What Surprised Us Most In 2025

2025 was the year AI stopped feeling chaotic and started feeling buildable. In this Lightcone episode, the YC partners break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started.

• 0:00Speaker 00

  I think perhaps the thing that most surprised me is the extent to which I feel like the AI economy stabilized. We 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 the models.

• 0:17Speaker 01

  Many episodes ago, we talked about how it was felt easier than ever to 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.

• 0:42Speaker 02

  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.

• 1:00Speaker 03

  Yes. In fact, we just wrapped up the winter 26 selection cycle for companies. And one of the questions we asked 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 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

• 1:42Speaker 03

  percent. And now Anthropic, who would have thought?

• 1:46Speaker 02

  Yeah. And you know, they'd 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.

• 2:01Speaker 03

  They had this hockey stick with the with the growth of over 52%.

• 2:06Speaker 02

  Why do you think that is?

• 2:07Speaker 03

  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 the models from Anthropic. And I think that's not by accident. I think from the hearing the conversation we had with Tom Brown not too long ago, he came and spoke. Is that was one of their internal evals. They on purpose made them their north star.

• 2:46Speaker 03

  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.

• 2:56Speaker 00

  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.

• 3:10Speaker 02

  Cause you'd be very familiar with like the personality of Claude Opus or whatever they're choosing.

• 3:16Speaker 00

  Yeah.

• 3:16Speaker 02

  Sonnet, I suppose.

• 3:18Speaker 00

  How about Gemini? How's Gemini doing in those rankings?

• 3:21Speaker 03

  Gemini is also pretty much has been climbing up pretty, pretty high. I think last year was probably single digit percent or even like two, three percent. And now for winter 26 is about 23%. And we've personally been using also a lot of Gemini 3.0 and we've been impressed with the, with the quality of it. I think it's really, really working.

• 3:43Speaker 02

  I mean, they have all different personalities, don't they?

• 3:45Speaker 03

  They do.

• 3:46Speaker 02

  Yeah.

• 3:47Speaker 03

  It's, 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.

• 4:00Speaker 00

  And how about Gemini?

• 4:02Speaker 03

  It's kind of like in between.

• 4:03Speaker 02

  Hard, you prefer Gemini, actually.

• 4:06Speaker 01

  Yeah, I've 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 grounding's 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, I personally, I found it was better than all the other tools for that. And it was better than perplexity on it too. Like 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.

• 4:45Speaker 02

  Even if you use Gemini as the reasoning engine in perplexity.

• 4:50Speaker 01

  Have not done that.

• 4:51Speaker 02

  Interesting, yeah. So it's hard to know like how much of it is the tooling and how much of it is like the base LLM.

• 4:57Speaker 01

  That's fair.

• 4:57Speaker 02

  Yeah, I mean, what are your 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, you know, I know is like a research task. Cause I think ChatGPT is still like a little bit of a step behind for searching the web. I don't know, I think memory is turning into an actual mote for like 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?

• 5:35Speaker 01

  The thing I'm still surprised about is why there just aren't more 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. Like, and we bought a house recently and like the whole thing, like I just had like a really long running ChatGPT 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.

• 6:12Speaker 02

  But simultaneously, I'm sure you took the PDFs and just like dropped them into Gemini and said like, well, summarize and tell me what's important for me.

• 6:21Speaker 01

  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 data 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.

• 6:38Speaker 02

  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 Claude open, you have Gemini open, you have ChatGPT 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.

• 7:00Speaker 03

  I actually think that that particular behavior at the consumer that level that we're doing. Startups are doing as well. They are actually arbitraging a lot of the models. I had some conversations with a number of founders where before they might have 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.

• 7:42Speaker 03

  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.

• 8:26Speaker 03

  It's almost like the era of Intel and AMD with new architecture would come up. People could just swap them, right?

• 8:32Speaker 01

  Yeah, if you're at the highest level, that angst around where's the value going to accrue? Is it 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 Claude Code, amazing launch. And it was like, oh, okay, like the model companies are actually going to play out the application layer. But then to me at least, it's all vibes based. Like Gemini, 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.

• 9:08Speaker 03

  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?

• 9:20Speaker 00

  Yeah. When I talk to undergrads, 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 round tripping going between Nvidia and OpenAI and like, is it, is it, is it all fake?

• 9:35Speaker 02

  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 is 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

• 10:20Speaker 02

  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 a long-term buy and hold, honestly, but for the moment, people are like, oh, well, Gemini is so good. And all the, you know, nobody seems to be Nvidia only now, and everyone's buying AMD, and everyone's, you know, and TPUs are working. So, you know, at the moment, it looks like

• 11:00Speaker 02

  there's, you know, what does that mean? Like, 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, you know, the AI labs, they get a little bit of power. But, 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 this 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.

• 11:39Speaker 00

  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, kind of doesn't really matter that much. Maybe Nvidia's 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.

• 12:10Speaker 01

  Yeah, towards Zach 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 and 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 case like, 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.

• 12:34Speaker 03

  There was this book written by this economist called Carolina 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 installation, which is where a lot of the very heavy capex investment come in. And then there's the deployment phase where really ripples, 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 GPT moment in 2023.

• 13:15Speaker 03

  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 say, 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, with the era of the internet. Before the 2000, there was a lot of heavy capex investment into the telcos, right? Those were giant projects that.

• 14:01Speaker 03

  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, I think things are still getting built up. I do think the foundation lab companies and GPUs very much are falling into the bucket of infrastructure.

• 14:33Speaker 02

  Yeah. 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 Star Cloud 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 laughed at them. Yeah. On the internet, yes. They said, that's the stupidest idea ever. You know, I guess 18 months later, suddenly Google's doing it. Elon's doing it.

• 14:58Speaker 01

  So he's not interested in every interview now, apparently.

• 15:00Speaker 02

  Is that right?

• 15:00Speaker 01

  It seems to be like his top talking point.

• 15:02Speaker 02

  Yeah. And so, I mean, why is that? Like, 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, like, are so bad, you know, the supply chain for jet engines to generate power for data centers is so backed up that, you know, you would have had to have ordered these things, you know, two or three years ago, just to even have it in two or three years from now.

• 15:43Speaker 02

  You know, these constraints end up like influencing, like, fairly directly what the giant tech companies need to do to win the game three or five years out. Like suddenly, there's not enough land. You know, 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, you know, the escape valve is like, actually, let's just do it in space.

• 16:20Speaker 00

  Yeah, come to think about it, we kind of have the trifecta of YC companies that are solving the data center build out problem.

• 16:25Speaker 02

  Well, you need fusion energy.

• 16:27Speaker 00

  Yeah, yeah, well, we have the company that's solving the no land problem by building the data centers in space. We have Boom and Helian, which are solving that we don't have an energy problem.

• 16:38Speaker 02

  I just funded a space fusion company that just graduated called Zephyr Fusion.

• 16:43Speaker 00

  Yes, that's a cool one.

• 16:43Speaker 02

  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, you know, Tokamaks and fusion energy. And they came into the lab one day, they looked at the physics, they, you know, 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 that's, they're on like this sort of grand next five, 10 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, you know, it might be, you know, an even more perfect trifecta shortly.

• 17:28Speaker 01

  Something else I feel like happened over the course of this year is the interest in starting model companies. Like, I guess I'd maybe at both ends, there's like the people who can raise the capital to go and actually try and build a head on competitor to OpenAI, which are very, very few, like maybe have Ilya with SSI. But then more so within YC, people trying to build like smaller models. I've certainly had more of those in the last few batches than before, like whether it's sort of like a model for on an edge devices, or maybe like 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

• 18:09Speaker 01

  What fed that was just knowledge about startups became more dispersed. There wasn't like a canon of library information on the internet about like how to start a startup, how to build software and then over like a decade that just became more commonplace. And that just exploded like society's knowledge of startups and how to build things. And it's made feels like maybe we're going through that moment in sort of the AI research meets like actually building things.

• 18:34Speaker 00

  With with training models, I think we are absolutely going through that right now. Yes, where it's going from being a very rare skill set to a more common one.

• 18:42Speaker 01

  Cause like OpenAI a decade ago was like a rare, like you need, 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.

• 18:55Speaker 02

  Wait, wait, wait. So did you just describe Ilya, Greg and Sam?

• 19:00Speaker 01

  You got it.

• 19:03Speaker 01

  That was like a rare team, right? There was 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 capital raising background, or at least can 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.

• 19:31Speaker 03

  I think so. I think the other thing that's even contributing and making this a ever 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.

• 20:13Speaker 03

  There's this particular YC startup that told me that they collected the best data set for. For healthcare and they ended up performing better than OpenAI and a lot of the benchmarks for healthcare with only eight billion parameters.

• 20:26Speaker 02

  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 that 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.

• 20:48Speaker 03

  Have to keep going, yeah.

• 20:49Speaker 02

  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?

• 21:00Speaker 03

  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.

• 21:23Speaker 02

  Varun Mohan had gone over to Google. He released Anti-Gravity. And did you guys see the video? I actually am 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 was very cinematic. Anyway, I think Sundar was, you know, also not only talking about space Data centers. 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,

• 22:02Speaker 02

  vibe coding is not, you know, completely usable and trustable for, you know, 100% of your coding, period, like this. You know, it is not true that you can like ship 100% solid production code today as of 2020 and the end of 2025.

• 22:23Speaker 00

  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. Like, 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 like, nobody knew when the other shoe might drop and like, what exactly was going to happen with startups and AI and the economy. Now, I feel like we've kind of settled into like, a fairly stable AI economy where we 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 the models. I feel like things really kind of matured in that way.

• 23:04Speaker 01

  Which feels is 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 is, 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, 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 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 in office hours.

• 23:37Speaker 00

  I agree.

• 23:38Speaker 02

  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. So it is slower. It requires like, 10x more compute and it's still sort of, you know, 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.

• 24:23Speaker 02

  In our previous episode where we sort of blew up that MIT report that said that, you know, 98% or 90% of enterprise AI projects fail. Well, it turns out that 90% of enterprises don't know how to do, you know, 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

• 25:08Speaker 02

  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, gonna 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 gonna accelerate this thing again.

• 25:48Speaker 01

  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 As without hiring, like, some places 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.

• 26:14Speaker 00

  No, they turned around and started hiring, like, actual teams.

• 26:17Speaker 01

  Yeah, like, 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 they're still just bottlenecked on how long it takes to hire people versus they have demand for less people.

• 26:36Speaker 02

  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 are like, there might be a case of two foie gras startups, like, one being Harvey. And the other one being open evidence, right? Harvey, the founders are incredible. They were very early. And then there's this 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, there may be 30 people who could write checks of like 10 to 100 million dollars. 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?

• 27:21Speaker 02

  So yeah, Harvey is interesting because, you know, Legora is coming fast for them. And obviously we have some skin in the game on Legora, but we think that they have as good a shot at any.

• 27:32Speaker 00

  I guess that's one trend that we saw in 2025 is that there is like a first wave of like AI hate of companies like Harvey.

• 27:38Speaker 02

  Who might have wasted a lot of money on fine tuning, actually.

• 27:40Speaker 00

  Totally. That like broke out really big in 2023 and kind of did a victory lap that, you know, oh, we've won the, the space. And now we're seeing a second wave of companies like Legora and Giga. And it turns out that like, oh, actually like it isn't so simple.

• 27:54Speaker 02

  Yeah, the beneficiary of, um, 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?

• 28:09Speaker 01

  Yeah. So at least as it relates to like the, the hiring and team size, I feel like of the two camps, one being the 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 the soft, what their customers expect. And they're all in the, you know, like Legora is racing with Harvey, Giga is racing with Sierra. Like they're all still competing for the same set of customers and they still ultimately are bottlenecked on like.

• 28:52Speaker 01

  People and like, I don't think anyone's bottlenecked on ideas, but they bottlenecked on like, people who can execute really well. I don't know, I think that's like, still, it's exciting. It feels like an exciting phase.

• 29:01Speaker 02

  I agree with you that like, the era of the one person running a trillion dollar company is not here.

• 29:08Speaker 01

  Not yet.

• 29:09Speaker 02

  Yeah, but I think it's gonna trend that way eventually. That'll be a wild time. Maybe that's a prediction for For next year? Twenty twenty six, yeah.

• 29:16Speaker 01

  You think it's coming?

• 29:17Speaker 02

  I mean, I don't think it'll happen in twenty twenty six either, honestly. I mean, I think you will have many stories of companies run by, you know, under a hundred people that are making hundreds of millions of dollars.

• 29:28Speaker 01

  Yeah.

• 29:28Speaker 02

  So, I mean, Gamma was interesting to see, like, 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 a hundred million dollars in ARR with only fifty employees. So, which is very different. It's, you know, such an inversion, right? Like, normally you have the big banner and the like, little X thing, you know, image. And it's like, oh yeah, like, 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.