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#2281 Garry Tan: Y Combinator Startups Growing 5X Faster – Here’s What Changed
Garry Tan of Y Combinator
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- [00:00]
A
Hey there, freedom fighters. My name is Andrew Warner and this is a new series for me. It's called the Next New Thing. Here's what's up in this interview, then an intro, then we'll get right to it. How do you compete with these bigger players?
- [00:13]
B
We're seeing routinely YC companies with 10 or 20 people get to 10 or $20 million a year in revenue in 10 or 20 months. That's like literally never happened before in software.
- [00:24]
A
Talk to me about how you use AI in your video creation.
- [00:28]
B
I took the scripts of all of the top videos that I ever made for my YouTube channel. I throw it at this prompt and then it would generate these beautiful three act narratives. I could have a new 10 minute script ready, whereas it normally would take me like several hours.
- [00:44]
A
How are you changing Y Combinator?
- [00:46]
B
Let's stop competing with all the other VCs. Let's be their partners.
- [00:49]
A
I'm gonna ask you to do something you're uncomfortable with.
- [00:51]
B
Oh yeah, what's up?
- [00:52]
A
Gary Tan is the president and CEO of Y Combinator, the Next New Thing. Why don't we start with the case Tech story? Because I feel like there is a before AI for that story. An AI experimentation. And then once it took AI, everything changed.
- [01:10]
B
I worked with Jay Keller, the founder of Case text, back in 2012, 2013, when he first went through Y Combinator. And that was also my first stint at YC as a partner. And they were sort of doing Basically your Web 2.0 for law. So literally what's happening with case law and new legislative? I mean, whether it's legislation or literally judgments, like all of the documents that the legal profession throws off, they would index, which would help you understand the law. And that was really what they built for something like going on 10 years, it grew by SEO. And Jake's both a great technologist and a great lawyer. And so he was really able to go into that market and make something based on what was happening in society and in tech at that time.
- [02:03]
A
I think there was also like a Q and A component of this, right? So they could go and talk to other lawyers. We're going to get into how things get better. Why wasn't that enough?
- [02:11]
B
Some things can become huge and drive billions or tens of billions of dollars in revenue every year. And some things really could only get to. They only provide value that, you know, and then you multiply it out by all the people who need it and that might only total up to 10 or 20 or 50 million. Like that's weirdly quite Common. I think a lot of founders are worried about that early, but my sense is maybe it's premature worry because embedded in that is also the case text pivot, the that they got users and an understanding and a useful corpus of data, all of which turned into a tremendous moat for them. Literally right at the correct moment, as technology itself shifted, something that could only make tens of millions a year could suddenly become something that could make hundreds to billions of dollars per year. And that was the dawn of the large language model in 2023.
- [03:08]
A
Tell me that story of, like, how they came up with that.
- [03:11]
B
The cool thing about YC was that Jake basically had access to early versions of ChatGPT, GPT3. These were sort of toy earlier versions of it. And they were certainly astonishing and interesting, but they were not useful yet because the LLMs actually would just hallucinate. They were early in the journey. So there wasn't enough data. The number of parameters, the sort of size of the models was too small. And that's what Jake found as he tried to use large language models to do a lot of the things that you and I take for granted today. Right at the dawn of this stuff, it was not that useful. You know, it was sort of a horseless carriage, if you will. You know, it was an oddity. You could look at it and say, well, maybe this will work, but it's mostly a toy and nobody will actually use it yet, possibly ever. Right. And certainly when I first saw it, I'm embarrassed to say, like, even as an investor and technologist myself, it's like that's. That was the consensus at the time. And at that moment, at the dawn of large language models, that was correct. Like you couldn't use it for useful things yet Jake being a great designer, engineer and lawyer, he tried really hard to make it work and it would hallucinate. And, you know, he also was operating in an area that in particular really had high sensitivity to hallucination. You get one thing wrong and you're fired as a lawyer. So, you know, his. His particular space was fascinating to me because it particularly could not withstand any hallucination. And as technology curves and cost curves go, this was something that I think surprised everyone. If you were Greg Brockman or Dario Amade, at that moment, you started internally, you were talking about the scaling laws and that the loss function was going down as log linear to the amount of data and compute you were putting in. And that was an astonishing realization that, like, there was a path to potentially AGI or asi. The rest of Us on the outside had no idea. And I think Jake also didn't have any idea. But because he was in the YC community, OpenAI itself was a spin out from YC research by Sam Altman.
- [05:33]
A
So then he starts adding this on once it's ready, once it's ready for lawyers. What was the original use and then how did it take off?
- [05:42]
B
I believe he basically started using it for being able to answer specific questions about legal cases. And once he got access to GPT4, he realized that if you cut down the size of the question to small enough, and today we call that context engineering. But at that moment he realized if you asked a very long ranging question like is the defendant guilty or something, you know, it's like such a big question that even GPT4, I mean you could argue that some of the reasoning models today are actually much more capable of doing it. But back then you didn't have multi stage like test time, compute reasoning at that moment. If you chopped it down to a bite sized chunk, like you gave it some amount of context that a human being given the same context and the same prompt would answer in a certain way. He found that he could, you know, given inputs and outputs have output that was usable, useful and reliable and not a hallucination, but it required you to chop that down into a particular small enough step. I think of Jake a little bit like the first man on the moon. You're like, oh, you can chop it down and then you should actually have tests for a bunch of different inputs and outputs and you should have evals that actually give you a sense and certainty about specific tasks. So you would sort of do tailored time and motion study of exactly how a lawyer might, you know, a lawyer often has to do a timeline, for instance. So what he would do is like chop it down into what would I do as a human being? Well, I would start skimming each, each chunk, whether it's a sentence or a paragraph. And then I would try to score it based on whether it was noteworthy or not for do I need to put it in a timeline or not? And so you can imagine, I mean he's like sort of very logically creating some of the first ways to do prompt engineering.
- [07:50]
A
So he's taking this technology and he's finding a way to make it useful by thinking almost like a human being, a little bit like a machine and then coming up with the answer. He now has a better tool that gives me two questions. What happened to the business? Did it immediately go from like sales cycles of I Think I saw you say in a video, a year or more to now, a month or less.
- [08:10]
B
I guess the really interesting thing was, you know, he built the first versions. It could do like, let's say it could do a, it could comb through thousands of pages of documents and give you an accurate timeline of events, for instance. And that's something you would hire a legal analyst or associate to do. And it would cost thousands, tens of thousands of dollars. Right.
- [08:30]
A
So instantly you can say, I will save you this much money direct to the bottom line, do you want to buy? And it's becomes an easy answer.
- [08:38]
B
I think that was the feat of strength. Like they took the corpus of Enron emails, for instance. And I think the example Jake likes to use is you could ask questions about emails where they're, you know, it involved like high nuance things. Like you could ask it about ironic jokes that the CEO had made and it would be able to like discern like, oh, they made a joke about their fraud in this particular way and it would like find the reference for you. So you know, that would, that was sort of the demo that they would show to lawyers and it would just be so astonishing that people would say, I need to buy it right now. This is the future. And it turned out to be very like not just a little bit correct. I, I think I'm still astonished day to day, especially as new model releases come out.
- [09:25]
A
That explains why every time I see what it does, I will see and it detects sarcasm. And now I get why that comes up. Then the other thing that comes up for me, Gary, is we keep hearing that maybe it's useless to create anything in this space because the big companies are just going to take it on. Right? You can imagine a world in which ChatGPT does all this or Google's Gemini and its consumer grade products that people are using to figure out what to do with their withdrawals, with their weekend plans, that it's natural for them to then ask the same question for it's not an unfamiliar tool. How do you, when you do this, how do you compete with these bigger players?
- [10:01]
B
Yeah, absolutely. I mean, I guess I have two answers. One is obviously the one that we're, you know, at Y Combinator, we, you know, we believe in that. We see it happen. Like we just see small teams of people go out into the world and create, you know, they're obviously using these incredible frontier models, but they're adapting them to the very specific things that real people in the economy actually need. And so they're not Necessarily glamorous scenarios. There are customer support scenarios for H VAC Consultants, for instance, this fragmented industry, but a very big industry, and they're taking, they're building software. There's a company called Avoca that we work with at YC. They're doing exactly this customer support for H Vac. But V1 of it was basically Servicetitan. So Service Titan is a incredible public company, but they're basically software. And H Vac consultants and firms spend about 1% of their dollar wallet. You know, for every dollar, for every hundred dollars they bring in in revenue, they spend about a dollar on software like Service Titan, but they spend five or six dollars on actual people picking up the phone and doing scheduling and doing all that stuff. So the wild thing that we're seeing is that if you like scope what you're doing and make the thing that is perfect for that set of people there, you can't just take ChatGPT and have it do this type of work yet. I mean it's entirely conceivable eventually, but it hasn't happened yet. And while that is still true, people are sort of building the next service titans. And then the wild thing about it is Service Titan is incredible business. But then you could have something that takes over that 1% and then expands like 5x or 6x bigger. So that's sort of why we're seeing routinely YC companies with 10 or 20 people get to 10 or $20 million a year in revenue in 10 or 20 months. And that's like literally never happened before in software. The average rate at which YC companies grow their revenue during the YC batch, which is 12 week process, is 10% per week on average. I mean some of the batches have been growing 15, 20% a week, but it's been at least 10% a week for more than a year. And so there's something in the water, like there's something happening.
- [12:37]
A
And what was it before? I thought it was 10% a week.
- [12:40]
B
Through going, oh, you'd have one or two companies grow 10% a week. That was like the aspirational, like if you could do it, that would be what good looks like. And then now on average everyone does that.
- [12:53]
A
So it went from on average 1 to 2% to 10 to 20%.
- [12:57]
B
Yeah, I think the average was probably close to 2 to 4%. And then now it's consistently 10 to 20%.
- [13:04]
A
And this is in revenue.
- [13:05]
B
Yep, in revenue. So. And you know, it all goes back to the case tech story, right? Like before, it's like, yeah, I know I need to replace my software, you know, oh, I'm still using SAP or I'm using whatever I was using. I'm still using spreadsheets. Like, it's, you know, having better software was much more of a nice to have. Like, it's something that you felt like you needed to do. And then today it's becoming, oh, like I see a demo, it's really impressive. I could see how that would hit my bottom line or basically create a better product or service immediately. And then, yeah, I need it right now. When can you start? Right.
- [13:46]
A
I totally feel that. I feel it in the air then. It kind of brings me back to the conversation that I had recently with the founder of Read AI, this is David Shim. And I said, when I do sales, I want a note taking app that keeps guiding me towards closing a sale or at least analyzes me afterwards based on a sale.
- [14:05]
B
That's a great scenario, great example.
- [14:07]
A
And he goes, andrew, that's not the way it's going to work in the AI world. What you're going to have is one tool, one note taker. He'd like it to be, obviously, Read AI that does everything. And if you say I'm a salesperson, it'll customize some of the feedback that you get for sales. To me, if I even have to customize it, it's an extra step. And so I've been seeing this, Gary, in conversations with builders. Some are saying AI doesn't need to be customized at all. And I see you squint as I say that. So I think that maybe you have a strong opinion here and others are saying, absolutely, what you are is go down to the level of H Vac.
- [14:39]
B
Yeah, I think that we are sort of, we might be at a moment where it's too early to tell. Obviously the stakes are very high. But if we got to a point where AI is truly, you know, not just AGI but asi, it can like far exceed that what hu of what humans can do. All bets are off at that point. Right. So I don't know, I feel like this is almost like the reverse pascal's wager for AI a little bit. He's like, well, it's entirely possible that ASI happens and you know, what some people think will happen happens. But you know, the society that we will need to live in will be reconfigured like in such a radical way that, you know, will there be jobs? And at that point, the hope is that if we have actually access to clean and Clean solar and wind and maybe even fusion. With Helion and things like that, over a 30, 50 year time frame, society can reconfigure into one that's really focused on abundance. When you're talking about startups and competition in markets, we still live in a market economy that is driven by should I buy X or. Yeah. And you know, I think it's like you're certainly in some sense a flawed system. On the other hand, it's certainly the best system that we have. Like the invisible hand.
- [16:07]
A
I want to know which direction you think, but I'm looking at the list of companies that you at YC have helped launch just in the fall of 2025. What I'm seeing here is there is a focus. It is companies like where is it? Market silver bullet for trade compliance. To give you an example of what I see, I see another one, Bluma automating short form video ads at scale. So you really are still saying I'm going to be focused narrowly on a vertical. Am I right? Or am I just looking at a handful and drawing?
- [16:40]
B
This is also about like making individual founders successful, right? I guess, famously. I think at some point Sam Altman came out and was, While working on OpenAI, he was, you know, sort of rethinking whether like the classic YC advice was correct. I hadn't, I mean obviously we're friends and we like hadn't like we had some exchanges about it. You know, I think that he's sort of changed his tune a little bit in that he's seen now that like AI, like all the startups out there using his APIs are sort of his commercialization arm. And that's not a bad thing, right? There was a time when I think he said he just wasn't sure if all of the advice around make something people want and like being lean was quite the right thing. And then to me, I think Looped had to be lean. You know, a lot of people who start really huge companies had to start companies that were much more specific. Elon Musk had to start Zip2. I think the reframe for us at YC is that we actually want people to be directly in control of their own destiny to the extent they can. Can we do that when you're starting a company?
- [17:55]
A
The thing that was exciting for me is like I'm looking, I'm holding here. This is well told. It's a mug by someone who I don't think 20 years ago could have created a mug. It's beautiful, it's got like the city that I'm in, he sent me a bunch of them. Gary one with every city that I've done mixergy in, which has been a lot cool and I think about him a lot because that kind of entrepreneur couldn't have existed before, but now they do. You are a video guy. Beautiful videos. You always had good taste in video. I don't think you would have existed, let's say 20 years ago because it would have been forever to videotape, to edit, to put your spin on it. Do you think the same thing now is going to happen with software that more people are going to be able to create it and it's going to be sustainable or it's going to give them enough money to sustain their lives?
- [18:42]
B
Absolutely. I mean that's certainly my hope. The reverse is like too dark to, you know, if anything. Like that's some of the reason why we spend a lot more time in dc.
- [18:51]
A
But Gary, even if it's the same like I, I'm wondering that because of all the vibe coding apps, I keep seeing vibe coded apps from people, will they turn into something significant or is it going to be like Most of the YouTube videos where there's no business from it, it's just fun to create? Or does that even matter?
- [19:07]
B
My argument would be, I mean especially vibe coding. The Claude code team apparently writes 95% of their code is written by Claude, which means very directly that each engineer working on Claude code themselves is doing the work of 20 people. That's sort of direct quote from a recent Like Lenny podcast with one of the co founders. And so I think that that's actually the good news. You know, I think if you look at tech across like 10, 20, 30 years, um, it's actually that like the access to good software is incredibly inaccessible. And one argument I often make is if you use an iPhone, you probably have hit bugs in Apple Calendar. And it's like very frustrating because come on guys, like this is the built in thing, the Apple, the iPhone, like the iPhone is the Apple is like one of the most dominant tech companies in the world and yet they cannot find good enough software engineers to fix the basic bugs that still exist in Apple Calendar. And so that's been true for time immemorial. Like if that's true for Apple, how could you possibly imagine an H Vac person ever getting access to good software? And that's the difference today it's like, hey, you can have it now and it can be customized to you. And if anything, like the funniest Thing is, if AI and Cogen gets even better than it is today, people can like, that might be one of the vectors by which H Vac people compete against each other. You might even choose the one that has the best status and the best app that can tell you exactly when things are done. Or if someone uses, they use repl.it to create software or maybe there's a vertical version of repl.it just for workflow for managing your workers, right? Like anything that you can imagine, it could actually create a better product or service. And then net net. What this might mean is that just like everything that we get in, you know, our day to day lives is just better, faster, cheaper, and then more is more actually like it's actually good that I mean, sometimes to link the abstract to the specific. I'm like, a good example of this would be I would love for every apartment in San Francisco, for instance, to have dishwashers and washing machines, right? Like in a weird butterfly effect sort of way. Like if you think about people doing better work, more meaningful work, doing it on time and at the right time for a better price, like that sort of, that's how the market creates like higher, basically higher standards of living, right. And so that's sort of like what I hope is, and what I think will happen is that as long as people can start businesses, they can make better choices, they can make better products. This is actually a much bigger engine for making our day to day better.
- [22:13]
A
You're imagining a world where instead of having HubSpot and Salesforce and Close and a couple of others that are really big, HubSpot, Salesforce, then you got the mid. You're imagining a world where there is a CRM for podcasters like me, a CRM for H Vac, in fact multiple of them. And so the revenue of Salesforce, I imagine, would then be spread across a bunch of companies and there would be a bunch of companies whose founders are not as wealthy, but they are making a strong enough living. That's the world you see.
- [22:45]
B
That's right. And then on the flip side, like even if they. Well, I realize like, you know, that's also often not how it works out.
- [22:55]
A
I also wonder if that's great for you because what you're looking for at YC is not to have a bunch of small companies where the founder can live a good life, maybe buy a second home, but where they're building the, the Marc Benioff size successes, right?
- [23:09]
B
Yeah, that's right. I mean, I guess YC is funny because even if someone doesn't end up making the salesforce size thing like they often sell their. I mean that was true for Posterous. My YC startup ended up selling to Twitter. Our YC batchmate back type is Chris Golda and Mike Montano's company. They sold to Twitter and then that team ended up creating Twitter ads. I think like our old teammates, imposters ended up making the, you know, you know, making the first Twitter, Twitter mobile apps and, or working on that team. So I don't know, there is like a creative destruction aspect and then on a sort of day to day career basis like it's better for people to become founders, learn how to create things for other people and then either, you know, you manage to get product market fit and you figure out a moat so that you can be, you know, as big a company as possible or even if you don't like, everything about your life and career moves ahead by five or 10 years faster than it would have been. Or you know, we have lots of friends who instead of starting companies, they stayed at Microsoft and it's better to be directly in the face of real users and shipping real code and product and then learning how to support that because that's just actually valuable. And I think there are lots of other jobs out there that are slow moving, bureaucratic. I actually sort of wonder like I was hanging out with another investor who sits on a lot of boards and we were thinking like, man, there's increasingly like a lost generation of people who work in big tech. And I think even at YC, like we are seeing the rate of 18 to 22 year olds at YC is up by more than 100% year on year. The rate of 22 year olds to 25 year olds is up about 20% and then the rate of 25 to 30 year olds is actually down by like 10 or 20%.
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A
Why?
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B
And a lot of those people started their careers actually during zirpins. So they're actually sort of, you know, hanging on for dear life at both startups. And Fang and those are also like, funny enough, some of the people who are the biggest AI deniers, like they just don't believe that the sort of revolution is happening.
- [25:40]
A
You're saying they got such a good job where they were, they don't want to lose it and go and kick off and start something different. Got it.
- [25:48]
B
Whereas the, the young generation, like they're incredibly hungry right now because Fang is not hiring. Like the zerp jobs are not there.
- [25:55]
A
And a lot of them I think are playing More with this. It used to be that playing startup was fun, then it became playing YouTuber was fun. But playing startups felt like now you're becoming the man. But now you see lovable and cursor and all these tools and you say, okay, they're making it more accessible, let's play with it. And then you lean and create something. I told you earlier that I would give you a great example of why combinators like inner Access. David Rogenmoyser he is a guy who started out with proof, a thing that was going to help e commerce companies. And I love after he got into Y Combinator he goes, what we're going to do is we're going to revolutionize E commerce. We're going to make every Shopify store customized based on who the person is because we're going to have a tool that goes across all of them. I go, boy, Y Combinator really gets people to think beyond the tool. Before he had a little frickin widget, now he has this changing the whole thing. And then suddenly he, he created what became Jasper and it's because he got to see the original OpenAI tools and he said, okay, I gotta pivot everything. We're gonna create an ad creation tool that uses AI. And then he changed it to based on user needs to create a writing tool for everything that is the inside Y Combinator access, right?
- [27:06]
B
Absolutely.
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A
What is like how do you stay in touch with people to keep guiding them after the time that they're in the program?
- [27:13]
B
Oh well, I mean these days everyone who gets into yc they have one particular primary partner and obviously when you apply it goes into sort of this giant pool. But then we have 15 equal partners on the investing side who like we basically are in there trying to fish like we're in there with our nets. Like let's take a look, let's watch the video, let's try the demo, let's read everything about the founders, what they've done and what do they know about their users, about the product. And then we try to figure out, well, who do we meet? And then anyone you choose, you meet. And then when you meet, it's up to you whether or not you accept them. And then when they're in like you always have one at least one person who is sort of like your investor at yc and that always, even after.
- [28:03]
A
The life of the company, you're still getting on calls with them a year, two years later.
- [28:07]
B
That's right. So you know, basically I think the bad version of thinking About YC is like, oh, it's like a summer camp and you have a camp counselor and you never talk to them again. And then the good version of it is like, oh, yeah, YC partners are, you know, actually renamed their title. It's like not group partners anymore. It's actually general partner. And we're actually investors invested in the future of the company for the life of the company. Right. It's like having your best angel investor who is there for you all the time, and then that's from for many years. So basically, on our end, the best thing we can do is like, understand the business, understand where the founders are coming from, who their customers are, and then just ask questions. It's like, could this be bigger? What's the most interesting thing that you've learned about your customer? What are things that get people promoted? What are things that are resonating? Let's double down on those things. Like, let's be super frank about what we've tried. That doesn't work. Like, if we're spending money or resources or, you know, people time or your time on it, maybe we, you know, we have that or set that to zero. And anything that's working, like, let's double down on that. And then you're just having someone who's outside of your day to day who could be a sounding board. I mean, there's that. And then honestly, the batch itself is perfectly designed to help. You have not just your partner, but dozens of other people who are all like, the outcome of a top 1% process. And they sort of help each other. Honestly. I mean, that was true for me. Like, when I went through yc, we got to know the founders of Heroku and they helped us raise our seed round and gave us a lot of advice about scaling. And then what's funny is about like, six months after we went through yc, they came to us with a problem which is like, we were becoming one of the biggest rail sites on the Internet. And they were worried that they were having some sort of scaling issue where they thought that our code base might not be able to run on Heroku. So they said, hey, would you guys, this is a big ask. Would you be willing to give us your GitHub access and code base? Can you imagine meeting someone at TechCrunch Disrupt and asking them for the code base? You'd never do it. But, you know, at yc, they gave us so much advice and we had really become friends with them. We said, you know what? Like, yeah, here it is.
- [30:32]
A
Which company Was it that you gave.
- [30:33]
B
Them the code base of Heroku?
- [30:35]
A
No. What was your company that you gave?
- [30:37]
B
Oh, Posterous. Yeah, Posterous was that big. Yeah.
- [30:40]
A
I'm glad to hear it. I told you before we got started, I freaking love Posterous. I'm still like. I feel like that was such an elegant app. It allowed you to post from anywhere, so it encouraged you to create a lot of. All right, talk to me about how you use AI in your maybe video creation.
- [30:57]
B
Yeah, absolutely. I mean, one of the things that we've been learning about, I mean, I think a lot of it came from talking to Jake about how he thought about breaking down the problem. You know, the cool thing about prompts is that it's actually an intelligible version of fine tuning the model. One of the things that I did recently, I took the scripts of all of the top videos that I ever made for my YouTube channel, and I just fed it in and the prompt to Gemini because it had long context at the time. I think a lot of other people have long context too, but Gemini 2.5 was extra good at this. You could feed in a bunch of scripts and say, help me extract the most salient features that are common across all of these scripts. And it actually extracted out, like, here are a bunch of the things that you did in those scripts that you can. That, you know, hook hard, hook fast, have an inner game lens. Think about what the founder and creator psychology is. Have sort of like a sentence rhythm of like a claim, a brisk explainer, and then a vivid example and then a takeaway. Right? And like, so these are all things that, like, you know, it just sort of figured out, like, this is sort of what goes into a script that performs very, very well for you. And then I took that, and then I'm coming up with sort of ideas for new YouTube videos all the time. And so then I would start. I took the prompt from this. The prompt is basically given a set of ideas. Write a script in this format. And then I started just taking ideas. You know, maybe I see it on X, or I'm sitting with the founder and we're talking about, you know, whether to pivot or not. And I just, like, could just scribble down, like, here's a bunch of notes, here's like a set piece idea, here's like a tweet or a video clip that I want to use. And then I'd throw it at this prompt, and then it would generate these, like, beautiful three act narratives that, like, look like. It was almost as if I, you know, it would take me normally probably like two, three hours to write out like the script for at this quality. And you know, I would have it when it first did it, it was like not that great, but iteratively what I would do, and this is something that anyone can do, is as you use the script, like you should like label it and number it and I would use the prompt, I labeled it and then I gave like what I wanted the next video to be about and then I would work with it. I would like edit the, you know, I would often tell ChatGPT to use a canvas and I would go in and sort of like instruct it, similar to if I had like a junior writer who was writing for me. And so I would like sculpt it into what I wanted. And then you'd have the output of an incredible script that you could have basically on my phone in between on a commute or something, I could have a new 10 minute script ready. Whereas it normally would take me several hours of just breaking my brain to write it out in my voice.
- [34:16]
A
Can you edit this or is this a bad first draft that you then get to go and put your spin on? Or are we talking about 80%?
- [34:23]
B
Oh, it's usable. Like I mean basically, yeah, well, I mean initially it was bad. And then what I would do is do this process, get the script to where I felt really good about it. And then at the last point I would say given what we did in this session to improve the prompt output, the next version of the prompt. And so now I've done this about 20 or 30 times. And so now I have a thing that has all of the different tricks. Like I even, you know, it started off as just like here's a format, here's sort of specifically how a good video might work. And then now what's crazy is because of the, once the reasoning models came in, now I can actually give it a grab bag of tricks. Some things that, I mean what's funny is like it's not entirely the AI coming up with it, it's not entirely me coming up with it. Like in the course of co writing something like 10 or 15 scripts, like it's figured out all of these grab bag of tricks like a pop culture cold open a 15 to 45 second film TV news clip that mirrors the thesis before the hook, like an authority pillar, like I love quoting Paul Graham or Alan Watts or Naval Ravikant and.
- [35:40]
A
Myself and it will find one of those people and maybe you've confined it to the type of people you want.
- [35:44]
B
Yep.
- [35:45]
A
I'm asking you to do something you're uncomfortable with.
- [35:47]
B
Oh, yeah. What's up?
- [35:48]
A
Share this.
- [35:49]
B
Oh, sure.
- [35:50]
A
Can we give one of these sessions to our people?
- [35:54]
B
Yeah, absolutely.
- [35:55]
A
Okay. I'm going to follow up with you. I would love to be able to.
- [35:57]
B
Give that link in the description for my prompt.
- [36:01]
A
Yeah, I'll put it up on X on everywhere else. I love seeing how people do it. It's so interesting to see how other people have these conversations. There was a period where OpenAI was trying to make these more public, and I get why they wouldn't. People were revealing too many things. But to see how other people prompt is a real eye opener.
- [36:19]
B
Yeah. And what it's taught me is, like, I think that ultimately almost anything that, like, you rely on humans for, like, you could probably do better. And these things are not writing it for me. Like, they're helping me, and I'm working with it. So it's a little bit more like a co writer. I still think that if you just say, like, write me a script and you give it no direction, it's going to be bad. Like, you know, you as the writer or creator, ultimately have to inject your voice into it. Like, and if you don't do it, then. Then it's slop.
- [36:54]
A
All right. One of the things that I've noticed that you've done different with Y Combinator is you added this sense of, first of all, video first. You're damn good with video. Always were. With visuals, you added a sense of cool. Like, I really thought once you started leading Y Combinator, that you were gonna dress differently. And then I'm looking at you, and I thought, he's not doing these videos anymore. Now someone else is gonna do the videos. He'll hire someone. But you've got these shoes, these sneakers that are on point all the time. You always have. Like, even for this conversation, I don't even know if you know that I'm publishing the video. Cause in the past, I didn't publish our videos. This I'm publishing, but it didn't matter. You set it up beautifully for me. What am I not seeing? This is the. Obviously the exterior stuff. This is an indication of, like, you modernizing the way the Y Combinator communicates. But what am I not seeing underneath the surface?
- [37:39]
B
Yeah, I mean, I guess I felt really, really inspired and just like, sort of filled with fire, actually, by One of our board members at YC is Brian Chesky of Airbnb. And so he was part of the selection process when they were looking at candidates for this job. And the second I got in the role, like, I mean, my board is Brian and obviously Paul Graham, Jessica Livingston, the original founders, Carolyn Levy and Harshtaggers, actually just joined as an observer recently. And so these are sort of like the stalwarts of yc. And then they basically just really enabled us to. I mean, think about it from first principles. Like, what does. I feel like yc 1.0 was the creation of Paul and Jessica and Trevor Blackwell and Robert Morris. I mean, the original founders of YC really set, like, the vibe and the course and, like, what YC is about, and they built it up. And then the second decade, you know, was really with Sam and Jeff, and Sam created. He took Google and turned it into Alphabet. And it was, you know, a lot of different competing things that all, like, sort of raised the ambition level of what YC was meaning, like, the nonprofit.
- [39:02]
A
Aspect and all those things.
- [39:05]
B
You know, working on continuity as a separate fund, like, all of these things basically broadened the aperture of, like, what YC was.
- [39:14]
A
Okay.
- [39:15]
B
And then in full transparency. I feel like when I came back, like, we explicitly decided as a board and as, like, the sort of steering body of what YC was supposed to be, we said, you know what? Like, Alphabet is great, but we're gonna go back to being Google, and it's easier. It's like, a thousand times easier to be Google than to make Alphabet work.
- [39:37]
A
What was one of the hard things to cut back on?
- [39:40]
B
Obviously, what's great is, like, all the people sort of involved in continuity are working on their own funds, and they're doing great, and we think the world of them. But, yeah, that was probably the hardest thing. You have a great team that's executing on a strategy. And then at some point, we realized actually, like, YC should be about the initial batch. And, like, rather than treat, like, group partners as kind of like camp counselors, it's like, oh, no, no. Those people are actually the partnership. Like, we're an equal investing partnership, similar to Benchmark, but we have 15 people. Like, that's actually the core of what YC is. And then we also have incredible staff. We have the world's best media team. We have the world's best software team. And those are sort of like the pillars of yc. And then it's just so much simpler. It's just like, let's do what we uniquely do the absolute best. Let's stop competing with all the other VCs in, you know, let's Be their partners.
- [40:40]
A
Actually, what was the problem with competing? When you say competing with other VCs, you mean founders would raise money from Y Combinator and then you say, okay, we can also give you the next round from Y combinators.
- [40:50]
B
And then what does it mean if you don't get it?
- [40:52]
A
And that was always an issue if you don't get it. That was always like a negative signal potentially. What other problems were there with keeping that on? Because otherwise it seemed like it made sense as the one thing to keep.
- [41:02]
B
Yeah, I mean some of it is, you know, partners really. You just didn't have the expectation that you would stay in touch with founders. And then as a result there was sort of like a throw it over to the other team.
- [41:14]
A
Why are these two connected? Whether you're investing with the continuity, whether you're getting continuity investment or not, why shouldn't you continue with the partner that you were working with in your batch?
- [41:25]
B
I mean that was just like a different vibe. And you know, when you have compartmentalization, I see the bureaucracy might come along and just say, well, like your time is for month zero to month four and after demo day it's someone else's job. And you know, it's actually easier to do it that way. But I don't, I think it's less fun. And then it certainly doesn't allow us to support founders the way that I really want us to because being a founder is so hard. And the most important thing actually is that you don't really know who to trust. And so if you have at least one person at YC who you know has taken an oath to look out for you and take care of you for the life of the company, that's actually, I mean that's better than I think 90% of people who start startups, period.
- [42:15]
A
I'm thinking about something like Whisper Flow. It's on my computer. You know, it, it like lets you dictate into your computer.
- [42:21]
B
Absolutely.
- [42:22]
A
Like what other rappers, if I were to pick a bad phrase, what other rappers are there that would develop or could develop?
- [42:28]
B
I think there's like infinity sort of off the beaten path, like underserved verticals. Like H Vac is just one of like, I think like accounting, compliance, audit, like, I mean there's like infinity things that I think are just where there's brass, there's gold. And so that's sort of the most obvious. The thing that I think is still non consensus, but I hope is correct and now sort of the moment to start working on it is Actually consumer. So. And you know, actually it shouldn't be so non consensus. You know, ChatGPT itself is actually the best and most astonishing consumer launch of any product in the history of products actually. And it was impelled by, by AI.
- [43:15]
A
But what do you see in consumer? Because I am always afraid of consumers. They don't make rational decisions. They in love or they don't where with a business, you know exactly how you can get to them. You know exactly how you can make the message stick.
- [43:25]
B
Yeah, I mean apparently one of the biggest behavior changes in ChatGPT recently, for instance, is that people sort of treat ChatGPT as a counselor or as a psychiatrist or therapist. And you know, I think that I'm pretty psyched about things like my buddy, my YC batchmate, Chris Bader, he has a company called Rosebud AI which is.
- [43:46]
A
I've seen your freaking videos for it. I'm a paid user of it. I've got some thoughts. Go ahead.
- [43:51]
B
I think that that's super interesting. On the one hand a small startup of two or three people and on the other hand, like people are paying for it. It's very high retention and so it's growing. You know, I think it has similar vibes to Ever. Like there's things like Evernote that grew very organically for a really, really long time and then suddenly everyone's using it. Right.
- [44:16]
A
Here's what I love about Rosebud. My therapist actually said, go sign up for Rosebud. So I signed up. It is like it remembers what you've done before. And so it's actually, this is actually a really good indication of the kinds of consumer products that could make sense because it's replacing a more expensive thing, which is a therapist, and doing it in many ways better because it's more accessible. I can't call up my therapist at any minute and say, come sign up. It's David Coates, by the way, who's one of the, one of the advisors of the app. The thought that I have on it is it doesn't have the Gary Tan design magic. Look at the text and the way that you read it. It doesn't look beautiful. Look at the voice interaction with it. It doesn't sound like an 11 Labs voice. It sounds like it's thinking for a moment and then it's talking to you like a robot and then you talk back to it. I need the Gary Tan magic. Like the fact that you would wear sneakers in a frickin podcast that no one could see and they're laced Just right. That level of detail to design needs to be applied there because the brains are good.
- [45:15]
B
Well, I think Chris Bader's got it. That's actually fantastic feedback for him. I mean that's the great thing. It's like that could be fixed tomorrow.
- [45:24]
A
I asked someone for a business coach. They go into 11 labs and they created an agent. This is a guy named Jeff Schenck. He said, okay, let me show you how I can do it. I can create a business coach for you. He did it overnight. It is good. He's making it better by adjusting it to me. Do you think one of these tools can that's built on using let's say ElevenLabs agent feature. Do you think they could become a business that eventually ends up on Y Combinator? Are we looking at stuff that's always going to be too small?
- [45:53]
B
I mean honestly, I think that even founders who have access to a lot more capital or better resources, we've been working with a lot more alums in the batch. Now Daniel Kahn for instance, who created co founder of Cruise Automation, he's in the batch and I'm pretty excited about it because whether it's like your first time or your fifth time, like being next to a bunch of people who are moving extremely fast, like that's probably the biggest thing that second time or multi time founders maybe struggle with is that like the next time you have plenty of resources but the one thing that's actually the most important is time. And so there's almost the only thing that matters up front is like can you speed up? And then I do think it takes a village to actually properly speed up.
- [46:44]
A
I do admire that. That's always been a Y Combinator thing that startups equal fast growing companies and that's the difference. But Gary, what I mean is can these apps that are essentially wrappers, that are essentially built on something else turn into real businesses? I guess in your recent PODC made the point that these are basically MVPs and once you get the proof that this works, you have to really have real engineers and maybe they're using cursor so they're much more advanced without than they would be without. But it's not enough to just build these simple tools.
- [47:14]
B
Ideally the founders themselves are actually really cracked engineers and then you basically 20x yourself by being able to use these tools. But if you actually learn how to prompt and you learn, you know, you would literally be a 100x engineer, right? You'd be a 200x engineer. You take your 10 and drop a 20 on top of that and you're 200x. And that's like the most powerful thing in the world is to be able to do so much more with way, way less. And then that's why this is sort of the golden age to be trying to create products for other people. Like, you know, I think it's, like, astonishing. And then it's sort of. It's not. Yeah, it's sort of like there's a. There was an earthquake. You know, you're walking around in this in San Francisco and there's a skyscraper chopped in half and, like, all the water mains are busted. And then everyone's walking around and they're just like, oh, wow, weird. Like, why is that happening? It was like, guys, there was a 7.5, you know, earthquake that just happened, like, and people are acting like it didn't happen. So, yeah, I think it's strange. I mean, it's 20, 25 now, and then, I don't know, like, the. The majority of things in our lives, day to day, you could still argue, are like, not quite touched by any of that stuff. And that just means that literally anywhere you go, like, you could do something that is better. And so this is by far the best time in the history of startups to be starting one.
- [48:46]
A
That really is true, actually. You're really seeing more and more people getting into it. I was starting to get Gary a little, like, down on startups because people, the energy was not on it, and now I feel like the energy is so on it. And I still want people to do this right and to not end up with these. With these products that people don't want to use the Y combinator phrase.
- [49:06]
B
Yeah.
- [49:07]
A
All right. Thanks so much for doing this. I love that you're more and more public. I wish that on Twitter. I love that you care about San Francisco. I wish on Twitter you would talk more about this type of stuff.
- [49:19]
B
Absolutely, I will. I appreciate the feedback.
- [49:22]
A
I like your thinking. I love. By the way, I love. As someone who lived in San Francisco for a decade and just felt like I moved out because nobody loved it and loved it. I feel like it needs the love that you have and a few other people have. But I like your insight a lot. Bring it into other places. Thank you.
- [49:36]
B
Thank you so much. I appreciate that.
- [49:37]
A
Hell, yeah, I'm looking.
- [49:39]
B
Thanks for having me.
- [49:40]
A
Thanks. Bye, everyone.