Dylan Patel – Anthropic & OpenAI will have most of the world’s compute by 2028

Dwarkesh Podcast

Dylan Patel discusses the accelerating concentration of global compute power in AI labs, particularly OpenAI and Anthropic, projecting that

Key takeaways

• OpenAI and Anthropic are projected to consume over half of incremental global compute by the end of 2025, with this share rising to 40–50% in 2026.
• The labs' revenue per megawatt has surged from $10–$15 million to as high as $50 million, enabling them to reinvest profits into training rather than relying on external capital.

Main topics

• AI lab compute growth
• Global compute distribution trends

Notable quotes

"By the end of 2028, if this trend continues, you've got them just controlling most of the usable flops in the world on their own."

Conclusion

As OpenAI and Anthropic continue to dominate global compute growth through massive capital investment, efficiency gains, and

Transcript preview

Speaker 1 (0:00) Okay, I'm back with Dylan Patel, founder of Semi-Analysis. Our version of a family Thanksgiving dinner is a regular yearly podcast. We are not actually related. Speaker 2 (0:08) Don't tell the people this. It will destroy the myth. Walk me through... Speaker 1 (0:13) 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, etc. 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. Speaker 3 (0:36) 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 open and anthropic. Now it may be built by others and then rented to them, but it's at the end customer, it's them. As we go forward into the future, the numbers for computer ballooning, right, 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. 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? 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 Codex 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. 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, you know, 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 bucks on inference capacity, actually generate 50 bucks of revenue, and then I can turn around and incrementally spend all of Speaker 1 (3:22) that profit on training. One thing I'm very interested in understanding is how you see decentralization of compute happening in 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 a vast majority of the world's compute? Speaker 3 (3:50) Yeah, so earlier this year, you know, the beginning of this year, Anthropic OpenAI started at two for OpenAI and less than two for Anthropic. End of this year, they're both above five. 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 percent 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. Now, who's building that compute for them will change. Next year, big at new entrepreneurs, 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 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. 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 Speaker 1 (5:05) year, Speaker 3 (5:05) it's Speaker 1 (5:06) already half of the incremental compute is going to Anthropic and OpenAI. Speaker 1 (5:10) computers 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. 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 like two at the beginning of this year to close to like six at the end of this year, just multiplying out by three. 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 do you see the world compute situation over the next few years? Speaker 3 (5:51) Yeah, so if the incremental compute adds this year 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 two years ago. So actually, 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 Tranium 3s, which are way, way, way more efficient, 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 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. So you've got another multiplier on that. So by the time you're in 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 flops in the world Speaker 1 (6:59) on their own. The thing I'm confused about is, why you think we only add 80 gigawatts in 2028 if we enter in a world in which the price, the value of computing increases so much. That's the upper bound, by the way. That's the like, I'm so fucking bullish. Right. Okay. So 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, one sec. You need 55,000 N3 wafers, 6K N5 wafers, and 170K DRAM wafers. I don't know those numbers might have changed. I'm Speaker 2 (7:34) going to Speaker 1 (7:34) troll you, but the way you said wafers was so fucking Indian. Wafers. By the way, when we first moved to the U.S., I had the VW thing pretty bad, and I was a vegetarian. 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? Speaker 3 (7:54) you Speaker 1 (7:58) Anyways, so that's for one 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 was at like $3 to $4 billion. Now, suppose you add in clean rooms and shell and everything else with the fab. So $6 billion of 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 five years, so, you know, the first gigawatt is generated five years of profits. The second gigawatt that the fab has produced is 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. Speaker 3 (9:01) There's a lot of OpEx along the way. There's a lot of Speaker 1 (9:04) other CapEx, like the data center, the power. And you had to pay like, you know, the opening AI for the R &D. There's a lot of different Speaker 3 (9:10) people Speaker 1 (9:10) who need money here. So, but yeah, it's huge. Take away half of it for all these middlemen. That still means there's a hundred X 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 going to figure, like we're going to be, you have this huge discrepancy where you can turn $1 into $100 and they're not going to figure out a way to make more mirrors. I mean, they are. Right. It's just these mirrors taking some time to bake, right? But the emergency are so big. We're like, anthropic and open-air, 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. And I'm just like, we're not going to be able to solve that supply constraint? That just seems quite hard to imagine. Speaker 3 (10:03) 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 thing bottlenecking a 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, wait, 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, 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 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, okay, we need to do that. But in reality, because of all the economics of what's going on, it should be even more. Speaker 1 (11:07) But it takes so long to pill. Suppose every single company in the firm, sorry, in the stack, got private equity. Like somebody came in who was super AGI-pilled and was like, we're going to maximize production. How fast, 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. Speaker 3 (11:40) 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, you know, 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 Speaker 1 (12:07) world is capital constrained. But in a world where, say, the top labs are generating, let's say, even combined, a trillion dollars in revenue next year, they're not able to say 10 of that. I don't think they're going to do that. Yeah, or Speaker 2 (12:19) hundreds of Speaker 3 (12:20) billions at least, right? It just Speaker 1 (12:21) seems like they realize where the world is headed. I feel like they could just make... Speaker 3 (12:24) 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, yeah, yeah. I Speaker 1 (13:00) mean, obviously, they will never get to that point, right? Because they want to keep... Yeah, Speaker 3 (13:03) you Speaker 1 (13:03) reinvest. You want to make your CapEx higher than your returns. But the key question I really want to understand is if the current continues to 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 more performance. by 2028 than they are now, right? Because the hardware has gotten better. So not only have like flops per watt increase, but also the hardware gets better at working with AI workloads. Okay, so 100 gigawatts for the lab's end of 2028. How much is like world compute? I think that Speaker 3 (13:40) may be a little difficult given 2028 you start to have... They've taken 70 80 percent 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 percent of compute is You know Google or meta or my 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 and tropic 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 Speaker 2 (14:17) a Speaker 3 (14:17) revenue share and credit back and all that. But ultimately, in 2028, 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 SGLang, 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 open router. It's very simple. Speaker 3 (14:55) 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? Does it get to Speaker 1 (15:19) $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 or 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. 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 you'd continue expecting them. to be able to gobble up, like bid for larger and larger shares of the compute. I Speaker 3 (15:54) 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 dollars a megawatt to really gobble up 70 % of the world's compute in 2028, 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, you know, sort of the open source Chinese language models. You know, OpenAI not releasing Astra, OpenAI stopping training for two 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. 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, 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 forces at play, which we cannot describe, that would potentially slow this down. Speaker 1 (17:37) 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? Like I think they're far from just being able to fully automate the job of like a full white collar worker. But white collar workers earn, you know, six figures or north of that a year. And if you have a gigawatt that can sustain a population of like, say a million white collar workers, let's say roughly, right? That's like, you could then off the back of that, Speaker 1 (18:11) That would be a hundred billion. That's actually surprisingly low. Speaker 3 (18:15) Yeah, a hundred K per person, million population, yeah? Speaker 1 (18:17) Yeah, yeah, yeah. Speaker 3 (18:18) I don't know. But it would be many hundreds Speaker 1 (18:20) of billions of dollars if you get like full AGI per gigawatt. I think the Speaker 3 (18:23) other aspect of this is, and we've continued to see this, most of the value capture is not happening, right? Like most of the value that these models generate does not get given to OpenAthentropic. Thankfully, so far, it is mostly just being given to the users, 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, you know, as much as 10 % of Anthropic's business. Speaker 3 (19:06) 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, that's what's required. So sure, if you had a million new software engineers, the cost per software engineer would also fall. Speaker 1 (19:28) One thing I'm confused about is does the market come in equilibrium? And if it comes in equilibrium, would you just expect... the price of compute to equal whatever anthropic and open AI can generate from it or be very close to it, but like a small amount of markup for anthropic and open