Things Benedict Evans Has Said on Podcasts

“There are two fundamental changes in AI this year. One is the massive product-market fit for software development. The other is that the capex is outgrowing the free cash flows at the hyperscalers. So they can no longer fund this out of their existing businesses… that now means it matters what the capital markets think, which it didn't in 2024.” — Benedict Evans, Another Podcast

Toni Cowan-Brown asked what Wall Street needs to see to keep funding AI. Evans says AI-related debt this year runs somewhere between half a trillion and a trillion dollars, which changes who gets a say in how far the build-out goes.

Another Podcast · 2026-11-03 Permalink → Listen →

Another Podcast Around 16:57 into the episode

Benedict Evans

…this might be the thing. This might be a thing that people do. But how is it going to fit together? We don't know. Can you plot that back against Anthropic? No. Back to my point. This is a snapshot of the unit economics in the summer of 2026. That doesn't tell you how any of this is going to work.

Toni Cowan-Brown

It feels like one maybe last question that I know we touched upon a little bit, but what does Wall Street need to see to keep funding this? Where do you think their head is at? Like what, you know, what happens when we run out of capital? What's next here?

Benedict Evans

Well, so one of the things I think I've been saying, I can't remember we said it on the last podcast, is that there's sort of two fundamental changes in AI this year. One of them is the massive product market fit for software development, AI for software development. And the other is that the capex is outgrowing the free cash flows at the hyperscalers. So they can no longer fund this out of their existing businesses. And so depending on who you ask, you've had something. Like, where's my slide? Something between half a billion and a trillion, half a trillion and a trillion dollars of debt issued around AI this year. There's well over a trillion dollars of leases, well over a trillion dollars of future purchase commitments. The Google, the hyperscalers on one side, and then the NeoClouds and Oracle on the other side have had to go to the capital markets. And so that now means that it matters what the capital markets think, which it didn't in 2024. So that's just the primary observation. Secondly, it's funny. Like, I remember when I was an equity analyst, I would talk to the credit analysts and realize that I had no idea what…

Toni Cowan-Brown

looking at a population. And something from completely different angles.

Benedict Evans

Yeah, but look, look, it's a completely different question. Because as an equity analyst, as an equity investor, what you want to know is how much is the share price going to go up. Whereas as a credit investor, what you care about is do they have the cash flows to pay me? And you don't care how…

Toni Cowan-Brown

no pivoting from that. Yeah, I am.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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“There's this slightly bizarre argument from NVIDIA that the chips are fungible, so if this NeoCloud goes out of business, they can sell the chips. But if they've gone out of business, it's because you can't pay the interest by selling the chips. Imagine I'm an Uber driver and I've bought a car with a loan, but I can't get enough Uber rides to pay it. The person who sold me the car says, that's okay, somebody else could drive the Uber. No, that's not why I can't pay off the loan. There aren't enough Uber rides.” — Benedict Evans, Another Podcast

Evans explains why lenders to NeoClouds and Oracle are in a different position from lenders to Google, which has ad cash flows behind it. He mocks the idea that GPUs make gold-plated collateral: if one data-center borrower fails because AI demand is weak, the resale market will be weak too.

Another Podcast · 2026-11-03 Permalink → Listen →

Another Podcast Around 19:17 into the episode

Benedict Evans

…ends up as a smoking hole in the ground, which it clearly isn't, but even if it did, you still got all that cash flow to pay your debts. Whereas for Oracle or for a NeoCloud, if it turns out you can't make any money from an AI data center, well, then you're just going to have to go home. There's

Toni Cowan-Brown

no pivoting from that. Yeah, I am.

Benedict Evans

No, I mean, there's this sort of slightly bizarre argument from NVIDIA that, you know, the chips are fungible. So if this NeoCloud goes out of business, they can sell the chips. And you think, but if they've gone out of business, it's because you can't pay the interest by selling the chips. So no one else is going to either. I mean, explain this to me like I'm five. Like, I've bought this thing. It's like, imagine I'm an Uber driver and I've bought a car with a loan, but it turns out I can't get enough Uber rides to pay the loan. And the person who's given me the loans, who sold the car, says, well, that's okay because somebody else could drive the Uber and pay off the loan. Like, no, that's not why I can't pay off the loan. If I can't pay off the loan, it's because there aren't enough Uber rides. So they're not going to be able to do it either. You know, there's a correlated downside here, put it like that. Which is why everybody pushed back massively on, I can't remember which NeoCloud it was that said they should be AAA rated because we can sell the chips and the chips are gold-plated collateral. Well, yeah, but no. And this is the, I think part of what's been going on, and again, you know, I'm not going to do financial markets commentary really, but part of what's been going on in the last…

Toni Cowan-Brown

Yeah, and it comes down to the thing that we always talk about. What's the value here? What are we building?

Benedict Evans

Depending on which numbers you're looking at. I mean, there's an old, you know, there's a very, very old stock market saying that there's no such thing as a bad company, only the wrong price. And, you know, back to the beginning, okay, the revenue on this thing is exploding. We really. Don't know…

Toni Cowan-Brown

To your point, we just don't know.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

“Anthropic's R&D is 59.5% of revenue in H1. Google was 9.5%, Meta 10.5%. So add the two together, 75% of revenue is going on running the existing model and building the next model. And 80% of Q3 revenue came from models built in the last six months. So people compare this to a mining company: running a gold mine is really profitable, but you've got to dig another one, and for Anthropic the gold mine only lasts six or eight months.” — Benedict Evans, Another Podcast

Evans compares Anthropic's S-1 cost structure with Google and Meta at their IPOs. The cost of serving customers looks similar; the cost of building the next model is far higher. It matters because the frontier moves so fast that each model goes stale within months.

Another Podcast · 2026-11-03 Permalink → Listen →

Another Podcast Around 07:36 into the episode

Benedict Evans

And Anthropic 85. So I mean, these are all, you know, with the, you know, close enough for government work. These are all good, close. It's all kind of the same number within French.

Toni Cowan-Brown

Yep.

Benedict Evans

Yeah, so they're spending sort of 15 to 20%. All of those companies are spending 15 to 20% of their revenue on actually serving the existing business. Then you look at the RD, which is the cost for Anthropic is the cost of building the next model. And at Google, RD was 9.5%. Meta is 10.5%. Anthropic is 60.5%. Sorry, 59.5% in H1. So plus the two of those together, 75% of revenue is going on running the existing model and building the next model. And then they say that 80% of Q3 revenue came from models built in the last six months. So this is why you've had people comparing this to a mining company or a pharma company in that actually running a gold mine is really, really profitable, but you've got to dig another one and that's really expensive. And for Anthropic, the gold mine only lasts for six months or eight months or a year or something, and then it becomes irrelevant because the pace of the frontier is going forward so much. You've got to have the newest model. So then you get questions which are, how does the ratio of that change? Because in second half of last year, Anthropic spent $4.5 billion on training. And in the first half of this year, they spent $9.7 billion of training. So they not exactly…

Toni Cowan-Brown

And is it fair to say that a big part of this IPO also is about bringing money? We've talked about this previously, but bringing money that's outside of the loop, because right now the money seems to be going in the circle of the, you know, the company's Anthropic rents its computers from also the…

Benedict Evans

So then there's a whole, so there's a whole second conversation, which is the amount of long-term commitments that Anthropic has made to purchase capacity in the future, which is hundreds of billions, trillions of dollars, depending on how you add it. It's something over a trillion dollars…

Toni Cowan-Brown

Okay.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

“You have pricing power in the sense that an oil company has pricing power. There is shortage. I can name my price. But you don't have pricing power in the sense that people have to buy my oil and not their oil because it's all the same.” — Benedict Evans, Another Podcast

Evans is going through what Anthropic's S-1 does and doesn't tell us. He argues the leading models are close to interchangeable, and many customers already use two or three of them. His point is that today's scarcity-driven pricing says little about what the business will earn once supply catches up.

Another Podcast · 2026-11-03 Permalink → Listen →

Another Podcast Around 02:55 into the episode

Benedict Evans

Or something.

Toni Cowan-Brown

Nice and short and sweet.

Benedict Evans

…So you don't know where supply and demand are going to be in two and three or four years' time. Secondly, the models themselves are more or less commodities. And certainly if you're deep into this, as again, we've discussed, you can argue about, oh, well, Claude is better than Gemini for this. But in generality, everyone is looking at using two and three models and you don't have pricing power. You have pricing power in the sense that an oil company has pricing power. There is shortage. I can name my price. But you don't have pricing power in the sense that people have to buy my oil and not their oil because it's all the same. And yes, sorry, I know different kinds of oil are different. But like the point here is that, okay, these are pretty fungible systems. And so you don't know where supply and demand is. Right now, you don't have differentiation on your own terms. We don't know if that will change. We don't know if a winner-takes-all effect will emerge. This is why people talk about recursive self-improvement.…

Toni Cowan-Brown

Is there anything in the document that you read as, okay, well, this actually makes sense and this is the reality as it is today? Or does everything feel like a lot of this is guesstimations? Because they have to be.

Benedict Evans

Oh, so the numbers in the thing now, I mean, these are all audited numbers. I mean, the numbers are the numbers. The point is that this isn't, you can't really draw straight lines from this and say, well, this is what's going on. And I think it's very kind of instructive to compare this with, as…

Toni Cowan-Brown

What are you going to compare this, Tim? I'm intrigued.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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