No Priors Ep. 81 | With Sarah Guo & Elad Gil

(music plays) Hi, listeners. Welcome to No Priors. Today, Elad and I are just hanging out. We're gonna talk about LM consolidation, what's going on in chips, I think an interesting dynamic a- around what type of risk you should take as an AI company in pushing the envelope, and, um, some big transactions. So let's get into it. First topic. Elad, are we done here? Is it too late? Is the LM market consolidated?

It's such a interesting question, right? Basically, what we're seeing is a number of model companies, um, are either, you know, having their teams join larger enterprises, so that may be parts of Inflection or Parts of Character or parts of other companies, parts of Adapt, AWS. And so that, you know, there's sort of one dynamic going on there with the model side. And many of those companies are continuing to exist, right? Like, you know, some of the products are still running and being used in different ways. Um, at the same time, there's, um, enormous capital moats, uh, emerging to get to the biggest scale for foundation models. And so if you look at it, you know, these companies are now raising billions or tens of billions of dollars, often either from hyper-scalers, so the Amazons and Microsofts of the world, or from sovereigns, right? Because those are the only people who can actually give you billions and billions of dollars. The venture capital industry is just too small to actually be able to support the next round for these companies, so everybody's kind of partnering up. And so it's a really interesting question to ask, well, for all the other players in the market, where are they gonna get these ever-rising amounts of capital and who do they partner with? Does Apple end up with a partner? Does Samsung end up with a partner? Does, you know, um, XYZ other company end up with a partner? And so you can kind of map, like, all the potential partners to all the model companies and just ask, how does all that fall out? And then in parallel, the big hyper-scalers have an incentive to fund these companies simply because it, you know, in some cases also translates over to more cloud utilization as an industry in general. The incentives start to flip between VCs, clouds, other strategics and sovereigns in terms of what they want to do. It does seem like it's increasingly hard to think that most companies will end up being competitive outside of a fundamental breakthrough in the model architecture or cost of actually training and then running inference on the model, or doing the post-training side. So I think it's a really interesting open question, but it does feel like we're moving into a stage of more and more consolidation. I don't know. What do you think about it?

Most of that makes sense to me. I would argue that the market has become more competitive, not less over the last, like, year and a half. Maybe it's competitive between a set of players that have, like, as you described it, you know, a capital moat, that there's some breakaway scale. But the dynamic now, at least from the consumption side, is there's continual and aggressive performance increases and, like, competition on the benchmark and price decreases, uh, uh, and also, you know, real open source players. And so you can have consolidation and people not necessarily making money yet.