Had a lot of fun chatting again with my twin brother Dylan Patel.

We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).

And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.

One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI.

Watch on YouTube：https://youtu.be/aV26V1UvkJw ; listen on Apple Podcasts：https://podcasts.apple.com/us/podcast/dylan-patel-anthropic-openai-will-have-most-of/id1516093381?i=1000785793715 or Spotify：https://open.spotify.com/episode/1chA0sqLyHUL684tUEE3ek?si=Dgplc0zpTz-H6O642qY5mA .

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(00:00:00 ) – Two labs will soon control most of the world’s compute

( 00:07:01 ) – $6 billion in fab capex enables $1t+ of end revenue

( 00:13:08 ) – Compute prices will rise if the labs outbid everyone

( 00:18:22 ) – Which layer will capture most of the surplus?

( 00:25:40 ) – What could slow down progress?

( 00:29:43 ) – Labs are shifting compute from inference to R&D

( 00:33:27 ) – China gets less than 10% of new compute, but its labs need less

( 00:48:48 ) – Will AI cause a sovereign debt crisis?

( 01:07:52) – Will the world’s future workforce belong to a few companies?

Okay, I’m back with Dylan Patel, founder of SemiAnalysis ：https://semianalysis.com/ . Our version of a family Thanksgiving dinner is a regular yearly podcast. But we’re not actually related.

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 ：https://fortune.com/2025/10/07/data-centers-gdp-growth-zero-first-half-2025-jason-furman-harvard-economist/ . 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 ：https://epoch.ai/data-insights/hyperscaler-capex-trend . We’re at a little bit over a trillion dollars of CapEx ：https://www.investopedia.com/terms/c/capitalexpenditure.asp 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 ：https://openai.com/index/introducing-the-codex-app/ and 5.6 ：https://openai.com/index/previewing-gpt-5-6-sol/ 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 ：https://www.nrc.gov/docs/ML1209/ML120960701.pdf .

The most interesting aspect about what’s happening now is this: Before, if they served a model — GPT-4 ：https://openai.com/index/gpt-4-research/ being served on Nvidia ：https://en.wikipedia.org/wiki/Nvidia Hopper ：https://en.wikipedia.org/wiki/Hopper_(microarchitecture) GPUs ：https://en.wikipedia.org/wiki/Graphics_processing_unit — it was generating negative gross margin ：https://www.investopedia.com/terms/g/grossmargin.asp for OpenAI. But now, when OpenAI serves GPT-5.6 or Anthropic serves Opus 5 ：https://www.anthropic.com/news/claude-opus-5 or Fable 5 ：https://www.anthropic.com/claude/fable , 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 ：https://cloud.google.com/discover/what-is-ai-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 ：https://en.wikipedia.org/wiki/SpaceX , which is building a ton of compute. They’re actively going to lease quite a bit of it to Anthropic ：https://www.wsj.com/livecoverage/stock-market-today-dow-sp-500-nasdaq-05-20-2026/card/anthropic-rents-1-25-billion-of-spacex-data-center-capacity-each-month-uNS0TiOKvaa8wo4GhxII 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 ：https://www.wsj.com/tech/ai/openai-broadcom-develop-custom-chip-for-ai-inference-beafd74a , Anthropic with TPUs that they’re purchasing from Google ：https://www.anthropic.com/news/google-broadcom-partnership-compute and deploying with Fluidstack ：https://www.anthropic.com/news/anthropic-invests-50-billion-in-american-ai-infrastructure .

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?

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 ：https://en.wikipedia.org/wiki/Floating_point_operations_per_second 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 ：https://www.dwarkesh.com/p/dylan-patel , you said that in order to make a gigawatt of, I think, Vera Rubins ：https://nvidianews.nvidia.com/news/nvidia-vera-rubin-platform , you need 55,000 N3 ：https://en.wikipedia.org/wiki/3_nm_process wafers ：https://en.wikipedia.org/wiki/Wafer_%28electronics%29 , 6K N5 ：https://en.wikipedia.org/wiki/5_nm_process wafers, and 170K DRAM ：https://en.wikipedia.org/wiki/Dynamic_random-access_memory 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.

In North Dakota, I was in elementary school, and I’d be like—

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 ：https://en.wikipedia.org/wiki/Cleanroom and shell and everything else at the fab ：https://en.wikipedia.org/wiki/Semiconductor_fabrication_plant . 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 ：https://www.zeiss.com/semiconductor-manufacturing-technology/smt-magazine/so-does-euv-lithography-work.html ?

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 ：https://en.wikipedia.org/wiki/ASML_Holding 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 ：https://en.wikipedia.org/wiki/Gas_turbine 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 ：https://en.wikipedia.org/wiki/Extreme_ultraviolet_lithography 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 ：https://en.wikipedia.org/wiki/Carl_Zeiss_SMT , 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 ：https://en.wikipedia.org/wiki/Artificial_general_intelligence -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.

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.

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?