AI Revolution: Why This Is The Best Time To Start A Startup

In this special episode of Lightcone, we’re joined by YC partner and creator of Gmail, Paul Buchheit, to dig into some of the latest trends in the world of AI and startups. We recorded our conversation at a recent retreat where 300 of the top AI founders in the world gathered to share expertise and make predictions about how this technology will shape our future. In the discussion, we cover a wide range of topics, including the future of work, the power of agency and taste in an AI world, and why this is the absolute best time to be building a startup.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  With AI, there's there's sort of two forks on the road. There's there's the bad direction and there's the good direction. And the good path, which I think we're, you know, we're moving towards, is looking to say, how do we maximize human agency and freedom? And our just potential to be kind of our best, the best versions of ourself.

• 0:17Harj Taggar?

  This is the first time, no one's saying no. Everyone is saying yes and like more. Like, there's just like unprecedented amounts of demand for just AI stuff.

• 0:26Speaker 00

  There's a whole category of businesses or products that would not have been economically viable or even possible to create before that are now possible. And so we've actually just like expanded the universe of possible businesses.

• 0:39Garry Tan?

  Never been a better time to be a founder, that's for sure.

• 0:50Garry Tan?

  Welcome back to another episode of The Light Cone. And we've got a special one today because we are in Sonoma and we just wrapped up a 300-person retreat of some of our top AI founders. And we also have a very special guest today, the creator of Gmail and our partner at YC, Paul Buchheit. Harj, why is this such a special episode? What are we doing here?

• 1:13Harj Taggar?

  Well, we're filming from a different place. So this weekend, we put on an AI retreat for some of our alumni companies to share ideas about AI and what they're seeing as they're building their startups. And we learned a bunch of really interesting stuff. So we thought we would film an episode to talk about it.

• 1:30Garry Tan?

  So PB, back in the day when we were working with companies, you know, what was sort of an aspirational growth rate? What would we tell people to try to do week on week?

• 1:39Speaker 00

  Well, 10% week on week is an amazing metric to hit.

• 1:42Garry Tan?

  Yeah, and I think back then, if you were like maybe the top one or two percent, you know, maybe even the top one or two companies in the whole batch, you'd be able to achieve that. And since summer of last year, the wildest thing is realizing that both summer and fall batch in aggregate, on average, over the batch in 12 weeks, averaged 10% week on week growth. So not just the very best, the Airbnb of the batch, but the batch overall.

• 2:14Speaker 00

  It's amazing.

• 2:14Jared Friedman?

  And it's not just during the batch. Diana and Hurd, you guys have companies that you've worked with that have continued an insane growth rate long after the batch is over. Do you want to talk about those ones?

• 2:24Diana Hu?

  One of the ones that really stands out is a particular company that went from zero to 12 million in 12 months. I'd never seen any growth like that. And I think we've seen this not to be just the exceptional different company of the batch, but actually more of them do that as well, right?

• 2:45Harj Taggar?

  Yeah, that was my general pickup from this weekend was that just the the rate of execution of startups is going is going much higher. And you can see it in both like how quickly companies are hitting like a million dollars in ARR. Like we used to, I think, say you should aim for that like 12 to 18 months out of the batch. And that's like the equivalent of the 10% week on week growth. Like that's what you should aspire to. Um, now it feels to me like that's probably like the minimum. Like an AI startup, um, I have companies hitting it within six months. And then I was just talking to some founders, we can just about their goals for this year. Like some of them have hit a million dollars ARR just now. Um, I I had one company say that their goal is 20, like another company say they're aiming for 10 at least. They just.

• 3:26Jared Friedman?

  Wait, going from one to 20 million in one year?

• 3:30Harj Taggar?

  Yes, yeah. And this is a goal, founders have goals and like we hope them, we hope they hit them. But my point is, I think like saying that like even a couple of years ago, hey, my goal is to go from like one to 20.

• 3:40Jared Friedman?

  People would have thought you were just.

• 3:41Harj Taggar?

  Yeah, you would have just, you would have been either like, uh, like just like, that's total nonsense. It's never gonna happen or like, um, it just, you just wouldn't have said it. And I just think like the general level of ambition has gone way up because of AI.

• 3:53Diana Hu?

  The things are starting to work and let's talk about why that is the case. Does anyone have any thoughts on that?

• 3:59Garry Tan?

  Well, I guess I have a meme that I showed you guys earlier. You know, I think the classic thing is you have a boss who's sort of like slave driving. And then, you know, I still believe this, like if you're a leader, you're not, you know, sort of slave driving from the back. You're way in the front, like sort of leading. And then the meme has, you know, this one person pulling the cart alone. And that's the introvert. And what might happen now is the introvert with AI can pull three times as many cards all alone, actually. Once intelligence is truly on tap, then it's actually a force multiplier for founders and people with really, really strong senses of agency.

• 4:35Harj Taggar?

  Shubham, why specifically is this happening? One of the interesting talks I heard was from Aaron Levy, the CEO of Box. And he was talking about, um, he's been through like multiple cycles of enterprise software. And he said that usually when there's sort of like a new cycle shift, but like cloud or mobile, there's always people in the room, decision makers at the big enterprise software companies saying like, no, like, you know, we're never going to shift to cloud. Like it's apparently like a famous quote from Jamie Dimon. Well, like whenever like mobile is not going to be a thing, it's not that important. But with AI, it's different. Like this is the first time no one's saying no. Everyone is saying yes and like more. Like there's just like unprecedented amounts of demand for just AI stuff.

• 5:19Jared Friedman?

  Yeah, it's notable that all these companies that are having these incredible growth rates, they're all the same flavor of startup, right? They're all basically selling AI agents to businesses. I mean, there's other companies that were funded that are doing well, but like all of the ones that you guys were talking about, right? They're all AI agents for businesses. And so they're all essentially, I think, riding on this wave of like enterprises are like, have enormous pressure internally to adopt AI.

• 5:42Speaker 00

  It seems like it goes back to our fundamental base advice, which is make something people want. And in this case, traditionally, the challenge was convincing people that they wanted the product. And I, it sounds like what's driving the growth is that the demand is already there.

• 5:56Harj Taggar?

  Yeah.

• 5:57Speaker 00

  And so you just have to show up with a product that works and you don't even have to be that good at sales.

• 6:02Harj Taggar?

  The point is that actually building the product that works is quite hard. Like a lot of, a lot of what the, the demand we're seeing is not for, is for software that can actually do the work of a person. So it's essentially services.

• 6:16Harj Taggar?

  And doing that to the equivalent level of a human doing the job, whether it's like customer support, sales, phone calls, whatever it is, um, it's actually very, very hard. So I think just like a trend I noticed is a lot of our heavily technical CEOs who aren't necessarily the strongest at sales are able to win big enterprise contracts now. Because although there's ten or fifteen other companies competing for the same contract, um, it's very, very hard to build a product. And so just building the thing that actually does the work well is enough to win these huge deals.

• 6:47Diana Hu?

  A lot of the details of how they build the products, they're really inventing a lot of new patterns on how to build the product. Because nobody knew how to really get L M's to behave, let's say, correctly and give very predictable results. And people thought that was impossible. That's because they only tried, maybe surface level, they play with chat G P T and oh, sometimes hallucinate and then people give up. That could be the, the random person just does that. But a lot of the technical founders, they don't, they find ways and whisery around on how to really state a problem, how to properly prompt it to actually be very accurate. And it is possible because we're actually seeing a lot of these products getting bought at businesses to handle all these complex tasks.

• 7:32Jared Friedman?

  One thing I noticed this weekend is that a lot of the talks that the founders gave were around evals and like testing, which I don't think that would have ever been true at a previous Y C conference, like testing was sort of this like afterthought thing that you try to do as little of as possible. I, I heard one really interesting comment from a founder who's building an A I agent, who said that he thinks that the most valuable thing that his company has built is not the codebase, it's the eval set. It's a gold standard labeled set of data of like, this is what, this is the correct answer for the A I to do. And that that was sort of a mental change for me that like there's, I think, this perception

• 8:13Jared Friedman?

  that would that like companies have like data assets, but just like general random data is actually not that valuable. The thing that's really valuable is like a gold standard meticulously labeled eval set.

• 8:24Garry Tan?

  I mean, this is exactly why the whole chat GPT wrapper meme is wrong in that actually it's the model that is changing very quickly. There are clearly five or more AI labs, all of which are right there at the frontier. So now there's a lot of alternatives to which model. But then the thing that, you know, nobody has that is actually hard to get is the eval set. And I'd argue the prompting, which is sort of like mirrors basically all of the opportunity for the people watching right now. It's like basically agency and taste prompting, just knowing what to tell someone to do, or, you know, what to tell

• 9:06Garry Tan?

  the agent to do. And then evals is taste. It's like, is it good? Is it beautiful? Is it useful?

• 9:12Harj Taggar?

  I heard one very interesting tidbit from a founder who said that his designer, that they've actually stopped using Figma mockup things and the workflow is interesting. So the designer is designing entirely with Claude and going from text to JavaScript. It was just counterintuitive to me because you assume design is this like very visual thing. But apparently their designer has figured like it has enough taste to be able to turn that into just like text prompts and like via prompt engineering essentially get to a actual like lines of code that they are like as tasteful and as good as any Figma mockup would have been.

• 9:51Jared Friedman?

  So it's like the pattern as always is whoever can iterate the fastest wins.

• 9:55Garry Tan?

  Yeah.

• 9:56Jared Friedman?

  And AI is an incredible tool for rapid iteration.

• 9:59Garry Tan?

  I guess people are, you know, sort of worried that the jobs are going to go away. But earlier we were talking about, um, there's this great Milton Friedman quote where he's visiting a developing country and he sees this large group of workers digging a canal using shovels. And he asks his government official host, you know, Why are you not using machinery? What's going on? And the guy says, it's a jobs program, actually. And what does Frieden say?

• 10:27Speaker 00

  He says, if it's a jobs program, you should give them spoons, not shovels.

• 10:30Garry Tan?

  I think that that's actually the most useful mental model for sort of the fear about job loss, at least for now.

• 10:38Speaker 00

  Certainly, you know, the the potential of AI is because it is this incredible tool where we're moving not from spoons to shovels or shovels to bulldozers, but to the point where the AI can do so much work that we're actually just able to create dramatically more wealth. And I think that's really the dream that we have for, you know, peering 10 years into the future is I think there's a potential for an unprecedented level of certainly scientific discovery. The AI is incredibly good at reading thousands of papers, you know, digesting textbooks, very good at chemistry. So I think we're going to see incredible levels of productivity.

• 11:19Garry Tan?

  The story is fascinating to me because the alternative is what? Replacing people's shovels with spoons? Like, I, you know, I think it's absurd on its face. Like, that actually is a little bit like torture. Like, I feel like, you know, growing up, my dad would force me to work in the gardens. That alone was barbarous to me. But like, if you made me do it with a spoon, what would we call that? That'd be torture, actually.

• 11:44Speaker 00

  I think the question that that I posed to Sam that that everyone seemed most interested in is is essentially, you know, are any of these startups actually going to exist in 10 years?

• 11:55Garry Tan?

  Well, certainly relevant to the audience.

• 11:56Speaker 00

  Yeah, everyone felt that was that was relevant because, yeah, if we if we do achieve AGI, you know, what does that mean? How how quickly can that actually just displace all of the work that we're doing here? And honestly, like, no one's quite sure, which makes this a very exciting time in technology. But, you know, it's it's very clear that we're able to achieve a lot more. And I think like throughout history, every time we've found ways to create more wealth more rapidly, that's actually worked out really well. You know, historically, 97% of people were farmers or something like that. And now it's, you know, I think maybe 3% or or even less. And so we seem to be very good at inventing new work for ourselves

• 12:40Speaker 00

  and new new ways to to find purpose and meaning.

• 12:44Garry Tan?

  Well, what was the answer to your question? I think.

• 12:47Speaker 00

  Luxury real estate. You know, what are the things that people will value in the future? And if we get to the point where we really do have just a real abundance of the kinds of things that machines are good at creating. And so this is actually an idea I've been talking about for 10 or 15 years of almost thinking about the world in terms of machine money and human money. That really what we want to do is is take the products of technology and create actually massive deflation. We want to drive the prices, you know, down to zero so that we're all able to afford. You know, certainly like medical care is something I think a lot about where it's really hard for most people to get really great medical care today.

• 13:31Speaker 00

  And I think that that's something where in 10 years, we're going to be able to make it so that, you know, the majority of humans on earth have probably better medical care than we here at the table have today, which is, I think, going to be a huge achievement.

• 13:47Speaker 00

  But at the same time, you know, that's kind of on the machine money side of things. But then you think about the human money. What are the things that that we get, that we really value from humans? Like, you know, if you go to see live music, we seem to have a preference to see a band live instead of just sit in front of a bunch of speakers, you know, or maybe robots playing music. Human money, I think, might be something that comes closer to just like an hour of your time, right? And that we actually have almost like a dual economy.

• 14:14Garry Tan?

  That's super interesting. Embedded in that is actually maybe a better version of UBI. I mean, a bunch of the studies around UBI are sort of showing that there are sort of nice benefits here and there, but fundamentally, it's not creating a greater sense of well-being in the way that people hoped maybe like five or 10 years ago.

• 14:33Speaker 00

  Yeah, it's definitely had mixed results. And I think a lot of that really comes down to, you know, people still need guidance in life, and especially a lot of the people who are targeted with UBI are not necessarily people who are in a great sort of social position to begin with. And again, I think that there's a lot of potential for AI to actually kind of act as like a life coach. And again, like, you know, if you're fortunate enough to grow up with great parents and a great. Culture or something, you have a lot of advantages that a lot of other people maybe didn't have. And so, again, I think like the great promise of of AI is kind of taking like the best of what we have available and then just making it universally accessible because we're able to drive the cost so low.

• 15:13Garry Tan?

  I mean, honestly, this past December, I spent a couple of weeks in Vietnam and you're in a developing country and then you realize like there is so much that needs to be developed. It's actually, you know, there's roads, there's infrastructure. Like the whole country seems like it's under construction. I imagine that's what China probably looked like in, you know, maybe the mid eighties or mid nineties. But there's also like this crazy optimism as it's building. If you have a robot, like it could build your house, it could clean your house. It could, you know, take care of all of these things for you. And that would like radically change your day to day, your standard of living. And how much more direct can you be if you, you know, rather than sort of just giving people

• 15:56Garry Tan?

  more human money, let's give them like the better way to live and that's sort of, you know, everyone can get here. But then I think there's like a special thing here around the human money where like the really remarkable things like, you know, nobody's guaranteed to have, you know, beachfront property in California or something like that. And that's where human money might go. Like everyone has the basics and actually something that's probably five or 10 times better than what even the most wealthy people have today.

• 16:26Speaker 00

  Yeah. The other way I like to think about it is I kind of think with AI, there's there's sort of two forks on the road. There's there's the bad direction and there's the good direction. And I think the bad direction is is one where it's used to just like constrain and control and essentially like imprison us. And and the good path, which I think we're, you know, we're moving towards is looking to say, how do we maximize? Human agency and freedom and are just potential to be kind of our best, the best versions of ourself. And we even have that, you know, today with some of the creative tools where, you know, I don't have like a lot of artistic ability, but with like AI image generation or something, I can convey, you know, funny concepts or whatever.

• 17:07Speaker 00

  And we see that again, like with the design tools, you know, someone who can't code can now all of a sudden create basic apps and things like that. And we're able to realize our visions in a way that we were never able to do before.

• 17:20Diana Hu?

  A conversation we were having earlier is how we are actually now on the good timeline on how AI is shaping.

• 17:26Speaker 00

  Yeah.

• 17:27Diana Hu?

  Because 10 years ago, this is very different view of how AI could turn out. Do you want to kind of talk about that?

• 17:32Speaker 00

  Yeah, exactly. So I, you know, I like to think about things on a time, 10 year time scale. In part because, you know, that's kind of how our startups work, roughly speaking. We seed fund them, they come through YC and then 10 years later they IPO. And so I've been asking a lot of people about your 2035, you know, what do you want to see in 2035? But also thinking backwards to 2015. And so if we go back to 2015, you know, 10 years ago was where we were having discussions inside of YC about artificial intelligence, because we believed that we had sort of crossed a threshold. Basically in the early teens, somewhere around 2012 is where we started to really believe that actually

• 18:14Speaker 00

  we had broken through. I think everything kind of prior to 2012 was fake, in my opinion. But it was really deep learning that, that, that started to really deliver on AI. But when we were looking at this 10 years ago in 2015, one of the big questions was, you know, it was all reinforcement learning and what is the thing that we were reinforcing? Because at the time they were playing video games and trying to make the score go up. And, and this is, I think, also kind of where the paperclip maximizer concept and fear came from is like if you gave it the wrong objective function. And so we had a lot of fear that based on our own evolution, our intelligence arose as a survival mechanism that we became intelligent and other animals became intelligent.

• 18:58Speaker 00

  As a way to survive or perpetuate themselves. And we thought that if AI did the same thing, it would by its very nature want to wipe us out in order to maximize its own odds of survival. And what's happened in the last 10 years is we actually found the right objective function, which is simply to predict the next token.

• 19:19Speaker 00

  And that actually, intelligence in its most raw form is simply predicting what comes next. And so all of our really at a root level, what we're predicting, our reinforcement function is simply predicting what comes next. And that is the fundamental core of intelligence. And the great thing about that is we've been able to create this intelligence that doesn't have this drive to survive. It doesn't mind that we spin up an intelligence and it does some work and then it disappears. Because it's just based on that ability to predict patterns.

• 19:52Garry Tan?

  I would argue like the most important part of this is actually the agency piece. Venkatesh Rao has this crazy thing. He talks about, and this is sort of a function of maybe the Uber and DoorDash era of things, where in society, there's this API line. So either you are above the line, meaning you create Uber, or you drive for Uber. And obviously, that's a distillation of like sort of the last idea. And then in this AI world, basically, if you're below the API line in the old model, like you don't have agency. You sort of have to like, you know, play this never ending game, like the human is doing the paperclip maximizing. And so there's this other sort of world that I'm hoping we live in where it's humans

• 20:38Garry Tan?

  not just writing the prompt and then the machine runs software and then this vast machinery and that's it. And you can never change the prompt. Like that would be tyranny, probably. It's conceivable that in the future, we might have, and I don't know if this is the right thing, but you know, this sounds like something the EU would do, for instance. It would probably mandate there to be a human in the loop on, you know, maybe the CEO of a company.

• 21:01Speaker 00

  Spoons.

• 21:02Garry Tan?

  And that might be the case, right? That, you know, it might be a form of like, we cannot use shovels here.

• 21:07Speaker 00

  Right.

• 21:08Garry Tan?

  We must use. Tiny little spoons just for this one part.

• 21:11Speaker 00

  Right. Yeah, I think kind of the fundamental error they keep making over and over again is taking a very static view of the world and then essentially trying to dysregulate the current structure. And that closes off our ability to evolve and see into the future. And it's, you know, again, very difficult and oftentimes implausible. So, you know, again, going back to 2015, the conclusion of our of our thinking was actually that we needed to create our own AI lab because at the time, you know, all of the best AI work was being done at Google and they had, you know, Google had all the money, all the data, all the users, all the researchers. And it kind of seemed possible that they were going to have essentially a monopoly and it was all going to be locked up inside of that system.

• 21:53Speaker 00

  And so we had this very sort of like loony moonshot idea to start. At the time, we called it Y C Research, but it eventually got renamed OpenAI. And so OpenAI, you know, we were going to take on Google with a small nonprofit, which was sort of doesn't quite pass the laugh test, right? Like, like, how is this little nonprofit, you know, going to be the one that that actually develops AGI when, you know, the other companies have dramatically more resources? And then here we are 10 years later, it actually happened. And at the time, it just seemed incredibly implausible, like no one would have believed it, yet here we are on, I think, basically the best timeline. Like, we actually delivered it.

• 22:33Speaker 00

  We have an open and competitive market with, I would say, at least kind of like six, basically, you know, foundation models that are competing, including an open source one from Meta. And I think that's our best shot for preserving freedom is is is is choice in competition.

• 22:51Diana Hu?

  And talking a bit about Google, it actually is digging their traffic, too. Do you want to talk a bit about that, about the stats that we're?

• 22:58Garry Tan?

  I mean, some of it is like, I don't think it's out there in the annual reports yet. And certainly, like, we did some, you know, research prior to this episode. We couldn't really find anything that conclusive, but maybe purely anecdotally, because we're in this pool of people who are very, very early adopters, very much software engineers and, you know, our behavior. Interacting with the internet has changed already. It's not a surprise to me. Some people are starting to report in their referral traffic. Google referrals are down maybe fifteen percent in the last year. And that certainly probably mirrors my own behavior. Like, I still use Google, but I'm increasingly not clicking on any links in Google because they're sort of the snippet at the top.

• 23:40Garry Tan?

  Or the first thing I think of is using ChatGPT with web or using perplexity directly.

• 23:45Speaker 00

  Yeah, exactly. I mean, if you want to understand the future, I think you always have to look at where the early adopters are. And so you say, you know, again, now if we go back twenty five years, right? If we go back to the year two thousand or nineteen ninety nine, you know, the early adopters were the people using Google. So at the time, you know, people are like, well, Google is just kind of this fringe thing that, you know, maybe techy people use or something. But at this point in history, those same people or those same kinds of people who are the early adopters of Google are now switching their behavior to where your default action, if you're looking for information, is, you know, ChatGPT or perplexity or one of these things. And even just, you know, observing my own behavior,

• 24:27Speaker 00

  I'll use Google mostly for kind of navigational, like if I'm just looking for a specific website and I know it's going to give the same thing. But it's starting to have that weird kind of like legacy website, like I'm using eBay or something vibe to it.

• 24:41Diana Hu?

  Even earlier sign was the drop in traffic for Stack Overflow that actually started back in twenty twenty two, even before ChatGPT, and this was primarily because of GitHub Copilot.

• 24:53Garry Tan?

  And they're down sixty percent this year.

• 24:54Diana Hu?

  Yeah.

• 24:55Harj Taggar?

  Yeah, the pool of people here have quite a good track record of predicting trends, right? If you think of, by the pool, I mean, just like technical startup founders at Y C. Like, I remember two thousand and seven, sort of, Apple was back on the rise, but you could tell because just everybody who was in a Y C batch was using a Mac. You could see the rise of AWS and like the shift from rack service to everything being in the cloud because all of the founders in the batch just started like using AWS. Same thing now. Like, I've spoken to a bunch of founders and just personal productivity, like they just have ChatGPT. Open all day that founders like say they're just constantly screenshotting their desktop and just like sending it to chat G P T if they need to debug something or figure out how to like navigate a government website.

• 25:39Harj Taggar?

  It's like one rather example, like I need to do set up some registration, like, you know, here's a screenshot, just tell me like exactly where I need to click in order to do this quickly.

• 25:47Diana Hu?

  Uh, the other thing that we saw last year in the summer batch was how so much of the batch is using cursor. And is one of the companies that's been growing a lot very quickly. Anecdotally, they hit fifty million in revenue.

• 26:01Harj Taggar?

  I think we may have mentioned this in another episode, but yeah, I can't think of another tool that's got adoption so quickly within a Y C batch as cursor. It just went from nothing to.

• 26:10Diana Hu?

  From one batch to the other, like up to like eighty percent of the batch using it from one to the other. When the previous batch was like single digit percent.

• 26:18Garry Tan?

  There are some people mentioned it felt like a like a technical conference a little bit, and a lot of people were trading notes on. How to hire the best engineers and a few people said, you know what, like if someone comes in. And I asked them if they use cursor or any code gen tools and they say no. Right now I can't hire them because they're not going to be able to be as productive as the rest of my team.

• 26:39Harj Taggar?

  I think that's an extension of um something Stripe started a decade ago, actually, like in general, engineering interviews and technical interviews. Most of the valley copied Google, I would say. It was like whiteboard C S problems, which probably made sense for Google or what Google was looking for. But I think Stripe were the first around like two thousand and eleven, I think they started doing this, where like. We don't really need you to whiteboard C S problems. We need you to develop web apps really fast. And so just give someone a laptop. And like the idea was you basically sit in a room and you just like build like a to do list app or whatever you can as quickly as you can. And you basically measured on your like your max output in those like two or three hours. And so I think if you follow that line through, then it's like, well, it doesn't really matter. Like if there's whatever tools they use, the question is just like the bar moves higher.

• 27:23Harj Taggar?

  Like you've got three hours, build what you can build. And it's like, you should be able to build a lot more with cursor than before. If you still believe that you're sort of looking for fundamentally, how clearly can people think or solve hard architecture problems, then you. Sticking to whiteboards.

• 27:38Garry Tan?

  What do you think this means for SaaS? Because, you know, one of the crazier things we've been seeing is that Klarna claims that they're not even buying new SaaS tools anymore. They're using CodeGen and not even hiring new engineers anymore, using their existing engineering set of people. They're just going to replace all the SaaS tools they use to run their fintech. And I definitely heard stories like that. One of the on-conference talks was actually specifically about that. This is a company, I think we mentioned before, is a company called Jerry that is now halfway to $100 million a year in revenue. But a few years ago, they were like still burning like $5 or $10 million a year. They had crazy customer support problems.

• 28:23Garry Tan?

  And basically, GPT-4 dropped, they implemented it, and then now it totally changed the way they hire. Like, the prompting itself is actually in the hands of their head of customer support. And so they have a PM, they have the head of customer support, the engineers made it, they don't have to touch it. It's mainly a prompt management and workflow tool. And it literally cut their customer support team and their budget for that side by half. And it turned a company that was not able to grow and burning $10 million a year to a profitable company that is cash flowing, that is also compounding its growth at north of 50% a year, which is like a dream scenario.

• 29:04Speaker 00

  Yeah, this is a great example, actually, I think, of the way in which AI is creating wealth, right? Because there's a whole category of businesses or products that would not have been economically viable or even possible to create before that are now possible. And so we've actually just like expanded the universe of possible businesses.

• 29:24Garry Tan?

  Yeah, it's never been a better time to be a founder, that's for sure.

• 29:28Harj Taggar?

  There's definitely been a vibe shift in.

• 29:31Harj Taggar?

  The attitude towards just building companies like uh this like start with like hiring a number of people, for example, if you're like certainly ten years ago, the general sense was that if you were growing, if your company was growing fast and revenue was ramping up, then you would go out and raise around and you would sort of a metric you'd hear a lot was like, how many people are you at? Like, how many people did you hire this year? How many people are you going to hire next year? So like a bit of a vanity metric. Um it just seems to me now like the companies that are reaching these numbers we're talking about, like a millionaire are trying to get to ten or fifteen or twenty are doing it with less people and expect to do it with less people.

• 30:04Diana Hu?

  Which is the new thing. This is why so many of them really haven't even raised a series A, which there's less need for for hiring a lot of people to do a lot of the operations on, or or maybe going to your analogy, Gary, the previous generation of startups had this concept of below the API or above the, below or above the API. So you had a bunch of people that had to kind of build and operate the API. Like if you had to build a business like Uber or Lyft, DoorDash, Marketplaces, you had to do that hyperscale of hiring lots of people.

• 30:35Garry Tan?

  The funny thing about that era was there's this concept that I think was probably appropriate for that era called blitzscaling. Uh, there was an entire book about it and the idea was basically, I think it was born out of this descending interest rate world while at the same time, like if you put more money into something, like you had these network effects. So if you played that out, yeah, you want to blitz blitzscale. You want to hire as many people as possible. You want to grow faster than everyone else. And then because of the winner take all dynamic, like the world capital markets were just going to funnel you tens of billions of dollars, hundreds of billions of dollars even to, you know, subsidize growth to be the winner.

• 31:15Garry Tan?

  And, you know, that was the game. And I think like, from what we can tell from all the people, you have more than three hundred founders right here, sort of sharing their stories. And I don't think I heard blitzscaling or I'm trying to hire as many people as possible, like at all. Nobody is bragging about, hey, you know who I'm hanging out with, these unicorn, you know, I'm going to be a unicorn. Like, people are literally not bragging about that.

• 31:38Speaker 00

  Yeah, it's all about leverage, right? Now, now the the real thing is how much you can do with a little bit of resources because we have these magical tools that give us superhuman leverage.

• 31:49Diana Hu?

  Part of it is like this, there's gonna be a longer tail of businesses that are possible only now because of AI. And this longer tail is gonna be also fatter. It's not just companies are doing twenty, thirty million revenue, but more than hundreds of millions of revenue. And it goes back to the episode when we talked about vertical fast. There's just more willingness to pay for this new category that people are still trying to figure out how to price. That's why there's just so much willingness to pay because people want it. It doesn't go just on the software budget for a company is there's budget from the AI chief officer or something. I don't know if that's like a title that has come out yet, but.

• 32:25Harj Taggar?

  Aaron Levy made this point to you that, I mean, one thing I'm certainly noticing is that the, the companies that are hitting these big revenue numbers trying to sign these contracts. It's actually sort of usage based pricing.

• 32:36Harj Taggar?

  And the detail, like, it's not necessarily they're paying per use, but the pricing is tied to like how much you use the product. Which is definitely how, it's close to how you would think about it as like selling services and software per se.

• 32:47Speaker 00

  Obvious ROI, right? So, so the problem a lot of times with selling a product is, is the customer doesn't really know if they're getting the ROI. And so that makes for a long and painful sales cycle. But if you're able to drop in something that pays for itself in the same month, that's an easy sale, right?

• 33:03Harj Taggar?

  Yeah.

• 33:04Diana Hu?

  I think the way they've kind of priced it is more like services and is really akin to this is how intelligence is getting priced.

• 33:10Harj Taggar?

  So another, on the, on the spectrum of like people thinking not worrying about just the big picture. Is AI gonna make us all obsolete on one end and the other end, like existential, like philosophical conversations? Some of the stuff I thought that's interesting in the middle is it's just hard to predict the timeline of the tools themselves. So there was some interesting talks about like RAG, for example. I, I think Sam maybe seeded this with, with his talk about like, if you have like infinite context or huge context windows, do you even need like RAG or retrieval tools at all? And I think that's like, that's the kind of thing where it's like, it's as a startup or a builder right now. I think people are more concerned about, am I using the right tools and like, is this gonna still make sense in three to six months?

• 33:51Speaker 00

  I think that's like actually a direct consequence of like, you know, if you're in AI lab, you're like on the frontier. And so how you know that you're.

• 33:59Garry Tan?

  Thing is working is actually like your model is bigger. You're like farther along on the scaling law. And so when I meet people from AI labs, like they almost all talk about bigger, better models, but they're model makers. And then obviously, you know, we also spend a lot of time with very scrappy founders who have very little capital. There are just as many talks sort of on the other side, which was, you know, I went to one that was very much about systems level programming. Like if you want to have, I think it was Tavis.

• 34:31Diana Hu?

  So Tavis is building this real time AI avatars with video and audio that are very realistic. Part of the trick is they got it to very low latency.

• 34:41Garry Tan?

  Six hundred milliseconds, which is really fast.

• 34:44Diana Hu?

  Which was even too fast that some of the customer, oh, no, no, it's too fast. It's a bit uncanny when it's too fast.

• 34:49Garry Tan?

  It's like now it's being rude. It's just interrupting me every time.

• 34:53Diana Hu?

  So a lot of the builders SDK for other companies. So a lot of the products that are getting built with this Zoom video interface with another human, it is using them.

• 35:05Garry Tan?

  So I love their talk because it's a good illustration of like, yes, like the labs are going to continue to do their thing, you know, and maybe on a more fast timeline than we even imagine, like, you know, nine months, 18 months, like, you know, maybe it's even every three months. There's sort of these like breakthroughs. So if I had to guess, like, that's sort of, you know, when people are in their heads being like, why should I do any of this? Because OpenAI models are just going to be infinitely smart and I should just lie down on a bed.

• 35:34Garry Tan?

  What I would say is like, I'm actually heartened by all the stories that I heard, like, will the models change? Will the technology change? Will, you know, will Tavis change its stack? Like, yes, like they've already seemingly rewritten their stack multiple times to take advantage of what's been going on. Their product has only gotten better in the marketplace as time goes on. And then what will it look like? Will there be like, you know, a mono model? Maybe not. The same AI labs today that are talking about, you know, there's going to be a trillion token context. It's like, man, how much is that going to cost? Ultimately, engineering and systems like those matter. Those are actually the most valuable. Valuable things right now. And then along the way, you're going to have these golden evals. I don't know, I hate to bring in, you know, consulting speak, but like, what are the moats, right?

• 36:19Garry Tan?

  And the moats in the end are a brand. It's your data that no one else has. Sometimes it actually literally is caring about customers that, you know, the giant company will never care about, right?

• 36:32Diana Hu?

  Actually, I think the other mode is going to be ultimately startups move quickly. One of the remarkable things that I observed, a lot of the founders actually had rebuilt a lot of their tech stack to be with the latest. They were very willing to, oh, this particular approach to RAG doesn't work or vector database, throw it away, PG vector became the better thing, let's use that and just throw it away and use the best thing. So what was fun to see is I think the best startups are going to be the ones that can build the fastest and be willing to be at the bleeding edge and be willing to reevaluate assumptions of what's the best approach. And I heard a lot of how, a lot of how things got built. They redo it or they do it again with the best, with the latest and best.

• 37:15Harj Taggar?

  Which would also, another reason to explain why they're securing enterprise contracts and these big contracts faster than ever, right? Like, big companies have never been great at continuing to build great software. But now, like, yeah, if you need to constantly rip and replace the tool you're using every three months to be like at the bleeding edge, like it's going to take three months to get like the meeting schedule to discuss

• 37:37Harj Taggar?

  whether we should reevaluate.

• 37:38Diana Hu?

  We're going to plan in the next sprint. It's going to take whatever.

• 37:40Harj Taggar?

  Yeah, we can get to that in like, you know, 2029 for sure.

• 37:44Diana Hu?

  Yeah, and these companies are getting to the six or 12 million example. I know they actually have rewritten a lot of their tech stack a lot of times. And the architect, every time I actually talk to them, oh yeah, we threw away that thing that you, that we told you is like, it's this new way of doing it. It's like, okay. And that's like every month, every other month.

• 38:02Jared Friedman?

  From talking to founders this weekend, what was your sense of the overall vibe?

• 38:07Garry Tan?

  It's pretty exciting. I mean, I, I don't know that there's ever been a better time. You know, again, just kind of looking back historically, really the foundation of Y C, if we jump back not 10 years to when we were starting OpenAI, but 20 years to the summer founders program.

• 38:19Speaker 00

  The thesis behind why Paul and team started YC was the realization that it was getting easier to build startups. You didn't need to raise a mountain of capital and hire a giant team that actually just a couple of smart kids could build a web app.

• 38:37Speaker 00

  And that trend has only accelerated now with AI, where you can build an entire $12 million business or something with just a handful of employees. And so it again goes back to technological leverage enables people who have sort of ambition and insight to do incredible things.

• 38:55Garry Tan?

  Well, that's all we have time for today, but we'll catch you next time on The Light Cone.