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

Pundits are screaming about the so-called “AI bubble.” But historically slow-to-adopt industries like medicine and law are actually embracing AI at an unprecedented speed. Sarah Guo and Elad Gil look ahead to 2026, breaking down the major trends that will define the next era of AI technologies. They explore the future of AI foundational models, predicting breakthroughs in solving complex scientific problems. They share competing views on the timeline for robotics and self-driving cars, debating whether startups have a chance for survival or if incumbents will dominate. Elad and Sarah also discuss the return of tech IPOs and M&As, forecast a new wave of AI consumer agent software, and explore why consumer product innovation has been slower than expected. Finally, the two offer bold non-AI predictions for the new year, including the acceleration of defense tech startups and the second-order underrated impacts of GLP-1 drugs on biohacking. Plus, stick around to hear predictions on what’s next for AI in 2026 from some of tech’s biggest names and industry leaders. We hear from Jensen Huang (Founder/CEO NVIDIA), Arvind Jain (Founder/CEO, Glean), Winston Weinberg (Founder/CEO, Harvey), Scott Wu (Founder/CEO, Cognition), Raiza Martin (Founder/CEO Huxe), Zach Ziegler (Founder/CTO, Open Evidence), Aaron Levie (Founder/CEO, Box), Misha Laskin (Founder/CEO, ReflectionAI), Noam Brown (Research Scientist, OpenAI), Joshua Meier (Founder/CEO Chai Discovery), Bryan Johnson (Living Man, Don't Die), Sholto Douglas (Member of the Technical Staff, Anthropic), Ben & Asher Spector (Stanford PhDs) and Dylan Patel (Founder/CEO SemiAnalysis). Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Chapters: 00:00 – Introduction 02:43 – AI Predictions for 2026 04:40 – Adoption of AI in Professional Fields 07:17 – Robotics and Self-Driving Cars 08:25 – Robotics: Incumbents vs. Startups 13:59 – Future of IPOs and M&A in AI 16:42 – Challenges in Consumer AI Innovation 21:08 – Funding of Neo Labs, RL Research 26:28 – Predictions for 2026 Beyond AI 26:44 – The Future of Defense and Technology 28:23 – Biohacking and Peptide Therapies 30:37 – 2026 Prediction from AI Industry Leaders 40:46 – Conclusion

• 0:05Speaker 00

  Hi listeners, welcome to No Priors. How can we even begin to wrap this year up? The AI field has grown, breaking out into 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 is eating up all of our inference capacity. Doctors are adopting clinical decision support en masse and in long 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.

• 0:49Speaker 00

  People are trying different ideas around diffusion, self-improvement, data efficiency, EQ, large-scale Asian 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 going to 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 Alad. Okay, Alad, it's been a year.

• 1:31Speaker 01

  I know, how the guys, 2026, baby.

• 1:34Speaker 00

  Are you feeling the AGI? Are you feeling AI winter in a good way?

• 1:38Speaker 01

  I think I'm actually just feeling microplastics. I think I'm now 80% microplastics. I'm just increasing my microplastic consumption. A friend of mine actually launched a new water brand that has no microplastics, by the way. It's called Blue. It's got like glass bottles and also the cap that's in that plastic.

• 1:53Speaker 00

  Does it come with continual testing?

• 1:55Speaker 01

  No, that's a good idea though.

• 1:56Speaker 00

  Does it come with continual testing for you?

• 1:57Speaker 01

  They did actually try to take out all the microplastics. And so they, I guess bottled water and actual bottles have more microplastics than plastic bottles because of the cap.

• 2:06Speaker 00

  Okay, we'll check back in with you in 27 to see if you feel...

• 2:10Speaker 01

  Yeah, and I'm just completely ossified out of plastic. I'm actually really worried about microplastics. What about all the little glass particles? Aren't you worried about that? People talk about microplastics, but not microplastics. I'm much more concerned about that.

• 2:22Speaker 00

  I don't think those particles end up embedded for you permanently.

• 2:25Speaker 01

  Silicon?

• 2:28Speaker 01

  You're not worried about silicones? I go to the beach, I'm like, oh no, microplastics everywhere.

• 2:33Speaker 00

  I'm actually very willing to insert silicon in my body eventually in my.

• 2:39Speaker 01

  Wow, that was, yeah, I'm not gonna say anything. We can keep going.

• 2:43Speaker 00

  What's, what's happening in AI, Alad? What do you, where, where are we? And what are you most excited about?

• 2:47Speaker 01

  Yeah, I guess for 26 is about this stuff that I think will be interesting that's 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 overhyped. And like that MIT report that people are quoting that I thought really didn't matter. And, and the reality of the technology ways take like 10 years to propagate. And people are getting enormous value out of AI already. And they're going to get way more out of it in the future. You know, so there's these, I'm undoubtedly next year, there'll be these overstated bubble claims as well as, um, hey, I actually isn't working that well kind of claims. And that happens over a technology cycle and we'll just hear it again. Next year, and there'll be pundits and discussions and just a bunch of waste of 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.

• 3:32Speaker 01

  I think this year we saw consolidation of coding into a handful of players and medical scrapping into a handful of players, legal into a handful of players, like Harvey and others. And so I think we'll see that next set of consolidated verticals happening. So I think that'll be interesting. I can keep going, I have like a bunch of these. Do you want to go next? We can alternate, I just did two, why don't you do two?

• 3:51Speaker 00

  Maybe I'll react.

• 3:52Speaker 01

  So react.

• 3:53Speaker 00

  I'll react and then I'll, 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 the adoption cycle and technical bets that people are making that they don't have full first principles confidence on coming to risk. So I think like any number of exogenous factors plus noise about the speed of adoption, which by the way, it seems like blinding overall. And we can talk about what the constraints are.

• 4:37Speaker 01

  Yeah, so fast. I don't even know what people are talking about.

• 4:39Speaker 00

  I just saw a report that talked about, it's from this group called Off Call, that talked about adoption of AI by doctors. And like, there is just amazing adoption of, of course, you know, several different categories, like documentation, clinical decision room support with things like a bridge and open evidence, and obviously the general models. But there's like massive enthusiasm for most of the physician profession here. And I'm like, okay, of all of the domains that were professional and considered more conservative, 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.

• 5:19Speaker 01

  I think this is, by the way, super under discussed. The people who 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, 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.

• 5:43Speaker 00

  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 going to be some like negative market current. Like, you know, if Nvidia doesn't overperform by some massive amount one quarter, everybody's going to freak out. But I, I think that has very little to do with the fundamental secular change.

• 6:03Speaker 01

  Yeah, it has to do with microplastics at Nvidia. It's my two cents.

• 6:06Speaker 00

  It has to do with, it has to do with microglastics, as you said.

• 6:09Speaker 01

  Yeah, that's true. Actually, the silicon there is in the air, I bet. I bet that microglastics all over the place. It's messed up, Sarah.

• 6:16Speaker 00

  It's part of the trade. If you make $20 million as an average Nvidia employee, then you also have to have microglastics in your blood. Don't listen to this Jensen. Jensen's our next guest.

• 6:25Speaker 01

  It's 1%.

• 6:26Speaker 00

  He can't hear that.

• 6:26Speaker 01

  1% microglastics in the blood. I think, you know, 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, and the next gen LLM, which of course will happen. But I mean, physics, materials, science progress by models, math progress. And I think what will happen is it'll be one or two, 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 going to change everything about physical sciences or whatever.

• 7:02Speaker 01

  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, that's another prediction for next year is 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.

• 7:17Speaker 00

  I have, okay, fine. Three, three quick predictions for you. One is there's going to be like some collapse of sentiment around a set of robotics companies next year. Not because it like actually isn't as a field going to progress, but because, you know, people are beginning to project timelines. And, you know, not everybody is going to deliver on those timelines.

• 7:42Speaker 01

  What's your timeline?

• 7:43Speaker 00

  I think that we will see humanoid and semi humanoid robots get deployed at small scale in environments, be the consumer or industrial next year, and not everything will work. And that like the, because there's this, you know, hype cycle around humanoids overall. As soon as something doesn't perfectly work, which it will not, people are going to freak out. Right? And then there's going to be some bifurcation about people investing.

• 8:08Speaker 01

  Yeah, I mean, we're near fifteen, seventeen, whatever, 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 big question for me on robotics, you know, it's interesting. If you look at self-driving, there was 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's Tesla. So I wonder what will happen in robotics. It feels to me like Optimus or some form of like Tesla Robot will be one of the winners, most likely, right?

• 8:49Speaker 01

  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? 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, maybe one of the startups.

• 9:30Speaker 00

  I think that's right, but that's like saying, I, 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.

• 9:40Speaker 01

  I don't, 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 certain kinds of expertise and supply chain, 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.

• 10:02Speaker 00

  Sure, but I, I don't, I don't argue that like some markets are struck, like the, the moats are structurally deeper, right? But one way that you might look at autonomous vehicles is it's one very complex, single-use case robot. And it mostly does locomotion. It does lots of other unnecessary types of prediction, defense, drive, whatever else, but it's, it's, it's a single use case robot.

• 10:25Speaker 01

  Yeah. And we, and we forget there's a lot of good ones like that. Dishwashers are great single use robots, vacuum cleaners, 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. Elevators are robots. No, seriously, escalators are robots.

• 10:40Speaker 00

  I'm going to use the language of like, for a robot to be a robot, it has to be somewhat intelligent, right? And so dishwasher doesn't count as an appliance. A self-driving car does count as a robot.

• 10:53Speaker 01

  Where is the border of intelligence for you?

• 10:55Speaker 00

  I, I think like, it's probably some level of generalization, right? It can work in different environments. It can work on different tasks. It can work on different objects.

• 11:02Speaker 01

  Otherwise, you know, 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. Maybe that's, maybe I have a better definition. I'm gonna look up what

• 11:17Speaker 00

  Yeah.

• 11:17Speaker 01

  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 them now. Your dishwasher has a chip in it, right? Or the computer in it.

• 11:30Speaker 00

  Okay, yes, but like, 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.

• 11:44Speaker 01

  Yeah, that's cool. I mean, I do think that on the, on the, 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 going to be, I think, one of the big things that's talked about next year. So I think, I think on the robotics scene, that's the biggie.

• 12:07Speaker 00

  I think if you look at all of the potential use cases for robots besides self-driving and say like self-driving, I mean, the optimist team actually proves this. Like if you take If you take a model that is powering Tesla self-driving and you put it in Optimus, it can do locomotion, but it can't do many other things and you still have to do the hardware, right?

• 12:26Speaker 01

  Sure.

• 12:26Speaker 00

  Like manipulation. And so I think that the advantages here are not as strong as you believe they are. And like startups, some set of startups.

• 12:34Speaker 01

  Yeah.

• 12:34Speaker 00

  Scariest competition is the Chinese, but I do think that there is opportunity here.

• 12:38Speaker 01

  Oh, I totally think there's opportunity for startups and I misinterpret me. I just think that it's not just the fact that you have a model or a base model, you have the expertise to build the model, but then you also have all the supply chain. And I think that's really important because a lot of the same sensors that you need to use are there. And you know, how you think about actually procuring and scaling things are there. You know, there's, there's good overlap actually in terms of some of the other skillsets that are needed that take a long time to build usually at a startup or that are a little bit painful to build. And people do it, it's fine. It's not, I mean, Anduril did it and SpaceX did it. You know, all these companies have done it. It's extra stuff. So that makes sense. I do think, I do think some startups will succeed here. I was just trying to think through, you know, besides the startups, who's going to be big? And then also I think there are one or two like incumbent slots that will just default happen.

• 13:23Speaker 01

  Unless something very strange happens. And you know, one could have argued that should have happened in foundation models where Google should have had a default slot. And in the end it did, right? It got there. And I think that was very predictable that the Google models will get there. I think I even may have read a post about this like two, three years ago that Google would be relevant, right? Because they just had all the assets that were needed for them to be a really important foundation model company. They obviously invented transformers, but they had all the data, they had all the capital, they had TPU's and GPT's. They had like the best people for all sorts of things or some of the best people. So it felt inevitable and I think this feels the same to me, but I don't think that's right. Do you want to talk about IPOs and M and 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.

• 14:08Speaker 01

  Four was robots and self-driving. And then five would be IPOs and M and A. What do you think? More IPOs, less IPOs, more M and A, less M and A, different types of M and A?

• 14:17Speaker 00

  It depends on whether or not the bottom falls out of the AI market at some point, right? But I think regardless.

• 14:23Speaker 01

  What do you mean by the, what do you mean the bottom falls out? Like what, what, what does that translate into?

• 14:28Speaker 00

  I think people just get skittish about, you know, the cycle here is like, what are people scared of? They're concerned that demand isn't real. No, demand isn't real for AI to support the capex cycle, that there is systemic risk from people passing the ball around in terms of who is actually responsible for the capex build out and these credit agreements, right? Or, you know, pay on delivery contracts for data centers and for chips. What else are they afraid of? They're afraid of like the microglastics, AKA like too much concentration in Nvidia and a small number of other players. If you're like a big public markets investor, you're just like, you know, you

• 15:13Speaker 01

  It's too much silicon.

• 15:15Speaker 00

  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 of 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 like 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.

• 15:58Speaker 00

  And so his view was like, 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. What do you think happens?

• 16:08Speaker 01

  I think there'll definitely be a lot more IPOs next year. I think if one of the main AI companies goes out, it'll be probably do extremely well, depending on where they price. I mean, obviously if they're overly aggressive, it won't. But in general, I think there's so much retail appetite to actually participate in AI decides Nvidia. And then that'll just get a lot of other people to go public just as followers on it. So I do expect there'll be a lot of them. It's just one even goes out. And then also it's a great way to raise huge amounts of money for some of these labs potentially. So it'll be interesting to watch what happens there. Any other predictions for 26?

• 16:41Speaker 00

  Yeah, I, I, I think that I did not believe that we were going to see that many like unique consumer experiences. Besides like chat GPT, I think we are going to see like a slate of consumer hardware that mostly fails. But I'm so 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 of like really different consumer agent software that I like, I actually want and will use. And I, I think people are like barely beginning to. Well, these companies are stealth right now, but I, I do think that like, there's going to be a lot more product people that experiment with this

• 17:24Speaker 00

  and model companies experiment with this next year. And so I'm, I'm pretty optimistic about that.

• 17:29Speaker 01

  Yeah, I agree with that a hundred percent. And I think the big question is what will end up being a breakout startup and undoubtedly be some. And then what will be a startup that'll grow really fast and then it'll get copied by the main lab slash 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 build that a network effect or something else really defensible. Usually incumbents can launch two, three years later and catch up. And so if they have the distribution and have their product and have, but I, 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, this guy, David Song, who was on my team at the time, ran a two quarter thing at Stanford where we had different key supply from the engineering programs there.

• 18:13Speaker 01

  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. It was just go do cool stuff because this is such a good playground. And those really neat experiences that were being prototypes. And then I was just shocked that nothing happened for a couple of 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 it because there's a different generation of founders who don't want to work on consumer? Or forgotten how, because you know, the big consumer companies have kind of aged out.

• 18:57Speaker 01

  Is it being incumbents are just too scary? Is it like, why, why is there so little innovation actually on the consumer side of AI? I still don't quite understand what the issue is.

• 19:07Speaker 00

  I, okay, let's, let's like list the, the reasons. I do think that the incumbents are pretty scary. 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 out.

• 19:22Speaker 01

  Yeah.

• 19:22Speaker 00

  So there's that. I also think like the first instinct that, that I've seen from companies, uh, from founders working on like new consumer experiences. Is essentially building like a better versions of like last generation experiences with this generation technology. And then it's 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. And so I think, like, 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.

• 19:57Speaker 01

  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 aren't working in consumer products could, you know, but of the people working in consumer products, I think that most was a few hundred people who are exceptional, who could actually come up with and launch their own product that would be interesting or good.

• 20:30Speaker 01

  And so you could also just 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.

• 20:42Speaker 00

  I would limit myself to that number if it's also the set of people who like have the context of like what is possible now. 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.

• 21:02Speaker 01

  Yeah, you're working on the little sub button in Gmail or whatever, instead of actually going off and doing this 100%.

• 21:07Speaker 00

  Yeah.

• 21:08Speaker 01

  Well, anything else we should talk about or any other big predictions for 26?

• 21:12Speaker 00

  I feel like a very big 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 all of the effort around like getting reinforcement learning to be more general continual learning, some of the research directions?

• 21:36Speaker 01

  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 gains for certain types of approaches or models, because we know scale really matters, which means that eventually you have to collapse into a handful of players because capital will aggregate to things that are working the most, no generating revenue. And so then the question is, what are those things? At what point do things just 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.

• 22:14Speaker 00

  I think to articulate what like the the arguments could be for, you know, new research directions is like Ilya, you know, did this interview recently where he described it as the age of research. And to paraphrase, he like basically says like, 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. 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, which like the rat race does feel a little bit like today.

• 22:58Speaker 00

  I think the other argument 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 usefulness. They just haven't been scaled, right? And like, there's enough capital out there to test them, be they like diffusion or SSMs or whatever, and that's going to happen this next year. 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, be it self-improvement or post-training

• 23:41Speaker 00

  or emotional intelligence or very large scale out agent stuff?

• 23:45Speaker 01

  Yeah, it depends on what you're doing because the inference is what ends up then raising you money to pay for everything else because you're generating revenue. So I think, sure, but it's effectively your way to bootstrap in and more and more scale. So 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 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, you know, highly sort of pre-wired to deal with vision really effectively. You have different areas of pirate thought and learning, you have memory, you have mirror

• 24:30Speaker 01

  neurons that are involved with empathy, right? Your brain is actually very 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 a few famous cases like that. But, you know, fundamentally, 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 a degree to which you reconstitute 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 try and make some

• 25:11Speaker 01

  of that work versus how much of it is a more analytical approach or a more experimental and iterative approach or, you know, so it's in a directed way. And so I think it's really interesting to ask because if you look again at biology as a, as a potential precedent, although maybe a very bad one, you look at protein design. And for a long time, there are these like super analytically designed proteins and then they came up with all these systems that involve this shit, 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 all these 3D structural predictions that are actually very good. That was not, that was AlphaFold and a few other things that really were breakthroughs

• 25:56Speaker 01

  there. So it feels like in the context of AI, maybe eventually we end up there as well, right? Where you just involve these systems. And then that may be a very different type of approach and training, you know, that, that, that may be where I think things really have an 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 the 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 get some really fast lift off, but maybe not, right? We'll see.

• 26:28Speaker 00

  What is the one prediction you have for 26 that has nothing to do with AI?

• 26:35Speaker 01

  Do you think about anything else, Sarah?

• 26:38Speaker 00

  I do.

• 26:39Speaker 01

  I'm joking.

• 26:42Speaker 01

  I mean, the other thing, by the way, one other prediction that does have to do with AI is I do think 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 going to be a huge shift that we'll see go even faster this coming year. I think this is accelerating in part to see how the Trump administration has been approaching it and the Secretary of War and everybody there have been thinking about it. I think in part this, you have enough density now of startups doing interesting things. So I think that's the other thing, this like a huge shift that. You know, it's a hype cycle right now. And I actually think, again, it's a little bit underthought about because it's it's going to be so big. Outside of AI, I mean, I think there's obvious really interesting things happening in space with SpaceX and Starlink.

• 27:27Speaker 01

  And I think about communications and telephony. So that's a big shift. There's really interesting things, in my opinion, happening in energy and mining. And, you know, I think there's a lot going on in the world.

• 27:37Speaker 00

  I agree on defense with some like concern that, 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 demands, like the need to be competitive in a world that's increasingly autonomy driven is like so obvious, right? And I think, you know, hype cycles and booms are good in that they bring a lot of people to the table, you know, capital, founders, people who want to work in the industry. And so you can make a lot of progress at a quick amount of time, even if a lot of companies die. And there's, there's more enthusiasm over a short period of time. So I agree with that. And I also don't think that's necessarily bad, right?

• 28:22Speaker 01

  What's your non-AI prediction?

• 28:25Speaker 00

  I think that, like, I'm not the only one, but I think the, like, GLP-1 thing is just, despite all of the enthusiasm, like, still underrated for how much impact it is having, 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. 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, I think, like, everybody understands now that, like,

• 29:11Speaker 00

  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 anything that looks like that type of opportunity. And so I think that's exciting.

• 29:23Speaker 01

  Yeah, I actually think 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, different, you know, different peptides that will do different things in terms of, you know, somebody will have some chronic purple tumor thing and they'll fly to Dubai to get, you know, peptides injected or whatever. And usually those are sort of early indicators of potential larger scale adoption societally. And so I think that's a really interesting trend right now in general, like this whole like 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.

• 29:58Speaker 00

  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 interested in longevity, neuromodulation with ultrasound, stem cell injection, for example, like that has been like a fringe, small community. And I think that like, I think it's going to get less fringe.

• 30:24Speaker 01

  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.

• 30:37Speaker 00

  And to round out this year end episode, we've asked some of our friends for their predictions for 2026. I'm so curious.

• 30:44Speaker 02

  My prediction for next year is that the reasoning systems are going to translate directly 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 self-driving cars to robotics. And so reasoning, I think, is, is the big, huge breakthrough that, that is going to transform a lot of different applications and industries.

• 31:26Speaker 03

  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 will 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 will be a coach that helps us improve our skills. It will be a 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.

• 32:04Speaker 04

  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 and chat GPT. 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?

• 32:41Speaker 03

  My prediction for 2026 is there will be a whole new suite of product experiences that run on much faster inference.

• 32:48Speaker 05

  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.

• 33:03Speaker 06

  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 ceded to labs as people assume. It really is anyone's ballgame.

• 33:31Speaker 07

  Hello, Aaron here. First of all, I get quite awkward around doing selfie videos. This is my ninth take of this video, so I hope it goes okay. But 2026 prediction would be that this is going to be certainly the continued year number two of AI agents, but in particular, AI agents in the enterprise in either deep vertical or domain-specific areas. 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.

• 34:15Speaker 07

  So this is going to be a year where we start to see this pattern emerge more and more, which equally means that we need to ensure that we have a lot more happening on agent harnesses. So shout out to Aporva Su-Hill and Dex for that answer. 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. And then finally, it will be the year of economically useful evals. So really starting to figure out how these models end up doing a lot more knowledge worker tasks in the economy. And that's going to, 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 and a handful of others. We're going to see way more of that.

• 34:59Speaker 07

  So those are the predictions and we'll see you in 2026.

• 35:04Speaker 08

  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 DTIC at the end of 2024. And American institutions were slow to notice this erosion of American leadership in open intelligence. But 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 Neo Labs starting to come out with open intelligence as their directive. And there are a few of these, not just reflection.

• 35:45Speaker 08

  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 weight frontier at the largest scale. And I'm really excited to see that.

• 36:01Speaker 09

  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, some people will come out strongly supportive of it. And I'm not sure which side's gonna win out.

• 36:22Speaker 10

  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.

• 37:07Speaker 11

  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. I think the shift coming is gonna be simple and radical, that we say yes to life and no to death.

• 37:48Speaker 11

  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 want to 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 going to be the year we end YOLO and the beginning of Don't Die.

• 38:14Speaker 12

  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 the software engineering itself goes utterly wild next year.

• 38:39Speaker 13

  My prediction for twenty twenty six 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 be flipped 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.

• 39:20Speaker 14

  Hey, I'm Benjamin Inspector.

• 39:21Speaker 15

  I'm Ash Inspector.

• 39:23Speaker 14

  And our prediction is that twenty twenty six of the year of energy efficient AI.

• 39:26Speaker 15

  Data center build outs are primarily constrained by energy. Power available, the grid interconnects, high voltage equipment, things like that, which is why XAI's Colossus was initially powered by onsite gas turbines.

• 39:36Speaker 14

  The thing is, the data for computer is continuing to grow. Labs, new labs like us, and startups like Curse have a pretty remarkably accessible demand for both training and compute. And this demand is currently on stripping our ability to push lots onto the grid. This means that in twenty twenty six, it will be really important to squeeze every available bit of tons out of every wallet.

• 39:54Speaker 15

  That said, in the long term, chips probably matter more than power because chips depreciate much more quickly than the underlying power infrastructure.

• 40:01Speaker 14

  So for example, with data center power supplies at tens of per kilowatt hour, the chips cost actually an order of magnitude more than the power in the five year depreciation cycle.

• 40:09Speaker 15

  So in twenty twenty six, we think intelligence for what is really important to squeeze as much intelligence you can out of every unit of energy. But in the long term, we think it's the chips that matter more.

• 40:18Speaker 14

  Happy holidays.

• 40:19Speaker 15

  Happy new year.

• 40:21Speaker 00

  Thanks for the year.

• 40:22Speaker 15

  Happy 2026.

• 40:24Speaker 00

  Happy 2026, listeners. Thank you.

• 40:28Speaker 00

  Find us on Twitter at NoCriersPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.