# AI Infrastructure Economics and Market Risks

**Podcast:** Big Technology Podcast
**Published:** 2026-10-07

## Transcript

How big does AI have to get to justify the trillions in infrastructure spending?
Let's talk about it with the leading authorities from Semi-Analysis right after this.
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Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond.
The AI infrastructure numbers just seem to be getting bigger with no end in sight.
So we're going to talk about what the physics of this entire buildout looks like and whether it can continue to sustain itself without maybe not destroying the world, but destroying the economy.
Because we have some very interesting guests to speak with us about it today.
We're joined today by Dylan Patel, the CEO and founder of Semi-Analysis, and Jordan Annos, member of the technical staff at Semi-Analysis to talk all about everything going on within the infrastructure buildout.
Dylan, Jordan, welcome to the show.
Thanks, great to be here.
Let's just start by talking about the scale of what we're seeing in this AI infrastructure build out, if we can.
Dylan, there's like a chart going around X right now that compares the AI infrastructure build out to previous build outs in the past, like railroads, like highways.
The chart is showing that the percent of GDP for the AI build out today is about 3.6 percent.
Oh, sorry, the median is about 3.6 percent the average year compared to 2.2 percent for the railroads.
And basically the commentary is that this is far bigger.
That would put it basically 50 percent bigger than railroads even.
in terms of its share of GDP.
And the commentary is this is bigger than anything we've ever seen before on a scale that is effectively unprecedented.
I'd love to hear your perspective on just how big this is, whether that's the right way to look at it, and then what it means if we're building out something this big.
Yeah, I mean, the first thing when that chart was going around, I was like, well, people's numbers are too low.
It's actually much higher than that already.
Really?
Okay, so share the real numbers.
Yeah, yeah.
So like...
I mean, if you look at like next year, CapEx across the US will be on the order of $2 trillion, not just data centers and chips, but also all the rest of the supply chain that people are investing in.
So you're close to $2 trillion of CapEx.
And then you stack on top of that, GDP is like close to 30, 30 and some change trillion.
So you're at like, you know, five or 6%, right?
Not 3% or 2%.
So sort of my thought process is actually like, oh, wow, these numbers are lower than the actual because people are understating the amount and size of CapEx that's happening in the US and North America and the world.
And I think like part of this is because, you know, with railroads, your CapEx is in the country that you're deploying it in with fiber, your capex is in the country you're deploying it in.
But for AI infrastructure, your capex in America is actually a lot of it's being used to serve outside of America uses, right?
So if you look at, for example, Europe, most of Europe's inference for anthropic is in America.
And most of OpenAI's inference in Europe is in America.
And similarly, if you look at a lot of Asian countries, and so there is a bit of this, like this, this, you know, CapEx in America is actually like much higher as a result because one, you're doing all the training here and then two, you're actually serving other countries from data centers in America.
And so ultimately you're sort of at this stage where capacity is much, the economic impact of it is much larger than any other sort of physical infrastructure investment cycle ever before.
All right, Jordan, and you are the author of ClusterMax 3.0.
This is Semi-Analysis Rankings of the GPU cloud systems, basically putting a lot of the hyperscalers and the Neo clouds in tiers, talking a little bit about their performance.
And we're going to get to that in a bit.
But, you know, you do look across the board in your report, and this is something that I'd like you to comment on.
Basically, we're looking at...
where the hyperscaler quarterly year-on-year CapEx growth is going.
And in the third quarter of 2026, the growth is at...
116% year over year, which is astonishing.
The consensus estimates, so what Wall Street is expecting to come after that, they dropped down in the fourth quarter to below 100% year over year growth, and then the first quarter next year in the 70%, and it continues to drop from there.
And I think this is a cycle that we're used to, right?
Where it's like, well, you know, we're on a bigger base, so it can't possibly exceed expectations again.
It can't possibly double again.
So I'm curious to hear from your perspective and your research.
Do you think that we're actually going to see that trend, like the growth is going to decelerate?
Or are, you know, are we again falling into this trap or is Wall Street again falling into this trap where it's like, all right, you're basically at the biggest numbers you could possibly imagine, but you still need to think bigger.
Yeah, I think we're definitely going to hit limits towards the end of next year.
Wait, Jordan, before you answer seriously, I got to, you know, it's like when you ask a kid, what's the biggest number they can think of?
And they say like, 1100, right?
And now, and it's like, no, no, no, no, we're at a trillion, babe.
Yeah.
Yeah, four commas club.
So it in Dylan, in this example, the kid is Wall Street.
Not making any any statements that people just go ahead.
Dylan may have a habit of referring to his employees and his customers as kids sometimes.
But that aside, I think I think I use a much more mean word.
Yeah, I like where this is going already.
We're running out of big numbers.
There are things to look at when it comes to the build out.
This could be everything from industrial equipment to firms that are prepared to do EPC work, like engineering for construction, to chips, to cooling systems, to all the components that go outside of that and an IT estate.
Basically, There's a lot of work being done to expand the supply chain in order to be able to accommodate for all of this capex to go into it.
And a lot of it's working.
And so we're going to see massive growth.
But yeah, you can't just have triple digit growth and accelerate the second derivative even further from where it is right now.
You run out of things.
And I think the biggest thing right now is twofold.
One is it becomes really hard for the biggest neoclouds in hyperscalers to just...
organize and marshal the resources required to build another gigawatt on top of what they're already trying to build, for example.
And it becomes pretty hard for them to raise the capital.
Even though there's incredible returns on a lot of these investments already, you still just need to find the capital to pour into these projects.
And that's a difficult thing to do when you're talking about numbers in the trillions of dollars.
I would say the other thing is, by definition, the numbers have to shrink.
This year, we have 10x growth.
Anthropic going from sub $10 billion of revenue to north of $100 billion, so they had 10x growth.
Definitionally, they're just not going to have a trillion dollars of revenue by the end of next year.
So yes, there's some decelerating growth there.
NVIDIA's revenue growth is still 2x year on year, but it's not as much as it was at an earlier point.
um just because it is definitely harder to grow 2x off the same base so 3x off the same base so it's sort of like um the same applies to like other firms so it is it is definitely like definitely decelerating okay so the growth on spending is going to need to decelerate at some point but the growth on revenue and profit for that matter is going to have to accelerate you know beyond It's basically going to have to trail this big infrastructure build out and accelerate probably beyond where we've seen the dollars being spent.
I mean, you would imagine to facilitate all the AI action.
And there's been further conversations that have circulated about this.
And Dylan, let me just throw these numbers out to you, right?
So this is a Goldman Sachs report.
Sorry.
So Goldman Sachs, they say Meta.
This is a tweet, right?
Meta's committed corporate suicide after taking on insurmountable debt that the company will struggle to pay back.
There's Bain.
Bain says hyperscalers will need six trillion in revenue.
Not ever.
This is per year by 20, by 2031 to break even.
And there's a Columbia professor, Sten Van Newberg.
who said that America will need to spend roughly 3.5 trillion annually on AI services by 2032, 8.8% of GDP to justify today's massive data center investment.
What do you think of these sort of assessments of where the spending and where the revenue needs to be to pay off the investments?
And do they have a chance at making good on those investments?
Yes.
I mean, I think everyone's numbers for revenue requirements are quite low.
I think revenue will be much higher than that by 2031, for example.
I mean, there's a couple points here, right?
One is like, you know, how much are people spending on capex and then revenue?
And the revenue always trails to capex.
these infrastructure investments are six years on an accounting basis, at least depreciable assets.
Although there's quite a bit of evidence that they're actually going to last longer than that.
But six years is let's just continue to use six years.
So if I spend a trillion dollars today on AI data center and GPUs, the GPUs are lasting six years, the data centers are lasting 15.
But that means definitionally in terms of revenue, either I could assume it's a straight line.
right so then over the six years i need to call it 1.5 trillion of revenue so 250 billion dollars revenue a year for that trillion dollars of capex or it's going to be something sloped where the first year is much less than the last year is much higher um to get the payback um as far as like you know like statements on meta it's like this is this is just kind of silly because meta meta at any point can just stop buying new stuff and their cash flow can easily pay for all the stuff that they've signed Right.
They just don't have to, you know, they don't have to build a hundred billion dollars of data centers next year.
They could pull the plug whenever they feel like it.
There's obviously contracts that they've committed to, but there's also a lot of stuff they haven't committed to yet.
And so as far as like the solvency of meta, like they're completely solvent, they're completely fine.
And then the question is like, what revenue do they make on top of that?
And as far as like AI's impact in the in the U.S.
economy.
Yeah, I mean, if AI revenue is only $3 trillion in 2031, then, yeah, we'll have big problems.
But, you know, I'm like of the belief that we can get to like 600, 700 million across open-ain anthropic alone by December of next year.
Really?
I'm in the opinion that you can get to, you know, one and a half trillion maybe even by end of 2027 with just those two companies.
So that's end of 27.
So then the payback is clear, right?
you know, where they're getting that from.
Ultimately, the capacity that people are building out is matched to what they think business is coming to.
And so there's always going to be a mismatch if we're in the building phase of how much infrastructure are you spending on versus what's the AI revenue?
Because that AI revenue for today is really based on the infrastructure you spent in prior years.
And right now, CapEx is not going up at 10x a year, but inference just revenue just went up 10x year on year.
for the AI labs.
And so we've seen Anthropic is now profitable in terms of compute cost minus revenue or revenue minus compute cost, both training and inference combined.
And OpenAI gets there within the next couple quarters, maybe two or three quarters.
So based on at least what we expect in the tokenomics model.
So I think those companies are already breaking even.
If you look at like Amazon, for example, the biggest builder of AI infrastructure, They all of their infrastructure investments are profitable today.
Now, you know, the customers that for them could go insolvent, those customers being open, I, Anthropic, etc.
But right now they are solvent on those, you know, making very strong gross margins.
In fact, AI has made their gross margin go up in the most recent quarter from bedrock and AI revenue from bedrock.
So so, yes, AI revenue needs to grow a lot.
But.
I would posit that like being able to automate all the stuff that we've been able to and being able to build all the stuff that the world has been able to is going to generate meaningful revenue and surplus for the consumer ultimately.
Yeah, I think we need to stretch it a little bit in our minds to understand what we are going to measure as AI revenue going into the future.
If these companies really do have the best models in the world that are capable of effectively all knowledge work, you can imagine them going into a whole bunch of different industries beyond coding and having returns on their investments similar to what they've seen in coding.
So you can look at Things like robotics and self-driving vehicles.
You can look at drug discovery.
You can look at just health and life sciences generally.
We can take this across all of these industries and say, if they want to bring a product to market, like a cancer drug or like a self-driving vehicle, you can just imagine how much revenue they can potentially return based on the investments they have today.
And if they have the AI, the system to be able to produce the knowledge work required to build that product incredibly well.
I think they're the only ones that can really compete on the frontier of being able to do these things.
And so I think people lack the creativity to understand how returns can be viewed beyond the like Claude code subscription and per token pricing regime of today.
Yeah, there's this chart that looks at how much we're spending as a share of GDP.
This is just 2025.
So a share of GDP, health is 18%.
That's the top category.
Food is 9.1%.
AI services is 8.8%.
It's the third category in 2025.
I imagine it's going to lap or it will pass food in 2026.
So guys, I'm just curious, where's this money coming from?
Like, you know, let's say OpenAI and Anthropic combined get to $700 billion in revenue next year.
Is the money coming from these companies?
labor pools?
Is it just additive because they can sell more?
Where is the money that these companies are, the customers of these companies, where's that money coming from that they can funnel it into the AI companies?
It's a few factors, right?
Well, first off is obviously companies are going to be able to accelerate what they're building and so they can invest more.
Increasing share of R&D dollars will go towards AI.
Increasing share of cost of goods sold dollars will go towards AI.
But also you'll have a significant amount of money sort of going, coming from, you know, labor as well, right?
Labor, and I don't think labor shrinks per se.
I think the rest of the economy grows so fast, at least in the short term, that labor sort of stays, you know, flattish.
And so like...
You know, in the past, we've had different cycles across the economy, but one longstanding one has been labor share of GDP has fallen over the decades.
And it's been a steady, slow line since the 70s.
And so a lot of the like strife that people say, oh, America's economy sucks.
And yet, oh, look, the economy is bigger is actually, well, like labor share of economy has shrank.
And that's why.
In reality, a lot more of the labor, the value is accruing to capital.
AI does is it's going to supercharge that trend, long-term trend of labor share versus capital share because AI tokens are basically just capital, you know, in the form of chips and data centers that are sitting there and creating labor.
And so that labor share is going to fall even faster.
And so, you know, I would hope and for society, that's my belief, you know, and maybe it's like a irrational belief, but I believe that we will just grow the economy so fast that labor, in terms of dollars will not shrink, and it might even grow slowly.
It's just that the rest of the economy grows so fast.
And that's sort of where it comes from, right?
So all these companies that do offer various services, their services are going to get better, they're going to be able to charge more, they're going to be able to sell more, and they're going to be able to do it with fewer people.
Or another way to look at it is they're going to have the same number of people in their business just going to get better.
And there's a number of industries where this is possible.
Right.
You know, obviously, coding is an area where people have done it quite a bit.
Obviously, general white collar work is getting rapidly automated, but also companies are just able to do a shitload more than they were able to do in the past.
But there's a lot of industries like pharma and trading and chip design and such.
Right.
Like chip design is a great example.
Right.
Chips.
The number of American R&D engineers working in chip design has been basically flat for 20 years, and yet the economy for it has exploded.
Now, obviously, manufacturing isn't we're not counting manufacturing, we're just counting the designers.
But the designers are like, you know, two of the most valuable companies in the world, right?
NVIDIA, Broadcom, as well as like Google and all these other companies that are making their own chips and many others, right?
These designers.
have not really grown that much in terms of number of people.
It's been flat as an industry for US.
And yet the value that's accrued has exploded.
And why is that?
There have been a lot of tools.
Now, what does AI-assisted chip design now do?
Well, now all of the chips that are across the economy get better and better.
Your phones are going to get better.
Your AirPods are going to get better.
The chips in them.
Chips for robots are going to get better.
Chips for obviously AI.
are going to get better chips for all sorts of things, Bluetooth and GPS and all these other things, sensors for gas leaks or whatever that Bosch makes.
These are all just going to get better because AI can help you design chips.
You do it with the same number of people, revenue grows, margins grow, everything looks fantastic, right?
And that's sort of like, there's many industries where this can get replicated in my view.
Dylan, you've said that, I think in the past, you've said something to the extent of knowledge work is F'd.
So do you no longer believe that?
I think most knowledge work, okay, like, let's separate like my belief in like Looney Tunes 2035 versus like, you know, 2028.
I think in 20, I think for the next, you know, X number of years, as long as AI is very powerful, but it is not better than humans in most every way, the most, the most high agency people are going to be able to do the coolest stuff.
They're going to be able to make way bigger impacts on the world.
Now, you know, in like the case where AI is just better than everyone, that could change drastically, right?
You know, then like knowledge work is screwed and actually like, you know, what matters is like people being able to like play games and have fun, people being able to act, people being able to like, you know, do the things that humans do that humans only appreciate in each other if the AI doesn't kill us all, right?
But that's sort of like, you know.
We won't go there today.
Jordan, you know, this is kind of in your area of expertise.
I want to get started talking a little bit about the risks to like what might derail the story.
And, you know, we'd already talked about the fact that there are some contracts that a company like Meta may not have to go through with.
You're obviously looking a lot at the businesses of the neoclouds, which are dependent on companies like Meta actually making good.
on the on the promises that they've they've put forth to build with these companies.
Is this sort of an underappreciated risk that like everything looks like fairly fine right now if you speak to one of these neoclouds because they'll tell you that they have the best customers in the world.
They have the metas and meta will make good on the on the on the promise and they're raising debt based off of this.
But in reality, the risk might be that a company like Meta might just decide.
yeah, we're not going to do that.
And the contracts might not be as binding as, you know, the Neo clouds tell us they are.
Okay, so I want to be clear about one thing Dylan was talking about there in terms of termination versus what's going on with meta and the Neo clouds, which is that generally speaking, these hyperscalers, or, you know, large frontier labs, or signing up with contracts for the Neo clouds, do not have arbitrary cancellation rights on like a monthly basis where they can just pull the plug and the contract's done.
Generally speaking, they're signing five-year, six-year term links and they're off taking the full capacity and they have a set hourly rate and total contract value that's very attractive to the neocloud providers because those guys have gone out.
and secured land and power and built a site and even in some cases purchased the chips prospectively before they've actually gone and done that contract.
What Dylan's referring to with the potential to slow down or pause or cancel projects is more with respect to the self-build sites, which is to say like Meta has massive campuses that are going to serve as more of a base load of capacity going into the future, which they can either choose to buy chips for or just leave as an empty shell for years if they want to slow down and not go as hard as they can.
And generally speaking, construction costs, while very expensive, are a small percentage of the total cost of a new chip project.
Like, in other words, the concrete and the steel and the walls is quite, you know, it's important and big and, you know, very real challenge to get it done.
But on a dollar basis, like the HBM and the optics and the GPUs themselves.
are the bigger portion of the bill.
And so, you know, what it's looking like right now is that all of these big companies are really needing to contend with the massive growth that they're expected to pursue by their demand from their customer base.
Either that's Meta or OpenAI or Anthropic who have demand on their consumer-facing applications like a ChatGPT or Cloud Code or Codex or even Muse from Meta, things like that.
In order to serve that demand, they need GPUs and it's profitable to serve the demand.
So they need to ramp up and be prepared to roll this thing out to the billions of monthly active users on the meta, different, you know, surfaces that they serve and approaching a billion of monthly active users for ChatGPT and hundreds of millions for Cloud Code and things like that.
So anyway, with that distinction in mind.
What's the risk with project cancellation or a downturn or things like that?
I mean, in order for us to forecast some sort of change in the demand supply curve there, you'd really need to see demand slow.
There are hard constraints on how many chips you can produce, how many data centers you can build, and how fast this stuff is going to come online.
And as of right now, all of the modeling we can do basically shows that demand outstrips supply.
Is demand 10% more than supply right now?
Is it 50%?
Is it infinite?
I mean, we don't get to answer that question because we are not able to catch up to demand with the supply of GPUs that you can bring online right now.
And so when that question gets answered, if it gets answered, if we find limits to the demand for knowledge work, to the demand for intelligence from the economy, then we'll be able to figure out.
who needs to slow at what time, where, and how that plays out.
Yeah, but so far, I guess those limits haven't been found.
So, all right.
Before we get into ClusterMax, one last topic I want to cover.
Big IPO on the way, which is, of course, the Anthropic IPO.
Dylan.
You had a very interesting tweet about it.
I'm just going to read it.
You said you can't invest in the Anthropic IPO at $2 trillion because it could be zero and I'm fucked or it could be $20 trillion.
But at $20 trillion, we're all fucked.
Can you explain that and tell us a little bit about what you mean by that?
To be clear, it's entirely a shitpost.
I was like listening.
You know what?
I get it.
But I think there's some truth to what you're saying at the high end.
So talk to us about it.
I'm generally extremely bullish.
Anthropic, I think.
Anthropic will make you make, you know, be a very successful company in the world.
Anthropic could even be the first company to be a $10 trillion valuation company in the world.
Like I'm like that fucking bullish Anthropic.
But at the same time, for them to be a 10 or $20 trillion valuation company, especially 20, they're, first of all, their multiples on revenue are going to have to collapse quite a bit.
Um, you know, so they used to, you know, they used to raise at 30 X and now they're, you know, IPO is going to be, you know, let's call it, they're at a hundred billion dollars of revenue and they're going to maybe be at 20 X.
Um, you know, if they get to $10 trillion, I bet you they would need to be at like a trillion dollars of revenue, um, or something crazy, right?
Like that.
Right.
Um, which could happen at some point in 28, um, or 29.
It depends, of course, like the stock market can also go crazy and like make multiples go crazy.
But, you know, in the world where they're doing, you know, where they're worth 20 trillion, you know, I don't think their revenue, I don't think they're even at a 10x sales multiple now, they're probably at like a 5x or even less.
In which case they're, you know, if they're at $20 trillion of revenue, they're at, are they at like, two, three, $4 trillion, $20 trillion valuation, two, three, $4 trillion revenue.
And at that point, you know, what is the state of AI?
If on a $150 billion global economy or trillion dollar economy, they're generating multiple trillion dollars of revenue and by far the largest company in the world, like AI's capabilities are freaking insane.
In which case, like, you know, all of this worry of like, whether it's AI kill us all or AI so good that everyone loses their jobs and because.
distribution of that earnings is going to be heavily leveraged to the people who own anthropic stock or constituent supply chains of it and not distributed well, then people are going to have disrest and overthrow governments because all of the growth is accumulating to a very small number of people.
So these things could tear the fabric of society apart.
And therefore, if you believe anthropic will get to 20 trillion, society as a whole could be fucked.
Jordan, do you have similar worries?
I think, yeah, I think there's a lot to do to get to that point.
Like, I actually don't think I'm necessarily as worried as Dylan.
I'm optimistic, by the way.
I just painted a pessimistic picture.
I'll go optimistic.
Yeah, I mean, like the rosy optimistic view is kind of what I described earlier, which is like, if they're getting to $20 trillion of revenue, this is not...
done on a per token metered basis for people using cloud code as a product.
And I don't think, you know, you go and see cloud code go 20 X better from where it is right now in terms of like quality.
And that drives the revenue.
I think that they get into other areas of business.
And I think I would trust Anthropic quite a bit to bring, like I said earlier, a cancer drug to market or something like that.
And we've seen similar stuff happen recently with GLP ones, just be such a meaningful thing for so many people in the economy.
And as a result, You know, companies like Lilly have had massive returns on that incredible research that led to those incredible drugs that help people.
And so I think I would be more optimistic for the future where theoretically Anthropics had a $20 trillion valuation.
But I do think that there are a lot of going to be a lot of bumps along the road along the way, which is to say.
Going back to your question previously, how can they possibly serve all of this demand and train these models and have intelligence defused to all of these different parts of the economy so that people can realize the benefits?
And I think the way you do that is to build compute, and it so depends on the nitty-gritty details of construction and installing GPUs and running them reliably and all of that.
To me, the center of the world is the neoclouts on that road from...
a $2 trillion IPO to a $20 trillion valuation in the public markets.
Yeah, Anthropic, kind of an interesting business case, right?
They're spending all this money on infrastructure.
Even in the S1, you saw big losses accompanying their revenue.
Although, Dylan, as you mentioned, it looks like what they've done is they've turned the tide a bit and they're making more money right now than they're spending on compute.
Talk a little bit, because I think the...
The belief here is that a company like Anthropic is a low margin business or at least much less than software, but that doesn't actually seem to be the case.
And you guys have run the numbers.
I would say like if you look at like Salesforce, which is probably like one of the greatest SaaS companies out there, you know, I mean, I think like at least like consistently grown to become one of the biggest SaaS companies, their gross margins are like 75% and Anthropic.
on their inference is doing 75% gross margins, a little bit higher even, you know, especially on the frontier models.
So it's sort of like, well, and it took Salesforce quite some time to get there, right?
They were not at 75% when they IPO'd, you know, they took quite a bit of time.
Actually they were, but then they like, you know, in the mid, in the late 2000s, like they were, you know, I think like it took, they were like not quite there.
It took, you know, Anthropic quite a bit less time to get to that strong levels of gross margin.
And if you look at many other SaaS companies, they're not even that high.
And then the other thing is like their customer acquisition cost is insane.
Right.
If you look at like a SaaS company, it's like, you know, fine, my COGS is really low, but then my customer acquisition cost can be as high as like 30%.
And Anthropic has near no customer acquisition cost.
Right now, obviously, they have a humongous R&D spend, but their R&D spend has grown slower than their revenue.
And so now they've reached profitability and there's no reason why that trend doesn't continue.
Right.
And they continue to grow more profitable.
So at least operating margin wise.
And then like scalability is the other thing, right?
Like the beautiful thing about a lot of SaaS companies, even if your customer acquisition cost is so high, you know, as long as your margins are high enough to pay for initially, eventually you don't have to reacquire that customer.
Now you've got this super profitable business.
But with like the AI labs.
their business seems somewhat sticky.
Yes, there's all this like messaging of like, oh, you know, Meta's cutting spend or Microsoft cutting spend or what have you.
But ultimately, the revenue has only been a straight line up on the chart and almost every customer's only grown on revenue for them.
And switching costs, even when, you know, some other Chinese model comes out or, okay, I can't stop with a better model, doesn't really shift the spend share that much for existing customers.
You end up with like this, like pretty sticky customer potentially.
who is growing revenue at some pace and there's no customer acquisition cost and the margins are high.
So it's arguably a better business than software is an AI lab.
If you can- But how much is the risk, the fact that like there, this pacing the frontier movement is coming out?
I think that's just signaling.
I think it's complete- Talk about that.
You don't think that's true at all?
I think pacing the frontier is a responsible thing to do if you believe that we could kill ourselves.
I don't necessarily think that they will pace the frontier too much, though.
I think there's very little pacing they can do given the race that they're in.
And people have made skits about this, but Dario can't slow down because he doesn't trust Sam and Sam's not going to slow down.
Sam can't slow down much because same with Dario.
Obviously, they're going to slow down a little bit when they have these massive security incidents.
and neither of them can slow down because china is not too far behind so it's like sort of like very difficult that's reassuring all right um look we're going to take a quick break and then when we come back we're going to talk a little bit about um the state of the ai clouds uh the cluster max of course uh and then we'll continue on with some more questions about the technology and the business so we'll do that when we're back right after this this episode is brought to you by intelligence ai Is your investment in AI coding tools helping your team ship better software?
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And we're back here on Big Technology Podcast with Dylan Patel and Jordan Anos from Semi Analysis.
Semi Analysis, I'm sure you're familiar.
It's the Bible for anything AI infrastructure.
These guys are the best.
We're very grateful to have your time.
both of you and you know definitely recommend you at least check out the blog you can also get semi-analysis on subsec it's newsletter.semianalysis.com it's the number one technology newsletter and well deserved so congratulations guys in the newsletter you can find the ClusterMax 3.0 It's actually, even if you're not technical, I was talking with Jordan about this.
It's a fun read.
It goes into the state of the build out.
And the subhead tells you, you know, it's already kicked off in the right way in the gory detail, the reliability, performance, support and pricing and security of the GPU cloud providers.
There's a lot in there.
There's a lot of areas we can go in this, Jordan and Dylan, but let's begin with the role of NVIDIA here.
The report.
You know, though it spends most of the time on the technical side of things, does go into the fact that NVIDIA is playing a major role in keeping these these these NeoClouds.
So the company's providing infrastructure, AI infrastructure, maybe to some of the frontier labs, but really across the board and making this whole thing work.
And NVIDIA has been backstopping a lot of their their businesses with, as you write, revenue floors, landlord guarantees, and leases that it plans to transfer to third parties, helping the Neo clubs and AI labs in obtain ingress investment grade financing without a hyperscaler being involved.
Jordan, to you on this one, you know, there's the criticism sort of like the Edzatron criticism of this of this industry is that Nvidia is basically funding the demand for all these NeoClouds and without that backstop, you know, this whole thing would basically evaporate and make NVIDIA look a bit smaller and make these NeoClouds, you know, non-viable.
What's your thought on that after going so deep into this?
Yeah, well, I mean, both in ClusterMax and in another article we did recently called NVIDIA's Backstop Universe, we go into a little bit of the dynamics of what backstops are and how they help the neoclouds raise the money they need to deploy the GPUs, which then customers are renting pretty strongly at this point.
And the number that we have is that currently we've got tracking of $588 billion of off-balance sheet backstops that NVIDIA is supporting right now, which is yet another unbelievable number in the list that we've been going through on this podcast so far.
I mean, but NVIDIA's revenue is approaching 400, about to cross $500 billion.
So we're talking about really big numbers there.
For context, that's like 2.5% of the M2 money supply in the US.
Obviously, there's massive, massive amounts of money going on in this universe, as we refer to it.
And I mean, the only thing I can say about this is that it just seems really pragmatic from NVIDIA.
They really want a wide diversity of customers.
They don't want to be dependent on the hyperscalers for all of their revenue.
They also don't want their customers to be dependent on getting their chips from a select set of three or four different hyperscalers.
And so they have every incentive to have a nice, healthy market with a diversity of options and lots of selection for all of the customers to go work with.
And in order to deliver on that, people have more problems than just.
raising the money required to buy the GPUs.
Well, first of all, they need the guarantee that if they raise the money, those creditors are going to see returns.
And that requires some sort of investment grade offtaker, which NVIDIA can serve in these cases, in many of these cases.
But the way it's worked so far is that NVIDIA signs up as that offtaker.
And then when it comes time to actually take those GPUs for themselves for their own internal research work, Somebody else comes along and wants them more at a higher price, and so then they sell them to those guys.
They don't take them for their internal research, and they roll on to the next one.
And for everybody who says that NVIDIA is doing this for some nefarious purpose or to keep some bubble going, I'd encourage you to look at the quality of NVIDIA's research.
They publish more models on Hugging Face than anybody else.
They have incredible CI across the suite of software that they support on their GPUs, and they trade models like Nemotron.
we're not the biggest fan of these models or the software.
We think there should be a diverse ecosystem.
But the idea that NVIDIA does not use their own GPUs for research that serves the purposes of their company is itself wrong.
And then the dynamic that we've seen is that this amount of compute is actually not a meaningful portion of NVIDIA's revenue.
It's a very small percentage of NVIDIA's total revenue that is going back for their own internal research workloads.
So what we're seeing is just that they are supporting NeoCloud's build-up the capacity for the demand from the customers, which is so incredibly strong right now and hard to serve.
And then maybe the last thing to say about this is that it's not just the chips.
We track a whole wide range of things that they're backstopping.
This is everything from the construction costs, the memory suppliers, the people building fabs, the people building industrial equipment.
I mean, it's not just like, I need help raising money for my GPUs.
It's I need help raising money for all of the other things in the supply chain, which is just the long pole in the tent for that specific project.
Yeah.
Let's talk about security.
because you know the first image in this report is a quote tweet of an Ilya Syskever post, which was made right after the Hugging Face incident.
Ilya says, NeoClouds have limited cybersecurity.
Next time agents successfully go rogue, they'll try taking over a NeoCloud to run more copies.
This is bad.
Thus, NeoCloud should greatly strengthen their cybersecurity and every company with strong cyber models should help with that.
This is a rare tweet from Ilya.
Dylan, what do you make of this?
Is this...
It sounds to me like if he's out saying this, right, remember he's building this stuff himself at Superintelligence that we might have a problem here.
Yeah, so we posted an article titled Most NeoClouds Suck at Security.
By the way, the art for that is incredible.
We'll put it on the screen for those who are watching.
It's like this guy getting stabbed by everybody and it's like it's SOC2 compliant.
But yeah, so we posted that report and then that, you know, like maybe eight or eight hours later or whatever, Ilya posted his tweet because they are very much, you know, using various clouds.
They're obviously super secretive and care a lot about the secrecy of like what they're working on, but also they are big believers in super intelligence.
I guess, Safe, Super Intelligence, SSI, they were the first.
Trump actually copied Ilya.
Ilya's first with another one, right?
First to reasoning, first to LLM scaling, and now first to super intelligence branding.
But Ilya sort of tweeted that out.
And it sort of echoed what we wrote, right?
Which is like, look, most of these clouds are terrible at security.
You can get open source models to just hack NeoClouds or get access to stuff within NeoClouds.
You know, GLM 5.3 especially is like really good at this.
So open source Chinese models can just hack dozens of the NeoClouds that exist out there and you can go look at what other people are doing.
And so we've seen stuff like this.
um at other clouds there was a cloud that you know we we didn't even intend to but we just accidentally ended up like seeing all of the other clients on that cluster seeing what stuff they had in storage see what stuff they had in slurm etc and there were like intelligence agencies right like oh so we immediately closed out like reported to them we're like yo which intelligence agencies we we cannot say i don't think that's a valid thing to say national like like federal well Security agencies of governments.
They were not American.
They were not American, but we said it was for a country that has a top 10 GDP.
It's not like some tiny country.
I'm losing my mind here that that was possible.
Yeah, it's like if we were malicious, and by the way, there were probably a dozen other customers, and maybe one of them was malicious, we could have just seen what they were training.
And they were training models and running models for themselves, presumably.
because this is a GPU infrastructure.
So what else would you do with a bunch of Blackwell GPUs?
Yeah, we throw around this word sovereign AI, and it gets entangled with the concept of a neocloud many times.
And people, even when they're raising money, they use this word a lot because they view compute as infrastructure.
And I think that's exactly right.
This is just like the telecommunications build out, where this infrastructure is viewed by the sovereign nations that are deploying it and are involved in this.
as a asset that the country would like to control and have sovereignty over.
And they believe that similar to what you're saying at the beginning of the show, they want to be a net token importer or exporter.
And that is driven by the infrastructure they build, which means data centers with GPUs inside of it.
And so I think it's actually no surprise that federal government research and intelligence agencies are on some of these clusters sharing.
infrastructure with others, you would like them to have an air gap data center.
But if there's exactly one facility in the entire country with GB 300 NBL 72, I mean, it's a seller's market.
Goodness.
All right.
Well, there goes my sleep tonight.
So, you know, you do in this report also rank.
you know, a lot of these hyperscalers into tiers and the neoclouds into tiers.
At the top tier is CoreWeave and Nebius.
I'm more interested in the second tier and that is Oracle and Google Cloud.
Mostly Jordan because, you know, I have like...
In the document that I put together for our conversation today, I have a lot of topics and sentences that I've pulled out of the report with questions.
And then with Oracle, I just have Oracle and then a bunch of question marks because that's kind of how I feel about that company.
I don't know how to feel about them.
Obviously, their debt has been rated not well.
by the rating agencies they're making this all in bet they rate pretty high and they actually in your report have been credited with pioneering some techniques um but i would love to get your read out on on oracle and you know whether whether this has been a smart bet for them and whether you know they're they're going to to rise in glory or or sort of fall in flames like a lot of people anticipate i think the key thing about cluster max is it is not stock recommendations right It is not anything to do with, hey, this NeoCloud is going to build a gigawatt next year or 200 megawatts, which is far more relevant for the stock than how good are they at operating a cluster.
It has nothing to do with, hey, this data center that they said they're going to deliver with this much capacity next year got delayed by three months.
That's not what ClusterMax measures.
ClusterMax is explicitly measuring sort of How good is the cluster for someone to use if you rent the GPUs?
How reliable is it?
How secure is it?
How's the storage?
How's the network?
How's the lifecycle?
How's the slurm on Kubernetes?
How are all these things?
In which case, you know, it's just sort of, we try to be more objective.
Obviously, making a rating pyramid is like, you know, it is something you kind of have to do.
It would be nice if you could just make a long report and no one cared if there were no rankings.
I mean, that's what would happen.
So you kind of have to do the rankings.
and set certain boundaries and you can always litigate the hell out of why and what is in what tier.
But like, it is an objective fact that we think Oracle is one of the top NeoClouds in terms of quality of service.
Now that doesn't mean on the other hand, hey, at Semi-Analysis in April, we published to our clients that their New Mexico data center was going to be delayed because they weren't going to get the pipelines.
And this was a big issue for OpenAI because OpenAI has contracted a huge data center with them in New Mexico.
But if these pipelines don't get built, this data center can't go up.
And then later, you know, we said it again in May and June.
And then most recently, Oracle announced a force measure on it and their stock tanked because of it.
right so so we're disentangling two things right yes oracle is an amazing cloud at managed for their managed cluster one of the top neo clouds but they also at the same time may have execution issues whether it's regulatory in the case of this new mexico data center where they can't get the pipeline there or other issues you know on their balance sheet right and so that is disentangling like what is good or bad for the stock versus what are we trying to test here the goal in first cluster max was create something that people renting gpus can use And it turns out now, like there's a lot of other uses for it that people try to ham fist in, whether it's like, you know, hey, we're going to write debt for this neocloud.
If they upgrade rankings from bronze to silver, their rate will actually go down.
You know, things like this or like, hey, I'm going to invest in stocks that are at the top of ClusterMax.
So obviously these are not the decision.
These are not what ClusterMax is designed for.
It's a data point that you should use if you're.
doing anything with these companies.
But as far as Oracle as a company, there's sort of like, you know, two things to go here, right?
Like one is they signed a ton of AI data center deals with OpenAI.
Was it last year?
Yeah, it was last year.
Holy crap, it was only last year.
It feels like eons ago.
I think it might have been exactly a year ago.
But the interesting thing is they signed these massive deals.
And there's a number of risks embedded with them.
One is they signed them with a fixed margin, essentially.
So now that the price of GPUs has gone up a lot for clouds, they can't take advantage of this higher margin.
So they're kind of hemmed in there.
Number two...
They raised a bunch of debt and then the market started getting freaked out about how much debt companies can have.
And so they spooked out, even though Oracle is completely solvent.
And then number three, they have some execution issues.
You know, I mentioned the regulatory one.
There's some other data centers that have some delays and such there as well.
But the main one being this, you know, such as in Wisconsin.
But the main one being this New Mexico site that has delayed potentially multiple years because of this pipeline issue.
And so, you know, these things can have very negative impacts on the stock.
But I think the company is still fine.
Now, you know, they're not Nebius.
Nebius gets to turn around and they have the shortest average contract length of the top clouds in ClusterMax.
And so Nebius has been able to turn around and really jack up pricing and take advantage of this, right?
Whereas CoreWeave and Oracle, despite having also good cluster management and reliability and security and all this stuff, were not able to take as much advantage because they locked themselves into long-term contracts primarily.
Oh, that is fascinating.
All right, Jordan, let me ask you a technology question then.
You know, as Dylan put out there, it is a pyramid.
And there's like these top tier groups like we talked about, CoreWeave and Nebius, Oracle, Google Cloud is up there.
But in the not recommended section, there's a lot of logos.
There's underperforming and unavailable.
I think there's more, something like more logos in the unavailable than, you know, the top four tiers combined.
What do you think that says about the fact that, I mean, what do you think that says about this technology that there's so many that are out there that just aren't meeting the bar for you?
Look, as people who are trying to represent the interests of buyers, I think it just makes us sad.
It's sad to see the, look, it's sad to see that like a year and a half, almost.
you know, since the first version of ClusterMax was published, actually approaching two years now, and since we coined the term NeoCloud at SemiAnalysis, that a lot of people have just not made progress towards the things that we were writing about.
And the things we were writing about is just exactly what we talk to the customers of these NeoClouds about what they want.
And so I think it's just like the dynamic of it being a seller's market means that the priority for these companies is how can they raise as much money as they can?
How can they build data centers as fast as they can?
And how can they deploy chips as fast as they can, rather than working on the true quality of the underlying service?
And that's a little bit of the dynamic that you see represented there, where people can have these great businesses without actually worrying too much about delivering a great service that their customers really enjoy and recommend to other people.
The other thing is that I think there's like this constant dynamic that we'll continue to see over time, which is that there can only be so many leaders because there's only so many people who truly innovate and then everybody else copies them.
And when you have a market that by our tracking is actually 323 providers as of this morning, which we track in our cloud TCO model for people who are subscribers to Semi-Analysis, they have this full list.
There's only a certain amount of them that are even relevant, like large enough to put on the list, and then a certain number of those that can pass the bar for what we believe is what we need to see to recommend them.
And, you know, in some ways, we think that the bar for being recommended is quite low.
Like the basic things that fail with respect to security and reliability, being the two top ones, are just, in our view, dead simple.
And it's just sad to see that others aren't paying attention enough or caring enough to actually implement this stuff.
Okay, two quick ones before we go.
Just a more broad question.
You know, Google, obviously, they've announced they have this Gemini Argon coming out, Gemini 4 Argon.
Is it your view that, you know, there have been some people who have called upon Google to just be, you know, basically a NeoCloud and rent out the TPUs from Google Cloud?
and the computing.
Do you see them going that way?
Or do you think that they're still going to use like a large percentage of their compute for model training and inference?
I mean, definitionally, they have allocated less compute to model training and deployment in the last six months than they had two or three years ago.
And that percentage allocation has fallen over time.
This is sort of, you know, one of the things that we made a joke about, like, maybe not even a joke, but just a statement that Thomas Kurian won and Dennis lost, right?
Now I guess Karai.
Google is producing significantly more compute, you know, year on year, but the amount that's going to DeepMind is not rising as fast.
It's not like, oh, DeepMind had a third of Google's compute, now it still has a third of Google's compute.
it's actually falling as a percentage, which is really interesting because now if I look at just DeepMind versus Anthropic and OpenAI, DeepMind has the least compute of the three by the end of this year.
So it's really a big deal.
And so Google, whether or not there's a NeoCloud, what the word NeoCloud even means, does selling tokens of other models count as NeoCloud?
In a sense, that's what the new cloud business is, is selling AI model tokens, right?
So maybe that's a NeoCloud, right?
The definition is quite- Blackstone and going and doing new construction projects to build your GPUs to deploy directly for customers outside of your core platform.
Or selling chips, right?
Is that NeoCloud?
No, I mean, they're selling a lot of chips to Anthropic.
So it's, you know, physically selling the chips rather than like just renting them out.
So I would say like Google's business model is evolving and they want to make money at every layer of the stack.
But by definition, you know, helping Anthropic this much with TPUs and helping them this much with these other things is hurting Google DeepMind's business.
They need to find a niche, man.
Like for Gemini, I mean, nobody wants the third or fourth or fifth best coding model.
So they need to find a niche that drives Gemini token sales if they want DeepMind to continue competing on the frontier of research.
Because right now, I mean, even with this Gemini 4 Argon release, it's just not there on coding compared to the top.
two or three.
And the risk here is what happens next year when OpenAion and Anthropic both attempt to add five to 10 gigawatts of additional compute for their labs and for OpenAion and Anthropic each.
And then Google only adds a few gigawatts.
Now their compute deficit, it was Google's way ahead in compute and they were kind of behind slash equivalent, mostly just a little bit behind.
And now they've fallen further behind, but their compute is the same.
So what happens when they have less compute?
Are they do they somehow magically catch up?
It's possible, right?
They have the talent, they have the people they need to reorganize and really focus up.
But also, like more and more, the best people just keep getting poached to Anthropic to open AI.
And it's meta discovery loop.
Yeah, discovery.
Right.
OK, I want to ask one more before we go, if that's OK.
You know, I think, Dylan, you've said in the past that the.
the people who are getting most of the value out of the AI models that are created are the end users, right?
The people that are using the clods, for instance, at the base.
And of course, Anthropic makes that model available via API.
And to go into all these various uses, it's going to have to do more licensing and who knows if they'll be able to do products for all this.
I'm curious if you see a scenario where given the big numbers that we talked about in the past, where do they keep their frontier models at home for longer?
much longer and use them to build their own products like we know they have the wet lab we know they have cloud code uh and co-work um and then they don't make them available because they want to get more margin out of the infrastructure that they've they've bought and built yeah so i mean i think i think we've already seen this right um anthropic had mythos ready in february uh they've their model safety cards noted noted uh noted that they have two other models since mythos that have released And publicly, we still don't have full mythos.
We have a neutered version of it called Fable.
And it took many, many months before Fable even came out.
They had the Glasswing, which was like mythos, but for only some subsectors.
And it was neutered in some areas, right?
Not all areas.
And so it's clear that Anthropic has not released new models.
OpenAI likewise, Astra was actually done a couple months ago.
And it only came out like, you know, it took a couple months before they released Astra.
And their new model, Bell.
They even showed a blog where they showed the performance of it in math and how it solved all this stuff.
And they have not released it.
So clearly they are just using it internally, not externally.
And so, you know, the labs have already moved along this line, right?
Where it's like, oh, yeah, the gap between them and Chinese open source is still six months.
They're kind of keeping that.
But in reality, they kept.
you know, months of progress internal only.
Because the moment you make it external, people can distill it, you know, and all these other things.
So I think, you know, Anthropic will likely release a new model in the coming weeks, you know, for their IPO.
I think OpenAI will likely have to release a new model in the coming weeks as well, just because it's such a competitive environment.
But at the same time, they're like keeping a lot of their stuff internal only.
Yeah, we were discussing internally some of the...
capitalistic incentives if the incentives are actually going to motivate people to release more models and get them in the hands of consumers because of the incredible gross margins on the per token API pricing, or if the incentive is to keep it internally so that you can pursue the end user applications, which are even more high value than selling the access of the model per token.
It's going to be interesting to see how it plays out.
But in one of those scenarios, the capitalist approach will be to pace the frontier for yourself, right?
And what's your gut on where it's going to go?
Which one of those scenarios you think plays out?
I think Anthropic has publicly stated that they are pacing the frontier by keeping themselves restricted.
And that's the only way that their blog posts have manifest in reality.
Nobody else is slowing down.
They're only slowing themselves down.
I think that's true.
I think that's what's happening.
And I think the incentives to Both improve the status of AI by solving things that are, you know, like cancer that are just unequivocally good that everybody can agree is a good thing would be, you know, a positive incentive.
And then I think there's also plenty of money to be made if you do something like that.
My bet is that we continue to see the minimum viable models released by both OpenAI and Anthropic to keep a lead over Chinese and the rest of the open source ecosystem.
The website is semianalysis.com.
Sign up for the blog, as Dylan says.
Sign up for the newsletter, as I have.
It's a wealth of insight and analysis, and I think you'll love it if you're not there already.
And this conversation has just been the manifestation of that.
So, Dylan, great to see you again.
Jordan, really nice to meet you.
You guys are welcome anytime and we appreciate your time today.
Thanks for having us, Alex.
Thanks, Alex.
All right.
Good stuff.
Thanks, everybody, for listening and watching.
And we'll see you next time on Big Technology Podcast.