Invest Like the Best with Patrick O'Shaughnessy
· Dylan Patel
[00:00:00] S1: Mythos is a materially larger model than prior models. And so yes, it is a much larger model. What chip it's trained on is not really relevant. It's the scale. And obviously to a hundred thousand black wells is equivalent to hundreds of thousands of prior generation chips. TPUs and Trainium have their different release cadence. So it's not exactly like mirrored one-to-one, but ultimately, yes, Mythos is a significantly larger model. It's proof that the scaling laws still work. Everything about it shows the trend line continues of models – more compute into model makes model better. And along the whole way, it's not just more compute into model makes model better. Along the whole way, we're also getting these compute efficiency wins, which are as all this research compute that the labs are spending is actually turning into, if I want X capability to your model every six months, that cost or every two months, that cost is dramatically decreasing. But then if I scale it up massively, I get a humongous capability jump as well. And so yes, it's proof that this is still happening. Google and Anthropic are not heavy, heavy users of GPUs on the training side, but OpenAI, they'll start having their new class of models. I think they're taking a more sensible principled approach to scaling in small steps. Anthropic really went for a huge jump. We'll see better and better models throughout the year. And the release cadence is only going to get faster.
· Patrick O'Shaughnessy
[00:00:00] S0: We've gone a long way in the conversation with saying almost nothing about OpenAI, which would have been so strange.
· Dylan Patel
[00:00:00] S1: So this is the interesting thing. Everyone's like, okay, so Anthropic's just won, right? They had Mythos in February. They never even released it because they didn't feel the need to. They're already sold out. Their revenue is already adding $10 billion a month. And then you've got Opus 4.7 today, all before OpenAI's alleged Spud release, which media such as The Information and others have posted about. So clearly Anthropic is in the lead and OpenAI is cooked. What's interesting is because Anthropic has such bounds on compute, and they can only grow it so fast and to the point of – Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. And now Anthropic is like, fuck, I wish we had a lot more compute. OpenAI is able to pay the bills perfectly fine. In fact, they've raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying from Oracle and CoreWeave and SoftBank and all these people in Microsoft, such as Trainium. Now they're getting Trainium as well from Amazon. They've done this insane thing on compute. They also know they need more. But what's interesting is if you were to say Opus 4.6, let's ignore models getting better over time. Let's just take diffusion of this technology. You and I may jump on the model immediately day one, but other businesses take time and it takes time for people to learn. And the spark of, oh shit, quad psychosis moment doesn't hit everyone at the same time. And so by the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. I don't think that's unreasonable. It's spending $40 billion right now.
· Patrick O'Shaughnessy, Dylan Patel
[00:00:00] S0: That's like a linear extrapolation. [00:00:00] S1: It's a linear extrapolation, not an exponential. To get the exponential, you need the better models. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon enough. Whoever hits that tier next – sure, Anthropic may get to charge 70 plus percent gross margins. But if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. And probably they also won't have enough compute to serve all the users. Sure, maybe Mythos is a model where if the world had enough compute, it'd be $500 billion of revenue or something crazy. There is such demand for these tokens and such limitations on compute. We see this with H100 prices skyrocketing and all these other things. The useful life of these GPUs continue to extend and extend. It's pretty clear even the tier 2 lab is going to be sold out of tokens, let alone the tier 1 lab. The tier 1 lab will have better margins, but the tier 2 lab will be sold out. And probably the tier 3 lab will also be close to sold out. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure. And so this gap will continue to grow and the model labs will continue to have expanding margins. Until people in the hardware supply chain, infrastructure supply chain are like – wait, no, why don't I just jack up my margins?
· Patrick O'Shaughnessy
[00:00:00] S0: So suffice to say, I think the assessment today or your assessment of the demand side is completely explosive in your own particular example here at SemiAnalysis. But just more broadly that – you call it AI psychosis, as people fall into this experience of what they can do, the implementation difficulty going completely away. I've certainly felt that. My own token spend is just through the absolute roof, just in a matter of weeks. So that feels like a pretty good assessment. Anything we're missing on the demand side?
· Dylan Patel, Patrick O'Shaughnessy
[00:00:00] S1: If you don't use more tokens, you'll never escape the permanent underclass. Either you use more tokens and you generate economic value, outsized economic value for the use of those tokens. A lot of people are doing it the boring, lazy way. Oh, I guess I'll just work one hour a day instead of eight hours a day and I'll have AI do most of my job. That's the boring way. The cool way is I'll still work eight hours a day and I'll do 8x the work and maybe I'll make 5x the money. You can't do this with a job, obviously. There's people who have multiple jobs and there's people who start companies and start selling stuff. There's people who are hustling, which is what I view like you and I is doing is we're mostly hustling. Get that economic value on this AI before everyone is using it and it's table stakes, because it's still not table stakes. So if you don't use more tokens and generate the value from them and capture that value, there's three different problems here. Using more tokens, generating value from those tokens, and capturing value from the value that you created from the tokens. If you don't do these three things, you'll never escape the permanent underclass, i.e. as models continue to skyrocket and capability and the concentration of resources potentially happens. [00:00:00] S0: All right, let's talk about supply. What is changing at the frontier of supplying the entire stack that's required to serve all these tokens as the demand curve explodes?