The State of Startups in 2026

YC works with thousands of founders every year, which gives us an early look at how startups are changing. Right now, the shift is striking: startups are moving from bits to atoms, nearly one in five YC companies has a solo founder, and companies are reaching meaningful revenue faster than ever. In this episode of The Lightcone, Garry, Jared, Diana, and Harj dig into what’s driving these changes and what they mean for founders. They discuss how AI is making it possible for smaller teams to take on more ambitious problems, why experienced founders are having a resurgence, and why knowing what to build is becoming more important than simply knowing how to build it.

• 0:00Speaker 00

  Frankly, some of the most powerful and badass founders that we've been seeing lately, they're might be in their late 30s, 40s, even 50s. I mean, there's a sort of resurgence of the experienced founder. A lot of people seem to say that they want to be YC for solo founders, but it turns out YC is the YC for solo founders. What a weird moment we are in history where you wake up in the morning, you like wire up a new model, and then these things that even a month ago, you're just like, why isn't it working? It just starts working.

• 0:41Speaker 00

  Welcome back to another episode of The Light Cone. At YC, we work with thousands of founders per year, which means we start to see things before they're obvious. So we wanted to share some of that with you today. What's the state of the art and what's coming next? What should you, the builder, know? Let's get started. Diana, you have a few things to share with us.

• 1:05Speaker 01

  So we did a bit of an analysis for all the companies we accepted in the last 18, 12 months, and we have some pretty shocking stats to share with everyone. So one of the big ones is the number of hard tech companies that are in the batch. It has gone from 8% to 20%. There's a lot of underlying reasons why that has happened. We will go deeper into that. The other one is the rate of growth of companies and what YC does to the companies has accelerated. So the median YC company, when it gets accepted, is at zero in revenue, is pre-revenue, pre-product. And by the end of the batch, in the past,

• 1:48Speaker 01

  companies would get to about 8K median revenue, and now the companies in median are getting to 20,000 monthly revenue as opposed to 8K. So those are the top two that we can dive deeper into.

• 2:02Speaker 02

  Yeah, let's dig into hard tech first. Like, what are these hard tech companies and what's, what's driving this?

• 2:08Speaker 00

  Things that actually touch atoms and not just bits.

• 2:10Speaker 02

  Yeah. And I, I think you have the, the category breakdown of the hard tech companies, right, Diana?

• 2:16Speaker 01

  Yeah. So specifically robotics has been a big one. It has gone from 1% of the batch to about 6%, 7% of the batch. Industrial manufacturing, building things back in the US has been a huge trend. It has, it has gone from about 4% to 10% of the batch. The other one is defense is a big one. We all have been working with a lot of defense startup. It has gone from about 1.5% to about 5% of the batch. The other big one is there's this compute need that the world is getting into with AI. So there's a lot of companies building the semiconductor stack or photonics. It has gone from about 1% of the batch from a year ago to about close to 4% of the batch. And the other one, even below the stack

• 3:00Speaker 01

  of compute is power. So there's a lot of power infrastructure as well. It's gone from also 1% to about close to 3% of the batch. So all these numbers across the physical atom stacks have somewhere triple or quintupled.

• 3:15Speaker 00

  Yeah, this is the age of the machine, I think. And that's, I mean, as the world goes, you know, our, our motto, the T-shirt says, make something people want, and people sure do want those things right now.

• 3:28Speaker 01

  And the other interesting factor about all these companies that are going deep into atoms is that we've been funding more technical founders and with more expertise than ever, right, Jared? We have this fun stat about the current summer batch with-

• 3:45Speaker 02

  In the current summer batch, one in six of the founders actually has a PhD. It's way more than that's been historically. And it's because, yeah, if you're doing, you know, something with like silicon photonics, you're probably going to need a pretty strong research background in that. And so we've been funding a lot more of those founders. And those founders, I think, have disproportionately been doing like especially well.

• 4:05Speaker 00

  I think AGI compounds this in a really fascinating and awesome way in that like you might think in the past, you actually, like, hard tech was hard because you had supply chains, you had an incredible software component often. I think Palmer Lucky talked about this a lot when it came to Anduril. It's like, having code gen means that suddenly even all the things that they do at Anduril can happen much, much faster, right? Even three or four years ago, you would talk about software engineering and like the top tier software engineers as one of, one of the limiting reagents to being able to do really, really top tier, full stack hardware. And that's less and less true. I mean, you

• 4:49Speaker 00

  still need one or two of them, or you need like a small team, but you don't need to hire a thousand great engineers versus Google or Meta or whoever else. And that really changes the economics.

• 5:01Speaker 02

  I mean, that's the, that's the true bull case for hard tech. It's that, it's not just that people are shying away from funding software businesses, but it's actually that the super smart models that we have now are actually accelerating scientific research and making it possible for startups to have bigger research breakthroughs earlier, and that therefore these deep tech companies will actually work better.

• 5:21Speaker 01

  I think the other factor is there's basically three macro trends that are also driving all this, all this growth on atoms and is seeing huge companies like SpaceX have such a successful IPO, has created a generation of founders wanting to build in space. So there's lots of these companies that are building across the whole stack. So there's been companies in the, in the current batch in summer 26. This is a company that we work with called Exosat that's trying to build basically a sovereign Startlink solution. There's other company that I work with in winter 26 called Beyond Reach Labs that's building solar panels for satellites in space. If you imagine companies like Star

• 6:03Speaker 01

  Cloud wanting to have all these data centers in space, they will need to have power. So this is an obvious solution. Now, the other macro trend is, I think we have a generation of current founders right now that have grown with the war, that's been very front and center and spoken a lot in social media, and they want to do something.

• 6:23Speaker 00

  Like, two of the companies I'm most excited about that I funded the last couple batches, one was Icarus last fall, and then Nine Mothers this last spring. And both of them were defense. Icarus is doing like a solar powered U2 spy plane that gives Overwatch and can also do comms, which is actually really important. The future of drone war is being able to actually communicate with your drones on the ground and see what's going on. They've been able to get to seven figure contracts with the new Department of War. And then likewise with drone war, Special Forces has been buying Nine Mothers anti-drone defense. So it's basically a shotgun turret with CV, but it's actually almost the only way that you could protect Special Forces deep behind enemy lines. I mean,

• 7:07Speaker 00

  these are people who have been training for years and years, you know, in a very elite special force that, you know, America doesn't have, you know, thousands of these people, you know? We, we have a very, very small set. And so protecting them from what could be like a commodity drone attack is actually really existential for the Department of War. So it's just really cool to see this new administration actually approach defense in a very different way. Like, you know, classically, there was just a lot of frankly capture from the big defense primes that are just doing sort of cost plus. They think of themselves as consultants. And, you know, to be able to see new startups that can

• 7:49Speaker 00

  actually take advantage of all of the AI, all of the tech, all of the new ways of building things, to build things that frankly, the defense primes can't build, you know, that's a really powerful mega trend right now.

• 8:01Speaker 01

  Now, the thing about defense is not just those full solutions that get sold to the government. There's also a lot of category of startups that are dual use, that they sell both to the private sector and to the government, and that have to do with everything down the supply chain. So things like manufacturing things back in America, building custom, I think you had this company, Knox Metal.

• 8:28Speaker 02

  Yeah, they're bringing metal manufacturing back to America. America has like largely lost its metal industry. It got hollowed out over the last few decades and can't build stuff without metal. And so Knox Metals is like rebuilding America's metal supply chain. And they're doing it in, in the heartland of America in, in Detroit, where there's all these like empty factories that have basically just been like sitting there.

• 8:50Speaker 03

  And they're a example of like the trend where the, it's not just people are doing hardware companies, but the hardware companies themselves are growing faster than ever. Like, I think I saw a PG tweet that Knox Metals is growing like at software growth rates.

• 9:01Speaker 02

  Growth rates, yeah.

• 9:01Speaker 03

  Do you understand that? Like, kind of what, how are they growing so fast?

• 9:05Speaker 02

  So one reason is that a lot of their customers are these new defense tech startups that have sprung up and need metal to build all their, all their stuff. And the existing suppliers that are these sort of like sleepy old businesses, mostly run by old people, just like can't keep up with the pace that the new defense tech startups want to build at. And it reminds me a bit of like when the Web 2.0 boom happened early in, in the YC days, we would have these new startups, but then they would prefer to buy from new startups that could sort of like move at their speed and like work well with them, like Stripe, for example. You could use a legacy credit card vendor, but like, it's just like way better to work with Stripe. And so I feel like they're sort of becoming that for the whole defense tech ecosystem.

• 9:48Speaker 01

  Now, the third trend is basically compute is a very heavy physical atoms.

• 9:57Speaker 01

  Process to get all these data centers live very quickly because a lot of the demand for AI that we've been talking has been skyrocketing. And there's a very interesting stat where GPUs from Nvidia, let's say like a A100 GPU per hour is actually appreciating in cost, which is unusual. In the past, when you get A100s by now are sort of old.

• 10:18Speaker 00

  They're pretty old, yeah.

• 10:19Speaker 01

  The price is going up because there's just too much demand and not enough supply and compute. So there's a lot of startups that are now working on bringing data centers live. And you have everything from the construction of the sites to the software to plan it, to actually doing the data center build out, to interesting solutions that have to do with how to power them and combination of energy, battery. So there's all these category of startups and even to the point of going down to the core compute silicones, there's a number of startups that are building new silicon for an alternative to Nvidia. There's this company that Tyler worked with called Lam Labs that's building new processors

• 11:03Speaker 01

  for compute. There's another one that I'm working on this batch called Bot that is trying to build basically new custom hardware architecture that's using ternary representation for models. Because what it turns out, which is a funny trend right now, if you look at all the Nvidia architectures from A100s to H100s and now the B300s, each of these generations, they're actually going down in floating point precision in terms of what they were. They're going from FP32, 16, 8, et cetera. And it turns out that the LLM architecture doesn't need the full precision floating point.

• 11:42Speaker 00

  FP2 is even somewhat usable.

• 11:44Speaker 01

  Right, so this is what Bot is trying to do. And I think you have an interesting one that's doing the interconnect with photonics.

• 11:51Speaker 02

  Yeah, there's a company called Dipole Labs in the current batch that is replacing the switches that are in data centers, which are essentially the like routing systems between different GPUs. If like GPU A wants to talk to GPU B, they talk to each other through this device. It's called a switch. And these switches right now are electronic. And so there's actually an issue, which is like the switches are not keeping up with the GPUs. The speed of the GPUs keeps going up and the switches are actually the bottleneck for many data centers and any different workloads. And so Dipole Labs is building the first fully optical switch where it's like all photons from GPU A all the way to GPU B. And so it will actually be much faster than the electronic switches that we use now.

• 12:32Speaker 01

  Now, the last one that's driving all this move to atoms is this aspect where robotics is going to happen. So there's a lot of companies building the stack around that and everything from vertical robotics in specific industries to the infrastructure to deploy robots, to data selling to the new robotics labs. Because there's this moment that everyone in the industry is feeling that we're going to get to the chat GPT moment is not quite there yet. And I think we're figuring it out and knew a scaling law around it. So there's a lot of that.

• 13:12Speaker 01

  And we had Kwan here a couple of episodes ago and we're believers that's going to happen. I mean, robotics.

• 13:19Speaker 00

  From pie.

• 13:20Speaker 01

  From pie, right? Half is AI and half is hardware.

• 13:22Speaker 03

  I was hearing this, reading this morning, that even Astro is like a big leap forward for robotics. I forget the benchmark, but there's a benchmark where like Fable was maybe at 10% and Astro is showing like you can do like 60 to 70% of the tasks.

• 13:36Speaker 00

  So data did just, you know, wake up in, you know, another couple of weeks and another breakthrough happens and we're a little bit closer.

• 13:43Speaker 02

  It's been really cool for me to see the resurgence of hard tech because, you know, at YC, we've been funding a hard tech company since 2014. That's really when we started, but it was like pretty hard to get these companies funded before. Like I remember like pre, pre this recent resurgence, we would like fund awesome stuff that we were super excited, like rockets and planes and chips and data centers and stuff like that. And then like VCs would just be like, oh, we only do B2B SaaS.

• 14:10Speaker 02

  And it's hard to bootstrap a company like this. So you really do need like downstream investors who can fund lots of, who can fund, you know, a full, a full capital build out. And so it's cool that like, it seems like Silicon Valley, which historically like venture was set up to fund hard tech, but it like drifted away from it for a decade or two because it was so profitable to just fund SaaS companies. And so it's cool to have it coming back to its roots.

• 14:33Speaker 03

  Yeah, on the point about like the investors wanting to do hard tech again, it does seem like that. I've never seen that happen so quickly. I mean, it seemed like it happened pretty immediately when like SaaS stocks were down earlier this year, Claude Code was surging and that just became like the, I mean, I feel like even at Demo Day, it literally happened. I feel like that happened probably mid the winter batch at the start of this year. And it seemed by even Demo Day that investors were starting to be a lot more interested in hard tech companies and that's just extrapolated. I mean, it is worth knowing though, on the other side, like since that, a bunch of the SaaS stocks have actually recovered and are doing better than ever. Like Salesforce is like the prime example of that. I think Snowflake recently had like, like two days ago had these like blowout earnings.

• 15:18Speaker 03

  And so it's possible that.

• 15:20Speaker 00

  It all hits.

• 15:21Speaker 03

  Yeah, maybe. I mean, that would be the dream case. I mean, I still think we're seeing real stuff though. Like clearly the software that gets built in the future and what's valuable is different. Like it just has to be. And so partly it seems like what we're seeing with Salesforce is the classic, the system of record argument is actually playing out.

• 15:37Speaker 00

  The moats are intact for now.

• 15:39Speaker 03

  Yeah. Like if you have a thing that agents can use, that is actually valuable. And if anything, you're just like agents will use software a lot more than humans will. And that seems to be driving Salesforce growth. And so it kind of takes us back to the other trend that we've talked a little bit about is if you think of agents as your customers and you make things that agents want and your software is something that agents want to use, then that seems like the right type of software.

• 16:03Speaker 00

  Yeah, Salesforce is super interesting because I think they started releasing their own Slack harness, Slack AI harness. And so I think we're right at the beginning of like the next AI harness wars. It's like Codex wants to be it, Claude Code wants to be it, Open Cloud could be it, Hermes, Open Code. It seems like there are going to be a bunch of them and it's not going to be quite like the browser wars and that the browser wars tend toward like one winner. But, you know, I guess it's anyone's guess. And then, yeah, Benioff has a pretty big advantage in that you have a lot of the most AI pilled people and companies in the world still use Slack. And, you know, if the harness is in there and it's your system of record for how people collaborate, then you have like this mega data moat.

• 16:48Speaker 00

  And then, you know, SaaS can still be as valuable as it's ever been valued if those moats hold.

• 16:55Speaker 03

  Yeah, I thought you had a really interesting tweet. Maybe we also go about how the software or system of record companies will have to become like harnesses.

• 17:01Speaker 00

  Yeah, I mean, and that's, that was about Slack, I would say. It's like basically if you are a system of record, you either will be preyed upon, like you'll release an MCP and then maybe like the data, you know, you lose your moat around the data, the data goes elsewhere, like becomes very trivial to switch. Or you kind of have to be a harness. You have to be the way people not just read and write, but actually do their work inside, you know, your system of record.

• 17:27Speaker 03

  And get the most value out of it. I mean, it's a little bit like the model companies. Like was the, was it RKGI was the benchmark where, or there was a benchmark where the.

• 17:36Speaker 01

  RKGI V3.

• 17:37Speaker 03

  Yeah, where the pre, or pre-Astra chat GPT model didn't do as well. But then they said, well, that's just because it was plugged into the wrong harness. So it's like the model plus the harness gets you the output.

• 17:47Speaker 00

  Oh yeah, I mean, with a custom harness, they claim Astro got to north of 90% on RKGI3.

• 17:54Speaker 01

  Yes, I think a couple of months ago, this was in the low two digits.

• 17:58Speaker 00

  Right.

• 17:58Speaker 01

  Which is an impressive leap. And I think you have a very good point around software. It's not that software and SaaS is that what people claim on the internet. It's just that it has transformed. We're actually seeing this in the batch. The percentage of companies we accepted that do sort of full stack end to end work or a task has gone from just 10% to over 25% of the batch. This has to do with actually doing the job, the agent does the job, not just like a point solution, which old SaaS in.

• 18:33Speaker 01

  Five, eight years ago was just like a point solution and you needed a, you needed someone to operate the SaaS software. Right now, it just runs by itself. And actually, in the batch, this is where we're seeing a lot of the growth in revenue. I think I gave that stat of the median startup when it gets into Y C is at zero in revenue. And it has gone from by the end of the batch, it was about eight K in MRR. Now it's like about 20 K in MRR and a lot of these are-

• 19:02Speaker 02

  That's a huge jump. That's like non-trivially big jump for the median, right? The average is even higher.

• 19:08Speaker 01

  And it has to do with doing the full end-to-end job with, for example, doing insurance broker, actually doing the clinical intake, doing the full end-to-end workflow of, I don't know, medical billing, et cetera. And these are the ones that are growing a lot. And I think there's another factor with this happen. I think we talked about this in a couple of episodes ago. We, right now, are about almost a year since agentic coding started to work since Opus 4.5, that we're seeing these workflows fully blossom. And the result are basically people want their job just be done and are willing to buy software that just gets the job done.

• 19:50Speaker 02

  I think when people hear these revenue numbers growing so fast, an easy knock on it is like, maybe it's just AI hype. And these companies are just like shelling out money for AI products because it's like the cool thing to do. And to be fair, that's probably some of that. But I think like the bull case is actually something that we said in an episode like two years ago, when agents were really just beginning to be a thing, where we were like, actually, the products are just going to be more valuable. Like if they automate the whole job, they will actually just be more valuable than some like system of record that tracks the job, but doesn't do the job. And therefore, companies will just like pay more money for the, for, for their product. And I definitely see that in companies that I work with where, yeah, they just go to a company, like the value proposition is so great that like, large enterprises are willing

• 20:35Speaker 02

  to write big checks very, very early.

• 20:37Speaker 03

  Yeah, a company that I'm seeing having this effect is Juicebox, an AI recruiting tool. Like, it's been on incredible growth rate for like the last couple of years now. But they'd started out as, I mean, I would say it was essentially sort of LLM-powered people search. Like, the thing that they did was you could type in sort of the spec of the type of person you wanted to hire and it did a really good job of pulling the good profiles of people that you may want to contact. But then you still have to go and contact the people. And recently, they've launched an agent product, which is really taking off. And the agent like, doesn't just search for the people, it like contacts the people. Eventually, you'll be able to schedule the interview, do a bunch of things.

• 21:13Speaker 02

  That sounds awesome.

• 21:13Speaker 03

  Yeah, and they're seeing that that's gonna, just on a like per account basis, I think is gonna double or triple like the revenue they make from a single customer because customers want more and more of these agents. I don't think it's fair to say that it's like, it's not like it's like automating the job with the recruiter at all. It's just, it's just changing it. Like, like the recruiters didn't necessarily want to be doing like that sort of rote reach out to like 500 people anyway. Like, the thing that makes the recruiter job, I would say like more skilled and interesting is like, there's like culture fit. That's just gonna be really hard for like an AI to do a phone screen that assesses like how well someone's gonna be like a culture fit and, and the human element of it. And so I think they're finding that the recruiters themselves are actually really

• 21:54Speaker 03

  excited to use the agents because it frees them up to do the work that they feel is like unique and interesting.

• 22:00Speaker 01

  The other shocking stat is that the companies that really accelerate during the batch, they really start taking off. One of the things that we started experiencing this year that we never experienced in the past is we have companies breaking from zero to seven figures in revenue during the batch. And that is in a span of three months. And that's shocking. In the past, that would have taken for a company to get to that about 18 months or more. And they're doing it in that amount of time. Part of it is they're solving real problems and because of agentic coding, they're actually building products that are a lot more mature as well.

• 22:41Speaker 01

  And they're able, these founders that are super AI-pilled, run, I don't know, 20 coding agent sessions to get to that product maturity.

• 22:51Speaker 02

  And there's another category of companies that's also been growing super fast recently, which is companies that sell data or RL environments to the labs. This one might be interesting to talk about because a lot of these companies are pretty stealthy. They tend to have a disincentive to talk about how well they're doing instead, you know, compared to most companies that like to talk about how well they're doing. And so I think people out there might not realize how big a category this has become. When Y C funded Scale back in 2016, this was like a tiny little niche thing. It wasn't even a category. There was initially, it was basically just Scale who was doing and then Mercor began to do it. And then like a couple of other companies. But in the last couple of years, it's become a big category. We pulled the data recently and just in the last two years, Y C has funded more than

• 23:35Speaker 02

  a dozen companies that are each making more than $10 million a year selling data or RL environments to the labs.

• 23:41Speaker 00

  And in many cases, hundreds of millions of dollars.

• 23:43Speaker 02

  And it may make it hundreds, hundreds of millions of dollars. And these are companies that were just, just a couple of years old.

• 23:48Speaker 00

  That's pretty fast to revenue, honestly.

• 23:49Speaker 02

  Yeah, it's like pretty bananas. Do you want to talk about any of them, Gary?

• 23:53Speaker 00

  I mean, the big ones, I mean, I think After Query and Data Curve, both really, really great. I mean, there are probably too many to name that are honestly like, maybe don't even want to be mentioned because, you know, once you have something that's working, you almost don't want people to know. I think that it's kind of natural to understand this, though. I mean, data is one of the legs of the scaling law. And, you know, much has been made of compute, but without the data, how are you going to make these models that much better? The RL environment thing is interesting. I mean, there's a lot there. I mean, there's a lot of like pure customization that's happening for specific use cases. Like, you'll have like RL environments for finance, for instance, and someone can go very, very infinitely deep with that.

• 24:38Speaker 00

  And it's like a little bit of expertise. It's a bunch of computer science. It's some systems work. But RL seems to be, I mean, one of the big engines for how, I mean, people are maybe benchmark maxing a little bit more than they should, but it costs money to do it. And it's seemingly here to stay in terms of how big model companies are going to approach it.

• 24:58Speaker 01

  Reportedly, the big labs are spending about a billion dollars on this. It's not a very known fact, but there's, there's actually a real business to be built around this. And RL environment is the current flavor of it. And there's things with long-term horizon tasks that are getting built up. And I think that is starting to also emerge in robotics. The labs also want to solve the problem of getting AI to work on the physical world. So they need a lot of the environments in the real world. So things with egocentric data, teleop, robotic tasks, starting to emerge as like big data category where labs are spending eight, nine-figure deals with these companies.

• 25:40Speaker 01

  We had a number of companies in the batch that work on that and been able to close, close revenues in that, in that space. Companies like, in the current batch of summer 26, there's Praxis Robotics. There's one that I'm working with that has like a network of places across the world where industrial manufacturing gets done. They collect data from that. There's this other company that Brad worked with called Deep Reach that also has data that local entrepreneurs and across the world do.

• 26:11Speaker 02

  And Human Archive in winter 26, yeah, there have been a bunch of these companies recently.

• 26:16Speaker 01

  Right.

• 26:17Speaker 00

  I think like, if I were gonna prognosticate, like, one of the things going back to the, you know, all systems of record need to be AI harnesses, they might also need to start training their own models. And that's where things like River AI or Tinker start becoming really interesting. Out of the box, like, you can sit there in Claude Code or even open, I use OpenClaw to train my own models, which is very fun. It'll do its own data cleaning and everything. But to date, like, that hasn't been a huge factor, but I can see that becoming a much, much bigger factor. I mean, when you have proprietary data and you can train, I mean, the open, open weight models are really nearly frontier. If you can like sort of special purpose, train these things to do even better than

• 27:02Speaker 00

  what the frontier can do, like, that, that's going to be really, really powerful.

• 27:06Speaker 01

  I think this is actually going to be even bigger in robotics. I mean, this is a hypothesis, it's not proven yet, but robotic foundation models in robotics, I think have a very different characteristics versus LLMs. LLM is, the whole thing is you model reality as language. And for robotics, you model reality in the physical 3D space, which has way more degrees of freedom. And perhaps in order to get robots to work in a specific vertical, like, let's say, robots that do operations in data centers. I have this company called Boost Robotic that build robots for data centers, like doing the cabling. It is possible for these robots to work. It's better to get a model that's fine tuned and trained and custom data that just works in that environment. Because the thing that's also challenging for robotics,

• 27:51Speaker 01

  they need to be in real time and respond very quickly to the stimuli and have an action plan, which is different than LLMs. LLMs, you can have this feature where you can just let it go and come back. But for robotics, you can't. Because if, I don't know, let's say you connect that cable to the data center and then someone comes in and knocks the robot out and things could get connected to the wrong plug, let's say.

• 28:17Speaker 02

  Yeah, my understanding is that all the YC companies that are using physical intelligence as models to deploy robotics, they're all fine tuning the pie models. I don't think any of them are able to use the pie models out of the box. Even if it's a great, like, starting point, you have to actually fine tune it for, like, your specific case, like, data center cables in order for it to work.

• 28:35Speaker 01

  You work with this company Ultra, right?

• 28:36Speaker 02

  Yeah, they start with the pie model, but then they have, like, thousands of hours of footage of, like, putting things in boxes. That makes it really good at putting things in boxes.

• 28:44Speaker 00

  I've heard the argument, basically, that, you know, you could look at Claude Code, like Claude Code can use its code transcripts to figure out who the top coders are. And you can take that and turn it around and, you know, basically train the next coding model to be even better. If you happen to own TikTok, you happen to have all of the data on what people watch and click on and what's compelling. And you can use that to make much more compelling videos in Seed Dance. So, you know, that's already been happening. I just, you know, I think that that trend is going to continue in a fairly spectacular way from here. So one of the things that we've been noticing, I think all of us have, is that frankly, some of the most powerful and badass founders

• 29:27Speaker 00

  that we've been seeing lately, they're might be in their late 30s, 40s, even 50s. I mean, there's a sort of resurgence of the experienced founder. A lot of people seem to say that they want to be YC for solo founders. But it turns out, YC is the YC for solo founders. Diana, you have a few stats that you found surprising.

• 29:49Speaker 01

  One of the shocking stats from analyzing the accepted companies from a year ago, we used to only have about 5% of the companies accepted be solo founders. And now we're over 18, 19%, which is a huge. This is the highest spike that we've seen.

• 30:04Speaker 00

  Almost one fifth of the batch, so. And it seems like it's going to keep going. You know, before, you had to have, like, you know, so many different skills. You had to be a great hustler, you know? You had to be able to explain. And, you know, we would say, like, they have to be good talkers, right? Like, you need someone who can, you know, be a hot person. Someone who can actually come in and convince someone of something. And then if you paired that with someone who is a world-class technologist, that's sort of the combo that is so ideal. And so classically, you would need co-founders to do that. Like, you know, you didn't necessarily need one, but like, it would increase your chances by so, so much. And I feel like a lot of that is, like, changing to this degree.

• 30:47Speaker 00

  It's becoming such that, like, knowing what to prompt and knowing what to build is so much more difficult and valuable than just knowing, you know, the CTO, being able to code the thing.

• 30:58Speaker 03

  I think what's going on is that we've always actually had hugely successful single founders. I think people don't realize that this about YC. There's different sort of definitions of it. But for all intents and purposes, Alpova with Instacart, Brian Armstrong with Coinbase, at least when the batch started, were single founders.

• 31:15Speaker 00

  Yeah, Parker Conrad got into YC as a single founder. And then I interviewed Lakshmi, who ended up being his CTO.

• 31:22Speaker 03

  The bar for being able to, like, have the idea, be able to sell it and be able to build it all by yourself was just really, really high.

• 31:31Speaker 00

  And now it's actually totally doable.

• 31:34Speaker 03

  Yeah, I think that's what's going on. In that case, like, those three are just incredibly exceptional people. And there's just, like, very, very few people who are capable of that. And now you can actually, like, get going. And so I think you just don't have to be quite that, like, exceptional, at least on one of those dimensions, the building part, to be able to get going.

• 31:49Speaker 00

  But net-net, it's still valuable to have co-founders. It's still a measure of, like, you know, if your co-founders are super elite, like, that means you're probably super elite. And it just increases the chance of success by a lot.

• 32:01Speaker 03

  Well, in each of those cases, they did bring on co-founders. I think in each of those cases, you just get going and they got traction. Then they added on co-founders sort of at a certain point. And so maybe, like, the equity ownership is different or maybe the dynamic is just slightly different to the traditional, hey, like, you start out and, like, the two of you in a room and you're completely 50/50. I don't know if you want to put equity stuff in, but.

• 32:23Speaker 02

  Yeah, I don't have the stats, but I have definitely seen a greater trend towards that. People adding co-founders later in the company lifecycle after the thing has already, like, gotten off the ground.

• 32:33Speaker 03

  I think that will be the trend. I think we'll see, like, more single founders in the batch which we're already seeing, like, starting the batch. But at least of the things that succeed, I still expect that they're going to be adding co-founders as the company progresses.

• 32:47Speaker 02

  Gary, do you also want to talk about the trend towards, like, more experienced people starting companies?

• 32:53Speaker 00

  So it does seem like people who have been around the block a few times are doing much, much better. I think of Peter Steinberger as, like, sort of the canonical example. Like, you know, he's, I believe, in his early 40s. And he'd been a dev manager, he'd worked on startups before. And then, you know, he sort of uniquely got extremely AI-pilled with the clankers early. But then he just tried a lot of stuff. And then he knows what to build. And so that's one thing that I think is actually really encouraging. It's like, basically, if you've been around the block, you know where the dragons are, you sort of have taste. And then those people in particular are, like, unusually powerful right now.

• 33:38Speaker 00

  Yeah, I mean, there are just so many classic gate-kept things that happen. It's like, oh, you know, you have to have a co-founder. You need, like, a certain set of, you know, cool investors to be into you. And like, now it's just less and less true. It's actually like, you need to know what to build. That's like, the higher order bit now is you need to know what to build. And if you've lived a little bit and you've been in places and you're very opinionated, like, actually, now you might not have an excuse. Like, what's your excuse? Like, you've been this loudmouth on the internet for so long. Like, you know, why are you not building something? Like, just pop open, open code and just go do it, you know? Like, put your money where your mouth is.

• 34:16Speaker 02

  I also wonder if managing coding agents is actually, like, in some ways, not that different from managing people.

• 34:22Speaker 00

  Oh, yes.

• 34:23Speaker 02

  And so, like, people like Peter or you or Boris Churney, like Toby from Shopify, who have had whole careers, like, managing teams of engineers, actually, like, take to this super well and can, like, spin up huge teams of coding agents and manage them maybe more effectively than even, like, a really smart 19-year-old who hasn't had those years of experience.

• 34:42Speaker 00

  Yeah, we can be a little bit less abusive to our agents, try to understand where they're coming from. You have to catch their emotions, like, 99.9% less. So, yeah, it's pretty helpful.

• 34:58Speaker 01

  I wonder what's the concrete advice for someone that wants to get started and want to build a company, like, right now in the current era?

• 35:06Speaker 00

  I mean, just start prompting. I mean, opening up GPT-6 today was pretty wild. I mean, just that moment where your agents are, you know, palpably smarter. You know, a bunch of things that you've been annoyed about, like these bugs that, you know, you haven't had time to deep dive yourself. You just be like, actually, could you just go back to the list of things that you couldn't figure out? Like, look at your, you know, all of our last chats and, you know, anything that looks like you didn't figure out? Like, try to figure it out now. And it'll do it, like, every single time. Like, you know, it's what a weird moment we are in history where you wake up in the morning, you, like, wire up a new model. And then these things that even a month ago, you're just like, why isn't it working? It just starts working.

• 35:51Speaker 00

  And like, you know, to think that that might be this thing that we get to do for the next 18, 24 months, 36 months, like, who knows? You know, I don't know when it ends, but that's coding in the time of AGI, I guess. Well, that's all we have time for for today. But if you can't tell, we're all pretty excited about what's going on right now. And you should be too. So we can't wait to see what you build.