No Priors Ep. 144 | The 2026 AI Forecast with Sarah & Elad

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 policy makers. Chat GPT

shipped massive numbers and asked for massive dollars. Gemini and Google

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

inference capacity. Doctors are adopting clinical decision support on mass 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 Neolabs, new research labs got funded this year. And the narrative is

this year. And the narrative is changing. Ilia is calling it the age of

changing. Ilia is calling it the age of research. People are trying different

research. 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

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,

volatile. It's hot in the hot tub. So,

get into it with me and Alad. Okay.

Alad, it's been a year.

>> I know. How's it going? 2026, baby.

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

>> I think I'm actually just feeling microplastics. I think I'm now 80%

microplastics. I think I'm now 80% microplastics and just increasing my microplastic consumptions. A friend of

microplastic consumptions. A friend of mine actually launched a new water brand that uh has no microplastics by the way.

It's called Loop and they have like glass bottles and also the cap doesn't have plastic.

>> Does it come with continual testing?

>> Yeah, that's continual testing for you.

>> 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.

>> Okay, we'll check back in with you in 27 to see if you feel >> Yeah. But I'm just completely oified out

>> Yeah. But I'm just completely oified out of plastic. I'm actually really worried

of plastic. I'm actually really worried about microlastics. What about all the

about microlastics. What about all the little glass particles? Aren't you

worried about that? People talk about microlastics, but not microlastics. I'm

much more concerned about that.

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

>> Silicon.

You're not worried about silicons. When

I go to the beach, I'm like, "Oh no, microlastics everywhere." I'm actually

microlastics everywhere." I'm actually very willing to insert silicon in my body eventually in my >> Wow, that was Yeah, I'm not gonna say anything. We can keep going.

anything. We can keep going.

>> What's What's happening in AI? Al, what

are you where where are we and what are you most excited about?

>> Yeah, I guess for 26 there's a bunch of stuff I think will be interesting that's coming. I think we will um I think

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 the reality of the technology ways to 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 undoubtedly next year there'll be these overstated but bubble claims as

well as um hey I 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 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. 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 verticals happening. So

that'll be interesting. I can keep going. I have like a bunch of these. Do

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?

>> Maybe I'll react.

>> Or react.

>> 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

reacting. So I'm glad I have at least two threads. Yes. I I think that the

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

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.

>> Yeah. So fast I don't even know what people are talking about. I I just saw a report um that talked about it's from this group called uh off call that talked about adoption of AI by doctors

and look there is just amazing adoption of of course you know several different categories like documentation clinical decision support with things like a bridge and open evidence and obviously

the general models but there's like massive enthusiasm from most of the physician profession here and I'm like okay of of all of the domains that were professional considered more

conservative. The fact that there is

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. I I think this is by the

professions. I I think this is by the way super underd discussed that 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,

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

stuff fast. So, I do think that's really notable and very under discussed.

>> 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.

>> Yeah. It has to do with microplastics and Nvidia. It's my two cents.

and Nvidia. It's my two cents.

>> It has to it has to do with um microlastics as you said.

>> Yeah, it's true. Actually, the silicon there is in the air. I bet that they have microlastics all over the place.

It's messed up. Sarah,

>> it's part of the trade. If you make $20 million as an average Nvidia employee, you also have to have microlastics in your blood.

>> Don't listen to this, Jensen. Jensen's

our next guest. You can't hear that.

>> 1% microlastics in the blood.

>> I think um you know, a third area is the next set of foundation models are going to come. And by that I don't mean the

to come. And by that I don't mean the neolabs and the and the nextgen 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 usea 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 going to change everything about physical sciences or whatever and that oneoff will be overstated And in the long run, the trend will be understated and will be incredibly important. So that's what

incredibly important. So that's what 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 has been solved and then later science will be solved.

>> I have uh 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 uh you know not everybody is going to deliver on those timelines.

>> What's your timeline? I think that we will see um humanoid and semihumanoid 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 human rights 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 >> yeah I mean we're in year 15 17 whatever

self-driving something around there and it's really working now but it seems like robotics should have maybe a faster curve but a similar curve right it's going to take some time to figure all this stuff out and then once it's figured out it's going to be really

valuable and the big question 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 Whimo and Tesla which were two incumbents right Whimo's Google Tesla is Tesla So I wonder what will 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 Whimo just adopt what it's doing for cars to robots as well? Because

there's some similar problems there. Is

it some other big industrial 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 going to 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 maybe one of the startups >> I think that's right but that's like 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.

>> 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 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.

>> Sure. But I I don't I don't argue that like some markets are like the modes 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 necessary types of prediction, defensive drive, whatever else. But it's it's it's a single use

else. But it's it's it's a single use case robot.

>> Yeah. And we 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

aren't, right? We forgot that they're robots. Elevators are robots.

robots. Elevators are robots.

>> No, seriously. Escalators are robots.

>> I'm going to use the language of like 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

appliance. Um a self-driving car does count as a robot, not just >> where's the border of intelligence for you? I I think like it's probably some

you? 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. Otherwise,

different objects. Otherwise, >> self-driving car is okay. Yeah, I don't know. I didn't have that complex of a

know. I didn't have that complex of a definition. I just had it as like

definition. I just had it as like something that will do >> certain pre-programmed types of labor for you. But maybe that's maybe I have a

for you. But maybe that's maybe I have a better definition. Let me look up what

better definition. Let me look up what 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

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.

>> 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

intelligence. And so that's the relevant set for maybe you and me and many people that are looking for something that changes quickly.

>> Yeah, that's cool. I mean, I do think that um 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 Whimo and Tesla caps. 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 team that's the big E.

>> I think if you um look at all of the potential use cases for robots besides self-driving and say like self-driving >> I mean the Optimus 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 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

are. And like startups, some set of startups, >> the scariest competition is the Chinese, but I I do think that there is opportunity here.

>> Oh, I totally think there's opportunity for startups. And don't misinterpret me.

for startups. And don't 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

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 is there you know there's there's good overlap actually in terms of some of the other skill sets 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 did it and SpaceX did it and you know all these companies have done it it's extra stuff so that makes sense I I do think I do think some startups will succeed here I 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 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 in the end it did right it got there and I think that was very predictable that the Google models will get good I think I even may have wrote a post about this like two

three years ago that Google will 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 TPUs

and GPUs had like the best people for all sorts of things or some of the people. So, um, it felt inevitable and I

people. So, um, it felt inevitable and I think this feels the same to me. That

doesn't mean it's right. Do you want to talk about IP as an 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 robots and self-driving, and then five would be IPOs and M&A. What do you

think? More IPOs, less IPOs, more M&A,

think? More IPOs, less IPOs, more M&A, less M&A, different types of M&A?

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

point, right? But I think regardless, >> what do you mean by the what do you mean the bottom falls out? Like what what what does that translate into?

>> Uh I think people just get skittish about you you know the cycle here is like what are people scared of? They are

concerned that demand isn't real. no

demand isn't real um 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 buildout and these credit agreements right or um you know pay on delivery contracts for data centers and for chips what else are they afraid of

they're afraid of like the >> microlastics aka like too much concentration in Nvidia and a small number of other players. If you're like a big public

players. If you're like a big public markets investor, you're just like, you know you >> silicon, it's too much silicon.

>> 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. Yeah.

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

AI revolution." And then if you're a hedge fun, you get benchmarked on annual performance and because of the retail pop and some set of investors wanting to buy into it as a pure play where you're

like, "Oh, I can't miss it like I missed Nvidia." Then you have to buy it. And so

Nvidia." Then you have to buy it. And so

his view was like you buy the IPO regardless of your fundamental view of the company. And I was like, "Wow, this

the company. And I was like, "Wow, this is not the investing job I know how to do."

do." >> What do you think happens?

>> I think there'll definitely be a lot more IPOs next year. Um, I think if one of the main AI companies goes out, it'll be probably do extremely well depending where they price. I mean, they 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 besides Nvidia. Um, and then that'll just get a lot of other people to go public to just followers on it.

So, I I do expect there'll be a lot of them. It's just one that even goes out.

them. It's just one that even goes out.

Uh, and then also it's a great way to raise huge amounts of money for some of these labs potentially. So, um, it'll be interesting to watch what happens there.

Any other predictions for 26? Yeah, I I uh 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 still openminded to it. And then

definitely actually like it remains to me see 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 barely

use. And I I think people are barely beginning to >> well I these companies are in stealth right now, but I I do think that like there's going to be a lot more product people that experiment with this and

model companies that experiment with this next year. Um and so I'm I'm pretty optimistic about that. Yeah, I agree with that 100%. And I think um the big question is what will end up being a breakout startup and it'll undoubtedly

be some and then what will be a startup that will 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 build 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 they have the core product and they have but 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

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 game supply uh from the

engineering programs there and it was like groups of people building consumer apps using AI because we said this wave of AI is so fascinating why didn'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 you know, in terms of us getting involved. It was just you go do

getting involved. It was just you go do cool stuff cuz this is such a good playground and it was 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

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 who've forgotten how because 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

of AI? I still don't quite understand what the issue is.

>> I Okay, let's let's like list the reasons. I do think that the incumbents

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 out.

>> Yeah.

>> 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 better

versions of like last generation experiences with this generation technology and it ends up like not being that interesting. And so I actually

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 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.

>> 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 and you know but of the people working consumer products he thinks at most there's a 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 say 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 >> I would limit myself to that number if it it's also the set 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 >> Yeah. Yeah. You're working on the the

>> 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%.

>> Yeah.

>> Cool. Anything else we should talk about or any other big predictions for 26? I

feel like a very big um emergent thing that happened this year was the surprising funding of like Neolabs like three through eight. What do you think of that? What do you think about

of that? What do you think about alternative architectures? Like do you

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

learning? Uh some of the research directions >> you know I think there's enormous amounts of really interesting research being done. So I, you know, there's a

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. Well,

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 have collapse into a handful of players because capital will aggregate to things that are working the most.

They're 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

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

exciting. I think we'll see how it plays out.

>> I think to articulate what like the the arguments could be for, you know, new research directions is like Ilia, you know, did this interview recently where he describes it 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. 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. 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, be they like diffusion or um SSMs or whatever. And that's going to happen

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 Ilia is describing that some

right? If Ilia 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 or post- training or emotional intelligence or very large scale out agent stuff.

>> 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

generating revenue. So I think uh sure that it's effectively your way to bootstrap into more and more scales. 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 maybe I'm overextulating up

a 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

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 high pier 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

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 there's a few

functionality. But um there's a few 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 ae or something,

tasks. It's almost like ae or something, you know. And the question is the degree

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 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 it's or in a directed way. And so I think it's really interesting to ask cuz 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 of this you know like phase display and like mutagenic scans and all sorts of things that give 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 prediction that are actually very good right that that was um alpha fold and a few other things that really were breakthroughs

there. So it feels like in the context

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 and 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 lift off but Maybe not, right?

We'll see.

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

>> Do you think about anything else, Sarah?

>> I do.

>> I'm joking.

>> Really?

>> I mean, the other thing, by the way, one other prediction that does 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. a massive reworking of how you think about war and defense and I think that's going to be a shoot shift that we'll see go even faster this coming year I think this is accelerating

in part 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 so I think that's the other thing that's like a huge shift

that you know it's a hype cycle right now and I actually think again it's a little bit under thought about because it's it's going to be so big um outside of AI I mean I think there's obvious really interesting things happening in

space SpaceX and Starlink and I think about communications and telefan 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.

>> 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

demand like the need to be competitive in a world that's increasingly autonomydriven um 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 um and so you can make a lot of progress in a quick amount of time even

if a lot of companies die >> and there's there's um more enthusiasm a very short period of time so I agree with that. And I also don't think that's

with that. And I also don't think that's necessarily bad, right? I

>> What's your high prediction?

>> I think that like I'm not the only one, but I think that the like GLP1 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 inexurable. I actually think it creates

inexurable. 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 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.

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 corporal cheerle 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 society >> 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 >> 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 um interested in longevity neurom modulation with ultrasound um 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 French.

>> 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 this stuff.

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

curious. 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

robotics. And so reasoning, I think, is is the big huge breakthrough that that um 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

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 our 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

product. And honestly, like one of the best experiences I've had with it so far is just memory and chatbt. 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

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 up front?

>> 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

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

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 the labs as people assume. It really is anyone's

people assume. It really is anyone's ball game. Hello, Aaron here. First of

ball game. Hello, Aaron here. First of

all, I get quite awkward around doing selfie videos. This is my ninth take of

selfie videos. This is my ninth take of this video. Um so I hope it goes okay

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

have. You have to have the right context engineering to make the agents actually work. And then you have to do the change

work. And then you have to do the change management that makes the agents effective. So this is going to be a year

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 Aorvosu and

harnesses. So shout out to Aorvosu and Dex for that answer. Uh but it's definitely going to be the year of age and 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

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 going to uh we're

economy. Um and that's going to 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 others. We're going to see way more of that. So, those are the predictions

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

models. Over the last year, the frontier of open intelligence shifted from America to China, starting with the release of Deep Seek 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 neolabs starting to come out with open intelligence as their

directive 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 weight frontier at the largest scale and I'm really excited to see

that. Hey folks, my prediction for 2026

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. Some people will come out. It's

it. Some people will come out. It's

probably supportive of it. And um I'm not sure which side's going to win out.

>> 2025 has marked an incredible year in AI drug discovery. In the past year alone,

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 fulllength antibodies with

drug-like properties zero shot on the computer. If 2025 has been the year of

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

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

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

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

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 going to 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

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.

>> The most striking thing about next year is that the other forms of knowledge work 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 sold in a satisfying way, that we see the first test deployments of home

robots, and the software engineering itself goes utterly wild next year.

>> My prediction for 2026 is that it's the year where everyone's perceptions are flipped. Currently, everyone believes

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

increase. Currently, most Americans think AI is not useful. That will flip as well. And so, everyone's priors will

as well. And so, everyone's priors will be flipped. That's because the

be flipped. That's because the transformative use of AI will be so prevalent. The the obvious utility of it

prevalent. The the obvious utility of it will be so high that there is no way for anyone's priors. You know, cognitive

anyone's priors. You know, cognitive dissonance will be wiped away.

>> Hey, I'm Ben Spectre.

>> I'm Ash Spectre.

>> And our prediction is that 2026 is the year of energy efficient AI. Data center

buildings are primarily constrained by energy, power availability, great interconnects, high voltage equipment, things like that. Which is why XAI's Colossus was initially powered by on-site gas trends. The thing is the

demand for computing to grow. Labs,

Neolabs like us and like Kurser have a pretty remarkably insatable 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 2026, it will be really important to squeeze every available bit of tons out of every wallet. 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 per kilowatt hour, the chips cost action order imaging more than the power than a 5-year depreciation cycle.

>> So, in 2026, we think intelligence per watch 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.

>> Happy holidays.

>> Happy New Year.

>> Thanks for the year. Happy 2026.

>> Happy 2026, listeners. Thank you.

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