Anthropic is asking Wall Street to believe in AGI

Today on Decoder, I’m joined by Joey Brookhart, an analyst at the chip and AI research firm SemiAnalysis, to help me dig into the story inside the numbers of the Anthropic IPO filing — a document known as an S-1.

Anthropic just filed its S-1 to go public on the Nasdaq, and it could start trading as soon as next month at a valuation of anywhere between $1.5 trillion and $2 trillion. That would immediately make it one of the most valuable companies on the planet.

But the price tag really isn’t the whole story here. The S-1 document published earlier this week is hundreds upon hundreds of pages filled with details that finally paint a picture of the economics of a frontier AI lab. As you might imagine, that picture is both really complicated and built on a series of very, very ambitious assumptions about the future and what AI will do to it.

So I wanted to have Joey on the show to help break down what this document tells us, both about Anthropic’s finances and the broader AI economy. Joey and his colleagues at SemiAnalysis actually built a model of Anthropic’s finances from the outside back in July, so he’s been waiting for this filing about as eagerly as anyone, and I was excited to hear his take on what we’ve learned and, importantly, what we still don’t know.

Okay: SemiAnalysis’ Joey Brookhart on the Anthropic S-1 and the costs and risks of building frontier AI. Here we go.

This interview has been lightly edited for length and clarity.

Joey Brookhart, you are an analyst at SemiAnalysis. You’re also co-lead on SemiAnalysis’ Tokenomics model, which is going to come up a lot in this episode. Welcome to Decoder.

Yeah, thanks so much, Nilay.

I am very excited to talk to you. We’re talking just a couple days after the big Anthropic S-1 was filed. It’s our first look inside of the finances of one of the leading frontier models. What’s your take? What’s your read into it?

It was an awesome S-1. It’s been a rough three weeks since the investor day. Some people were somewhat disappointed in the September [annualized recurring revenue] numbers, but what we saw here was a company that has efficiently scaled to an amazing level, with a $1 trillion-plus valuation, $70 billion-plus of ARR in a very short amount of time.

In gross margins, compute margins, and free cash flow, everything is what you’d want to see. So I don’t think there was much to nitpick in this filing. It’s the reason you’ve seen a lot of the semiconductor complex, and the neoclouds and compute stocks, really rally again in the last two days.

It’s funny, I don’t think I can nitpick the S-1 itself. I want to get into some of the details because there are a lot of numbers in there that are worth thinking about. I actually just want to start at the really big picture. You run a thing called Tokenomics. I’m going to explain what Tokenomics is to people.

I look at Anthropic and where it could go. I look at Dario Amodei saying that he can see his way to $1 trillion in revenue. Where does that money come from? Because so much of these businesses are about eating every other business and it feels like someone else has to make some money to pay Anthropic to generate these revenues. I haven’t actually seen that laid out very clearly.

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It’s so interesting. Today, because even the scale we’re at is $120 billion of ARR between the two leading labs, it’s still an extremely narrow market. The top 1 percent of customers are the ones that are spending $50,000 to $100,000 per employee. Your broader Fortune 500 or Global 2000, which typically do a lot of IT spend, isn’t spending much.

A lot of the spending, especially at Anthropic, is coding-specific. It’s almost a single vertical company. We look in the future to 2027, you think to this broader adoption and white collar work. This typical enterprise Q4 budgeting season, we actually haven’t gone through with one of these labs yet in any meaningful way because their ARR did really start to accelerate until the beginning of the year. We think of other cases like cyber, which can be similar to coding, where these are eight-figure-plus contracts with leading enterprises that can scale very quickly due to all the cybersecurity incidents we’re seeing. You get to other verticals like bio.

To get to $1 trillion, we do have to move to some of that white collar work, where it goes from AI augmentation to labor saving or replacement in some areas. You look at things like Microsoft’s buyout offers to employees as an example of this, but there’s no wealth of other verticals that they can get into and really start to move the needle.

This is the single biggest question I have, not just about the S-1, but the entire industry. I would understand that if there are one or two leading labs. There are two big companies that have a technology that can automate a whole bunch of things. We will see how well that automation goes sector by sector, but there are not just two leading labs. There are quite a few of them. There are open weight models.

I’ve heard from a lot of CEOs who are very nervous about giving all their data to an OpenAI or Anthropic and they would like to run some things on-prem. There are law firms running on-prem AI now, which is the funniest turn in maybe all of history.

How do you see that playing out? Because so much of the pressure on this S-1 was the numbers themselves, which I want to talk about, but then there’s the external pressure of, “You’ve got to grow and growth is not as short.” So how do you see all of that playing out?

It’s very interesting because when we look at coding today, for example — and I think we saw this in Q3 with OpenAI kind of catching up — there are areas of coding where there are very low switching costs, and I can just move between that. What we hear from enterprises and maybe, too, on the OpenAI side — obviously I think they somewhat botched the Fable release, and the lack of zero data retention is why we saw some of the slowdown. That’s something they can fix with Fable 5.5 and maybe drive more enterprise adoption there.

That’s a huge thing for enterprises when we talk with them. We definitely see enterprises now, and more so, too, a lot of the AI natives, doing custom training and post-training on open weight models, and then running either their inference off of that or even their products for internal or external use cases.

Obviously, DeepMind at Google and [Meta Super

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