Hyperscaler strategy in AI, the application landscape heats up, and what we know now about agents with Sarah and Elad

**Elad Gil** (0:06)
Today on No Priors, we are going to have a host-only discussion. There's so much going on over the last couple of weeks in AI. We just thought it would be good to take a big, deep breath and a step back and talk through some of the really big changes that seem to be happening in the landscape.
Sarah, there's been a lot of new models that have come out over the last, even just week or two. This cloud, Grok, Databricks, a variety of things, what do you think? What's going on?

**Sarah** (0:30)
Yeah, I think it's a huge update for most people's priors versus a year ago.
I think it's very likely at this point that you end this year with a handful of GPT-4 level models and that some of those are open source. I think Mistral first, but then also Databricks with DBRX, they change the point of view on what you can do with a relatively small amount of compute, tens of millions of compute, and then also from a scale perspective. The Databricks team in particular just declared a very strong point of view that they call Mosaic's Law, where a model of a certain capability will require a quarter of the dollar capital investment every year due to a bunch of improvements on the hardware and algorithmic side. And I don't know if that's grounded in any particular technical belief, but I do think that the model landscape completely shifts versus what people expected to be, I think most people expected to be quite monopolistic or at least oligopolistic a year ago, right? And I think there's still a really big question at the state of the art, because if you go up one level of scale in terms of capital investment, if you're still, the dominant factor is compute scaling. I think that question remains, but there's an awful lot you seem to be able to do with the GPT-4 level model. So I think the net impact of that is pretty good from the application or the enterprise adoption side.

**Elad Gil** (2:09)
Yeah, it definitely feels like the most cutting edge, smartest models in some sense, you're going to end up with an oligopy at least in the next couple of years, just because of the scale of capital needed, but also just how far ahead you start to be as you have a model that can help you build the future models, right? Even just things like data labeling or certain forms of reinforcement learning through AI feedback or other things like that. And so as you get better and better model capabilities, you start bootstrapping the next generation of models, although obviously you have to do other breakthroughs to get there.
And then to your point, I think under that, you have this broader swath of different models and companies and things that are available. And one could argue part of what that's going to do is just kind of flip some of the value capture, the revenue, the margin, the people, whatever metric you want to use over to the clouds because they're going to be hosting all these things, right? So whether it's Llama or whether it's Claude or whether it's one of these other entrants, there's just going to be a lot of room, I think, for the clouds to make money over time as well, which I think is a little bit under discussed in terms of who captures value in this market besides the model providers. Related to the clouds, how do you think about the recent inflection Microsoft deal?

**Sarah** (3:17)
I think the first reaction is like they're true believers in AI and Microsoft and SIA is a live player, right? And so I think these sort of obvious observations with Microsoft here would be they both see a product, they see a product opportunity that they need AI aware product leadership and research leadership to go after across Microsoft properties. Despite all of the initial real traction around copilot in the code domain, I think we're still far short of what revenue Microsoft actually expects to drive in terms of across its productivity suite and in search. And they're ambitious to go after that. And I think there's a leadership change that supports that. Now they're clearly still working with OpenAI given like direct statements from both companies and the Stargate data center effort. But it's also hard from the outside not to see this as somewhat of a hedge, right? Not in a criticism of OpenAI, but if you are a true believer that this is the most important technical driver for your company and then you're reliant on an outside player, that's not a position that a trillion dollar company likes to have. You know, Mustafa has had more capital and more compute available to him than the vast, vast majority of entrepreneurs and research teams. And I think one big argument you can make from Microsoft is just like you have direct access to that if you're focused on the research, right? And so I think it supports what you said where the spend required at the, perhaps not even this generation, but the next generation really requires a certain level of sponsorship that is challenging for most independent players.