I think perhaps the thing that most surprised me is the extent to which I feel like the AI economy is stabilized. You have like the model layer companies and the application layer companies and the infrastructure layer companies. It seems like everyone is going to make a lot of money and there's kind of like a relative playbook for how to build an AI native company on top of models. Many episodes ago we talked about how it felt easier than ever to pivot and find a startup idea because if you could just survive, if you could just wait a few months, there was likely going to be some like big announcement that would completely make a new set of ideas possible. And so like finding ideas is sort of returning to sort of normal levels of difficulty.
Welcome back to another episode of The Light Cone. Today, we're talking about the most surprising things that we saw this year in 2025. Diana, you found a pretty crazy one. It's sort of a changing of the guard almost in who is the preferred LLM at YC during the YC batch. Yes, in fact, we just wrapped up the winter 26 selection cycle for companies. And one of the questions we ask to all the founders that apply to YC is what is your tech stack and model of choice? And one of the shocking things is that for the longest time, OpenAI was the clear winner.
for all of last year, last couple of batches, though that number has been coming down. And shockingly, in this batch, the number one API is actually Anthropic. It came out a bit more than OpenAI, which who would have thought? I think when we started this podcast series back then, OpenAI was like 90 plus percent. And now...
Anthropic. Who would have thought? Yeah. And, you know, they've been hovering around like 20%, 25% for most of like 2024 and early 2025. And then only even in the last three to six months did this sort of changing of the guard actually happen. They had this hockey stick with the growth of over 52%. Why do you think that is? I think there's a couple of things in terms of the tech stack selection. I think as we've seen this year, there's been a lot of wins in terms of vibe coding tools that are getting built out out there. And coding agents are so many categories that this ended up being a bigger...
problem space that actually is creating a lot of value. And it turns out the model that performs the best at it is actually models from Anthropic. And I think that's not by accident. I think from hearing the conversation we had with Tom Brown not too long ago, he came and spoke. That was one of their internal evals. They, on purpose, made them their North Star. And you can see it in the model taste as a result of what's the best choice of model for a lot of founders building products is Anthropic. The vast majority of the use cases people are using it for, though, is not coding. So I wonder if there's like a bleed through effect where people are using Claude for their personal coding.
And then as a result, they're more likely to choose it for their application, even if their application is not doing coding at all. Because you'd be very familiar with the personality of Cloud Opus or whatever they're choosing. Sonnet, I suppose. How about Gemini? How's Gemini doing in those rankings? Gemini's also pretty much has been climbing up pretty high. I think last year was probably single digit percent or even like three or three percent. And now for winter 26, it's about 23 percent. And we've personally been using also a lot of Gemini 3.0 and we've been impressed with the quality of it. I think it's really, really working. I mean, they have all different personalities, don't they? Me too. Yeah.
It's kind of the classic where OpenAI sort of has the black cat energy. And almost like Anthropic is kind of more the happy-go-lucky, a bit more very helpful golden retriever. At least that's what I feel when I talk to them. And how about Gemini? It's kind of like in between. Harj, you prefer Gemini, actually. Yeah, I switched to Gemini this year as my go-to model. I think even before 2.5 Pro came out and just seemed better at reasoning for me, it was just like the increasingly I replaced my Google searches with Gemini. And I just sort of trusted that Google's, I think like the groundings API and its ability to actually like use the Google index to give you like.
real-time information correctly. I just found it was better than all the other tools for that and it was better than Perplexity on it too. Perplexity would be fast but not always accurate and Gemini was not quite as fast as Perplexity but was always pretty accurate if I asked it about something that happened today. Even if you use Gemini as the reasoning engine in Perplexity. I have not done that. Interesting. Yeah. It's hard to know how much of it is the tooling and how much of it is the base LLM. That's right. Yeah. I mean, what are you guys' tools of choice? I haven't switched off of ChatGPT. I mean, I find the memory very sticky. It knows me. It knows my personality. It knows the things that I think about. And so I'll use perplexity for fast web searches or things that I know is a research task because I think ChatGPT is still a little bit of a step behind for searching the web. I don't know. I think memory is turning into an actual moat for that consumer experience. And I don't expect Gemini to ever have the personality that I would expect from ChatGPT. It just feels like a different...
like entity you know this thing i'm still surprised about is why there just aren't more um consumer apps around like all the various things we do like if i think back one of the big changes for me this year is just the amount of prompting and context engineering i do for like my life and we bought a house recently and like the whole thing like i just had like a really long-running chat gpt conversation stuffing it full of context of like every inspection report or like wanting it to be like level the playing field between me and like the realtor to understand kind of all the dynamics and things that are going on and it just feels like there should be an app for that but simultaneously i'm sure you took the uh pdfs and just like dropped them into gemini and said like well summarize and tell me what's important for me i guess i worry about i worried about i still don't trust the models enough to be accurate without lots of prompting and it's a high value transaction so you don't want to like get incorrect days out of it so i still feel like you need to put in the work and it feels like there should still be apps that just do all the work for you did you see karpathy release like sort of a llm arena of a sort which i mean i do by like hand right now using tabs it's like you have uh claude open you have gemini open you have chatty open and you give it the same task and then you take the output from each and then i usually go to claude at that point and i'm like all right claude this is what the other one said what do you think and check each other's work actually that particular behavior at the consumer that level that we're doing
startups are doing as well. They are actually arbitraging a lot of models. I had some conversations with a number of founders where before they might've been loyalists to, let's say, OpenAI models or Anthropic. And I just had some conversations recently with them. And these are founders that are running larger companies like Series B level type of companies with AI. They're actually abstracting all that away and building this orchestration layer where perhaps as each new model release comes out, they can swap them in and out, or they can use specific models that are better at certain things for just that. For example, I heard from the startup, they use Gemini 3.
to do the context engineering, which they actually then fed into OpenAI to execute it. And they keep swapping it as new models come up and the winner for each category or type of agent work is different. And ultimately, they can do this because it is all grounded based on the evals. And the evals are all proprietary to them because they're a vertical AI agent and they just work in a very regulated industry and they have this data set that just works the best for them. I think this is the new normal right now where people are expecting, yeah, it's cool that the model companies, they're spending all this money and...
making intelligence faster and better and we can all benefit let's just do the best it's almost like the era of um intel and amd with new architecture would come up people could just swap them right yeah it feels like the highest level that angst around where's the value going to accrue is going to go to the model companies or like the application layer i.e the startups feels like that ebbs and flows in either direction a little bit throughout the year to me like i feel there are moments where called code amazing launch and it was like okay like the model companies are actually going to play out the application layer but then to me at least as a live space like the gemini surge especially over the last few months just feels like it returns us to a world of where exactly that like the models are all essentially commoditizing each other and it's just like the application layer and the startups are going to set up to have another fantastic year if that continues. I'm curious what you think, Jared, with some, a lot of perhaps the negative comments on Twitter around is this a bit of a bubble, AI bubble?
Yeah. When I talk to underguides, this is like a common question that I get is like, oh, like I heard it's a big AI bubble because like there's all this like crazy roundtripping going between NVIDIA and OpenAI and like, is it all fake? Yeah. No, this is fantastic, right? Like people look at the telecom bubble and it's like, there's just, you know, billions of dollars, like tens of billions, hundreds of billions, just like sort of sitting in a bunch of telecom back in like the, you know, 90s. Actually, that's why YouTube was...
able to exist, right? Like if you just have a whole bunch of extra bandwidth that isn't being used and it's relatively cheap, the cost is low enough for like something like YouTube to exist. Like if there wasn't a glut of telecom, then like maybe YouTube would have happened and just would have happened later. And then that isn't that like sort of what we're talking about here? Like how do we, we have to accelerate, right? We have.
The age of intelligence, the rocks can talk, they can think, and they can do work. And you just have to zap them more. And you get like smarter and smarter stuff at this point. I think the argument to college students is actually like, because there will be a glut, there is an opportunity for you. And if there was not a glut, then there wouldn't be as much competition. The prices would be higher. The margins lower in the stack would be higher, right? And then, you know, what's one of the big stories this year? Like Nvidia suddenly is on the outs. Like I think their stock is today is like around 170s or something. You know, I think I'm still at long-term buy and hold, honestly. But for the moment, people are like, oh, well, Gemini is so good.
Nobody seems to be NVIDIA only now and everyone's buying AMD and TPUs are working. So at the moment, it looks like there's, what does that mean? There's competition and it means that there will be more compute, not less. And then that means that probably a little bit better things for all of the big LLM companies, like sort of the AI labs, they get a little bit of power. you know, they too are in competition with one another. So then what does that mean? Well, then it's, you know, go up another level in the stack, right? Like, as long as there are a great many AI labs that are in deep competition with one another, then that's even better for that college student who's about to start a company at the application level. Yeah, I think that's exactly right. It's like...
People are asking this question, like, is it a bubble? That's maybe a question that's really relevant if you're like the equivalent of like Comcast. Like if you're Nvidia, that's a very relevant question. Like, oh, are people overbuilding GPU capacity? But like the college students, they're not Comcast. They're actually like YouTube. If you're doing a startup in your dorm room, it's like the AI equivalent of like YouTube. And like, it kind of doesn't really matter that much. Maybe Nvidia stock will go down next year. I don't know. But like, even if it does, that doesn't actually mean that it's like a bad time to be working on an AI startup. Yeah, it's what Zuck said on a podcast earlier this year, I think, right? It's like, Meta may end up over-investing like a significant amount in like the capex infrastructure, but like they essentially have to, the big companies have to do it because they can't just like sit on the sidelines. And in the
demand falls off a cliff for some reason it's their capex not the startup's capex and there's still going to be tons of infrastructure and ideas to still continue building there was this book written by this economist called carlota perez who studied a lot of tech trends and it studies a lot of technology revolutions and it summarizes that there's really two phases there's the phase of uh installation which is where a lot of the very heavy capex investment come in and then there's the deployment phase where really ripples it where it rips and then everything explodes in terms of abundance and during the initial phase of installation is where it feels like a bubble there's a bit of a frenzy because it starts first with a there's this new technology that's amazing which happened with the chat gp moment in 2023 everyone got super excited about the tech and then everyone got super hyped and got into investing into a lot of the infrastructure with buying a lot of gpus and all the giant gigawatt data center built out and then people's like but what is the demand what are going to be all the applications to be built out i think right now we're in that transition which is actually really good news for startup founders because they are not involved into the building, the data centers, but they're going to build the next generation of applications in the deployment phase when it really proliferates. And what happened, just going back to the analogy with the era of the internet, before the 2000s, there was a lot of heavy capex investment into the telcos, right? Those were giant projects that college students wouldn't be involved, but they were very heavily invested. And in some cases were over-invested. I mean, there's a whole thing with dark fiber and some pipes that are not used, and that's fine. The internet ended up being still a giant economic driver.
And what that means is startups like the future Facebook or the future Google are yet to be started because those come in in the deployment phase. Because right now, things are still getting built up. I do think the Foundation Lab companies and GPUs very much are falling into the bucket of infrastructure. I mean, it's interesting to watch how this stuff is evolving a little bit. So do you remember summer 24, there was a company called StarCloud that came out and was one of the first to come out and say, we're going to make data centers in space. And what was the reaction when...
You know, people laugh at them on the internet. They said that's the stupidest idea ever. You know, I guess 18 months later, suddenly Google's doing it. Elon's doing it. It seems to be like his top talking point. Yeah. And so, I mean, why is that? I feel like one of the aspects is that like part of the infrastructure build out right now that's so intense is like we literally don't have power generation. Boom supersonic instead of making supersonic jets right now is on this good quest to create enough power for a bunch of these.
AI data centers that are being built right now. They use jet engines. And even those are so bad. The supply chain for jet engines to generate power for data centers is so backed up that you would have had to have ordered these things two or three years ago just to even have it in two or three years from now. These constraints end up influencing fairly directly what the giant tech companies need to do to win the game three or five years out.
suddenly there's not enough land. In America, we can't build. The regulations are too high. In California, we have CEQA, which is totally abused by the environmental lobby to stop all innovation and building housing, by the way. We just don't have enough terrestrially to just do the things that society sort of needs right now. So the escape valve is like, actually, let's just do it in space. Yeah, come to think about it, we kind of have the trifecta of YC companies that are solving the data center buildup problem. Well, you need fusion energy. Yeah, well, we have the company that's solving the...
no land problem by building the data centers in space. We have Boom and Helion, which are solving that we don't have an energy problem. I just funded a space fusion company that just graduated called Zephyr Fusion. And they actually had a great seed round out of Demo Day. They're in their 40s. They're national lab engineers who their entire careers, they were building tokamaks and fusion energy. And they came into the lab one day, they looked at the physics, they looked at the math and the models, and they said, you know what, if we did this in space, it would actually pencil.
And so they're on this sort of grand next five, ten year quest to actually manifest it, to actually create it in space, because the equations say that it is possible. And if they do it, it's actually the only path to gigawatts of energy up there in space. So it might be an even more perfect trifecta shortly. Something else I feel like happened over the course of this year is the interest in starting model companies. I guess maybe at both ends, there's the people who can raise the capital to go and actually try and build a head-on competitor to OpenAI, which there are very few.
ILEO with SSI, but then more so within YC, people trying to build smaller models. I've certainly had more of those in the last few batches than before, whether it's models run on edge devices or maybe a voice model specific to a particular language. And I'm curious to see if that trend continues, going back to this early era of YC, actually. We sort of saw the explosion of just startups being created, and maybe especially SaaS startups. Partly what fed that was just knowledge about startups became more dispersed. There wasn't the canon of library information on the internet about how to start a startup, how to build software. And then over a decade, that just became more commonplace, and that just exploded.
society's knowledge of startups and how to build things. And it feels like maybe we're going through that moment in sort of the AI research meets like actually building things. With training models. I think we're absolutely going through that right now. Yes. Where it's going from being a very rare skill set to a more common one. Because like OpenAI a decade ago was like a rare, like you need like a unique combination of skills, right? You need like your researcher brain, your sort of like engineering brain, maybe like your sort of finance business brain. Wait, wait, wait. So did you just describe Ilya, Greg?
You got it. That was like a rare team, right? There just wasn't that configuration of team around very much. And now a decade later, there's like a plethora of people who have like the research background, the engineering background, the startup capitalizing background. He's going to be taught how to do all of that kind of stuff. And I'm curious if that would just mean we'll just see more.
applied AI company starting and maybe there'll be like even more models to choose from for all the very specific tasks. I think so. I think the other thing that's even contributing and making this a even bigger snowball is because of RL. I think there's all these new open source models that people are doing the fine tune on top of it with a particular RL environment and task. So it is very possible that you can create the best domain specific, let's say, healthcare.
model trained on a generic open source model by just doing fine-tuning on it and doing RL. It beats the regular big model. Actually, I've heard and seen a number of startups where their domain-specific model beats OpenAI, let's say, on healthcare. There's this particular YC starter that told me that they collected the best data set for healthcare, and they ended up performing better than OpenAI and a lot of the benchmarks for healthcare with only 8 billion parameters. I guess what's funny is that you do need to have a post-training infrastructure. We've also had YC companies where they had something to beat.
OpenAI, you know, GPT 3.5, and they were doing fine-tuning with RL. But then, yeah, GPT 4.5 and then 5.1 came out and, you know, basically blew their fine-tuning out of the water. You gotta keep going, yeah. Yeah, you gotta keep going. Yeah, I mean, you actually have to continue to get to the edge. Anything else that really sort of stood out from this past year that jumps out to you? It's funny. We started the year with one of our episodes that got a lot of views around Vibe Coding. I think we were talking about it more as observing a behavior that was happening from our founders. And I was surprised to see that this became like a...
giant category. There's lots of companies that are winning. I mean, we have Replit, there's Emergence, there's a bunch of them. Varun Mohan had gone over to Google. He released Antigravity. And did you guys see the video? Actually, I'm sort of curious whether they actually used Nano Banana or any of these video gen things, because it's like a little too perfect, but Google has the budget to do the high production value video. But it's, you know, Varun at the keyboard and then, you know, Sergey is like right behind him. So I was like, it's very cinematic. Anyway, I think Sundar was, you know, also not only talking about...
space data centers uh he was also talking about vibe coding and i knew that i was a little bit trolling back but knowing what we know i mean yes vibe coding is not you know completely usable and trustable for uh you know 100 of your coding period like this you know, it is not true that you can ship 100% solid production code today as of the end of 2025. Yeah, I was thinking about things that surprised me in 2025. And I think perhaps the thing that most surprised me is the extent to which I feel like the AI economy stabilized. I feel like when we did this episode at the end of 2024, it felt like we were still in the middle of a period of incredibly rapid change where the ground was shifting under our feet and nobody knew when the other shoe might drop and what exactly was going to happen with startups and AI in the economy. Now I feel like we've kind of settled into a fairly stable AI economy where we have the model layer companies and the application layer companies and the infrastructure layer companies. It seems like everyone is going to make a lot of money.
And there's kind of like a relative playbook for how to build an AI native company on top of the models. I feel like things really kind of matured in that way. Which feels it's all downstream of like the models themselves have incrementally improved this year, but there haven't been like major steps forward that have shaken everything up, which has a knock on effect. Many episodes ago, we talked about how it was felt easier than ever to pivot and find a startup idea. Because if you could just survive, maybe just wait a few months, there was likely going to be some like big announcement that would completely make a new set of ideas possible and create more opportunities to build things. It certainly feels like that has slowed down. And so like finding ideas is sort of returning to sort of...
normal levels of difficulty in my experience. I agree. I'll tell you what's not a surprise. Do you remember that report, AI 2027, where it was just sort of like this doomer piece that said like, oh, well, society is going to start falling apart in 2027. But, you know, at some point they quietly revised it to say that it wasn't 2027, but they kept the title. Maybe it's not a surprise. Like I was always a little bit of a skeptic of like this fast takeoff argument, because even with the scaling law, it is log linear.
It is slower. It requires like 10x more compute and it's still sort of topping out, right? And that's one form of good news. Another form of, it's weird to call this good news, but human beings don't like change. In our previous episode where we sort of blew up that MIT report that said that 98% or 90% of enterprise AI projects fail. Well, it turns out that 90% of enterprises don't know how to do IT, let alone AI.
It's weird to say that that's a good thing, but in the context of fast takeoff, like that is a real break on the ability of this new really insane technology from actually permeating society. I love to accelerate, but like, it's weird to say like, oh, well. Actually, in this case, maybe that's a good thing, right? Like it is a shockingly powerful technology, but, you know, between being log linear scaling and human beings really don't like change, like organizationally speaking, society will absorb this technology. Everyone will have enough time to sort of process it. Like culture will catch up. Governments will be able to respond to it. Not in like a frantic SB 1047 sort of like, you know, let's stop all the compute past 10 to the 26, right? Like just these knee jerk responses to technology. We're excited about the ARC AGI prize is, you know, going to come in and do the winter 26 batch as a nonprofit.
The funny thing about that is like, yeah, maybe there's a team right now that is climbing the leaderboard of Arc AGI and they're going to accelerate this thing again. Something that surprised me related to that with the startups is I remember around this time last year, we were talking about how companies are getting to a million dollars ARR and raising Series A's without hiring, like simply not hiring anyone, just the founders, maybe hiring one person, which just felt very unusual. I feel like a year on... that hasn't translated into, okay, and then they went and hit like 10 million ARR or they scaled without adding any more people to. No, they turned around and started hiring like actual teams. Post-Series A, it actually largely feels like the playbook is the same and the companies might be smaller for the same amount of revenue, but it feels it's entirely because they hit the revenue so fast and there's still just bottleneck on how long it takes to hire people versus they have demand for less people. I still think there is like, you know, some effect, but it is not like open and shut. It is not like you don't have to hire executives anymore. I think there might be a case of two foie gras startups, like one being Harvey and the other.
of one being open evidence, right? Harvey, the founders are incredible. They were very early. And then there's a sort of idea of like for VCs, you could just go down Sand Hill Road and like the fix is in. Like you just sort of block out all of them. And then all the people, you know, maybe 30 people who could write checks of like 10 to $100 million. And if you just sort of get all of their money, like there's sort of no one who can actually come in and do the next Series A. And then basically you're safe. Like you have capital as a bludgeon is capital as a moat in that case, right? So yeah, Harvey is interesting because, you know, Lagora is coming fast for them. And obviously we have some skin in the game on Lagora, but we think that they have as good a shot at any. I guess that's one trend that we saw.
is that there was like a first wave of like AI companies like Harvey. Who might have wasted a lot of money on fine tuning, actually. Totally. That like broke out really big in 2023 and kind of did a victory lap that, you know, oh, we've won the space and now we're seeing a second wave of companies like Flagora and Giga. And it turns out that like, oh, actually, like it isn't so simple. Yeah, the weak beneficiary of, you know, burning some non-trivial double-digit percentage of your capital stack on fine tuning that buys you no advantage is like basically the investors are the only winners there because they just own more of your company, you know? Yeah, at least it relates to like the hiring and team size. I feel like...
Of the two camps, one being that AI is going to make everything more efficient, you will need less people. And the other, AI is going to reduce the cost of like producing the time to produce things. And so then the expectations from your users and customers will just go up and you'll need to keep hiring more people to satisfy like the growing expectations. I feel like this year has been more in that second camp. And I think that is what's driving the fact that the companies are still just hiring as many people as they were pre-AI. It's just like the bar for what their customers expect. And they're all in, you know, like Lagora's racing with Harvey, Giga's racing with Sierra. Like they're all still competing for the same set of customers. And they still ultimately are bottlenecked on like...
people and like i don't think anyone's bottleneck on ideas but they bottleneck on like people who can execute really well i think that's like so it's exciting for an exciting phase i agree with you that like the uh era of the one person running a trillion dollar company is not here not yet yeah but i think it's going to trend that way eventually that'll be a wild time maybe that's a prediction for 2026 yeah you think it's coming i mean i don't think it'll happen in 2026 either honestly i mean i think you will have many stories of companies run by under 100 people that are making hundreds of millions of dollars. So, I mean, Gamma was interesting to see. One of the biggest things that they said in their launch that I think is a very good trend is they said they got to $100 million in ARR with only 50 employees. So, which is a very different, it's such an inversion, right? Like normally you have the big banner and the little X image and it's like, oh yeah, we raised all this money and look at all the people who work for us. It's a good trend to have the reverse flex, which is like, look at all this revenue and look how few people work for us.
Well, that's all we have time for this time. We just wanted to wish you a really happy holidays and happy new year from all of us to you and yours. See you next time.