Okay, I'm back with Dylan Patel, founder of SemiAnalysis. Our version of a family Thanksgiving dinner is a regular yearly podcast. But we're not actually related. Don't tell the people this. It will destroy the myth. 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. I want to understand where the crazy future ends up within a few years. But let's start with where we are today. Walk me through lab compute and lab revenue right now, and maybe project out a year or two.
When we go back to last year, even at the end of the year, most of GDP growth in America was just AI infrastructure. As we look towards this year, about a third of the compute coming online is for the labs, for OpenAI and Anthropic. 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 compute are ballooning. We're at a little bit over a trillion dollars 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. 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. This is at least some of the contracts they've begun signing with their partners. This requires a big reshaping of what happens with their economics. 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 start turning a profit even, with the bigger rise of Codex and 5.6 and all this. But if we go back a year ago, all the money they had was venture-funded losses. 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. 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, 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 this: Before, if they served a model — GPT-4 being served on Nvidia Hopper GPUs — it was generating negative gross margin for OpenAI. But now, when OpenAI serves GPT-5.6 or Anthropic serves Opus 5 or Fable 5, their revenue generation has passed well beyond the incremental $10-15 million per megawatt. In the case of Anthropic, the revenue has gone as high as $50 million per megawatt. What that now enables them to do is: 'Hey, if I spend 10 bucks on inference capacity, I actually generate 50 bucks 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 the labs. If you say right now a third of marginal compute is going to the labs, by when is over half of the incremental compute in the world going to the labs? By what point do the labs have basically a vast majority of the world's compute?
At the beginning of this year, OpenAI started at 2 gigawatts and Anthropic at less than 2. End of this year, they're both above 5. So they've 3-4x'd compute as a whole. When you look at the incremental compute added, that's about 30% of the compute added this year. As we step forward to next year, given what's already been signed and penned and inked, you've got something even more dramatic. Anthropic and OpenAI are taking as much as 40% to 50% of compute next year. This centralization doesn't look like it's slowing down or stopping. In fact, it looks like it's only accelerating. Who's building that compute for them will change. Next year, a big new entrant is, for example, SpaceX, which is building a ton of compute. They're actively going to lease quite a bit of it to Anthropic and OpenAI, most likely, because they're the ones 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. So you ask, 'Hey, when does half of the world's incremental new compute go to just OpenAI and Anthropic?' It's really by the end of next year when half of the incremental compute is already 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 two years — that most of the world's compute is owned by two labs, or at least is serving the demand from two labs. There's this trend where maybe world compute in gigawatts doubles every year, but the compute at the frontier labs triples every single year. 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. It's 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'? How do you see the world compute situation over the next few years?
If the incremental compute this year adds 30 gigawatts, next year 50 gigawatts, and the year after that roughly 70, you end up with this really interesting phenomenon. A new watt deployed this year is significantly more efficient than the watts deployed two years ago. A humongous percentage of the world's compute was deployed this year. Even though it didn't double the number of watts deployed, I'm deploying GB300s and TPUv7s and Trainium3s, which are way, way, way more efficient. They're 3-5x more performance per watt than the prior-generation chips. So ultimately you've got a huge ladder here. If Anthropic and OpenAI take on 45% of compute next year, you've got them in, let's say, December '27 having 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. So you've got another multiplier on that. By the time you're towards the end of 2028 — if this trend continues, and I see nothing that's stopping it — you've got them just controlling most of the usable flops in the world on their own.
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 value of compute increases so much. That's the upper bound, by the way. That's the like, 'I'm so fucking bullish.' Okay, let's do some chain of thought here. When I interviewed you a few months ago, you said that in order to make a gigawatt of, I think, Vera Rubins, you need 55,000 N3 wafers, 6K N5 wafers, and 170K DRAM wafers. I know if those numbers might have changed. I'm going to troll you, but the way you said wafers was so fucking Indian. Vafers.
By the way, when we first moved to the US, I had the v/w thing pretty bad, and I was a vegetarian. I remember you told me about this. In North 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 one gigawatt.
I had an LLM run your wafer fab equipment model and figure out how much the tooling costs to produce a gigawatt of compute basically every single year. It said $3-4 billion. Now suppose you add in cleanrooms and shell and everything else at the fab. So $6 billion of fab CapEx produces a gigawatt every single year. 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 over the course of five years, the first gigawatt has generated five years of profits, the second gigawatt the fab has produced has generated four years of profits, and so on. $6 billion of CapEx at the fab level will have generated over a trillion dollars of end AI revenue.
Yeah. There's a lot of OpEx along the way. There's a lot of other CapEx, like the data center, the power. And you had to pay OpenAI for the R&D. Installation. There's a lot of different people who need money here. Take away half of it for all these middlemen. That still means there's a 100x discrepancy between fab CapEx and end revenue generated. More than that, actually, but we're just being very conservative. As a result… This is capitalism. You have this huge discrepancy where you can turn $1 into $100. They're not going to figure out a way to make more mirrors? They are. It's just that these mirrors take some time to make. But the emergency is so big where 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. How can we make more mirrors if we spend $100 billion on this?' That's the situation we're going to be in pretty soon.
We're not going to be able to solve that supply constraint? That just seems quite hard to imagine. You've seen people do funny arbitrages here where they buy turbines and then try and resell them, because the value of a turbine is way more since it's 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, wait, and then sell it for north of a billion dollars. But ultimately, yes, capitalism will cause these things to expand. But it's a whip. It takes a long time for the whip signal to get to the tail end of that. The supply chain doesn't react immediately. In fact, you go 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.' 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. Now they're like, 'Okay, we need to do that.' But in reality, because of all the economics of what's going on, it should be even more. It takes so long to pill. Suppose that every single company in the stack got private equitied. 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? 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.
I do agree generally. There's obviously some physical constraints. The way the supply chain is expanding currently, 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. You would have to do this with every company in the supply chain. But you don't think that's gonna happen next year? 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, even combined, a trillion dollars in revenue next year, they're not able to take $10B of that— I don't think they're going to do that, but… Or hundreds of billions at least? It just seems like they realize where the world is headed. I feel like they could just make… The thing is, the labs are 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. 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. Obviously they will never get to that point, because you want to keep your CapEx higher than your returns. Yeah, you reinvest.
The key question I really want to understand is: if the current trend continues, it'd 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 many-fold more throughput or performance by 2028 than they do now, because the hardware's gotten better. 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 by the end of 2028. How much is world compute? I think that may be a little difficult, given that by 2028 they've taken 70-80% of incremental compute. And I'm not sure what happens to markets then. How much does the price of compute skyrocket for them to actually be able to buy 70-80% of compute? Is Google or Meta or Amazon willing to sell even that much? Also, there's one caveat when we're talking about these gigawatt numbers. When Amazon is serving Bedrock Anthropic models, that counts as Anthropic compute in our worldview, because it is effectively, at the end of the day, counted as revenue for Anthropic even though there's a revenue share and credit back all that. But ultimately in 2028, if they get to 100 gigawatts combined, they have done really disruptive things to the market. Because anyone can make money off of $10-15 million per megawatt compute today. I kid you not, it's not that hard. Go get a GB300 rack, go download the Kimi weights, go download vLLM or SGLang, set it up. Codex and Fable can actually help you do this. It's pretty simple. It's not trivial, but it's not rocket science. Go put it on OpenRouter. It's very simple. You'll start generating more revenue than you're paying for the compute. This has already led to this compute pricing, $10-15 million per megawatt, starting to inflect up. 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? Does it get to $40 million a megawatt?
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 continue being the case. If there's some kind of recursive self-improvement where the AI labs are relatively uplifted — or they have models internally they're not releasing externally that are helping them make their next model better — you'd expect that to be even more the case. Aren't you already seeing this, where SpaceX, or whoever is slightly further behind, will just sell compute to the highest bidder if they can't internally monetize it as well as the labs? You'd expect them to keep bidding for larger and larger shares of the compute market. I think that is my worldview. 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. They do have to start paying $25, $30, $50 million a megawatt to really gobble up 70% of the world's compute in 2028, to get to 100 gigawatts by 2028, which is a very aggressive goal. The other aspect of this that's really challenging is that we've already seen a huge slowdown for the AI labs. 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 two weeks. Anthropic not releasing what their safety assessment says is Model 2, which is widely believed to be the next version of Mythos. They're clearly not releasing their best models, in which case their revenue per megawatt stalls or can even start to decline again because other models are competitive again. It's not that they're falling behind. It's just that they're not releasing their best stuff. 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. 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. But in a world where safety doesn't matter, I do believe that's exactly what happens. They can start generating $100 million per megawatt or more, and they can pay $50 million a megawatt. 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 forces at play, which we cannot describe, that would potentially slow this down.
I think a good intuition pump is: what if the AI models were literally as good as a fully automated software engineer? They're not currently there yet. I think they're far from being able to fully automate the job of a full white-collar worker. But white-collar workers earn six figures or north of that a year. If you have a gigawatt that can sustain a population of, say, roughly a million white-collar workers. Then off the back of that…That would be $100 billion. That's actually surprisingly low. Yeah, $100K per person, million population. I don't know. But it would be many hundreds of billions of dollars per gigawatt if you get full AGI. The other aspect of this — and we've continued to see this — is that most of the value capture is not happening. 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. Jane Street, with their exclusive contract with OpenAI for GPT-5.6 Ultrafast mode, or Jane Street where they're one of Anthropic's biggest customers, is generating way, way, way more value out of the tokens they're paying for than Anthropic is generating in terms of profit, because they get to make money off of the market. Or take Meta, who at one point was 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, getting engagement time 5% longer, all these things. They're making way more money off of using these models than Anthropic is. That's what's required. Sure, if you had a million new software engineers, the cost for a software engineer would also fall. One thing I'm confused about is, does the market come into equilibrium? 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. In a world where the revenue per gigawatt continues to increase, if Anthropic's ability to monetize a gigawatt doubles or triples, it'd be weird if the gap continued to increase. Anthropic, just by having some weights, can take something that cost them $10 and turn it into $100.
This is always a fun question. 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. Then you have the app layer. So far the app layer's generated very little value. Then you've got the model layer, which up until a year ago was generating negative gross margins and is now generating massive positive gross margins. It looks like it's on the path to generating $100 million per megawatt. So turning $10-15 into $100, as you said. But if we go back 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, as were many other startups. Many of these hyperscalers were 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, if you will, because they were selling the tokens for less than it cost them on the infra side. All the value was being captured at the chip, the fab. Initially in 2023, the memory guys were making no money off of HBM or memory for AI, even though theoretically the value they were delivering was humongous. Now you've got… Well, actually TSMC captures way less value than the memory guys. So the value capture's shifted around a lot, which is very fun for people tracking the market or participating in the market, like Jane Street as an example. This is not an ad. This is not an ad. This is not an ad. They're a sponsor but you don't have to plug them that hard. So what happens going forward? Anthropic and OpenAI have 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.'
What's your prediction of how much the relevant tranche of compute — B300s or whatever 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. If Meta, Microsoft, Amazon, SpaceX can build compute without finding a customer, just saying, 'Fuck it, I'm going to build this compute,' and then turn around and wait till it's already built, they now control what's going on. Most compute is contracted well before it's built. This is what Elon took advantage of in the market. He actually had all this compute. He was like, 'Hey, Anthropic, I know you're making $60-plus billion per gigawatt. Why don't you just buy my stuff for a crazy amount of money?' Obviously it's not like Elon decided this or Anthropic decided this. The market figured itself out. Other people, you go to a random cloud, they're like, 'Okay, I'm going to 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 want to find a customer, I need to find the capital. Who's going to give me the capital and the customer? The customer has to sign a deal. Then I take the customer's commitment to the credit markets and I raise the capital.' So there's this completely different power structure where Meta is effectively hoarding compute. Them and SpaceX are the only plausible #3, because they're hoarding all this compute. They're using their balance sheets and capabilities to build compute without an end customer that's monetizing at a huge degree. They have an actual balance sheet, so they can go to the credit market. You build a gigawatt, you can make your margin, not a crazy margin, but a good margin. Now I have all this compute. Now Meta and SpaceX have this optionality of looking around and being like, 'Is my internal use case going to make me more money, or should I go out there and sell it to Anthropic or OpenAI at crazy margins?' So now we've entered a regime where SpaceX and Meta are saying, 'Actually, I'm going to build the compute, and I can rent it out for not $13. I can sell it for $25, $50, and more.'
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What do you think their revenue per gigawatt is by the end of 2027? For Anthropic or OpenAI, by the end of '27.
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-plus million a megawatt. By the end of '27. Oh, by the end of '27? That's where it gets more challenging, but I think it could get higher than that, to like $70, $80 million a megawatt, blended across the company, if not higher. Seems low. So if that's the case, 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. If I'm SpaceX, I look to the supply chain and 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? Then SK Hynix and Micron and Samsung look at it and they're like, 'Well, why don't we raise our prices?' So with the value capture, I think there's a bullwhip effect here. Just because someone has raised prices doesn't mean the entire supply chain rebalances immediately. But over time, the supply chain will rebalance and things will cost more and more. To get that incremental capacity, you sort of have to. So TSMC raising prices very slowly, but memory companies raising prices very quickly. Substrate companies raising prices very quickly. Elon wouldn't have sold if it was $15, but he's selling because it's $25+. So obviously he raised his prices really quickly.
I'm surprised you think that revenue per gigawatt doesn't increase way more than even 100 per gigawatt by the end of next year. When does RSI happen? When does takeoff happen? Or even if RSI doesn't happen, just say the current rate of progress continues. Just look at how much progress we've made in, let's say, the last year and a half. What was the model from a year and a half ago? Claude 3.5 or something?
My problem with this is that the best model that exists in the world was trained in February. So you're saying maybe we just won't be allowed to release the labs' best models. OpenAI says they're not training models for two weeks, man. What the hell? There's one thing where internally, are they getting enough use for it that they'll bid up the price of compute? Another is, does AI progress as a whole slow down because of regulation? Yeah, but they're not even allowed to use this new model internally. Astra's not even widely deployed internally. But still, if you have a model that is… What was the model released at the beginning of last year? GPT… 4o? Was that 4o? Yeah. You're talking about a GPT-4o to Mythos 2-size leap by this point, again, by the end of 2027. Yeah, but Mythos 2's not out. Or even Mythos. That leap again. Even Mythos is not allowed to be out. They've neutered it. We can't use it to optimize inference performance. We can't use it to optimize all sorts of things. Yeah, maybe there's some slowdown in AI progress or the deployment of AI that means the revenue per gigawatt can be lower. But that's the only way I could see it being only $100 million per megawatt by the end of next year. 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, 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. That's going to get passed on as well. You start to end up in a spot where progress does slow, at least in the external sense, even if the models internally keep getting better and better.
In a takeoff scenario, why would Anthropic not have their best model six months ahead of what is externally available? Because of safety and regulation, but also the competitive advantage? That six-month difference, if progress accelerates, is actually a bigger differential. So that's the thing that would cap revenue-per-megawatt gains to much lower growth than we've seen in the first half of this year. Here's something I'm very interested in. As these companies go public and they're accountable to investors, let's say by the end of next year they have close to 20 gigawatts. So 10% of compute is 2 gigawatts. Let's say they want to go from 60% of compute to training to 70% of compute to training. And their investors are like, 'Well, if you're going to be able to generate $100 billion per gigawatt, you're basically saying no to $200 billion of revenue in order to increase your training compute.' 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'?
This is what I personally believe. The labs are going to allocate less and less compute to inference over time. I think that's very non-consensus. 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 necessarily revenue-generating inference. Ultimately, 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? I think the obvious answer from Anthropic and OpenAI, not just at the executive level but also their board, is to go build AGI, because it's way more profitable. 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. The whole point is, if I'm selling tokens… Is OpenAI releasing Ultrafast mode for just external, or are they doing it internally too? It turns out, no. Actually, I'm going to allocate it to internal and external, because the internal value I'm generating from super-fast AI or the best AI model is way more than what someone external is. 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? What does that do towards my future earnings potential, the discounted cash flows of whatever the hell I've done? They're not going through that calculation, but ultimately it makes more sense to dedicate more and more compute internally. The only reason to have inference compute be so large is so you can grow your training fleet.
I think this is an interesting economics question that I feel we can have the models digest. What would have to be true about a world where they reduce the fraction of compute spent on inference?
I think they have been over the last three months already. I think at parts of this year, they were increasing the fraction of compute… Let's just take it 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. So in January, they added less compute than December, and yet their revenue adds skyrocketed. Then they've sort of plateaued. They're not adding $25 billion of ARR every month now. That means the marginal megawatt they're getting is going as a higher percentage to R&D than it is to inference. So they are factually increasing their compute towards R&D today. I think this is self-evident if you look enough at what they're doing.
If I look at the numbers you said for how fast world compute grows, here are some things I want to understand. It seems like if I add up the numbers you just said, it would be over 200 gigawatts of world compute by the end of 2028, right?
Yeah, globally. Okay. How fast can that continue growing, global AI compute after 2028? 30 this year, 50 next year, 70 in '28. '29 should be on the order of 90-100. Then just 100 more every single year or something? I think the slope can continue to go upwards. It's hard to predict anything more than four years out. Who knows whether we're in an RSI regime, or when is the world economy growing at 10% a year? Because if you're at 100+ gigawatts a year, you're at absurd GDP growth.
If you think there's 200 gigawatts globally in 2028, how much is in China by that point? 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.
If we level-set back to 2022, the US was adding about 45-50% of the world's compute. China was adding about 30-35%. The rest was 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. China is really a very small number. Sub-10% of watts being deployed for data center AI compute is in China. 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, Malaysia or what have you. So ultimately, China domestically still continues to have sub-10% of incremental new compute. In 2028 it might start to inflect up, I think. But it's pretty easy to say China will have 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. In 2026, they're still mostly relying on a lot of the smuggled chips, 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. 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. Now they're incrementally adding 5-10 gigawatts, in just 2028, of domestically produced chips. 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 30 gigawatts, but it's really much worse chips. But if you think the world is going to add 100 gigawatts the following year — 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 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. If there's anything China's really good at, it's scaling manufacturing really, really quickly. I imagine 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 Nvidia to sell them, or what have you. But do you think China could be adding 50 incremental gigawatts in 2029? I think that's completely reasonable. 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 from American chips.
Right. So you're actually projecting a world where maybe the leading lab in 2028 has more compute than all of China will have in '29 or even '30, if you weighted gigawatts by their quality. Implying that there's nothing done to slow down the US labs.
That's right. But clearly the government and politicians are starting to do that. Whereas China's not going to slow down AI. In fact, the only thing they're going to do is accelerate it.
Honestly, when I interviewed Jensen and asked about export controls — I am a libertarian person — I wasn't genuinely sure what I thought about this issue. I was steelmanning the opposite view from what he has, because I think it's important to hash out ideas. 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.' But I didn't realize the compute situation was as fucked as you're saying. Actually, the export controls do seem to have really… If they ship the amount that you're saying, that's a huge difference. By the time we have automated coder and are getting into automated researcher, China is way far behind on the compute stock. If that ends up being the case, that would have worked.
I think that's actually a notable success. The only caveat there is that some of it is export controls, but some of it is also just financial systems. American financial systems are more willing to YOLO into startups than Chinese financial systems. But once Chinese financial systems choose an industry to focus on, they'll subsidize it a hell of a lot more. So the Chinese semiconductor industry has significantly more subsidies than the rest of the world's semiconductor industries combined.
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 noteworthy aspect of this is that Chinese companies today are not that far behind in AI models, at least perceivably by the public, relative to the amount of compute they have. 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 Kimi is not running a gigawatt or anywhere close to it. Whereas Anthropic is more than 5 gigawatts by the end of the year.
So the question is, does it matter? I think right now this difference in compute doesn't matter that much. When we break down the compute ratio or budget of a lab, so far it's been 60% training, 40% inference. But that training gets broken down further. Actually 50% of the compute is research, 10% of the compute is development, and then 40% is inference. What I mean by research and development is: researchers are generating ideas, testing new architectures, testing new data mixes, testing new hyperparameters, new attention techniques, blah, blah, blah. But ultimately when they do the training run, when Anthropic trains Mythos, it's sub-200 megawatts.
The pre-train or the whole thing?
The pre-train. It's sub-200 megawatts for, call it, two months. Then the RL is even less.
You think the RL was less compute than the pre-train?
At least in terms of single site of pre-training, yeah.
But total compute was probably higher, right?
But it's sequential. At most, the most they ever used at one point in time was maybe 200 megawatts. In reality they had multiple gigawatts, so most of their compute was going to the research, not the development of a model. There's reasons for this. 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. 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 all two gigawatts that you have onto training.
Actually, I can only leverage 200 megawatts. As we get further and further down automated coding and automated researcher, I actually expect the percentage of the compute budget that goes to research versus training to become a lot more fuzzy, or even higher for training. Also things like continual learning. All of these things start to mean that more and more is actually going to training the model. 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.
Then stack on the fact that you have to build the power plants way before then. It's also 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, once you account for future years' growth, is actually going to be more like $7 or $10 trillion of CapEx.
Wait, I didn't understand. That doesn't include the fact that there's not the infrastructure for the power generation in the data center itself.
Right, exactly. When you talk about AI CapEx, people are saying $40, $50 billion. But that's really just the critical IT: 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. If I'm building 100 gigawatts this year and 150 gigawatts next year, then all of the buildings for that 150 gigawatts need to be built in CapEx this year. 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. So actually, it's much bigger than even $5 trillion if you're building 100 gigawatts.
Right. Very plausibly, incremental CapEx every year is getting close to $10 trillion by the end of 2030, which is going to be close to a tenth of the world economy. If all of it's going up in the US… 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 just going towards data centers.
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.' Because for this exponential to continue, a quarter of America's economy is just building data centers. I believe in capitalism and reallocation of resources towards the most profitable thing. But at the same time, politics exist, 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: over $2.5 trillion towards IT CapEx, and then another $1 to $2 trillion on data center and energy, and all the supply chain downstream, like semiconductors and all that stuff. 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.
Hyperscalers 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. In addition, they raise debt and spend everything on CapEx. You've seen Meta do it, even Amazon, even Google. Microsoft will be there soon. Everyone is raising debt to pay for their CapEx. Now who is the incremental person to pay for this that was not doing it before?
In the case of Google, it was pretty simple for them to stop doing buybacks, or Meta stop doing buybacks, and turn around and buy computer infrastructure. 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?
So there's a few different ways. There's 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 gathering capital and investing in infrastructure. Instead of bridges, it's data centers. 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 that's helping people buy homes, or maybe I shouldn't buy government debt. I should just buy hyperscaler debt, or I should buy this data center's debt, or I should buy Anthropic's debt.'
Because Anthropic's willing to pay 20% rates for 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. So you've got all of this contention. But if you now do this, the whole world economy is really shifted around.
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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. The logic is this. As we were mentioning, you have a situation where very little investment turns into a lot of money. 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.
So the rate of return is incredibly high. Even at the data center level, if you build a data center and you're trying to get rented out to an Anthropic or an OpenAI for 10x what it costs you on a depreciated basis to build it, it's fucking crazy. You turn $1 into $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… 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. That's making it more expensive for everybody else to borrow. This has huge implications for tons and tons of people.
Sorry, I'm going to go on a bit of a monologue here, but we've been thinking about this together. I think the US will be fine at the end of the day. Because 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. 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, paying interest payments on the debt. Now, a lot of the debt is short duration, so it rolls over every five years. Why are you fucking laughing?
Because it's things you've learned in the last month.
Like it's any different for you. Like you got a degree in fucking financial economics. I didn't. The internet thinks I'm a beekeeper.
Few months, few months. This is our business, Dylan.
I know, I know. Sorry, sorry. Now I'm self-conscious. Fuck.
