems. systems, but once Chinese financial systems choose an industry to focus on, they'll subsidize it a hell of a lot more.

Yeah.

And so the Chinese semiconductor industry has significantly more subsidies than the rest of the world's semiconductor industries combined, which points to if takeoff is not as fast as you're implying, but actually takes longer, then ultimately China will catch up drastically on the semiconductor side, which then is compute at some point. The other aspect of this that I would think is I think is like noteworthy is Chinese companies today are not that far behind in AI models, at least percept— perceivably by the public, relative to the amount of compute they have, right? The leading Chinese labs have 100, 200 megawatts total of compute at most. ByteDance Seed being the one outlier where they have significantly more than that. But, you know, KIMI is not running, you know, a gigawatt or anywhere close to it.

Yeah.

Whereas Anthropic is nearly 5 gigawatts by the end of the year, right? Or more, sorry. And so the question is sort of, well, does it matter? And I think right now it doesn't matter that much, this difference in compute, because when we break down the compute ratio or budget of a lab, historically it's been, let's say, so far it's been like 60% training, 40% inference. But that training gets broken down further. And as it's actually like 50% of the compute is research, like 10% of the compute is development, and then 40% is inference. And what I mean by research and development is researchers are generating ideas, testing new architectures, testing new data mixes, testing new hyperparameters, whatever it is they're doing, new attention techniques, blah, blah, blah. But ultimately, when they do the training run, when Anthropic trains Mythos, It's sub-200 megawatts, right?

The pre-train or the whole thing?

The pre-train. It's sub-200 megawatts for, call it, 2 months. And then the RL is even less.

But you think the RL was less to compute than the pre-train?

At least in terms of single-site inference. I mean, single-site of pre-training, yeah.

But total compute was probably higher, right?

But it's sequential, right? So at most, the most they ever used at one point in time was maybe 200 megawatts. And then In reality, they had multiple gigawatts. So most of their compute was going to the research, not the development of a model.

Yeah.

And there's reasons for this, right? You can't, it's hard to coordinate all these clusters. It's hard to co-locate all of them. It's hard to do multi-site training. It's hard to do RL, you know, generating even more rollouts during RL does not necessarily make it better. There's all sorts of reasons why you may not be able to leverage, you know, all 2 gigawatts that you have for training onto training, right? Actually, I can only leverage 200 megawatts. As we get closer and as we get further and further down, implement automated coding, automated researcher, I actually expect the percentage of the compute budget that goes to research versus training to start to, and to start to like become a lot more fuzzy or even higher for training. Also things like continual learning, right? All of these things start to mean that more and more is actually going to training the model.

Okay, so if you end up in a world where you're doing 100 gigawatts a year, at current prices, that would be $5 trillion of CapEx every single year.

And then stack on the fact that you have to build the power plants way before then, slash it's a 30-year asset. You stack on the fact that the data centers are a 15, 20-year asset, and you have to build that then too. So the $5 trillion, you have to account for future years' growth, so it's actually going to be more like $7 or $10 trillion.

I didn't understand because you're not including the fact that, like, that doesn't include the fact that there's not the infrastructure for the power generation or whatever in the data center itself.

Right, exactly.

Yeah.

And the data center itself is— when you talk about AI CapEx, people are saying $40, $50 billion, but that's really just the critical IT.

Yeah, right.

The servers, the networking, the fiber, the transceivers, optical communications, all this sort of stuff. It doesn't account for the data center itself or the power plants themselves, which are being built ahead of time. Yeah, yeah, yeah. If I'm building 100 gigawatts this year and 150 gigawatts next year, well then all of the buildings for that 150 gigawatts need to be built in CapEx this year. And if I'm building 200 gigawatts the year after that, all those power plants need to be spent. You have to buy the turbines this year.

Yeah, yeah, yeah.

Right. And so you've got this like, this like actually it's much bigger than even $5 trillion if you're building 100 gigawatts.

Right. So very plausibly, incremental CapEx every year is getting close to $10 trillion.

By the end of the decade.

Right, which is going to be close to a tenth of the world economy and a third of— if all of it's going up in the US, it's like, well, the US economy will have grown as well, but still, at the current size of the US economy, it'll be like a third to a quarter of the US economy. would just be going towards data centers. And as I say that out loud, I'm like, maybe you're right and we just won't allow it. And that's the reason this doesn't happen, right? Because like for this exponential to continue, just like a quarter of the world, a quarter of America's economy is just building data centers.

Yeah, I mean, I believe in capitalism and reallocation of resources towards the most profitable thing, but at the same time, politics exist.

Yeah, yeah.

And credit markets exist and capital markets exist. So to enable Let's say that 100 gigawatts by 2030, or let's even pare it down to 2028 where it's like $3 or $4 trillion of CapEx across all of these items. A couple, over $2.5 towards IT CapEx and then another $1 to $2 on data center and energy and all the supply chain downstream like semiconductors and all that stuff. So if you're at $3 or $4 trillion of CapEx, where does all this cash come from? No one is generating that much cash from the business yet. Right? Hyperscalers, they funded all of the growth up until now. Google, Microsoft, Amazon, Meta, they funded a huge percentage of it. They were more than half of compute, but they now don't generate cash. They actually spend everything on CapEx. And in addition, they raise debt and spend everything on CapEx, right? You've seen Meta do it, even Amazon, even Google. You know, Microsoft will be there soon. Everyone is raising debt to pay for their CapEx. So now who is the incremental person to pay for this that was not doing it before? In the case of like Google, it was pretty simple for them to stop doing buybacks or Meta stop doing buybacks and turn around and buy computer infrastructure. And that doesn't have a huge effect on the market, but it does have some effect. But as you step forward to 2028, where the hyperscalers are now raising hundreds of billions of dollars of debt, and then all of their supply chain is raising hundreds of billions of dollars of debt, who pays for this? And so there's a few different ways. You know, there's the semiconductor companies like Nvidia and Broadcom and the memory companies turning around and deciding to fund some of this CapEx. There's the traditional infrastructure investors who are turning around and gathering capital and investing in infrastructure, and instead of bridges, it's data centers. And then lastly, there's everyone in the economy who's realizing Maybe I shouldn't buy a home, or maybe I shouldn't invest in credit for a home that's helping people buy homes, or maybe I shouldn't buy government debt. I should just buy hyperscaler debt, or I should just buy this data center's debt, or I should buy Anthropic's debt because Anthropic's willing to pay 20%, you know, rates for, you know, the incremental billion dollars to build their capacity because they know their revenue from it's going to be huge and they're going to pay 20% because it's still better than renting it from SpaceX for $50 billion a gigawatt.

Yeah.

So you've got all of this contention, but if you now do this, the whole world economy is like really shifted around.

Antithesis is a deterministic software testing platform that enables perfect reproducibility. It also unlocks some pretty insane approaches to debugging, like time travel. With Antithesis, you can jump to any point in the trajectory and start from there. So when there's a crash, you can rewind to the exact moment that something went wrong and freeze the entire system. The application, the database, even the environment itself. This lets you do something that would otherwise be impossible, which is to observe every part of a distributed system at the exact same instant. Time travel also allows you to add telemetry and logging to an event that has already happened. For example, you can rewind to 5 seconds before a crash and decide to capture all the network traffic. Most powerfully, Antithesis gives you a live terminal into your system that you can use to perturb anything you wish. Kill a node or disable a feature and then hit play and see what happens. Then go back and try something else. In production, you often only get one shot on goal with this sort of destructive analysis. If you restart a deadlocked service, for example, the exact deadlock you needed to study disappears. But with Antithesis, the original timeline is always reproducible, so you can test as many hypotheses as you need. And if you don't want to do all this time traveling yourself, you can just have your agents do it for you Via the Antithesis API. Go to antithesis.com/thewarcash to learn more. So you and I have been debating off air for the last few days whether there will be a sovereign debt crisis as a result of AI. And the logic is this: AI is— you have a situation where, as we were mentioning, very little investment turns into a lot of money, right? So the rate of return—

What a fucking problem, dude. Oh my God.

Can't believe it.

No, it is a huge problem for everybody else who can't turn a little money into a lot of money, right? So the rate of return is incredibly high, even at the data centre level. If you build a data centre and you can rent it out to Anthropic or OpenAI for like 10x what it costs you on a depreciated basis to build it, it's fucking crazy. And so you turn $1 into like $2 or $10 or something at the end of the year. That raises the rate of interest higher. Now, if the rate of interest goes higher, and if it does that for the entire economy, and people are borrowing more and more money, they're competing against the other lending that the government would've done, or that other companies would've done, or that you as a consumer or a mortgage buyer would've done, then that's just making it basically more expensive for everybody else to borrow. This has huge implications for tons and tons of people. Sorry, I'm gonna go on a bit of a monologue here. But we've been thinking about this together. So I think the US will be fine at the end of the day because they can, if the data centers are built in America, you can fundamentally just tax the data centers. But the way the current tax system is set up, corporate income is less than 10% of federal revenues and 80% plus is payroll taxes and income taxes, which as more and more automation happens will shrink. At the same time, on the spending side, currently 20% of tax revenue spending goes towards servicing the debt, basically paying interest payments on the debt. Now, a lot of the debt is short duration, so it refurbishes every 5 years, it rolls over. Why are you fucking laughing?

Because, you know, it's like things you've learned in the last month.

Yeah, like it's any different for you. Like you got a degree in fucking financial economics. I didn't. I didn't.

The internet thinks I'm a beekeeper. Few months, few months, few months.

This is our business, Dylan.

I know, I know, sorry, sorry.

And so now I'm self-conscious.

Fuck. No, it's good. You're doing good. I just think it's funny. Million people, listen to this guy who just learned about debt this month.

So you go from 20% of— suppose interest rates rise 1%, then over a 5-year basis, the amount of the fraction of tax revenue that goes towards servicing the debt basically goes from 20% to 25%. If they rise 5 percentages, that would go towards like north of 40%. But if you take into account the fact that the government is borrowing $2 trillion every single year, then that goes from like 40% to like north of 60%. So 60% of tax revenue basically just goes towards paying interest payments on the debt. Now, I think the US is going to be fine because also the tax base will increase if we let data centers get built in America. Other countries are absolutely fucked, in my opinion. I was just looking at which countries have a lot of debt, have very little tax revenue, and also a lot of their debt is serviced quite often. And those countries like Pakistan or Nigeria or something, I think are just gonna be very fucked in this new interest rate regime.

So this crowding out effect is actually like the thing that I've like, is the reason it's not like YOLO 1 billion gigawatts.

Yeah, yeah.

Right? You've got all these industries and countries that use a lot of debt, whether it's, you know, all these impoverished countries that you mentioned earlier that are just gonna default. You've got like consumer packaged goods, right? Like all of these like companies that make things you see at Trader Joe's or wherever use a lot of debt. All these telecom companies use a lot of debt, and banks use a lot of debt. And so if interest rates go up, um, in the market, not necessarily the government-set interest rate, but in the market, the spread of interest rate, uh, between what the government says their federal rate is versus what everyone else is charging because Amazon wants to raise $100 billion of debt next year or whatever the hell the number is, You know, probably less, but you end up with this like really challenging problem of where does the cash come from? There is some level that is funded by cash flows and cash flows keep going up. But the logical thing to do is to invest way more than your cash flows because then the returns in future years will be amazing.

Yeah.

So you have this delta and then what's pushing down on the delta is all of these other things, right? There's regulations against data centers, regulations, you know, consumers getting mad. Politicians getting mad, regulations against AI, the AI labs not releasing their latest models because of safety reasons. All of these things and interest rates going up are an influence on all of these things. So all of these things bend the curve from what does capitalism want in terms of just pure simple economics to what does the complex system that we have want and bends it lower and lower and lower to where not as many gigawatts as should be built will be built.

Well, the interest rate is part of capitalism, right?

Yeah, but like, you know, like in the simple economic model versus like the more complex what we have.

Yeah. What is the rate at which you think Amazon or Anthropic or whatever will be releasing bonds for debt next year? They do hundreds of billions of dollars of debt. What is the rate? What is the average rate?

I don't think Amazon will do hundreds of billions of dollars of debt.

The total, let's say the big tech—

The hyperscalers in total will raise, and all the clouds.

Yeah, yeah.

In the modeling that we do, we have about, $11 trillion of CapEx from 2024 to 2029.

Total?

Total. And if you do, you know, if you fund a lot of this with cash flows and as much as you can, you still end up with north of $5 trillion of credit that needs to be issued for this $11 trillion-plus buildup.

You don't think the AI revenue continues even 3xing year over year?

AI revenue does go up. I don't think it can go up forever. I don't, you know, like just like without like certain constraints being hit, I think certain labs will have certain incentives. And labs are not the ones building all the compute in many cases, even though they're increasingly trying to go that way.

They'll have all these cash flows, like if their revenue keeps increasing, whatever, that's fine. But if you— how much did you say the revenue will be? You think they'll not have that much revenue?

No, I'm just saying till 2029, there's, you know, something on the order of $11 trillion of CapEx, and $6 of that is funded with cash, and $5 of that is funded with debt. And if that's the case, $5 trillion of debt being raised, across the whole ecosystem does make interest rates go up. And then what prevents that? You know, there's a couple of things. One, do labs increase their revenue per megawatt more and keep inference allocations large, in which case they're taking all this profit, they're accumulating all the profit across the S&P 500 because everyone's paying to, you know, reduce their costs. Of course, their profits will also go up. But, you know, cash has to come from somewhere. So there's an upper limit on how fast their revenue can grow versus the value they deliver into the world. And there's a diffusion aspect of the technology. But ultimately, labs' revenue keep going up. They can't cash flow fund everything. The optimal scenario is you actually use credit as much as you can to fund, because even if cash flows from the labs fund a lot of stuff, you want to build more than that.

Of course.

And so there is some amount of credit that gets built. Our current modeling has $5 trillion of credit and $6 trillion of cash-funded infrastructure investments through '29. And when you take that, You've sort of got, you know, this is not enough compute relative to what this demand growth is from the AI models. And so you've got the obvious answer, which is revenue per megawatt keeps going up.

Yeah, that makes sense. So how much do you think interest rates will increase by 2029 as a result of all this?

Dude, you know, it's just vibing a number, but if you're vibing a number out, you know, growth in the world and economy is going up a lot, so why wouldn't interest rates for Amazon go from, you know, from where they are today. I think Meta paid— okay, let's take— so this is going to be extremely vibed out, but recently Meta's raised at like 5 to 6%. I don't see why they wouldn't pay 8% because they would happily pay 8% because the return from the compute that they're going to build is humongous.

Right.

And the market won't want them to, but if they want to pay 8%, the flip side is if they pay 8%, versus the 5 they do, 5.5, 6 they do today, you know, 250 bps increase, that makes everyone else in the economy also pay 250 bps more.

Yeah, yeah.

Which then causes a lot of things, right? Banks will scream because if their credit spread goes up, then their assets don't reprice, their debt themselves reprices faster than their assets reprice. And you ultimately end up with they're losing tons of money if their credit spread blows up.

The other consequences of this are, this is a point you made, but if interest rates rise, the discount rate increases, which means that the discount cash flows of all equities crater, which means that even though the stock market as a whole might be doing fine, like S&P 500 will be fine, Yeah. Any individual stock will probably have just like cratered in value, especially the Buffett, like Berkshire type, pay good cash flows for 30-year type stocks.

Yeah, it's like, why would I pay this much for Johnson Johnson? They're seen as a stable stock, good cash flows, they'll return their cash flows over time, or a railway company. Why the fuck would I invest that much if my discount rate isn't 3% or 5%, it's now 8% or 10%?

And for developing countries, Basil Halperin, who's a good friend and he's an economist, he made this point that we'll see a second Volcker shock. So in the '80s, to fight inflation, Fed Chair Paul Volcker raised interest rates more than 5%, or at least something like 8%, real interest rates, 8%. And that caused some 40 different countries, mostly in Latin America, to default in that decade. And I think that will probably happen again. In fact, okay, now we're getting into like singularity talk. So we've been talking about what happens if interest rates rise.

I think this all happens before singularity, by the way.

Yeah, that's what I'm saying. That's what I'm saying. So if we were to talk about like, you know, before singularity, interest rates rise 2%, 3%, et cetera. At some point, I think it's very likely that the world economy will be doubling every single year. Okay, this is not happening in 5 years, but it'll happen eventually. It's just like, there will be, there's this, There's a researcher, Damon Binder, who's done great work on this. But basically, if you look at input-output tables in a fully automated economy, just what would it take to double the entire stock of things in the economy every year?

Yeah, if economy grows at 3% a year, then it's like rule of 70, it's like 20-something years.

Right. But he was like, okay, well, right now we're bottlenecked by the fact that there's people and you can't double people every single year. But in a world where you can also double labor force every single year, how fast can the economy grow? And I think it could double every single year. At the very least, it would be like tens of percent every single year. Okay, the rate of interest should be pretty close to the growth rate. It won't be exactly that because of consumption, but it should be pretty similar. So then we'll go into a world, I think in the 2030s, where the rate of interest is like tens of percent. And I don't know, part of my brain is like, it might be hundreds of percent, but okay, let's say it's at least tens of percent. I'm just like, okay, every country that is not involved in the production of AI Defaults. Every stock that is not an AI stock is worth basically zero because discounted cash flows are worth nothing. If the federal government can't figure out a way to tax AI, servicing the debt is more than the current tax revenue. All these other effects that I'm sure we're not even pricing in, like you can't get a mortgage, et cetera, et cetera. Because fundamentally, what is happening in this world? This is all nerd speak, right? But let's step back. What's happening?

Just now it started, the nerd speak.

We'd be entering a regime where just we're in a totally different growth regime, basically. And the economy is basically saying, hey, you like paying people, you borrow, the government borrowing money to pay people pensions, the opportunity cost of that is extremely high now because that money could be spent building a robot factory that builds a robot factory that builds a robot factory. And so the opportunity cost of capital is going to increase a ton. And that just like, that's fundamentally what the cause of All of these things we're talking about.

Yeah. So, so as interest rates go up, equity markets get pummeled.

Yeah.

And even AI companies, right? People are like, you know, some people who really believe in AI are like, why does Micron or Hynix or Kioxia trade at 2 or 3 times earnings? And it's like, well, if you're really AI-pilled, everything in the economy should trade at like 2 or 3 times earnings. And if you're not AI-pilled, then sure, they're, they're over-earning.

Yeah.

Um, so it's sort of like a, an argument for why, like, I think memory is going to do great, but, you know, memory, memory stocks shouldn't, you know, 10x or whatever again. Because if they were— if we're in the market where there's that much demand for memory, which means AI's caused this drastic change in the economy, then everything should trade at like 2 or 3x multiples and the stock market should fucking crash.

Right.

Right? And so in a sense, like, Meta trading at— I don't know, I think Meta trades at like some— they're like $1.5 trillion company. It's What? Silly. They're worth way more than that, at least in a logical sense. You just look at their cash flows and all the infrastructure they're hoarding and all the compute that they're going to be able to sell for crazy amounts of dollars per watt, either as tokens because they're lab works or just Anthropic and OpenAI. Ultimately becomes a question of you have to reallocate all the capital to the AGI, and you do that by pricing everyone else out. And, and, and so the limiter on AGI is not how fast can the research engineers, like, you know, our roommate Sholto, can crank the gears. It's actually just like, how much does the rest of the world let that happen?

Right.

Because they're going to regulate, they're going to obviously increase interest rates, they're going to say no data centers, they're going to say stop building fabs, they're going to say, oh shit, every company's equity value is tanking, so how can I pay for AI? you know, to increase my business, well then, you know, like, okay, then Anthropic and OpenAI have to start like building their own stuff. And obviously they're gonna eventually focus on, you know, they're building their own chips already, or at least designing their own chips, and it'll expand out. They're, you know, they're contracting their own data centers and building their own, uh, infra in the next couple years. Um, you know, there's sort of like, how does this reallocation of the economy happen? But there's a lot of downward pressure on it not being You know, just straight takeoff, even if the models were capable of it, which I think you and I believe we're in a world where models are capable of that. But slow takeoff is, at least my hope, possible because everything in the economy and regulatory world, like governments saying, don't release your models, governments saying, actually, you can't even use your models internally that much, because that's going to happen soon. They're already saying you can't release your models.

Which is actually the thing I'm most worried about is, you know, a singularity, which external deployment is actually helping. So the fact that we're preventing external deployment—

Well, does that prevent singularity?

I mean, right now it leads to more revenue because the models aren't capable of RSI. But I'm worried about a world where it's 2030 and the government's like, we're going to wait 6 months before you can release your newest model to the public.

6 months, 100x, let's go.

Yeah. And in that 6 months, they do recursive self-improvement internally. They just have all kinds of crazy shit happening in the company. Meanwhile, the rest of us are stuck with models that are, like, at current pace, years behind.

Yeah.

So here's my thought. Okay, suppose that the whole world gets in on this conspiracy to, like, try to slow down AI.

I don't think it's a conspiracy. It's outwardly written.

Yeah, yeah.

You know, from, like, every politician.

Suppose they basically prevent an entire— they slow down AI by a year. If compute is increasing 2 to 3x every single year, they prevent a whole year of AI deployment such that you're a year behind where you would otherwise been. During RSI, you're getting 3 to 6 years of AI progress in a single year.

But they can't, they don't just limit compute, right? They also limit the lab's ability to release the model internally, right? We saw that Anthropic had to stop giving Mythos to foreign employees for a bit.

I didn't know that was true. Like internally as well?

I mean, that's what they claimed.

I thought that was just a different checkpoint that was not Mythos, but it was basically Mythos. Yeah, yeah, yeah.

But I mean, like stuff like that is not gonna be allowed either, right? is dumb, but they're not that dumb, right? Like, you know, I, I would hope at least. Um, you know, governments are going to not want companies— at least the US government has the cards here— is not going to want Anthropic to use Mythos 4 internally. They're gonna be like, hold the fuck on, right? Like, slow down, you know, because, because all of these regulatory reasons. Everyone who's elected is going to hate AI. Even the people who are elected already hate AI. Um, All the constituents, you're gonna literally have like I bet you at some point your parents are gonna call you and be like, "Dwarke Shabita, you're doing a terrible job. You're making every AI progress happen faster." Like it's my podcast. It's gonna happen.

It's gonna happen. It's my podcast. I'm accelerating AI progress.

I mean, maybe you educate people, right? And maybe if they're smarter, they're progressing AI faster. But anyways, like you're going to have real world constraints on the progress and development and deployment of AI. Even though, you know, it will happen eventually, it's like we could tear ourselves apart before we get there.

Jane Street is hiring for 2 separate ML internships right now, one focused on ML engineering and the other focused on ML research. I sat down with Alok, who helps run the research track, to learn more about that program.

I think this domain is fundamentally understudied. Often we have unanswered questions within our deep learning research team where we don't understand some, say, some market participants' behaviors or certain dynamics of how trading happens. These unanswered questions make for really good intern projects because they are ultimately topics that we care about and just haven't gotten around to figuring out yet.

So even as an intern, you'll be contributing to real research, not working on some sort of contrived exercise. The Jane Street team follows frontier LLM research closely. A relatively common intern project is adapting a recent paper to financial markets, which come with their own set of gnarly problems.

Ultimately, we're trying to model thousands of interconnected irregular time series. The signal-to-noise ratios are extremely low because we have a lot of competitors trying to do the same. So we have this like adversarial, non-stationary, extremely high-dimensional problem that we were trying to solve.

To be clear, you don't need to know anything about finance in order to be a good fit. As long as you have a background in ML research, Jane Street can teach you the rest. Their 2027 internship applications are open now. Apply at jane street dot com slash dwarkesh. One thing I find crazy about these scenarios is just how much of the world's future labor supply ends up in very few companies, and also how fast that labor supply grows year over year. So if like compute at the frontier. In FLOP terms, is growing 4 or 5x a year. And further, the compute required to achieve the capabilities is decreasing 3x a year. So the compute at the frontier, basically the effective AI population size at the frontier labs, is increasing 10x year over year. And so that doesn't really matter that much right now because AIs are not good enough to do full jobs or be as autonomous as people in their capacity to do work or pull off schemes or whatever. But if the current trend continues, you have a world where OpenAI goes from having, say, 10 million basically AI labourers this year to 100 million the next year to a billion the year after that. And then pretty soon, even if compute scaling slows down, it doesn't take many more years before each company individually has more labour equivalents than there are people on Earth. And I think that's like a thing that is very plausible by the end of this decade. That there's more AI labour, more effective population within a single lab than there are people on Earth. So we talk often about centralisation of power because of nationalisation or whatever, but we don't think enough about the fact that we're actually moving very fast into a regime where most AI labour— or sorry, most people, in terms of work output or something, is just concentrated within 2 labs who are consuming more and more of the world's compute. And so if these AIs are misaligned, then most of the world is misaligned basically, because most of the world's minds are there. But even if they're not, it's just very few companies have a lot of influence or a lot of control over people.

Yeah, it's sort of, there was the whole spat recently where it's like, I think Gavin Baker was like, Dario believes that there's only gonna be one company in the world. And then Sholto and Dario came out and were like, no, no, no, we didn't say that. But ultimately, if you believe in RSI, you believe in the labs are the most effective user of compute and can generate the most value from the compute, then the only thing that's going to happen is centralization of compute. And if you believe in AI researchers, RSI, AGI, then all of this exists. All of this is the base.

This is even true if there's no RSI. The current effective population of the frontier is currently increasing 10x year over year for a given level of capabilities. So if you get to the level of capabilities, which is a human, a very competent remote worker, or a very competent software engineer, or a very competent researcher, that population of those would 10x year over year at the current rate of capabilities growing.

Without RSI. Then once you have RSI, it's even faster.

Then it's maybe growing 100x a year or 1,000x a year, or their intelligence is increasing, but their population isn't increasing, or some mixture of the two, right?

Yeah. I mean, I guess what world do you see, Dwarkesh, where everything is not centralized? Because it seems to me that every force is screeching towards centralization, right? And that's scary as hell.

Yeah.

I don't, I don't, you know, I would love for it not to be centralized completely, but maybe that's, that's the whole point of a machine that loves grace, right? Is it is everything and it makes our lives great. Yeah.

It's so hard to think about the future, but I agree with you that I think the fundamental problem is that lab AI training has huge economies of scale because any effort you spend into training an AI for a specific skill or specific set of knowledge gets amortized across billions of sessions or billions of users. So that's one effect. The other effect is if you're slightly ahead in the AI race and compute is in shortage, you can charge a much higher markup because you can better economize this scarce resource. So there's 2 effects which give more and more to the person who's ahead in the AI race. There might be more, right? So if there's models that are learning from deployment and one model is deployed much more widely than another one, it's getting much more real-world data.

Yeah, your point is taken that whether it's user deployment and continual learning, whether it's training, having these economies of scale, whether it's the incremental progress that the best AI model helps you to make the next AI model, RSI, all of these things— Oh, sorry, I didn't even mention RSI.

All of these things point to So I think one of the big intellectual projects, honestly, that we should spend some time thinking about, Async, or at least I'll spend some time thinking about, is what is a vision of a decentralized, broadly empowered future after AGI that takes these economies of scale seriously? The alternative vision is that the government controls it, and maybe you think that you can trust the government more because it's not a private corporation.

I don't trust the government, and I don't trust Dario, and I don't trust Sam.

Yeah, yeah, that's a problem, right? But there's no, at least, obviously it's very easy to be wrong about the future and you don't anticipate a key effect or something that changes everything. But ex ante, it's very hard to see a reason why there, or how we avoid a scenario where we have to choose one source of information.

I mean, it's why capitalism worked, right? It's the decentralized decision-making and decentralized power and why super centralized capitalistic economies actually grew slower than super decentralized capitalist economies. To some extent, you have to have rule of law and all this. But then AI flips all this on its head, right? And ultimately you're like, actually, private ownership is probably not the most efficient economy, and therefore it grows slower than an AI economy, which is centralized.

It's still private ownership, but it's like, how many firms are really involved in this? Well, this is like the share of the economy that's not— what, like 5% of the economy or something like that in the US? Sorry, $1 trillion divided by 30— less than that, sorry. But, um, yeah, maybe 2% of the economy right now. It's like Nvidia has a huge share of it, and Anthropic, and OpenAI, and these hyperscalers. And obviously there's other firms involved, but a large share of the AI stuff is just happening from very few companies. So it's like it could be private property, but very few companies are involved.

I mean, this is what the structure of the market is doing. So what can prevent it? I don't know. Unless AI progress slows down, unless governments regulate the fuck out of it, this is all that happens. In which case, we're headed for a world where either we have super concentration of resources and we pray that that one company gets everything right, or we have governments slow everything down and people slow everything down and you have a slowdown of progress somehow, hopefully.

Yeah.

And there is more of a balance of power. And even as we go towards an AGI, ASI, RSI, everything along the way will still lead to someone's going to allocate, going to capture more resources. So it's kind of hard for a framework in which AI doesn't lead to super concentration.

Yeah.

Now, the one positive thing here is that today Anthropic does not capture most of the value. So we can talk all we want about, oh, they went from $20 million per megawatt to $100 million a megawatt, but they're still paying $13 for a lot of the compute they're buying. But at the end of the day, the reason they've gone to $100 million per megawatt is because Jane Street is capturing $300 million per megawatt or $500 million per megawatt. Um, where Dwarkesh, from researching his podcast and learning about credit, is capturing, you know, how many dollars per megawatt. Now, how much can you use? Tough.

Yeah, yeah, yeah.

Um, but, you know, I think, I think that's the, like, one saving grace is that the rest of the economy maybe profits so much more from Anthropic.

Yeah, but the whole logic you were laying out earlier of them reallocating inference to AI R&D The whole logic of that is that the returns to labor inside AI labs is much higher than the returns outside.

Yeah, this is my cope. I agree. In all scenarios of the world, you know, there's 80,000 worlds and only one of them Anthropic doesn't own the whole world. Is that, is that, you know, again, power concentrates because I don't want to send the tokens outside. They're more valuable inside. And so it's the same thing, right? Why would I let Jane Street, you know, make all this money off of these degenerate options traders.

Hey, they're a sponsor. Come on. Jesus Christ.

No, I think it's great.

I think it's great.

It's a good value for the world to make it an efficient market. Yeah, yeah, yeah. You know, Jane Street making all this money off of getting the worldview correctly, making money off of degenerate options traders, whatever it is, you know, why would Anthropic allocate compute to that if the end monetization that Jane Street has per megawatt is $200? So they're willing to pay Anthropic $100. Well, what if Anthropic can just generate hundreds of millions of dollars per megawatt by using that compute internally? And that's what's happening.

Yeah, yeah, yeah. Well, on that somber note, I guess we'll meet again when the RSI is officially kicked off.

You're not gonna have me on your podcast again for like 2 months? All right, cool.

Thanks, dude.