No PriorsNo Priors Ep. 144 | The 2026 AI Forecast with Sarah & Elad
EVERY SPOKEN WORD
85 min read · 16,575 words- 0:00 – 2:43
Introduction
- SGSarah Guo
Hi, listeners. Welcome to No Priors. How can we even begin to wrap this year up? The AI field has grown, breaking out into the mainstream and taking center stage with policymakers. ChatGPT shipped massive numbers and asked for massive dollars. Gemini and Google roared back strong. And on the application front, AI coding has shifted to agents, and it's eating up all of our inference capacity. Doctors are adopting clinical decision support en masse. And in law and customer support, enterprise adoption is accelerating. What's next? On the research front, the race has multiple live players with open source closing the gap too. A handful of neo labs, new research labs, got funded this year, and the narrative is changing. Ilya is calling it the age of research. People are trying different ideas around diffusion, self-improvement, data efficiency, EQ, large-scale agent collaboration, continual learning, energy transformers. It's more open than it's ever been. Finally, we had a lot of attempts to make AI reach into the real world, with renewed optimism around robotics. Next year, those companies are going to start making contact with reality. From a prediction standpoint, personally, I think we're gonna see somebody make a lot of money, hundreds of millions of dollars, trading markets with LLMs next year. It's inevitable. So, we're in the second or third inning. Markets are running a little hot and a little volatile. It's hot in the hot tub. So get into it with me and Elad. Okay. Elad, it's been a year.
- EGElad Gil
I know. How's it going? It's 2026, baby.
- SGSarah Guo
Are you feeling the AGI? Are you feeling AI, AI winter in a good way?
- EGElad Gil
I think I'm actually just feeling microplastics. I think I'm now 80% microplastics. I'm just increasing my microplastic consumptions. A friend of mine actually launched a new water brand that, uh, has no microplastics by the way. It's called Lute, and it's gonna have, like, glass bottles and also the cap doesn't have plastic.
- SGSarah Guo
Does it come with continual testing?
- EGElad Gil
Yeah. That's a good idea though but-
- SGSarah Guo
Does it come with continual testing for you? Yeah.
- EGElad Gil
They, they, they did actually try to take out all the microplastics and so they, uh... I guess bottled water in actual bottles has more microplastics than plastic bottles because of the cap.
- SGSarah Guo
Okay. We'll check back in with you in '27 to see if you feel-
- EGElad Gil
Yeah, when I'm just completely ossified out of plastic. I'm actually real- really worried about microglastics. What about all the little glass particles? Aren't you worried about that? People talk about microplastics but not microglastics. I'm much more concerned about that.
- SGSarah Guo
I don't think those particles end up embedded for you permanently.
- EGElad Gil
Silicon? (laughs) You're not worried about silicones. (laughs) I go to the beach, I'm like, "Oh, no, microglastics everywhere."
- SGSarah Guo
I'm actually very willing to insert silicon in my body eventually in my
- GUGuest
(laughs)
- EGElad Gil
(laughs) Wow, that was... Yeah. I'm not gonna say anything. We can keep going.
- 2:43 – 4:40
AI Predictions for 2026
- EGElad Gil
- SGSarah Guo
What's, what's happening in AI, Elad? What do you... Where, where are we and what are you most excited about?
- EGElad Gil
Yeah, I guess for '26, there's a bunch of stuff that I think, uh, will be interesting this coming... I think we will, um... I think there's probably four or five things. One is I think people will proclaim yet again that AI is not doing much and it's over-hyped and might... like that MIT report that people were quoting that I thought really didn't matter. And, and the reality of the technology will always take, like, ten years to propagate and people are getting enormous value out of AI already and they're gonna get way more out of it in the future. You know, so there's these... Um, un- undoubtedly next year there'll be these overstated but bubble claims as well as, um, "Hey, AI actually isn't working that well" kind of claims, and that happens every technology cycle and we'll just hear it again. Next year and there'll be pundits and discussions and just a bunch of wasted time on it. So I think that'll happen. I think another prediction for '26 is the next set of verticals will hit massive scale. I think this year we saw consolidation of coding into a handful of players, of medical scribing into a handful of players, of legal into a handful of players, like Harvey and others. And so, I think we'll see that next set of consolidated protocols happening. So I think that'll be interesting. I can keep going by the way. I have, like, a bunch of these. Do you wanna go next? We can alternate. I just did two, why don't you do two?
- SGSarah Guo
Maybe I'll react.
- EGElad Gil
Yeah. Uh, react.
- SGSarah Guo
I'll react and then I'll g- and then I'll give you two predictions. Um, I have to think of my predictions while I'm reacting, so I'm glad I have at least two threads. Yes. I, I think that the overall sentiment on AI in the investing landscape is a lot of people getting stressed about the amount of capital they have at work, and then just a level of uncertainty around, uh, the adoption cycle and technical bets that people are making that they don't have full first principles confidence on coming to roost. So, uh, I, I think, like, any number of exogenous factors, plus noise about, um, the speed of adoption, which by the way seems like blinding overall and we can talk about what the constraints are.
- EGElad Gil
That was so fast. I don't even know what people are talking about.
- 4:40 – 7:17
Adoption of AI in Professional Fields
- EGElad Gil
- SGSarah Guo
I, I just saw a report, um, that talked about... It's from this group called, uh, Ofqual, that talked about adoption of AI by doctors. And, look, there is just amazing adoption of, of course, uh, you know, several different categories, like-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... documentation, clinical decision room support with things-
- EGElad Gil
Yeah.
- SGSarah Guo
... like Abridge and OpenEvidence, and obviously the general models. But there's, like, massive enthusiasm from most of-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... the physician profession here. And I'm like, okay, of, of all of the domains that were professional and considered more conservative-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... the fact that there is this, like, you know, desire to have things that make work better seems like obviously to continue in the other professions.
- EGElad Gil
I, I think this is, by the way, s- super under-discussed, uh, that people who t- tended to be the slowest adopters of technology love AI. That's physicians, that's lawyers, that's certain accounting types, it's... You know, it's, it's actually kind of fascinating. It's compliance, you know? (laughs) It's all the people who always never adopt technology are now adopting this stuff fast, so I do think that's really notable and very under-discussed.
- SGSarah Guo
It will keep happening. There are actually lots of professions where, like, being able to reason and interact with unstructured data is very useful.... like, I expect that there's gonna be some, like, negative market current. Like, you know, if NVIDIA doesn't over perform by some massive amount one quarter-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... everybody's gonna freak out. But I'd... I think that has very little to do with the fundamental secular change.
- EGElad Gil
Yeah. It has to do with microplastics at NVIDIA. That's my two cents.
- SGSarah Guo
It has to do with-
- EGElad Gil
I think, uh-
- SGSarah Guo
It has to do with, um, micro-glastics, as you said.
- EGElad Gil
Yeah. Uh, that's true actually. There's silicon there. It's in the air, I bet. I bet they have micro-glastics all over the place. It's messed up, Sarah.
- SGSarah Guo
It's part of the trade. If you make $20 million as an average NVIDIA employee-
- EGElad Gil
Yeah.
- SGSarah Guo
... then you also have to have micro-glastics in your blood.
- EGElad Gil
I know, it's awful.
- SGSarah Guo
Don't listen to this, Jensen. Jensen's our next guest, so he can't-
- EGElad Gil
It's one percent-
- SGSarah Guo
... he can't hear that.
- EGElad Gil
One percent micro-glastics in the blood. I think, um, you know, a third area is the next set of foundation models are gonna come, and by that I don't mean the Neo Labs and the, and the next gen LLMs, which of course will happen, but I mean, uh, physics, materials, um, science progress by models, math progress. And I think what'll happen is there'll be one or two use ca- one or two cases where it works really well for something, they'll invent some new material or 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 will be incredibly important. So, that's what... That's another prediction for next year, is there'll be a, a couple of 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.
- 7:17 – 8:25
Robotics and Self-Driving Cars
- EGElad Gil
- SGSarah Guo
I have... Uh, okay, fine. Three, three quick predictions for you. One is there's gonna be, like, some collapse of sentiment around a set of robotics companies next year. Not because it-
- EGElad Gil
Hmm.
- SGSarah Guo
... like, actually-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... isn't as a field going to progress-
- EGElad Gil
Yeah.
- SGSarah Guo
... but because, you know, people are beginning to project timelines-
- EGElad Gil
Yeah.
- SGSarah Guo
... and, uh, you know, not everybody is going to deliver on those timelines.
- EGElad Gil
What's your timeline?
- SGSarah Guo
I think that we will see, um, humanoid and semi-humanoid robots get deployed at small scale in environments, be they consumer or industrial, next year, and not everything will work. And that, like the... Because there's this, you know, hype cycle-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... around humanoids overall, as soon as something doesn't perfectly work, which it will not, people are gonna freak out. Right? And then there's gonna be some bifurcation about people investing.
- EGElad Gil
Yeah. I mean, we're in the year 15, 17, whatever of self-driving. Something around there. And it's really working now, but it took a long time. So, it seems like robotics should have maybe a faster curve but a similar curve, right? It's gonna take some time to figure all this stuff out, and then once it's figured out, it's gonna be really valuable. And the, the big question
- 8:25 – 13:59
Robotics: Incumbents vs. Startups
- EGElad Gil
for me on robotics... You know, it's interesting, if you look at self-driving, there's like 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'll happen to robotics. It feels to me like Optimus or some form of, like... 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? 'Cause there's some similar problems there. Is it some other big industrial company? Is it startups? Like, who are... 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, which is self-driving, right? Um, I guess arguably the other winners in self-driving are Chinese companies, right? Chinese car companies, which are banned from coming into the US market, and those will probably also be winners in robotics, right? The most likely global winners in robotics will be some subset of China plus Tesla plus something else, right? Maybe it's... Maybe one of the startups.
- SGSarah Guo
I think that's right, but that's like saying... I think in most industries, like, you know, the incumbents are more likely to win than the startups, if you're just looking at it, like, as, as a numbers game.
- EGElad Gil
Uh, I don't-
- SGSarah Guo
The way I-
- EGElad Gil
I don't know. Yeah, I don't know. I don't think so. I think, um... I think there's startup industries where startups should win and there's incumbent industries where incumbents should win. And they have different characteristics in terms of market structure, in terms of capital needs, in terms of structures of expertise and supply chain, and, you know. So, I do think there are markets where incumbents should definitionally do better. They don't always, but they typically do. And then I think there are markets where startups will do better.
- SGSarah Guo
Sure. But I, I don't, I don't argue that, like, some markets are stru- like, the, the moats are structurally deeper, right?
- EGElad Gil
Mm-hmm.
- SGSarah Guo
But one way that you might look at autonomous vehicles is it's one very complex single use case robot.
- EGElad Gil
Mm-hmm. Yeah.
- SGSarah Guo
And it mostly does locomotion. It does lots of other unnecessary types of prediction, defensive driving-
- EGElad Gil
Yeah.
- SGSarah Guo
... whatever else. But it's, it's, it's a single use case robot.
- EGElad Gil
Yeah. And we for- and we forget there's a lot of good ones like that. Dishwashers are great single use robots, vacuum cleaners are great... You know, like, there's all these things that we actually have that are robots in the home that we pretend aren't, right? We forgot that they're robots.
- SGSarah Guo
It d- it-
- EGElad Gil
Elevators are robots. No, seriously. Escalators are robots.
- SGSarah Guo
I'm gonna use the language of, like-
- EGElad Gil
(laughs)
- SGSarah Guo
... for a robot to be a robot it has to be somewhat intelligent, right? Um, and so dishwasher doesn't count as an appliance. Um, a self-driving car does count as a robot.
- EGElad Gil
Good. Interesting.
- SGSarah Guo
Not just like-
- EGElad Gil
Where is that border of intelligence for you?
- SGSarah Guo
I... I think, like, it's probably some level of generalization, right?
- EGElad Gil
Oh, really? Interesting.
- SGSarah Guo
It can work in different environments, it can work on different tasks-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... it can work on different objects. Otherwise, you know-
- EGElad Gil
So a car... A self-driving car is... Okay. Yeah, I don't know. I didn't have that complex of a definition, I just had it as, like, something that will do certain pre-programmed types of labor for you. But maybe that's... Maybe you have a better definition. I'm gonna look up what-
- SGSarah Guo
Yeah, I-
- EGElad Gil
... the definition of robot is. A machine capable of carrying out a complex series of actions automatically, especially when programmable by a computer. But, you know, all these things have chips in 'em now. Your dishwasher has a chip in it, right? Has a computer in it.
- SGSarah Guo
Okay. Yes, but, like, uh, I would argue that robotics has not been an interesting area of innovation without intelligence, and so that's the relevant set for maybe you and me and many people that are looking for something that changes quickly.
- 13:59 – 16:42
Future of IPOs and M&A in AI
- EGElad Gil
about IPOs and M&A next year? What do you think will happen there? I think that's another big... That's theme number four, five. I guess, you know, three was different types of models, four was ro- robots and self-driving, and then five would be IPOs and M&A. What do you think? More IPOs, less IPOs, more M&A, less M&A, different types of M&A?
- SGSarah Guo
It depends on whether or not the bottom f- falls out of the AI market at some point, right?
- EGElad Gil
Uh, what- what-
- SGSarah Guo
But I, I think regardless-
- EGElad Gil
... th- what do you mean by the... What do you mean the bottom falls out? Like, what, what, what does that translate into?
- SGSarah Guo
Uh, I think people just get skittish about... You, you know, the, the cycle here is like, what are people scared of? They are concerned that-
- EGElad Gil
Hey, robots.
- SGSarah Guo
... demand isn't real. No. Demand isn't real, um, for AI to support the CapEx cycle, that there is systemic risk-
- EGElad Gil
Okay.
- SGSarah Guo
... from people passing the ball around in terms of who is actually-
- EGElad Gil
Yeah.
- SGSarah Guo
... responsible for the CapEx build out and these credit agreements, right? Or, um, you know, pay on delivery contracts for data centers and for chips. What else are they afraid ab- of? They're afraid of, like-
- EGElad Gil
Micro-glastics.
- SGSarah Guo
... the... Micro-glastics, AKA, like, too much concentration in NVIDIA and a small number of other players.
- EGElad Gil
See?
- SGSarah Guo
If you're, like, a big public markets investor, you're just like, uh, you know, "You, um-"
- EGElad Gil
Too much silicon. It's too much silicon.
- SGSarah Guo
It's too much silicon. You're damned if you do, you're damned if you don't. I was talking to a friend of mine who runs a large tech hedge fund, and they're already, like, a foundation model investor in, like, multiple significant labs that may or may not go public in the next couple years.
- EGElad Gil
Yeah.
- SGSarah Guo
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-"
- EGElad Gil
Mm-hmm.
- SGSarah Guo
"... because they, like, want to be part of the AI revolution." And then-
- EGElad Gil
Yeah.
- SGSarah Guo
... 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-
- EGElad Gil
Yeah.
- SGSarah Guo
... "Oh, I can't miss it like I missed NVIDIA," then you have to buy it.
- EGElad Gil
I see.
- SGSarah Guo
And so his view was like, you buy the IPO regardless of your fundamental, uh, view of the company. And I was like, "Wow, this is not the investing job I know how to do."
- EGElad Gil
Yeah.
- SGSarah Guo
What do you think happens?
- 16:42 – 21:08
Challenges in Consumer AI Innovation
- SGSarah Guo
I, uh, I think that... I did not believe that we were gonna see that many, like, unique consumer experiences-
- EGElad Gil
Hm.
- SGSarah Guo
... besides like ChatGPT. I think we are gonna see, like, a slate of consumer hardware that mostly fails.
- EGElad Gil
Okay.
- SGSarah Guo
But I'm still open-minded to it. And then definitely actually, like, it remains to be seen if any of these scales, but I am seeing magical experiences-
- EGElad Gil
Okay.
- SGSarah Guo
... of, like, really different consumer agent software that I, like, I actually want and will use, and, uh, I think people are, like-
- EGElad Gil
What's an example of that?
- SGSarah Guo
... barely beginning to... Well, I, these companies are in stealth right now, but I, I do think that, like, there's gonna be a lot more product people that experiment with this, and model companies experiment with this next year. Um, and so I'm, I'm pretty optimistic about that.
- EGElad Gil
Yeah, I agree with that 100%, and I think, um, the big question is, what will end up being a breakout startup? And there'll undoubtedly be some. And then what will be a startup that'll grow really fast, and then it'll get cop- copied by the main lab Google, and then it just gets incorporated into the core product? And the, the interesting thing is unless a company truly hits escape velocity and builds up a network effect or something else that's really defensible, usually incumbents can launch two, three years later and catch up. And so, if they have the distribution and have the core product and have... But, uh, you know, to your point, I think it's very exciting, and I've been waiting for this for a while.I think two years ago, three years ago, um, this guy David Song, who was on my team at the time, ran a two-quarter thing at Stanford where we had different teams apply, uh, from the engineering programs there, and it was like groups of people building consumer apps using AI. 'Cause we said this wave of AI is so fascinating, why isn't anybody building anything consumer? So we basically just gave people free GPU to go and try stuff. And there was no, like, obligation on their side to do anything with it, you know, in terms of us getting involved, right? It was just, "Go do cool stuff 'cause this is such a good playground." And those were really neat experiences that were being prototyped. And then I was just shocked that nothing happened for a couple years in terms of, you know, really interesting consumer products. So I agree with you, there's so much room for that, and I always wonder, is that because there's a different generation of founders who don't want to work on consumer or forgotten how? 'Cause, you know, the big consumer companies have kind of aged out. Is it the incumbents are just too scary? Is it, like... what, why is there so little innovation actually on the consumer side of AI? I still don't quite understand what the issu- issue is.
- SGSarah Guo
I... okay, let's, let's like list the, the reasons. I do think that the incumbents are pretty scary. Um, and anybody who was around for the last generation of interesting consumer ideas saw actually the ingestion of those ideas into the existing platform, as you put up.
- EGElad Gil
Yeah.
- SGSarah Guo
So there's that. I also think, like, the first instinct that, that I've seen from companies, uh, and f- from founders working-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... on, like, new consumer experiences is essentially building, like, better versions of, like, last generation experiences with this generation technology, and it ends up, like, not being that interesting. And so I actually think you have to be, like, either quite close to research or pretty creatively ambitious to build, like, something very different that has any chance.
- EGElad Gil
Yeah.
- SGSarah Guo
And, and so I think, li- I think, like, there's just not that many people who have had that experience set or that creativity, and now we're going to see it.
- EGElad Gil
Yeah, I think it's pretty exciting. The other thing is, um, I was talking to a really well-known consumer founder who's running, you know, a giant public company, and his view is that perhaps in the entire world, there's a few hundred great product people for consumer. At least in terms of who are actually working on it; obviously there's enormous human potential and people who aren't working in consumer products could, you know. But of the people working in consumer products, he thinks that most are the few hundred people who are exceptional who could actually come up with and launch their own product that would be interesting or good. And so you could also just sa- say that maybe there's just a limitation on how many of these things can exist, just given human potential within the set of people who are already doing it. Which I think is kind of an interesting argument. I don't know if I agree with it, but I thought it was an interesting argument that he made.
- SGSarah Guo
I would limit myself to that number if i- it's also the set of people who, like, have the context of, like, what is possible now.
- EGElad Gil
Mm-hmm.
- SGSarah Guo
If you've got great consumer product instinct, but you're like work- you're like grinding away on the like 50th iteration of an existing product, like...
- EGElad Gil
Yeah, yeah. You're working on the, the, the little sub button in Gmail or whatever instead of actually going off and doing this, 100%.
- 21:08 – 26:28
Funding of Neo Labs, RL Research
- EGElad Gil
- SGSarah Guo
Yeah.
- EGElad Gil
Cool. Anything else we should talk about, or any other big predictions for '26?
- SGSarah Guo
I feel like a very big, um, emergent thing that happened this year was the surprising funding of, like, Neo Labs, like three through eight. What do you think of that, what do you think about alternative architectures? Like, do you have any point of view on, um, all of the effort around, like, getting reinforcement learning to be more general, continual learning, uh, some of the research directions?
- EGElad Gil
You know, I think there's enormous amounts of really interesting research being done. So I, you know, there's a lot of juice to be squeezed out of these models still in different ways, and I think that's really exciting. Ultimately, these things become capital games for certain types of approaches or models, because we know scale really matters, which means that eventually you have to have collapse into a handful of players because capital will aggregate the things that are working the most, you know, generating revenue. And so then the question is, what are those things? At what point do things just get kind of locked in from a usage perspective for whatever reason? And there's all sorts of ways you can imagine this being built over time against some of the models. So, I think it's interesting. I think it's exciting. I think we'll see how it plays out.
- SGSarah Guo
I think to articulate what, like, the, uh, the arguments could be for, you know, new research directions is like... Ilya, you know, did this interview recently where he describes it-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... as the age of research. And to, to paraphrase, he like basically says that, yes, I believe in scaling, of course, but, you know, there's, there's some floor of compute that is not infinite where we can test ideas at scale.
- EGElad Gil
Mm-hmm.
- SGSarah Guo
And then if we have, let's say, secret ideas around like how to get to more rapid or more compute efficient improvement, then it actually isn't just a straight resource battle-
- EGElad Gil
Mm-hmm. Yeah.
- SGSarah Guo
... which, like, the rat race does feel a little bit like today. Um, I think the other argument you, you could take is actually, like, multiple architectures - and people have done some research on this - but multiple architectures are really relevant at big domains of, of, um, usefulness. They just haven't been scaled, right? And, like, there's enough capital out there to test them-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... be they like diffusion or, um, SSMs or whatever. And that's gonna happen this next year.
- EGElad Gil
Mm-hmm. Mm-hmm.
- SGSarah Guo
And then I think there's like a, like a resource focus argument, right? If Ilya is describing that some set of labs, they have an enormous amount of compute, but they have to spend a lot of that compute on inference today, then how much do you spend on your particular research direction, uh, be it self-improvement-
- EGElad Gil
Yeah.
- SGSarah Guo
... or post-training or emotional intelligence or very large scale out agent stuff?
- EGElad Gil
Yeah, it depends on what you're doing, because the inference is what ends up then, uh, raising you money to pay for everything else, because you're generating revenue. So I think, uh, sure, but it's effectively your way to bootstrap into more and more scale. So, I always thought, perhaps incorrectly... I, 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, and maybe I'm over-extrapolating off of biology where, you know, effectively your brain has a series of modules that have different functions or tasks, right? You have a visual system that's, um, you know, highly sort of pre-wired to deal with vision really effectively. You have, uh, different areas of higher thought and learning. You have memory, you have, uh, mirror neurons that are involved with empathy, right? Your brain is actually very, um, specialized in some ways, although obviously there's people who are born with literally, like, half a brain hemisphere, and the brain rewires and sort of covers all the functionality, but, um... Like, there's a few (laughs) famous cases like that. Uh, but, you know, fundamentally, um, you have a lot of stuff that evolves into very specialized tasks. It's almost like a MOE or something, you know? And 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 kind of do to, 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 or, you know, so... And i- or a- in a directed way. And so, I think it's really interesting to ask, 'cause if you look again at biology as a, as a potential precedent, although it may be a very bad one, you look at protein design, and for a long time, there were these, like, super analytically designed proteins, and then they came up with all these systems of this evolution. You know, like phase display, and like, 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, where you have, um, all these 3D structural predictions that are actually very good, right? It's not... That was, um, AlphaFold and a few other things that really were, were breakthroughs there. So it feels like in the context of AI, maybe eventually we end up there as well, right? Where you just evolve these systems. And then that may be a very different type of approach and training, and every... You know, that, that, that may be where I think things really have a interesting break. And that's one of the reasons 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, the potential, really interesting approach to it, to, to get some really fast liftoff. But maybe not, right? We'll
- 26:28 – 26:44
Predictions for 2026 Beyond AI
- EGElad Gil
see.
- SGSarah Guo
What is, um, the one prediction you have for '26 that has nothing to do with AI?
- EGElad Gil
Do you think about anything else, Sarah? (laughs)
- SGSarah Guo
I do.
- EGElad Gil
(laughs) I'm joking. Um... (laughs)
- SGSarah Guo
Really?
- EGElad Gil
I mean, the other thing, by the
- 26:44 – 28:23
The Future of Defense and Technology
- EGElad Gil
way, one other prediction that doesn't have to do with AI is I do think, um, defense will accelerate in terms of startups and defense tech, and the shift to autonomous... or not autonomous, but to drone-based systems in general. It's a massive reworking of how you think about war and defense, and I think that's gonna be a huge shift that we'll see go even faster this coming year. I think this is accelerating, in part due to, you know, how the Trump administration has been approaching it, and the Secretary of War and everybody there have been thinking about it. But I think in part just you have enough density now of startups doing interesting things. I think that's the other thing that's, like, a huge shift that... Uh, you know, it's a hype cycle right now, and I b- I actually think, again, it's a little bit under thought about because it's, it's gonna be so big. Um, outside of AI, I mean, I think there's obvious really interesting things happening in space with SpaceX and Starlink, and how you think about communications and telephonies. That's a big shift. There's really interesting things, in my opinion, happening in energy and mining and... You know, I, I think there's a lot going on in the world.
- SGSarah Guo
I agree on defense with some, like, concern that w- you know, we have to wait for budget to actually shift from contracts to primes to some of these new companies at scale. But the demand, like, the need to be competitive in a world that's increasingly autonomy driven, um, is, like, so obvious, right? And I, I think a l- you know, hype cycles and booms are good, in that they-
- EGElad Gil
Agree.
- SGSarah Guo
... bring a lot of people to the table, you know, capital, founders, people who want to work in the industry. Um, and so you can make a lot of progress in a quick amount of time, even if a lot of companies die. (laughs)
- EGElad Gil
Mm-hmm. Yeah.
- SGSarah Guo
Uh, and there's, there's, um, more enthusiasm in a very short period of time. So I agree with that, and I also don't think that's, um, necessarily bad, right? I-
- 28:23 – 30:37
Biohacking and Peptide Therapies
- SGSarah Guo
You're welcome.
- EGElad Gil
What's your non-I-I prediction?
- SGSarah Guo
I think that, like, I'm not the only one, but I-
- EGElad Gil
(laughs)
- SGSarah Guo
... I think the, the, like, GLP-1 thing is just-
- EGElad Gil
Mm-hmm. Mm-hmm.
- SGSarah Guo
... despite all of the enthusiasm, like, still underrated for how much impact it has happened.
- EGElad Gil
Hey. Mm-hmm.
- SGSarah Guo
Right? And so, I think that the continual adoption of these is, like, inexorable. I actually think it creates a path that is interesting for, like, other peptide and hormone therapies.
- EGElad Gil
Mm-hmm. Mm-hmm.
- SGSarah Guo
I think the fact that it has been so effective has, like, lots of second order effects, both from people way... Like, just being a lot less overweight, like directly, and the willingness to look at other engineered peptides, or, like-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... I- I think it... Like, everybody understands now that, like, delivery matters. There are these really incredible medicines, and I think that the impact of that is going to, like, fuel much more investment in, um, anything that looks like that type of opportunity. And so, I think that's exciting.
- EGElad Gil
That makes sense. Yeah, I actually think, um, one thing that you mentioned is really interesting, where if you look at the sort of biohacking community, there's a lot of peptide use now of different, you know, different peptides that will do different things, in terms of, you know, somebody will have some chronic carpal tunnel thing, and they'll fly to Dubai to get, you know, peptides injected or whatever.
- SGSarah Guo
Yes.
- EGElad Gil
And usually those are sort of early indicators of potential larger scale adoption societally.
- SGSarah Guo
Yeah.
- EGElad Gil
And so I think that's a really interesting trend right now in general, like, this whole, like, um, world of peptides and their uses, and is there a hymns of peptides? Like, what's the, what's coming there? So I think that's super interesting, you know.
- SGSarah Guo
I also think, like, the biohacking community, as you said, it, like... The set of people who were really, really early off-label GLP-1 adopters-
- EGElad Gil
Yeah.
- SGSarah Guo
... um, interested in longevity, neuromodulation with ultrasound, um, stem cell injection, for example-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... like that has been like a fringe small community-
- EGElad Gil
Mm-hmm.
- SGSarah Guo
... and I think that like... I think it's going to get less fringe.
- EGElad Gil
Uh, and a lot of these things traditionally, 10 years ago, came out of the bodybuilding community, right? The bodybuilding community was like creatine and all these things that are more broadly used now, but also other... other things for sleep aids or other... you know, magnesium and all those stuff.
- 30:37 – 40:46
2026 Prediction from AI Industry Leaders
- EGElad Gil
- SGSarah Guo
And to round out this year-end episode, we've asked some of our friends for their predictions for 2026. I'm so curious.
- GUGuest
My prediction for next year is that, uh, the reasoning, uh, systems are going to translate directly, uh, to AIs that are much, much more versatile, much, much more robust. And reasoning is going to impact, is going to revolutionize, not just, not just language models, but reasoning is going to impact every single industry, from biology, to, uh, self-driving cars, to robotics. And so reasoning, I think, is, is the big, huge breakthrough th- that, that, um, uh, is going to transform a lot of different applications and industries.
In 2026, AI will stop being a reactive tool that waits for us to prompt it. Instead, it will become very proactive and get deeply integrated in our work life. It'll go where we go, hear what we hear, know what tasks we need to work on, and in fact, most of the times, complete those for us before we even ask it to do so. It'll be a coach that helps us improve our skills, it'll be our manager who helps us prioritize our work and manage our time. In short, it's going to be the best work companion we could wish for.
I think the main AI prediction that I have for next year is I think context is just going to be the most important part of every single product. And honestly, like one of the best experiences I've had with it so far is just memory in ChatGPT. Like, I think that there are going to be a lot more features that basically their goal is to extract the user intent and make the onus less on the user to basically give all of the models or the system or the product more and more context. So, in other words, how do you put the onus on the product to actually extract that from the user instead of the user having to do all of the work to do this upfront?
My prediction for 2026 is there will be a whole new suite of product experiences that run on much faster inference.
My prediction for 2026 is that we'll finally stop copy-pasting stuff into chat boxes. Instead, I think we're going to have applications that have better use of screen sharing and context management across the sources that matter the most.
One prediction for 2026. There's so much talk of agents right now, and there has been for a while, but no one has truly created a mass-scale consumer agentic AI. I think the models are there today for this to be possible. And in 2026, we will see the group that figures out the right interface and system and product that creates as big a step function and overall experience as Chat did when it first came out. And I think this area is not nearly as seated to labs as people assume. It really is anyone's ball game.
Hello, Aaron here. First of all, I get quite awkward around doing selfie videos. This is my ninth take of this video. Um, so I hope it goes okay. But, uh, 2026 prediction would be that, uh, this is going to be certainly the continued, year number two, of, uh, AI agents, but in particular AI agents in the enterprise in either deep vertical or domain-specific areas. Um, I think this is going to be the main way that we actually take all of the progress that we're seeing in AI models and actually deliver them into the enterprise. You have to be able to tie to the workflow of the organization. You have to be able to get access to the data that they have. You have to have the right context engineering to make the agents actually work. And then you have to do the change management that makes the agents effective. So, this is going to be a year where we start to see this pattern emerge more and more. Uh, which equally means that we need to ensure that we have a lot more happening on agent harnesses. So, shout out to Apoorva Suhail and Dex for that answer. Uh, but it's definitely going to be the year of agent harness and seeing how do you start to get, you know, an order of magnitude improvement on the model's capabilities by having all the right scaffolding around the model. Uh, and then finally, it will be the year of, uh, economically useful evals. Um, so really starting to figure out how these models end up doing a lot more knowledge worker tasks in the economy, um, and that's gonna... uh, we're going to see a lot more of that in 2026. We saw some previews of that this year with Apex and GDP-Val, uh, and a handful of others. We're going to see way more of that. So, those are the predictions, and we'll see you, uh, in 2026.
I think 2026 is going to be a very interesting year for American open models. Over the last year, the frontier of open intelligence shifted from America to China, starting with the release of DiTik at the end of 2024. And American institutions were slow to notice this erosion of American leadership in open intelligence. But, uh, I think they've noticed in a big way over the last half-year, both from the government level, from the enterprise level, and there are some really interesting, uh, neo-labs starting to come out with open intelligence, a- as they are directed. And there are a few of these, not just Reflection. And these companies are starting to produce some very interesting small open models. And next year, I think we'll see the US regaining leadership at the open-way frontier at the largest scale, and I'm really excited to see that.
Hey, folks. My prediction for 2026 is that I think we will see AI become much more politicized. I think we'll see it become a major...
... point of discussion for the 2026 midterm elections, and some people will come out strongly against it, and some people will come out strongly supportive of it. And, um, I'm not sure which side's gonna win out.
2025 has marked an incredible year in AI drug discovery. In the past year alone, we've gone from being able to design simple molecules on the computer to designing simple antibodies, and now most recently, full-length antibodies with drug-like properties, zero-shot on the computer. If 2025 has been the year of research in AI drug discovery, 2026 will be the year of deployment. The models have finally entered an era where they're becoming really useful for drug discovery. Not only do they make things faster, but they're also allowing us to go after really challenging targets which have been traditionally really difficult to do with traditional techniques. I'm really excited to see what comes next because the models show no signs of slowing down.
Okay, my prediction for 2026 is it will be the year that YOLO dies. We will begin transforming ourselves from a you-only-live-once to don't die. I think right now we're kind of a suicidal species. We do very primitive things. We poison ourselves with what we eat. We design our lives so that we slowly kill ourselves. Companies make profits by making us addicted and miserable. We destroy the only home we have, and somehow we celebrate these things as virtue. I think it's all backwards, and I think one day we'll look back and we'll be pretty astonished that we behaved like this. Um, I think the simp- the shift coming is gonna be simple and radical, that we say yes to life and no to death. It's simple, but I think it could be in response to AI's progress. And we do this defiantly as a form of unification. Um, I think it does require a lot of courage for us though to say we recognize how sacred our existence is. We don't wanna throw it away, and we want to defend it with every bit of courage and strength we have, uh, because it is so precious. I think it's gonna be the year we end YOLO and the beginning of don't die.
The most striking thing about next year is that the other forms of knowledge work are going to experience what software engineers are feeling right now, where they went from typing, you know, most of their lines of code at the beginning of the year to typing barely any of them at the end of the year. I think of this as the Claude code experience but for all forms of knowledge work. I also think that probably continual learning gets solved in a satisfying way, that we see the first test deployments of home robots, and that software engineering itself goes artfully wild next year.
My prediction for 2026 is that it's the year where everyone's perceptions are flipped. Currently, everyone believes that you can only use NVIDIA outside of Google, and that will be obvious that that's not the case. Currently, about a third of Americans hate AI and think it's really bad. That number will increase. Currently, most Americans think AI is not useful. That will b- flip as well. And so everyone's priors will be flipped. That's because the transformative use of AI will be so prevalent. The, the obvious utility of it will be so high that there is no way for anyone's priors... You know, cognitive dissonance will be wiped away.
Hey, I'm Benjamin Spector.
I'm Asher Spector.
And our prediction is that 2026 is the year of energy-efficient AI.
Data center build-outs are primarily constrained by energy, power availability, great interconnects, high voltage equipment, things like that, which is why x.ai's, uh, colossus was initially powered by on-site gas turbines.
The thing is that as the internet continues to grow, labs and new labs like us and startups like Cursor have a pretty remarkably insatiable demand for both training and compute, and this demand is currently outstripping our ability to push, uh, watts onto the grid. This means that in 2026, it will be really important to squeeze every available bit of in terms out of every watt.
That said, in the long term, chips probably matter more than power, because chips depreciate much more quickly than the underlying power infrastructure.
So for example, with data center power supplies at 10 cents per kilowatt hour, the, uh, chips cost actually an order of magnitude more than the power in a five-year depreciation cycle.
So in, in 2026, we think intelligence per watt is really important, to squeeze as much intelligence as you can out of every, uh, unit of energy. But in the long term, we think it's the chips that matter more.
Happy holidays.
Happy New Year.
- SGSarah Guo
Thanks for the year.
- GUGuest
Happy 2026.
- SGSarah Guo
Happy 2026, listeners.
- GUGuest
(laughs)
Episode duration: 40:46
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