Decoder with Nilay Patel
00:03:33 · Nilay Patel, Joey Brookhart
[00:03:33] S2: I am very excited to talk to you. We're talking just a couple days after the big Anthropic s one 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 what's your read into it? [00:03:44] S4: It was an awesome S1. I think, you know, it's been a rough three weeks since the Investor Day. I think, you know, some people were somewhat disappointed in the September ARR numbers, but what we saw here was a company that has capital efficiently scaled to an amazing level, trillion plus valuation, know, 70,000,000,000 plus of ARR in very short amount of time. And gross margins, compute margins, 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 in the last two days. I think a lot of the Semi Connector Complex and Neo Clouds and compute stocks really rally again. [00:04:19] S2: I'm actually... It's funny, I don't think I can nitpick the S1 itself. I want to get into some of the details because there's a lot of numbers in there that are worth thinking about. I actually just want to start a really big picture. I look at Anthropic and where it could go. I look at Dario saying that he can see his way to a trillion dollars in revenue. Where does that money come from? Because so much of these businesses are about eating every other business and it feels like somewhere someone else has to make some money to pay Anthropic to generate these revenues and I haven't actually seen that laid out very clearly.
00:04:51 · Joey Brookhart
[00:04:51] S4: It's so interesting I think today because even though the scale were at you know 120,000,000,000 of ARR between the two leading labs, it's still an extremely narrow market. The top 1% of customers are the one that are spending you know 100,050 thousand per employee. Your broader Fortune 500 Global two thousand, who typically has a lot of IT spend, isn't spending much, and then two, lot of the spending, especially at Anthropic, is coding specific. So we're kind of in, it's almost a single vertical company, and then we look in the future in a '27, you think of just this broader adoption in 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 ARR didn't really start to accelerate till the beginning of the year. We think of other cases like cyber, where that can be similar to coding, where these are 8 figure plus contracts with leading enterprises that can scale very quickly due to all the cybersecurity incidents we're seeing. And then I think you get to other verticals like bio, and then to get to a trillion, I think we do have to move some of that white collar work and where it's more of AI's augmentation to labor saving or replacement in some areas. And I think you look at things like Microsoft's offerings to employees as you know, maybe down the line at some point, but you know, there's no wealth of other verticals that they can get into and really start to move the needle.
00:06:11 · Nilay Patel
[00:06:11] S2: This to me is the single biggest question I have, not just about the S1 but the entire industry. I would understand that if there are one or two leading labs, right? There's two big companies, they've got a technology that can automate a whole bunch of things. We will see how well that automation goes sector by sector. But there's not just two leading labs. There's quite a few of them. There's open weight models. I've heard from a lot of CEOs who are very nervous about giving all their data to an open air Anthropic and they would like to run some things on prem. There's like law firms running on prem AI now, is the funniest turn in in maybe all of history. How do you see that playing out? Because so much the pressure on this S1 was the numbers inside themselves which I want to talk about, but then there's the external pressure of well you've got to grow and it growth is not as short. So how do you see all of that playing out?
00:06:58 · Joey Brookhart
[00:06:58] S4: And when we look at like coding today for example, and I think we saw this in Q3 with OpenAI kind of catching up, there's areas of coding where there's very low switching costs and you know, I can just move between that. I think you know, what we hear from enterprises and maybe too on the open wait side, like obviously I think they somewhat botched the Fable release, and why we saw some slowdown because of the zero data retention or lack of zero data retention. And is that something they can fix with Fable 5.5 and maybe drive more, enterprise adoption there? I think that's a huge thing for enterprises when we talk with them. We definitely see enterprises now, and more so to a lot of the AI natives, custom training, 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. So there's that. Then obviously DeepMind at Google and MSL with Meta are very well funded labs that have pretty similar training compute capacity, and they're bringing on a ton of and not even to mention SpaceX as well, with what Elon said on how much 10 net new gigawatts next year, and half of that kind of is the long term, internal use case. I think in the first half of the year, Anthropic ran away with it. They were 90% of the industry's net new ARR, and then we saw in OpenAI was probably actually the majority of industry net new ARR, and then open source has grown from probably a billion dollar ARR in Q1 to 10,000,000,000 plus here in Q3. So yes, the industry has gotten much more competitive, but I think all these new use cases too, and some of these verticalized use cases are really where we're going to see the growth, you know, going forward, because it's obvious coding isn't going to power us to, you know, even triple from here.
00:08:34 · Nilay Patel
[00:08:34] S2: It's interesting, the concept is called tokenomics, you run that practice alongside a co lead at SemiAnalysis. Explain what it is because to me, it's such a smart way of thinking about, okay, we're going put some money in and maybe we'll get some money out.
00:08:47 · Joey Brookhart
[00:08:47] S4: I think of it as like the unit economics or even just the broader everything from a macro to microeconomics of running an AI business or even looking at AI demand. So we cover the hyperscalers, now some of the neo clouds as well, in kind of a separate product, but then too just the AI labs. And I think for investors or just people that are interested in the business, Anthropic had negative 94% gross margins inference two years ago, now they have positive 85% and people didn't understand kind of how do they allocate compute, what are actually how do we think about the inference business model? I think part of that gross margin conversation is the revenue per megawatt in inference, and why are all companies that are running inference seeing massive gains in inference and compute efficiency? So we try to model out these businesses, and obviously when Anthropic was private, we're dealing with information we can get from the press and things like that. But now that we have numbers, concrete audited numbers, it's easy to explain the unit economics of this and two, how the businesses differ. OpenAI is a much different business than Anthropic, just in both their consumption versus subscription and then historical weight versus, you know, consumer versus enterprise. So trying to understand across the demand level, you know, the economics of these businesses.