The 2026 AI Forecast: Foundation Models, IPOs, and Robotics with Sarah Guo and Elad Gil

Elad Gil: I think a third area is the next set of foundation models are going to come. And by that, I don't mean the neo labs and the next Gen LLMs, which of course will happen, but I mean physics and materials, science progress by models, math progress. And I think what'll happen is there'll be one or two cases where it works really well for something, they'll invent some new material, there'll be some conjecture proved or something. And then it'll fall into this overstated hype cycle of it's gonna change everything about physical sciences or whatever. And that one-off will be overstated, and in the long run, the trend will be understated and it'll be incredibly important. So that's another prediction for next year. There'll be a couple anecdotal one-offs in science that will make people say, look, science is solved and they'll realize science isn't solved and then later science will be solved.

Elad Gil: I think what'll happen is there'll be one or two cases where it works really well for something, they'll invent some new material, there'll be some conjecture proved or something. And then it'll fall into this overstated hype cycle of it's gonna change everything about physical sciences or whatever. And that one-off will be overstated, and in the long run, the trend will be understated and it'll be incredibly important. There'll be a couple anecdotal one-offs in science that will make people say, look, science is solved and they'll realize science isn't solved and then later science will be solved.

Elad Gil: The big question for me on robotics, it's interesting, if you look at self-driving, there's two dozen, three dozen, whatever, legitimate self-driving companies, really good teams and good approaches and all the rest. And then arguably the two biggest winners, at least now, are Waymo and Tesla, which were two incumbents, right? Waymo's Google, Tesla is Tesla. So I wonder what will happen to robotics. It feels to me like Optimus or some form of Tesla robot will be one of the winners, most likely, right? High probability. And then the question is, does Waymo just adopt what it's doing for cars to robots as well? Because there's some similar problems there. Is it some other big industrial companies? Is it startups? Who are the winners and why? And structurally, when you have a lot of capital needs, but also a lot of hardware and manufacturing needs, that's gonna favor incumbents.

Elad Gil: I do think that on the topic of robots, the biggest trend perhaps, or one of the biggest trends of 2026 100% will be that self-driving will really begin to matter. And that'll be both in terms of your own car, it'll be in terms of Waymo and Tesla cabs. It's gonna be, I think, one of the big things that's talked about next year. So I think on the robotics team, that's the biggie.

Elad Gil: I was talking to a friend of mine who runs a large tech hedge fund. And they're already a foundation model investor in multiple significant labs that may or may not go public in the next couple years. And they're like, okay, well, the question is, do you buy the IPO? Their game theory on it was like, actually, no matter what I think about it, I have to do it because retail will want it because they want to be part of the AI revolution. And then if you're a hedge fund, you get benchmarked on annual performance. And because of the retail pop and some set of investors wanting to buy into it as a pure play, where you're like, Oh, I can't miss it like I missed Nvidia, then you have to buy it. And so his view was you buy the IPO regardless of your fundamental view of the company. And I was like, Wow, this is not the investing job I know how to do.

Elad Gil: They're like, okay, well, the question is, do you buy the IPO? Their game theory on it was like, actually, no matter what I think about it, I have to do it because retail will want it because they want to be part of the AI revolution. And then if you're a hedge fund, you get benchmarked on annual performance. And because of the retail pop and some set of investors wanting to buy into it as a pure play, where you're like, Oh, I can't miss it like I missed Nvidia, then you have to buy it. And so his view was you buy the IPO regardless of your fundamental view of the company. And I was like, Wow, this is not the investing job I know how to do.

Elad Gil: I always thought that eventually you end up with evolutionary systems is really how you build AI. Because, and maybe I'm over extrapolating up a biology where effectively your brain has a series of modules that have different functions or tasks... The question is the degree to which you recapitulate that as you're doing further development of AI and when do you start just spawning off a bunch of instances of something and just have some utility function they're evolving against that you then have some selection and recombining and all the other stuff that you do to try and make some of that work versus how much of it is a more analytical approach... you look at protein design. And for a long time, there were these super analytically designed proteins, and then they came up with all these systems that disabolish it, like phage display and mutagenic scans and all sorts of things that gave you dramatically better results than if you just sat and thought about it. And now, of course, we kind of solved it with AI.

Elad Gil: I always thought perhaps incorrectly, I actually probably think it's incorrect, but I always thought that eventually you end up with evolutionary systems is really how you build AI. Because, and maybe I'm over extrapolating up a biology where effectively your brain has a series of modules that have different functions or tasks, right? The question is the degree to which you recapitulate that as you're doing further development of AI and when do you start just spawning off a bunch of instances of something and just have some utility function they're evolving against that you then have some selection and recombining and all the other stuff that you do to try and make some of that work versus how much of it is a more analytical approach or a more experimental and iterative approach. If you look again at biology as a potential precedent, although maybe a very bad one, you look at protein design. And for a long time, there were these super analytically designed proteins, and then they came up with all these systems that disabolish it, like phage display and mutagenic scans and all sorts of things that gave you dramatically better results than if you just sat and thought about it. And now, of course, we kind of solved it with AI. So it feels like in the context of AI, maybe eventually we end up there as well. And that may be a very different type of approach and training. That may be where I think things really have an interesting break. And that's one of the reasons that arguably people are so focused on code because code is arguably a bootstrap into moving faster on development of AGI. But I think it's kind of code plus self-evolution is really the potential really interesting approach to it to get to a really fast lift off.