Shane Parrish (7:17)

Actually go deeper on that.

Gary Tan (7:18)

Yeah. Earlier, just now we were chatting about this podcast setup, but we spend a lot of time writing essays and putting out content on our YouTube channels and just trying to teach people how do you actually do this stuff? There's like a lot of mechanical knowledge about how do you incorporate or how do you raise money for the first time. And all of that is out there for free. And you know, on the other hand, I think of doing YC being in the program. It's a 10 week program. We make everyone come to San Francisco. Now at the end of it, it culminates in people raising, you know, sort of the median raise is about a million to a million and a half bucks for, you know, sometimes teams that are two or three people. Just an idea starting at, you know, at the beginning of the match.

Shane Parrish (8:12)

That's the demo day. Is that the. Yeah.

Gary Tan (8:14)

And yeah, we have, you know, I think we have about a billion dollars a year in, you know, funding that comes into YC companies. And that's because the acceptance rate to get into YC is only one.

Shane Parrish (8:28)

Let me get this straight. You have, I think I read somewhere 40,000 applications a year.

Gary Tan (8:33)

Yeah, I think it's close to 70, 80,000 at this point.

Shane Parrish (8:36)

How do you filter those?

Gary Tan (8:37)

Well, we ourselves use software, but we also have 13 general partners who actually read applications and we watch the one minute video you post. And the most important Thing to me is that I want us to try the products, right? You know, sure, we can use the resume and you know, people's careers and where they went to school. You know, we're not going to throw that out. Like it's a factor in anything. But the most important thing to me is not necessarily the biography. It's actually, you know, what have you built? What can you build?

Shane Parrish (9:15)

Go deeper on the software thing. I don't think I've heard that before that you guys, obviously you have to use software, but what does the software do? How does it filter?

Gary Tan (9:23)

Yeah, I mean, ultimately the best thing that we can do is actually brute force read. And on average, I think a group partner will read something like a thousand to fifteen hundred applications for that cycle that they're working. So the best thing we can do is like not. It is basically like humans trying to make decisions, you know, which is maybe a little antithetical to, you know, the broader thing right now. And now it's, you know, let's just use, use AI for everything. But I think that the human element is still very important.

Shane Parrish (9:59)

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Gary Tan (11:05)

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Gary Tan (11:47)

I guess the surprising thing that has worked over and over again ultimately is in those 10 minutes, either you learn a lot about both the founders and the market or you don't. So we're looking for incredibly crisp communication. So I want to know, you know, what is it? And you know, often the first thing I ask is not just what is it, but why are you working on it? Like, I want to sort of understand, where did this come from? Did you just read about it on the Internet? Or. A much better answer is, you know, well, I, I spent a year working on this and I got all the way to the edge of, you know, what people know about this thing. And, you know, what's cool about, you know, the biographical is that then it invites more questions, right? It's the best interviews in 10 minutes. Like, you learn about an entire market, you learn about a set of people that, you know, normally you might not ever hear of. It's like you're traveling. It's like you're traveling the idea maze with the people you're talking to. This is all over zoom. And you know, at the end of those 10 minutes, like, sometimes the 10 minutes becomes 15. Like, you want to talk to people longer. Cause that's what a great interview feels like to me. It feels like I'm a cat and I see a little yarn and I'm just pulling on the yarn, I'm just pulling on the thread. Cause it's like this, you know, there's something here, this person understands something about the world that, you know, actually makes sense to me. And I, I think what we're looking for is actual signal that there's there's a. There, there, there's a real problem to be solved. There are people on that end who are willing to pay. And then you're working backwards. What a great startup ultimately is, is something real that people are willing to pay for that probably has durable moats that, you know, it doesn't mean that, you know, it means that that company could actually become much bigger than you. You don't want to start a restaurant, for instance, because there's infinite competition for restaurants. But you do want to start, you know, something like airbnb. That has network effects or that can really scale. Exactly. Or you know, in AI today, one of the more important things is, you know, are people willing to pay. And today because people are not selling software, they're increasingly actually selling intelligence. They're like, you know, like it or not, like these are things that you could not buy before. Like, you know, probably the most vulnerable things in the world today are things that you could buy, you know, farm out to an overseas call center. That's sort of like the low hanging fruit today. And you know, basically how do you find things that people want and how do you actually provide it for them? And the remarkable thing is that, you know, in that's why it only has to be 10 minutes. You know, one of the things I feel like I learned from Paul Graham interviewing alongside him so many years was that sometimes I'd go through and this person would come in. They had an incredible resume. You know, they're like had a PhD or they studied under this famous person or you know, they worked at Google or Facebook or all these really famous places. They had an impressive resume or they had the credentials of someone who I felt like, you know, should be, should be able to do it. But then they had a mess of an interview. Like we didn't get any signal from it, we didn't understand or like it just, it seemed garbled or you know, at the end of it sometimes they're asking like, oh, we just, you know, 10 minutes is too short, we need more time. And one of the things I feel like I learned from Paul was that if in 10 minutes you cannot actually understand what's going on, it means the person on the other end doesn't actually understand what's going on. And there isn't anything to understand, which is surprising.

Shane Parrish (15:50)

That's a really good point. I bet you that holds true. Do you look at people that you've been successful with that don't work out and then people that you filtered out that do become maybe successful and try to learn from that?

Gary Tan (16:02)

Oh, definitely, all the time. I mean, I think that's the trickiest thing. You know, I think the system, it's like will always produce both false positives and false negatives because it is only.

Shane Parrish (16:15)

10 minutes, but you have the highest batting average. Y Combinator. My understanding is it's like 5% of the companies become billion dollar companies.

Gary Tan (16:24)

Yeah, about 2.5% end up becoming decacorns sooner or later.

Shane Parrish (16:28)

But that would be the highest batting average of any VC firm. Maybe with Sequoia being The exception. What's interesting to me is most of the people that I know in that space are doing hundreds of hours of work per company and you guys can't do that because you have 80,000 people applying and you're still the most or at least top tier in terms of success.

Gary Tan (16:51)

Yeah. I mean, what's great is I don't want to compete with Sequoia or Benchmark or Andreessen Horowitz or. They're our friends. Honestly. Done right. We're much earlier than everyone else because we want to actually give them half a million doll when they have just an idea. Or maybe they don't even know their co founder yet.

Shane Parrish (17:11)

That's what makes it more incredible. It's because the batting average should be way lower based on where you're at in the stack in terms of funding.

Gary Tan (17:18)

Yeah. You know what it is though? I spent five years, seven years actually away from YC before coming back a couple years ago. So I ended up, I think, in the top 10 of the Forbes Midas list as my final year before coming back to yc. And why haven't other people, you know, we ask this all the time, why haven't other people come for us? You know, I think there are lots of people who are doing various things that might work and I guess so far people sort of lose interest or, you know, float off and go do higher status things. Working with founders when they're just right at the beginning and just an idea is actually, you know, relatively low status work because, you know, it's very high status to work with a company that is, you know, worth 50 or $100 billion now. But guess what, like that's 10 years from now or sometimes 15 or 20 years from now. You know, the. It all starts out very low status and all the way in the weeds. Like you're ask, you're answering sort of relatively simple questions and you're giving relatively small amounts of money.

Shane Parrish (18:31)

Well, you were giving 20 at the start, right now you give 500.

Gary Tan (18:35)

Is that the half a million dollars today? Yeah.

Shane Parrish (18:37)

Has that changed the ratio of success?

Gary Tan (18:40)

I think some of it is. Well, we find out in 10 years, if anything. I think that the unicorn rate has gone up over time. You know, 10, 15 years ago, I think it was closer to maybe three and a half to 4%. And now we're around five and a half percent. Some batches from maybe 2017, 2018 are, you know, pushing 8 to 10%. Oh, wow. Some of those companies in that area, in that vintage, about 50% of companies end up raising what looks like a Series A. And then the wild thing about it is it is it actually takes a long time for people to get there. So, you know, I think that YC has actually flipped a lot of the, I guess, myths of venture. You know, one of the myths of venture maybe 10, 15 years ago was that, you know, within nine months of funding a company, you will know whether or not that company was good or bad. And you know, going back to that stat, you know, about half of companies that go through YC will end up raising a Series A that's, you know, much higher than any other precede or seed sort of situation that I know of. But about a quarter of those who raise the Series A, they do it in year five or later. And that's a function of like we're funding 22 year olds, you know, 19 year olds, 24 year olds. I mean, we're funding people who are so young that sometimes they've never shipped software before. Sometimes, you know, they're fresh off of an internship, you know, let alone it takes three to five years to mature, to learn how to iterate on software, how to deliver really high quality software, how to manage people, how to manage people effectively, give feedback. And so the wild thing is, I mean, sometimes it takes five years for those things to come together in my head.

Shane Parrish (20:39)

And correct me if I'm wrong here, there's a bit of like misfit geek. People have told me this won't work or won't be successful. And then when I get to Y Combinator, I'm around a whole bunch of other people who are exactly like me. Oh yeah, for the first time in my life. And they're super ambitious. To what extent do you think that that environment just creates better success or better outcomes?

Gary Tan (21:01)

Oh, that was definitely true for me. I mean, without that I feel like what my, I mean I had a good, a really great community at the end of the day, like it was, you know, my fellow Stanford grads. But I guess the weird thing to say is that like being around people who are really earnestly trying to build helps, you know, 10x war, the, the default startup scenario out there is not about signal, it's about the noise. Like you're playing for these other things, like how much money can I raise? And from what, you know, high status investor, like, you know, some people sort of float off and they become scenesters. They're like, oh, let me try to get a lot of followers on Twitter. That's the most important thing. And then, and what we really try to do at YC during the batch and then afterwards and you know, in our office hours working with companies is like when we spot that kind of stuff, it's like, oh no, no, like maybe don't do that. Like, you know, let's go back to product market actually building and then iterating on that, getting customers, you know, long term retention. All of those things are the fundamentals and everything else is like the trappings of success or, and those will always feel I I. What's funny is like in other communities, all of those things will always feel more present to hand and they're easier. Like you can just get it like you're, you know, on stage keynoting or you know, even doing the podcast game. I feel like guilty, you know, like it's kind of funny. We see that in people and then sometimes, you know, often that will kill their startup. Like they take their eye off the ball, you know, angel investing. If you're a startup founder and suddenly people have heard of you and people try to add you as a scout. People kill their startups all the time by that, just by taking their eye off the ball.

Shane Parrish (22:59)

Go deeper on that a little bit in terms of focus and how people sort of lose their way unintentionally and then do they catch it before it starts to go off the rail or does it, it sort of just crashes and then there's no coming back from it.

Gary Tan (23:13)

I mean, it crashes and then, you know, sometimes you have to go and do your next startup or you know, or I don't know, sometimes people just go off and become VCs after that and that's okay too.

Shane Parrish (23:24)

Is that the difference between somebody who like wants to run a company and start a company versus somebody who wants to be seen as running a company and starting a company?

Gary Tan (23:32)

I think that that's probably the biggest danger to people who want to be founders. I mean, I think I've seen Peter Thiel talk about this. Like he doesn't really want people who want to start startups. From my perspective, it's certainly much better to find people who have a problem in the world that they feel like they can solve and they can use technology to solve. And that's like sort of a more earnest way to look at it. And if you look at the histories of some of the things that are the biggest in the world, they actually start like that. You know, there are lots of interviews with Steve Jobs and Steve Wozniak saying, you know, I never meant to start a company or Ever wanted to make money, All I wanted to do was make a computer for me and my friends. And so, you know, many, many more people kept coming to me saying, can you build me a computer? And they just, you know, like a cat were pulling on this thread.

Shane Parrish (24:28)

It's like the company was a reluctant side effect almost right in history. It seems like a lot of innovation comes from great concentration of people together, whether it's a city or the Industrial revolution or all these things together tends to be localized and then spread over the world. If I understand it correctly, why Silicon Valley, why San Francisco? And why haven't other countries been able to replicate that success inside?

Gary Tan (24:54)

Well, at yc, what we hope is that people actually come to San Francisco and we do strongly advocate that they stay, but it's no requirement. And then what we hope is that, that if they do leave, they end up bringing the networks and know how and culture and, you know, frankly, vibes, and they bring it back to all the other startup hubs in the world. And I think that that's some of the stuff that has actually come about. I mean, Monzo was started by now my partner, Tom Blomfield, he's a partner at YC now, but he started, you know, multiple startups and a few of them, you know, multiple unicorns, actually. And both of them are some of the biggest companies in London, for instance. So what we hope is that San Francisco becomes sort of really Athens or Rome in antiquity. You know, send us your best and the brightest. You know, ideally you stay here. One thing we spotted is that the teams that come to San Francisco and then stay in San Francisco or the Bay Area, they actually double their chance of becoming a unicorn.

Shane Parrish (26:03)

Oh, wow.

Gary Tan (26:04)

So if it's one, one thing that you could do, it's be around people and be in the place where making something brand new is in the water.

Shane Parrish (26:14)

So if hypothetically you created a new country tomorrow and you wanted to spur on innovation, what sort of policy you got to compete with San Francisco now? What sort of policies would you think about? Like, how would you think about setting that up to attract capital, to attract the right mindset of people, to attract and retain these people?

Gary Tan (26:36)

I think what I want for San Francisco, for instance, is I think the rent should be lower. And so rather than subsidizing demand, we actually need to increase supply, like fairly radically, actually. And that just hasn't happened. I think I was looking at it for the entire last calendar year. I think maybe Scott Wiener had just posted this on X, that literally there were no new Housing starts in all of San Francisco proper for the last year. So how are we supposed to actually bring down the rents and make this place actually livable if San Francisco is the microcosm where people build the future and it is sort of the siren song for, you know, 150 IQ people who are very, very ambitious and have our, you know, techno optimistic ideology. And it's also where they are most likely to succeed society and certainly, you know, America is not serving society the right way. If we're getting in the way of these smart people trying to solve these problems, trying to build the future.

Shane Parrish (27:48)

But just continuing on the Y Combinator theme for a second. Are there ideas that you've said no to, but you think they're going to be successful? They just scare you and you're like, no, that's too scary.

Gary Tan (27:59)

I mean, if it's scary, but might or probably will be good, I think we want to fund them. And certainly there are things that would be bad for society but are likely to make money. And, you know, the history is our partners are everyone's independent. You know, we have a process that is very predicated on, you know, if you're a general partner at yc, you know, you pretty much can fund what you want. You know, we run it by each other to make sure you sort of double check, like the thinking. But I think we're pretty aligned there. Like, there are lots of examples of, you know, maybe five or six years ago there was a rash of telehealth companies that are focused on, for instance, ADHD meds. And I distinctly remember one of our partners, Gustav Alstroemer, he met that team and he said, you know what, what? We're not going to fund these guys. You know, it's going to make money. But I don't want to live in a world where it is that easy to get, you know, people on these drugs. Like, they're ultimately methamphetamines and, you know, these are controlled substances. And this is the wrong vibe. Like, we did not like the vibe that we got from the founders of that company. So, you know, I hope that YC continues that way and I think it will. Ultimately, we want people, we want, want people who are, I mean, ultimately trying to be benevolent at least, you know.

Shane Parrish (29:29)

How would you think about, like, just the idea of spitballing if I were to come to you and be like, I'm starting a cyber weapons company?

Gary Tan (29:38)

I guess some of it is like, are you only going to sell to five eyes? Because, you know, I really Liked what MIT put out recently. They were very clear. They said, you know, MIT is a. An institution, and that institution is an American institution. And so being very clear about that, I thought was totally the right move for mit. And, you know, I think that YC needs to be an institution of similar character.

Shane Parrish (30:08)

I like that. What do you wish founders knew about sales coming in?

Gary Tan (30:12)

Oh, how hard it is. And I mean, like it or not, the ideal founder is someone who has lived, like 20 lifetimes and has the skills of 20 people. And the thing is, you know, you can't get that. And so probably the first conference that we had, the first mini conference we have when we welcome the batch in, is the sales mini conference. And essentially it is don't run away from the. No. Spencer Skates of Amplitude has this great analogy that he told, you know, some companies when he came by to speak recently that I've been thinking a lot about, which is sales is about, you know, having 100 boxes in front of you, and maybe five or six of those boxes has a gold nugget in them. And if you haven't done sales before, you think, I really. I'm gonna gingerly, in a very gingerly way, open that first box and hope, Hope, hope that. That, you know, I have a gold nugget. And then, you know, I don't. I almost don't want to know that there isn't a gold nugget in there. Like, I'm so afraid of rejection. It's sort of remarkable how often high school and family and, you know, the 10,000 hours of human training people get from their childhoods comes up in Paul Graham's essays. I always think about that because I think that most people's backgrounds just don't prepare them for sale. It's a very unnatural thing to do, sales. But then the sooner that you. You acquire those skills, like, the more free you become. And what Spencer says about those hundred boxes is instead of, like, being incredibly afraid of, you know, getting an F, or, you know, nothing's gonna happen to you, just like flip open all hundred boxes immediately. And then, you know, you should aggressively try to get to a no, and, you know, you'd rather get a no so you can spend less time on that lead and you can get onto the next one. I mean, I think that that's like a very interesting example of the mindset shift that you can read about. But you sort of need. It takes a village. Like, you sort of need to be around lots and lots of people for whom that is true. That has been true. And I think that maybe that's actually one of the reasons why YC startups are much more successful. Like other people give as much money, or, you know, as you said, like venture capital, VC firms tend to give, you know, a lot more money. I mean, there are clones of YC right now that give like twice as much money, for instance. But I don't think that they're gonna see this level of success because they're not going to have as earnest people who become as formidable around you. Like, it's. It's actually a process.

Shane Parrish (33:01)

It's so interesting to me because as you're saying that there's something that strikes me about the simplicity of what you're doing. And then also like Berkshire Hathaway, you know, everybody's tried to replicate Berkshire Hathaway, but they can't because they can't maintain the simplicity. They can't maintain the focus. They can't do the secret sauce, which obviously has a lot to do with Charlie Munger and Warren Buffett. And with you guys, it has a lot to do with the founders that you attract and you can bring together, but you have billions of dollars effectively trying to replicate it. Nobody's able to do that. I think that's really interesting. And it's not like you're doing something that's super complicated. It doesn't sound like it, unless I'm missing something. It's a very simple sort of process to bring the people together. And obviously there's filtering, and you guys are really good at doing that.

Gary Tan (33:49)

I mean, what my hope is, I feel like when Paul and Jessica created YC for my. I went through the program myself in 2008, and I came out transformed. And then that's very explicitly what I want to happen for people who go through the batch today. It's, you know, it isn't just, like, show up to a bunch of dinners and network with some people who happen to be. It's much deeper than that. Like, I want people to come in maybe with like, you know, the default worldview, and then I want them to come out with a very radically different worldview. I want someone who is much more earnest, someone who is not necessarily trying to sort of like, hack the hack. They're trying to, you know, and I think this mirrors what you were saying from, you know, what, you know, rest in peace, Charlie Munger talks about. And what Warren Buffett talks about around all of these things are in the short term, popularity contests. But in the end, all that matters is the weighing machine. So you can Raise your Series A. You can throw amazing parties. TechCrunch can write about you. All these Twitter anons can fit you as like the next greatest thing and you could get, you know, hundreds of thousands of followers on X or whatever. But you know, at the end of the day you look down and did you create something of great value? Like did you with your hands and you know, did you assemble people and capital and you know, create something that you know, when all is said and done, solve some real problem, put people together, is there real enterprise value? And that's the weighing machine. And the way that YC makes money, the way that the founders make money, it's all aligned at that point. Yeah, there's a way to hack the hack and I don't really know what the end game is on the other stuff. It's just very short term. Whereas, you know, on a 5, 10, 15 year basis, like if you are nose to the grindstone, earnestly working on the thing, you know, you will succeed. Like I think that that's what Paul Graham's essay about being a cockroach actually is. And you know, that's why 25% of the people who reach some form of product market fit at YC do it in year five or later. It's like they don't quit year one, they don't quit quit year two. Like they are learning and growing. I have one other really crazy stat that I'm thinking about all the time right now. There's a founder, there's a VC actually. His name is Ali Tomasab. He works at Data Collective. He wrote a book called Super Founders. And I get this email from him out of the blue. He says, did you know that about 40% of the unicorns from the last 10 years in the world were started by multi time serial founders? And was like, okay, that's a cool stat. Like, makes sense. Like multi time founders are, you know, they know a lot more, they have networks, they have access to capital. Like that's not a surprising stat. If anything, it's a little surprising that it's only 40%. Like you would have guessed maybe that was 80. But the, the thing he said after that really shocked me. He said, did you know that 40, you know, of those 40%, 60% of those people, the people who created unicorns the last 10 years are YC alumni.

Shane Parrish (37:26)

Oh, wow.

Gary Tan (37:27)

So I'm like, that's crazy. Like I'm really glad that YC exists now because you know, even if you know, YC today is basically a thing that is for first timers, you know, we do have second timers apply. We have, we do accept them. But, you know, we primarily think of the half a million dollars. You know, it really is for people who are starting out and it's kind of hilarious. Like, I have no product right now for people who are, you know, for my YC alums. And maybe that's okay, you know, it's, you know, that's our gift to the rest of Sandhill Road because, you know, they're the ones who are going to be the fun returners for all of the rest of Sandhill Road.

Shane Parrish (38:06)

Would you say, like, in terms of personal characteristics? It sounded like determination was definitely one of the most important outside of the company or venture. What are the other personal sort of skills or behaviors or characteristics that people have that you say you would think correlate to not only the successful first time, but second, third, fourth?

Gary Tan (38:29)

Yeah, I mean, the number one thing that I want that comes to mind for me is, I mean, maybe it's even surprising because that's not a word that you might associate with Silicon Valley founders. I think of the word earnest. So what does earnest mean? Like, incredibly sincere? I think basically what you see is what you get. Like you're not trying to be something else. It's like authentic, but like, you know, even humble in that respect. Right. Like, I'm trying to do this thing the opposite. I mean, and it's, it's surprising because, you know, I don't know if people associate that with Silicon Valley startups, but I see that in the founders that are the most successful and most durable. I see it in Brian Armstrong at Coinbase, like, and which is fascinating because that's definitely not the trait that you would apply to most crypto founders. And, you know, I would use Sam Bankman Fried as sort of the opposite of that. Like, you know, Brian Armstrong is an incredibly earnest founder who literally read the Satoshi Nakamoto white paper and said, this is going to be the future and let me work backwards from that future. When you talk to him, the reason why he wanted these things comes directly out of his own experience at Airbnb. They were dealing with the financial systems of myriad countries, and it's international. Just sending money from one country to another was totally fraught and totally not something that was accessible to normal people. Remittances, this crazy scam. It's insane how many fees that people have to pay just to like send money home or do cross border commerce. Right. So this is something that was incredibly earnest of Brian Armstrong. To do. He said, here's a thing that is broken in the world that, you know, he saw personally, I think he spent time in, you know, Buenos Aires in Argentina, and he saw hyperinflation. And he said, you know, this is a technology that solves real problems that I have seen hurt people and I know that this technology can solve it. And then after that, he's just like nose to the grindstone, working backwards from that thing that he wants to create in the world. And, you know, it's no surprise to me. I mean, there were many years in there that I think our whole community were looking at. We were looking at someone like Sam Bankman fried and just wondering like, what's going on over there. He speed ran this sort of money power, fame game to an extreme degree. So much so that he stole customer funds to do it. And like, that was the answer. Like that. That's anti earnest. That is the definition of he was a crook. He's in jail now. And my hope is that people who look, if you just look at Brian Armstrong versus sbf, I'm hoping that young people listening to this right now take that to heart. It's like the things that actually win. I mean, and going back to Buffett, I went to their, you know, sort of conclave in Omaha.

Shane Parrish (41:40)

Oh, you went to the Woodstock for capital?

Gary Tan (41:42)

Yeah, yeah. I mean, amazing. And I think those guys are by definition extremely earnest. You know, I don't think it's an affectation. I think it's like, it's like legit and serious. Like those guys did everything, you know, what is it? It's their thing, right? It's, you know, work on high class problems with high class people. I mean, that's very, very simple. You just do it the right way. Right. And so that's what I want. I think that if YC is the shelling point for earnest, friendly, ambitious nerds to steal something from. You know, I have a friend on Twitter who goes by Visa visacon and you know, he has a whole book on it. I think it's called Friendly Ambitious Nerd. If you look it up. I mean, I think that that's what YC by definition should be attracting. And you know, Brian Armstrong is like the best. Found one of the best founders I've ever met and gotten, gotten the chance to work with and fund. And I think the world desperately needs more people like that where, you know, in the background, just like consistent, doing the right thing, trying to attract the right people, like, you know, chop wood, carry water. That's It.

Shane Parrish (43:03)

He also took a big stand before it became popular that the workplace is like a performance place. It's not. You don't bring all of your politics and all that stuff in. But he did that at a time when it was courageous. Like, it was really. He was one of the people.

Gary Tan (43:20)

Yep.

Shane Parrish (43:21)

Out of the gate. And he took so much flack for that.

Gary Tan (43:23)

Yeah, I'm vindicated now.

Shane Parrish (43:26)

I know. But I remember reading, like, his thing, and I was like, oh, this is great. But, like, why. Why are we. Why are we even pointing this out? You know, like. And then he got like, I read the stuff online. I was like, this is crazy.

Gary Tan (43:38)

That's the media environment. Right.

Shane Parrish (43:39)

I thought it was interesting anyway that he came out and did that. And I think where it relates to the earnestness is only somebody who's really comfortable with themselves and, like, trying to do good in the world could really come out and take that stand at that point in time.

Gary Tan (43:54)

Yeah, that's true leadership.

Shane Parrish (43:55)

Yeah. What's the biggest unexpected change you've seen in building companies in the AI world?

Gary Tan (44:03)

I think the biggest thing that is increasingly true, and we're seeing a lot of examples of it in the last year, is blitzscaling for AI might not be a thing.

Shane Parrish (44:16)

What's blitzscaling?

Gary Tan (44:17)

So I think Reid Hoffman wrote a whole book about it. It was definitely true in the time of Uber. So, you know, that was sort of a moment when interest rates were descending and then these sort of international, increasingly international marketplaces, this sort of, you know, offline to online marketplaces like Uber in cars or delivery, or you could say instacart doordash. You could throw, grow in, you know, Lyft. There was sort of this whole wave of, you know, sort of the top startups were marketplace startups, but also in software too. This idea that, you know, scale could be used as a bludgeon, that, you know, the network effects grow, you know, sort of exponentially. And then because you could have access to more and more capital, whoever raised more money would have won. And I feel like that was extremely true in that era. Sort of the 2000 and tens. And then in the 2000 and twenties, especially by, you know, we're in the mid-2020s now, I think that we are seeing incredible revenue growth with way fewer people. And that's very remarkable. We have companies basically, you know, going from zero to $6 million in revenue in six months. We have companies going from zero to $12 million a year in revenue. Revenue in 12 months. Right. And with under a dozen People like usually five or six people. And so that's brand new. Like this is the result of large language models and intelligence on tap. And so that's a big change. Like, you know, I think we are seeing companies that in the next year or two will get 250, $100 million a year in revenue, really with under you know, maybe 10 people, maybe 15 people tops. And so that was relatively rare. And my prediction would be this becomes quite common. And my hope is that's actually a really good thing. Like this is sort of the silver lining to, you know, what has been really a decade of big tech. Right. Like it's more and more centralized power. You know, what might happen here is that, you know, and what we're actively trying to do at YC is we hope that there, you know, are thousands of companies that each can make hundreds of millions to billions of dollars and give consumers an incredible amount of choice. And we hope that that will be very different than sort of this. The opposite, I think was increasingly true. Like we have fewer and fewer choices in operating systems, in web browsers, across the board, just more and more concentration of power in tech.

Shane Parrish (47:05)

Two thoughts here. One, how much do you think that cloud computing plays into that? Because now I don't have to buy $6 billion in infrastructure to be that five person company. I can rent it based on demand. So that's enabled me not to compete on a capital basis.

Gary Tan (47:23)

Yeah, that was true. That was even why y combinator in 2005 could exist. Exist. You know, I remember working at a startup in 1999, 2000 or at like Internet consulting firms and these were like million dollar projects because you had to actually pay $100,000 or hundreds of thousands of dollars to Oracle. You had to pay hundreds of thousands of dollars to your colo. To like rack real servers.

Shane Parrish (47:48)

So the cost of even starting a company was just huge.

Gary Tan (47:51)

Yeah, I mean, I remember, remember Jeff Bezos actually launched aws at a YC startup school at Stanford campus in 2008, right when I was starting my first company. So I think, you know, cloud really opened it up and that, you know, that's part of the reason why startups could be successful. You know, you didn't need to raise 5, $10 million just to rack your server. And you know, that's the other big shift. Like I think in the past it was very, very common to have, you know, Stanford MBAs or Harvard MBAs be the CEO and then you would have to go get your hacker in a cage. You had to, you know, get your cto. And, you know, there was sort of that split, and then now what we're seeing is, you know, what, like the CEO of the majority of. Of YC companies, they are technical.

Shane Parrish (48:48)

Is this the first revolution, like, technological revolution, where the incumbents have a huge advantage?

Gary Tan (48:54)

You know, I think they have an advantage, but it's not clear to me that they are conscious and aware and, like, at the wheel enough to take real advantage of it because they have too many people, right? And then it's all. I mean, I think this is what Founder mode is actually about. So last year we had a conference with Brian Chesky. We invited our top YC alums there. We brought Paul and Jessica back from England, and we had this one talk that wasn't even on the agenda, but I managed to text Brian Chesky of Airbnb, and I got him to come and speak very openly and honestly in front of, you know, a crowd of about 200 of our absolute top alumni founders. And he spoke very eloquently and in a raw way about how your company ends up not quite being your own. Unless you are very explicit. Like, you know, I. This is actually my company. I am actually going to have a hand and a role to play in all the different parts of this company. I'm not going to. You know, basically the. The classic advice for management is hire the best people you possibly can and then give them as much rope as you possibly can. And then somehow that's going to result in, you know, good outcomes. And then I think in practice, and this is sort of the reaction that is turning out to create a lot of value across our community, certainly. But I think the memes are out there and it's actually changing the way people are running businesses. It's sort of a shade of what you were saying earlier with Brian Armstrong. Like, you know, you can sit back and allow your executives to sort of run amok. And, you know, if the founder and the CEO does not exercise agency, you know, then it's actually a political game. And then you have sort of fiefdoms that are fighting it out with one another and the leader is not there. Then you enter the situation where neither the leader nor the executives have power or control or agency, and then you have your. Everyone's disempowered, everyone is making the wrong choice. You know, retention is down, you're wasting money. You have lots and lots of people who are sort of working either against each other or not working at all. And that's, you know, know, I think a Pretty crazy dysfunction that took hold across arguably every Silicon Valley company, period. And it's still taken. It's still, you know, mainly in power at, you know, quite a few of those companies. Actually, though I think people are aware now that that's not the way to run your company.

Shane Parrish (51:50)

Are the bigger companies sort of, like, shaping up or. No, the way that I think about this analogy is sort of like if I'm the young, skinny kid and I'm competing against the fat, bloated company I want to run upstairs, it's going to suck for me, but it's going to suck way more for them.

Gary Tan (52:07)

Right. I think this is maybe a function of blitzscaling and using capital as a bludgeon gone wrong. You can look at almost any of these companies. They probably hired way too many people. And at some point they were viewing smart people as, you know, maybe a hoarded resource that, you know, if you were playing some sort of adversarial, you know, Starcraft, and you didn't want, you know, the ironic thing is like, they themselves were not using the resources properly either. Right.

Shane Parrish (52:46)

They just didn't want somebody else to have.

Gary Tan (52:48)

Exactly. I guess it felt like a little bit of a prisoner's dilemma, because I think the result is that, you know, tech progress itself decelerated. You have, like, the smartest people of a generation basically retired in place, working at places that, you know, the world is actually full of problems. Like, why. Why are people sort of retired in place, pulling down, you know, insane by average American standards, absolutely insane salaries to build software that doesn't change, doesn't get better. I mean, sometimes I sit there and I run into a bug, into whether it's a Google product or an Apple product or Facebook or whatever. I'm like, this is an obvious bug. And I know that there are teams out there, there are people getting paid millions of dollars a year to make some of the worst software, and it will never get fixed because there's no way, like, you know, people don't care. No one's paying attention. Yeah, that's just one symptom out of a great many that is, you know, the result of, I don't know, basically treating people like, you know, hoarded resources instead of like they should. You know, the world is full of problems. Let's go solve those things.

Shane Parrish (54:02)

When it comes to AI, the raw inputs, I guess, if you think about it that way, are sort of the LLM. Then you have power, you sort of have compute, you have data. Where do you think incumbents have an advantage? And where do you think startups can successfully compete?

Gary Tan (54:20)

Yeah, I mean, we had a little bit of a scare I think last year with AI regulation that was potentially premature. So, you know, there was sort of a moment maybe a year or two ago and you sort of see it in the shades of it did make it into say Biden's eo. These sort of, you know, past a certain amount of, you know, mathematical operations like that's banned or not banned, but you know, we require all of this extra regulation. You have to report to the state, like you better get a license. You know, it's. That felt like the early versions of potentially regulatory capture where, you know, they wanted to restrict open source, they wanted to restrict, you know, the number of different players. You know, sitting here a year after a lot of those attempts, I feel pretty good because it feels like there are five, maybe six labs, all of whom are competing in a fair market trying to deliver models that, you know, honestly any startup, anyone, you know, any of us could just, you know, pick and choose. And you know, there's no monopoly danger. There's no, you know, crazy pricing power that one person, one entity wields over the whole market. And so I think that that's actually really, really good. I think it's a much fairer playing field today. And then I think it's interesting because it's an interesting moment, I think that, you know, basically there's a new Google style sort of oligopoly that's emerging around like who provides the AI models. But because it won't be, it probably won't be a monopoly, that's probably the best thing for the consumer and for actually every citizen of the world because you know, you're going to have choice.

Shane Parrish (56:18)

Let's go deeper on the regulation, then come back to sort of competition. How would you regulate AI? Or how do you think it should be regulated? Or do you think it should be regulated?

Gary Tan (56:28)

It's a great question. I guess there are a bunch of different models that I could see happening. I think what's emerging for me is that the two things that I think the first wave of people who are really worried about AI safety, not to be flippant, but my concern is that they basically watch Terminator 2, you know, and I'm like, I like that movie too. But you're right now, you know, there's sort of that moment in the movie where they say suddenly the, the AI becomes self aware and it becomes, you know, it, it takes agency, right? And I think the funny thing, at least as of Today, you know, these systems are, are. It's just matrix math and there is no agency yet. Like, there's basically they're equivalent to incredibly smart toasters. And some people are actually kind of disappointed in that. And personally I'm very relieved and I hope it stays that way because that means that there's still going to be a clear role for humans in the coming decades. And, you know, I think it takes the form of two very important things. One is agency. I mean, people often ask like, what should we be teaching our kids? And you know, the ironic thing is we send them to a school system that is not designed for agency. It is literally designed to take agency away from our children. And maybe that's a bad thing, right? Like, we should be trying to find ways to give our children as much agency as possible. That's why I'm actually personally pretty pro screens and pro Minecraft and Roblox and, you know, giving children like this sort of playground where they can exercise their own agency.

Shane Parrish (58:23)

Have you tried Synthesis Tutor?

Gary Tan (58:26)

Oh, yeah, yeah, yeah. I'm a small personal investor in them and, you know, I think that we're just scratching the surface on how education will actually change. But that's a great example. Like those synthesis is like designed around trying to help people, have help children, like, you know, actively be in these games that increase instead of decrease agency.

Shane Parrish (58:47)

And it's crazy. So it teaches the kids math. And my understanding just from reading a little bit is El Salvador just replaced like The K through 5 math with synthesis Tutor and the results are like, astounding.

Gary Tan (59:00)

Incredible.

Shane Parrish (59:00)

Yeah, it's way better. I mean, the kids get involved and they're obviously invested, invested in it. The regulation question is really interesting too, because it begs the question of it's a worldwide industry and so regulating something in one country, be it the United States or another country, doesn't change what people can do in other countries. And yet you're competing on this global level.

Gary Tan (59:23)

Yeah, I think the biggest question around it is, of course, I mean, the existential fear is like, where are all the jobs going to go? And then my hope is that it's actually two things. One is like, I think that robotics will play a big key role here where I think that if we can actually provide robots to people that do real work for people, that will actually change people's sort of standards of living in like, fairly real ways. So I think universal basic robot is relatively important. You know, I think some of the studies coming back about UV have not, you know, universal basic income where you just give money to people. It's just not really resulting in a different.

Shane Parrish (60:11)

I think they've never read a psychology textbook. I mean, just going away from the economics of it, people need to feel like they're part of something larger than themselves. And if they don't feel like they're part of largers, then something like they're contributing to something, they're part of a team, they're. They're bigger than what they are as a person, then it leads to all the problems.

Gary Tan (60:32)

Yeah, exactly. And then I think that we really need to actually give everyone, you know, on the planet some real reason why this stuff is actually good for them. Right. Like, I think if, if there is only sort of a realignment without a material increase in people's day to day livelihoods and, you know, their quality of life life, like maybe we're doing something wrong actually. And left to its own devices, like it's, you know, it's possible. So I don't know what the specific things are, but I think that that's what it would look like, you know, if, if regulation were come to come into play or there was some sort of realignment in reaction to, you know, the nature of work changing. That would be the outcome that, you know, know the majority of people, if not all people, like, see the benefit in some sort of direct way. And if we don't do that, then there will be unrest. I think that that's one of the criteria. I don't have the answer, but I think that's sort of one of the things I'd be on the lookout for at Capella University.

Shane Parrish (61:42)

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Gary Tan (61:44)

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Shane Parrish (61:48)

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Gary Tan (62:00)

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Shane Parrish (62:02)

Learn more at capella.edu. fastest 25% of students cost varies by pace. Transfer credits and other factors. Fees apply. This episode is brought to you by Indeed. When your computer breaks, you don't wait for it to magically start working again. You fixed the problem, so why wait to hire the people your company desperately needs? Use Indeed sponsored jobs to hire top talent fast and even better. You only pay for results. There's no need to wait. Speed up your hiring with a $75 sponsored job credit@ Indeed.com podcast. Terms and conditions apply. At what point do you think the models start Replacing the humans in terms of developing the models. So like at what point in the models doing the work of the humans in OpenAI right now and they're actually better than the humans at improving the model.

Gary Tan (62:55)

We're not there yet. So there's some evidence that synthetic data is working and so some people believe that synthetic data is where the models are sort of self bootstrapping.

Shane Parrish (63:07)

So just to explain to people, synthetic data is when the model creates data that it trains itself on.

Gary Tan (63:12)

That's right. And so, so I guess the other really big shift is actually test time compute. Like literally 01 Pro is this thing that you can pay $200 a month for and it actually just spends more time at the sort of query level. It might come back five minutes, 10 minutes later, but it will be much more correct than sort of the predict next token version that you might get out of, you know, standard chatgpt. Yeah, from, from what I can tell, that's where a lot of the wilder things might come out. You know, level four AGI as defined by OpenAI is innovators. So we have, you know, lots of startups, both YC and not YC that are trying to test that out right now. They're trying to apply the latest reasoning models from OpenAI that are about to come out, you know, like O3 and O3 mini, and they're trying to apply them to actually scientific and engineering use cases. So there's a cancer vaccine biotech company called Helix that did YC a great many years ago, but what they've figured out is they can actually hook up some of these models to actual wet lab tests. And you know, that's something that I'd be keeping track of like over the next couple years. Like if only by applying, you know, dollars to energy that then goes into these models, will there be real breakthroughs in, you know, biological sciences like being able to do new processes or come to a deeper understanding of, of whether it's cancer or cancer treatment or anything in biotech. The first experiments of that sort that's happening in the next year. Even in computer aided design and manufacturing, there's a YC company called Camphor that is trying to apply. They actually were one of the winners of the recent YC01 hackathon we hosted with OpenAI and their winning entry was literally hooking up 01 to Airfoil Design. So being able to increase the sort of lift ratio just by applying, you know, spend more time thinking about this O1 and it's able to create a better and Better airfoil given a certain number of, of constraints. So, you know, obviously these are like relatively early and toy examples, but I think it's a real sort of optimistic point around how do we increase the standard of living and push out like sort of the light cone of all human knowledge. Right. Like, you know, that that is like a fundamental good for AI. You know, between that and the inroads it might make in education, these are like some real, you know, white pill things that I think are going to happen over the next 10 years. And these are the ways that AI becomes not, you know, sort of Terminator 2, but instead like, you know, sort of the age of intelligence. As you know, Sam pointed out in a recent essay. Like, I think that if we can create abundance, if we can increase the amount of knowledge and know how and science and technology in the world, that solves real problems. And you know, I don't think it's going to happen on its own. Like, you know, each of these examples are there's, you know, frankly a YC startup like right there on the edge trying to take these models and then apply them to domains that, you know, it's kind of like, you know, Google probably could have done what Airbnb did, but it didn't. Because Google's Google. Right, Right. And so in the same way, I think that whether it's OpenAI or anthropic or Meta's lab or Deep Seq or some other lab that wins, like I think that we're going to have a bunch of different labs and they're going to serve a certain role like pushing forward human knowledge that way. And then, you know, my white pill version of what the world I want to live in is one where, you know, our kids, or really any kid with agency can get access to a world class education, can get all the way to the edge of, you know, what humans know about and are able to do or are able to like sort of affect and then, you know, sort of empowered by these agents, empowered by ChatGPT or perplexity or you know, whatever agent, you know, it's gonna look like her from the movie, right? Like we're going to have these, you know, basically super intense, intelligent entities that we talk to. I'm hoping that they don't have that much agency. You know, I'm hoping that actually they are just like sort of these inert entities that are your helpers. And if that's true, like that's actually a great scenario to be in. You know, that's the future. I want to be in. Like, I don't want to be. I don't think anyone wants to be sort of, you know, to borrow a term from Venkatesh Rao. Like, I don't think any of us want to be under the API line of, you know, these AIs. Right? Like, and I think that really passes through agency.

Shane Parrish (68:42)

The minute a robot can do laundry, I'm in. I'll be the first customer.

Gary Tan (68:47)

Yeah. There are YC companies and many startups out there that are actively trying to build that right now.

Shane Parrish (68:53)

My intuition is that it strikes me as immediate progress could come from just ingesting all of the academic pace papers that have been done on a certain topic and either disproving ones that people think are still correct and thus cutting off research on top of something that's not likely to lead to anything or making connections. Because nobody can read all these papers and make the connections and make maybe the next leap. Right? Not the quantum leap, but the next logical step. Who's doing that?

Gary Tan (69:24)

I mean, that's inevitable. And then someone listening here might want to do it, and then in which case they should apply to yc and maybe you should. We should do a joint request for startup for this next YC batch.

Shane Parrish (69:36)

I like it. I want equity there.

Gary Tan (69:37)

All right.

Shane Parrish (69:38)

But it's also interesting because then you think about that and you're like, if I'm a government and I'm funding research, that research should all be public because I want people to be able to take it, ingest it, and make connections that we haven't made yet. And it seems like a lot of that research these days is under lock and key. So you get this data advantage in the LLC LLMs, where some LLMs buy access or steal access or whatever, have access to it and then some don't. How do you think about that from a data access LLM quality point of view?

Gary Tan (70:08)

Hmm. It's a good question. I mean, yeah, it's a bit of a gray area these days. I mean, I'm not all the way in. I don't actually run an AI lab, even though, you know, and I was not actually.

Shane Parrish (70:18)

You run the meta AI lab.

Gary Tan (70:20)

Yeah, that's right. Not the meta AI lab, not meta.

Shane Parrish (70:25)

The company, but like meta as in all of them.

Gary Tan (70:28)

Anyway. That's a good question. I guess the funniest thing, my main response to all of that around, like, provenance of the data itself is at some point, like, it feels like it actually is fair use, though. I mean, that's all the way into case law.

Shane Parrish (70:46)

Well, here's another interesting twist on this then, like, so the airflow, they designed this new airfoil, is that patentable?

Gary Tan (70:53)

I mean, at least in terms of generated images, My understanding is generated images are not copyrightable.

Shane Parrish (71:00)

But if AI generates not only the science behind it, maybe we're at a point where maybe in the next couple years AI is doing more science than we've done. Is that going to be copyrightable or patentable or withheld? Or is that public access, public acknowledge that?

Gary Tan (71:18)

Well, my intuition would say people are just going to take the outputs of, you know, these AI systems and as far as I know, you know, you can submit a patent and there's not a checkbox yet that says like, was this, did you use AI as a part of it?

Shane Parrish (71:34)

Why wouldn't, here's another startup idea for anybody listening that we both want in on. Why wouldn't somebody just read all the patent filings in the US and be like, make the next logical step for me and patent that, Like a attempt to just patent it.

Gary Tan (71:46)

People might already be doing.

Shane Parrish (71:47)

A one person company could literally ingest the US patent database and be like, okay, here's the innovation in this. What's the next quantum leap or the next, even the next step that's patentable. Okay, automatically file and you're funded. I'm in. I got two ideas now. I love those.

Gary Tan (72:07)

I don't know, I think these are all totally open and fair game. And then I guess maybe going back to regulation, that's one of the stranger things that is happening right now. You know, one of the pieces of discourse out there during the AI safety debates, like in the last year, for instance, are about bioterror. And you know, the wild thing is, you know, basically possessing instruments of creating bioweapons is already illegal. So do you really need special laws for a scenario that are already covered by laws that exist that. I mean that's just like my sort of rhetorical question back when people are really, really worried about bio terror. You know, I think there's this funny example where AI safety think tanks were in Congress and they were sort of, you know, going to ChatGPT and you know, typing in sort of a doomsday example and it spits out this, you know, kind of like an instruction manual on like, well, you need to do this. You'd have to acquire this, you know, here's this thing you would do in the lab. And you know, of course like those steps are illegal. And then I think a cooler head prevailed in that. You know, the rebuttal Was someone next went to Google, entered the same thing and got exactly the same response. So, you know. Yes. Like, I've seen Terminator 2 as well. You know, am I worried about it? You know, my pdoom score is 1%. Like, like, I'm not totally, you know, unworried. Right. It would be a mistake to completely dismiss all worries. It would also potentially be worse to prematurely optimize and basically make a bunch of worthless laws that slow down the rate of progress and prevent things like better cancer vaccines or better airfoils or, you know, frankly, like, you know, nuclear fusion or like clean energy or better solar panels or engineering, manufacturing, you know, manufacturing methods that are better than what we have today. I mean, there's so many things that technology could do. Like, why are we going to stand in the way of it until we have a very clear sense like that is actually what we need to do?

Shane Parrish (74:24)

What does scare you about AI?

Gary Tan (74:26)

I mean, it's brand new, right? So the risk is always there. You know, it's so funny though. I mean, I'm. I'm not unafraid. On the other hand, like, you know, this principle of you can just do things still applies to computers, right? Like, if the system becomes so onerous, like, maybe you would go and like, let's shut down the power systems, let's shut down the data centers themselves. Like, why wouldn't people try to do that? Right? And they might do that. And, you know, I think that people.

Shane Parrish (75:01)

Try to do that every day now.

Gary Tan (75:02)

Right before AI, Right. If it became that bad, like, you know, I'm sure there would be some sort of human solution to try to fix this. But, you know, just because I read about the Butlerian jihad in the Dune series doesn't mean that I need to live. Like, that's what's going to happen.

Shane Parrish (75:23)

So you don't believe there's going to be one, One winner that dominates, like OpenAI or anthropic or it might still happen.

Gary Tan (75:29)

Right? You know, I think that there are lots of reasons why it won't happen right now, but, you know, who's to say everything is moving so quickly? Like, I think that, you know, these questions are the right questions to ask. I just don't have the answers to them.

Shane Parrish (75:42)

Like, I know, but you're the person to ask.

Gary Tan (75:45)

It's like asking, like, I guess, will Windows or Mac win? Or, you know, we're just literally living through that time where very, very smart people are, you know, fighting over the marbles right now.

Shane Parrish (75:57)

Totally.

Gary Tan (75:57)

And then to me, though, like, Working backwards. The best scenario is actually one where we have lots of marble vendors and you get choice and nobody has sort of too much control or, you know, cornering of all the resources.

Shane Parrish (76:12)

What's your read on Facebook? Almost doing a public good here and spending. I think it's over 50 billion at this point. And just releasing everything open source.

Gary Tan (76:23)

Yeah. I think that what Zuck and Ahmad and the team over there are doing is, frankly, God's work. I think it's great that they're doing what they're doing, and I hope they continue.

Shane Parrish (76:35)

What would you guess is the strategy behind that?

Gary Tan (76:38)

It's kind of funny because my critique on Meta would be they vary openly. Make everyone. They put it in everyone's faces, right? Like, you can't use Facebook or Instagram without. Or even WhatsApp without seeing, like, hey, Meta has AI now. But the funniest thing is, like, I'm very surprised that they don't think about sort of like the basic product part of it. Like, I went to Facebook Blue app recently and I was going to Vietnam, and I just wanted to say, okay, Meta AI, you're so smart. Tell me, my friends in Vietnam and it didn't know anything about me. I'm like, this is some basic rag stuff. Like, I get it. Like, you're already spending billions of dollars on training these things. How about, like, you know, spend a little bit of money on, like, the most basic type of, you know, retrieval augmented generation for me and my, you know, like, it's. They're just sort of sprinkling it in and it's a little bit of a checkbox. So, you know, I'm a little bit mystified. Right. Like, if they were very unified about it, I would really get it. Right. Like, clearly the way that we're going to interface with computers is totally going to change. What Anthropic is doing with computer use is, you know, I think that, you know, what I've heard is basically every major lab is probably going to need to release something like that, whether it's an API, the way Anthropic has or literally built into this, you know, the runtime that you run on your computer. Like, there's going to be a layer of intelligence. Like, you can sort of see the shade of the very, very dumb version of it from Apple and Apple Intelligence. It's like sort of sprinkling in intelligence into notifications and things like that. But I think it's virtually guaranteed that the way we interface with computers will totally change in the next few years. You know, the rate of improvement in the models as of today, all the smartest things that you might want to do, there's still actually things that you have to go to the cloud for and then that opens a whole can of worms. But there's some evidence that, you know, in the frontier research of, you know, the best AI labs, it's pretty clear that there's sort of parent models and child models. And so there's distillation happening from the frontier, very largest models with the most data and the most intelligence down into smarter and smarter tiny models. There's a claim this morning that a 1.5 billion parameter model, I think got 84% on the AIME math test.

Shane Parrish (79:14)

Oh, wow.

Gary Tan (79:15)

Which is like 1.5 billion parameters is like so small that it could fit on anyone's phone. Yeah. And that was like deep seq r1 just got released this morning, so hasn't been verified yet. But I think it's super interesting. Like we are literally day to day, week to week, learning more that these intelligent models are going to be on our desktops in our phones and we're right at that moment.

Shane Parrish (79:43)

So is the model better? Is the LLM better? Like, what makes that model so successful with so few parameters?

Gary Tan (79:50)

Oh, I don't know. I haven't tried it yet. But I mean, some of it is you can be very specific about what parts of the domain you keep.

Shane Parrish (79:59)

Okay.

Gary Tan (80:00)

And then, you know, I guess, you know, math might be one of those things that just isn't, you know, it doesn't require, you know, 1.5 trillion parameters. It takes 1.5 billion to do an 84% job of it, which is pretty wild. I mean, that's another weird thing of AI regulation. You know, I think Biden, for instance, his last EO was sort of this export ban. And Deepseek is a Chinese company releasing these models open source. And I believe that they only have access to last generation Nvidia chips. And so, you know, some of it is like, why are we doing these like, measures that like, may not actually even matter?

Shane Parrish (80:42)

It's interesting, right, because you think of constraint being, being one of the key contributors to innovation. By limiting them, you also maybe enable them to be better because now they have to work around these constraints or presumably have to work around them. I doubt they're actually sort of working around them.

Gary Tan (80:58)

That sounds right. I mean, I think the awkward thing about AI regulation is there's something like $4 billion of money sloshing around think tanks and AI safety organizations and someone was Telling me recently, like, if you looked at, on LinkedIn for some of the people in these sort of giant, the giant NGO morass of think tanks. Sorry if people are a part of that and getting mad at me right now hearing this. But, you know, there's a lot of people who went from, you know, bioterror safety experts who, like, you know, one. One entry. Right. You know, right above that, in the last even six or nine months, they've become. Become AI bioterror safety experts. And I'm not saying that's a bad thing, but it's just, you know, very telling. Right. Like, anytime you have billions of dollars going into, you know, a thing, maybe prematurely, you know, people have to justify what they're doing day to day. And I get it.

Shane Parrish (81:55)

So many rent seekers. I want to foster an environment of more competition within sort of like general safety constraints. But I don't think we're pushing up against those safety constraints to the point where it would be concerning. But we also operate in a worldwide environment where other people might not think the same way about safety that we do. And then it's almost irrelevant what we think in a world where other people aren't thinking that way and it can be used against us.

Gary Tan (82:21)

I think we're going into a very interesting moment right now with the AI czar is Sriram Krishnan, who used to be a general partner at Andreessen Horowitz. And I think that that's a very, very good thing. Like, we want people who have the networks into, people who have built things, who have built things themselves, you know, as close to that as possible. And, you know, I think that it is actually a real concern that the space is moving so quickly that, you know, if it takes legislation two years to make it through, that might be too slow. And so it's sort of even more important that the people who are close to the President and the people who are in the executive branch, at least in the United States, they should be able to respond quickly, whether it's through an EO or other means.

Shane Parrish (83:12)

I don't know what it's like in the States, but in Canada, I was looking at the Senate the other day, and I was just trying to like, is there anybody under, like, 60 in the Senate kind of thing? Does anybody understand technology? Or do they all grow up in the world where Google became a thing after they were already adults? And it strikes me that there's a difference, the pace of technology improvement versus the pace of law or regulation, but also the people that are enacting Those laws don't tend to. They have a different pace as well. Right. Our kids are in a different world. My kids don't know what a world without AI looks like. Neither do yours. But we do, you know, because we're similar age. And then, you know, our parents have this other thing where it's like, well, we used to have landline phones and like all of these other things. And it strikes me that those people should maybe not be regulating, you know, AI.

Gary Tan (84:04)

That sounds right. I mean, I think it's more profound now than ever before. I mean, the other thing that's really wild to think about is it's. I. What comes to mind is that meme on the Internet where, like, there's the guy at this dance. It's this like, you know, that everyone else is dancing and they're in the corner and it's like they don't know. Yes. You know, if you go any. Almost anywhere in the world, you know, people maybe have heard of Chat GPT. They definitely haven't heard of Anthropic or Claude. Yeah. You know, it just hasn't touched their lives yet. And then meanwhile, like, the first thing they do is they look at their smartphone and, yeah, you know, they're using Google and, you know, they're addicted to TikTok and things like that.

Shane Parrish (84:47)

So do you think we get to a point where. And this is very like Ender's Game, if I remember correctly, in the movie, where, you know, you pull up an article on a major news site and I pull up an article on a major news site and at the base it's sort of like the same article, but now it's catered to you and catered to me based on our political leanings or what we've clicked on or what we watched before.

Gary Tan (85:10)

Well, my, my hope. Hope is that there's such a flowering of choice that, you know, it's going to be your choice, actually. I mean, the difficulty is like, well, then you have a filter bubble, but, you know, that exists today with social media today. Yeah. Okay, so here's a white pill that I don't know if it's going to happen, but I hope it happens. You know, one of the reasons why it's so opaque today is literally that, you know, X has, you know, or X or, you know, before it was called Twitter. And Twitter had, you know, thousands of people working at that place. And, you know, you needed thousands of people maybe. Right. Or I guess the tricky thing is like, Elon came in and quickly axed like 80 or 90% of the people. And it turns out you didn't need 80 or 90% of the people. So that's like another, you know, form of founder mode taking hold. But like it or not, you know, I can't go into Twitter today and tool around with my for you. Like my for you is written for me. Right. It's in some server someplace and there's a whole infrastructure thing.

Shane Parrish (86:21)

Yeah, you don't control it, but it's.

Gary Tan (86:23)

Conceivable, you know, today with Codegen, you know, today engineers are basically, you know, writing code about 5 or 10x faster than they would before, and that sort of capability is only getting faster and better. Like, it's sort of conceivable that you should be able to just write your own algorithm and maybe you'll be able to, you know, run it on your own, you know, and you'll want choice. And so, you know, the kind of regulation that I would hope for is actually open systems, right. Like, I would want to actually write my own version of that. Like, I don't want. The best version of that is actually like, I want to see an exp. You know, I maybe want to see my for you algo, like very plainly. And then I want to be able to see if I can convert that into the one that I want. Or I can choose from 20 different ones.

Shane Parrish (87:16)

Two ideas here, you know, as you're mentioning that one, like, your list could be your default, like I want this list to be. But the other one is like, Maybe there's just 20 parameters and you get to control those parameters. And it could be, you know, you could consider it political as one parameter from left to right.

Gary Tan (87:33)

Right.

Shane Parrish (87:34)

But you can, you could be like happy, sad, like you could sort of filter in that way. I know that'd be super interesting.

Gary Tan (87:40)

So, I mean, if, if regulation is coming, like, give me open systems and open choice, and that's, you know, sort of the path towards liberty and, you know, sort of human flourishing. And then the opposite is clearly what's been happening, right? Like Apple, you know, closing off the imessage protocol so that, you know, it's literally a moat. Like, oh, no, like that person has an Android. So they're going to turn our really cool blue chat into a green chat.

Shane Parrish (88:07)

We don't talk to those people, do we?

Gary Tan (88:09)

Yeah, right, I know, right? I mean, that's just a pure example of, you know, Apple, even today, still, you know, they're opening it up a little bit more with rcs, but. But those are actually in reaction to the work of Jonathan Kanter and the doj. So there are efforts out there that are very, very much worth our attention around reining in big tech and reining in the ways in which these sort of subtle product decisions only make money for big tech and they reduce choice and ultimately reduce loads.

Shane Parrish (88:46)

Liberty It'd be super interesting to be able to have an advantage if you're big tech and your company and you come up with this, but have that advantage erode automatically over time in the sense that you might have a 12 month lead, but what you're really trying to do is foster continuous. Like if you're a government and you're trying to regulate, it's like, I don't want to give you a golden ticket. I want you to have to earn it and you can't be complacent, so you have to earn it every day. And so, yeah, maybe you have like a two year window on this blue bubbles and then you have to open it up, but now you got to come up with the next thing. You got to push forward instead of just coasting. Like Apple really hasn't come up with a ton lately.

Gary Tan (89:28)

Yeah. And then I think the reason why it's so broken is actually that government ultimately is, you know, very manipulatable by much money.

Shane Parrish (89:40)

Yeah.

Gary Tan (89:41)

And, you know, that's sort of the world we live in.

Shane Parrish (89:43)

Do you think that'll be different under Trump? I don't tend to get into politics here, but so many people in the administration are already incredibly wealthy.

Gary Tan (89:50)

Oh yeah, that's the hope. I mean, we're friends with a great many people who are in the administration. We're very hopeful and we're, you know, wishing them. We're hoping that really great things come back and, you know, in full transparency. Like, I think I was too naive, even didn't understand how anything worked in 2016. That's not what I was saying. In 2016 I was fully an NPC in the system. But also, that being said, I'm a San Francisco Democrat. So I really have very, very little, have very little special knowledge about how the new administration is going to run, except that I really am rooting for them. I'm hoping that they are able to be successful and to, you know, make America truly great. Like I am 100%, you know, even though I didn't vote for Trump, I am 110%, you know, down for making America truly awesome.

Shane Parrish (90:46)

What do you believe about AI that few people would agree with you on?

Gary Tan (90:50)

It might be that point that I just gave you Like I think that a lot of people are hoping that the AI becomes self aware or you know, have agency. And from here the kind of world we live in will be very different if somehow the, you know, literally AI entities are given. You know, maybe the line is actually will we have an, an AI CEO? Like will we have a company that just like literally gives in to, you know, whatever the central entity says, like that's what we're going to do. Every problem, every, you know, you know, it's sort of the exact extreme opposite of founder mode. It's like AI mode. Like will we live in a world in the future where you know, corporations decide like, you know, what a human is messy and kind of dumb and doesn't have a trillion token context window and like won't be able to do what we want it to do. So we would trust an AI and you know, an LLM based consciousness more than a human being. Like I'd be worried about, about that.

Shane Parrish (91:57)

I was thinking about this last night watching the football game actually and I was like, why are humans still calling plays? Like yes, for coaching, but like calling players in the game and AI? I feel like at this point with like 01 Pro or something, we'd be ahead of where we are as human. I'm wondering if team should try that. That'd be a super interesting.

Gary Tan (92:18)

Oh, that's going to be the next level of Moneyball.

Shane Parrish (92:20)

Then we'll just try it in preseason, right? Like, or try it in a regular season game. I don't know but it strikes me that like they would know who's on the field, who's moving slower than normal. Like all these, a million more variables than we can even comprehend or compute or. And historical data, you know, the last 16 weeks this team has played, you know, when you run to the right after they just subbed or something, like they can see these correlations that we would never pick up on. Not causation, but correlation. It'd be super fast. Fascinating.

Gary Tan (92:49)

Yeah. I mean what's funny about it is I think in, in those sort of scenarios you might just see a crazy speed up because of human effects. I mean when you look at organizations and how they make decisions, so many of them, you know, there's sort of like a, a Straussian reading of them. They're sort of like at the surface level you're like I want to do X, but like right below that is actually something that is not about X. You know, for a corporation it has to be like we have a fiduciary duty to our shareholders. And we need to maximize profit, for instance. And then right below that, you know, corporations or, you know, entities of any set of people, like, they do all sorts of things not for reason X on the top. It's actually like, oh, actually, you know, the people who are really in power, you know, don't like that person or, you know, they rub them the wrong way or.

Shane Parrish (93:47)

We're human.

Gary Tan (93:48)

Yeah, exactly. Right. It's like these are like extremely influenceable systems.

Shane Parrish (93:53)

Your idea might be best, but I'm gonna disagree because it's your idea, not my idea.

Gary Tan (93:56)

Right. And then I think that's why in general, we really hate politics inside companies, because, you know, it sort of works against the collective.

Shane Parrish (94:07)

Do you think we'd ever see a city like a mayor then first, before even a CEO, as like an AI mayor?

Gary Tan (94:15)

You know, I guess, like, now that we're sitting here thinking about it, it's like sort of conceivable. But, you know, in sort of all of these cases, I would much rather there be a real human being.

Shane Parrish (94:26)

Kind of like a plane, Right? Like, we want a physical pilot, even though the plane is probably better off by itself.

Gary Tan (94:31)

Yeah, that's right. And that, that might be what, what ends up happening. Like, even if 90% of the time you're using the autopilot, like, you always need a human in the loop and, you know, I'd be curious if that turns out to be one of the things that society learns. One of the crazier ideas I've been talking to people about that, like, I feel like would be a fun sci fi book, would be just speculation playing out on, you know, sort of how this interacts with nation states. Like, you know, China obviously is run by a central committee and arguably Xi Jinping, you know, seemingly, if you had asi, you would only want, you know, sort of the Central Committee to have it. And so that might turn into like a very specific form of that. You know, it's. You know, China might end up having one ASI that is totally centrally controlled, and then everything else about it, you know, sort of comes out of that. And then you might end up with, I mean, controversially, like, I think often they're trying to be benevolent. Right. Like, if you spend time in China, it's incredibly clean. It's, you know, I'm sure there's all sorts of crazy stuff that happens that is quite unjust. You know, I have no idea. It's not really even my place to like, argue one way or another what, what it's like to Be in China. But that's an interesting idea. It's like, you know, that society probably, you know, unless there's other changes there, like, that's. You can sort of count on a single artificial superintelligence, like sort of setting the. How everything works over there. I mean, probably internal to the Politburo itself. You know, they're going to have to have all these different discussions about what do we do with this ASI and who gets to, you know, where does the agency, the ultimate agency of that nation, come from?

Shane Parrish (96:27)

Going back to something you said earlier, I think the ultimate combination, at least for right now, is human and machine intelligence working in concert, where the machine intelligence might be the default, and then the human opts out.

Gary Tan (96:38)

Right.

Shane Parrish (96:38)

And that's exercising judgment. It's like, no, we're not. And when you look at chess, that tends to be the case where the best players are usually using computers, but they know when, oh, there's something the computer can't see here, or there's an opportunity that it just doesn't recognize. And I think it was Tyler Cowan who said that he had a word for it, mixing the technologies.

Gary Tan (97:00)

Fascinating.

Shane Parrish (97:01)

Yeah.

Gary Tan (97:01)

And then, yeah, the question is, how does America approach it? Potentially, it's much more laissez faire. And then in that case, my argument would be the most American version of it is that, like, you know, you and I have our own asi, and, like, each, you know, each citizen should be, you know, issued an ASI and be taught how to get the most out of it. And, you know, maybe it needs to be embodied with a robot. Like, we should all, you know, we should all be Superman in that sense. And that would be, like, the most empowering version of a society that of, like, free and, you know, free people created equal. Right, right. And then, you know, there might be other versions in Europe. I mean, I'd be curious, like, you know, what's the European version of it? Maybe that version has, you know, all the check marks and, like, oh, is, you know, every decision has to be, you know, was this AI assisted or not? And, like, let's check the provenance on, like, yeah, you know, how that AI was, like, trained. And, I mean, I don't know. There are all these different. There's like a billion different ways all of these different governments are going to sort of approach this technology.

Shane Parrish (98:07)

What are the smartest people at the leading edge of AI talking about right now?

Gary Tan (98:12)

I mean, the hard part is I spend most of my time not with those people. I spend most of my time with people who are commercializing it. So the very, very smartest people are clearly the people who are in the AI labs actually actively doing sort of creating these models, but sort of the people who I know who are in those rooms. I mean, it sounds like test time compute is really it. The reasoning models are sort of the thing that will really come to bear this year. Like we're sort of understanding that right now. For now, it sounds like pre training might have hit some sort of scaling limit, the nature of which I don't understand yet. There's a lot of debate about it. Will there be new 4.0 style models that have more data or more compute? And seemingly there's just rumors of training runs gone awry that basically the scaling loss may have petered out, but I don't know.

Shane Parrish (99:18)

So we have sort of like the LLM, we have the reasoning. The LLM and the reasoning model are different. Correct.

Gary Tan (99:24)

The way OpenAI talks about O, there's sort of connected but like different steps.

Shane Parrish (99:30)

Okay. And so we have progress there, then we have progress with the data and then we have progress with inference.

Gary Tan (99:38)

Yep. Well, we just don't have enough GPUs really. Like, you know, I think what's funny is like I'm still pretty bull on Nvidia in that they more or less have, oh, talk to me about this monopoly on, you know, sort of the best price performance.

Shane Parrish (99:53)

And so you think this is going to continue? Continue?

Gary Tan (99:56)

Like, well, the demand for trillions of.

Shane Parrish (99:58)

Dollars of investments in AI, basically.

Gary Tan (100:02)

I think you can live in two different worlds. One world says like, all of this is hype. We've seen AI hype before, it's not going to pan out. And then I think the world that we're spending a lot of time in, the world really wants intelligence. And then the scary version of this is like, yes, some of it actually is labor displacement. Right. Like in the past, what tech would do is we'd be selling you hardware, we'd be selling you a computer on every desk. Like everyone needs a smartphone. You know, we're selling you Microsoft Office, we're selling you packaged software, we're selling you Oracle SQL Server. Like, you know, we're selling, you know, SaaS, apps like Salesforce, like, you know, it's $10,000, you know, per seat, per year, that kind of thing. Or we're selling, you know, classically Palantir was selling, you know, million dollar or 10 million dollar ACV, you know, very specific vertical apps. Right. And so all of those things are selling software or hardware. And that's like selling technology. And so increasingly what we're starting to see is like, you know, especially the bleeding edge is probably customer support and all of the things that you would use for a call center, like those are sort of the things that are already so well defined and specified. And there's a whole training process for people in, you know, usually overseas to do these jobs. And AI now is just coming in and like it's, you know, the speech to text and text to speech, those things are indistinguishable from human beings now. And you can train these things. The evals are good, the prompting is good. You know, I, you know, going back to what we were saying earlier, like what we're seeing is like, you know, like it or not is actually replacing labor.

Shane Parrish (102:07)

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Shane Parrish (103:05)

@Mintmobile.Com has anybody created an AI call center from scratch and now is ingesting customers?

Gary Tan (103:13)

Yes, I mean I, I funded a company in this very current batch that, you know, it's called Leaping AI. They are, they are working with some of the biggest wine merchants in Germany, which is fascinating. So I mean that's another fascinating thing. Like these things speak all human. They certainly speak all the top languages very, very well and are indistinguishable and, And I think 80% of the ordering volume for some of their customers is entirely no human in the loop.

Shane Parrish (103:46)

I would love to see government call centers go to this. It would scale so much better. I was on the hold for three hours the other day for a 15 minute question that I had to answer. And it's like, this could be a. It could be done so much quicker by somebody who's not a human and probably more securely and reliably and more consistent, regardless of who's on the other end or how they're talking. How would you define AGI?

Gary Tan (104:14)

I guess the funniest thing is Microsoft, I think, is defining it when it gets its $100 billion back. But I'm sort of skeptical of that because I think basically only Elon Musk then would, you know, qualify as a human general intelligence. I think, like AGI, the thing is, like, in limp, in a lot of domains, it feels like it's here, actually. I mean, you know, can it have a conversation with someone and take, you know, give incredibly good wine pairing recommendations and have a perfectly fine indistinguishable from a real human, you know, sort of, or even better than human sort of interaction and also, like, take orders for very expensive wine and have that just work? Yes. Like, that's happening right now.

Shane Parrish (105:04)

Yeah.

Gary Tan (105:05)

So I think in a lot of domains, and this is sort of the year where, like, Maybe there's like 5 or 10% of things that, like, it's, you know, sort of hitting the Turing Test and, you know, really satisfying that. But, you know, I think maybe this is a year where it goes from like, 10 to 30% and the year after that it doubles again. And the next few years are actually the golden age of building AI.

Shane Parrish (105:28)

Totally. I think I'm super optimistic, at least for the next five years, about the things we'll discover, the progress we'll make, the impact we'll have on humanity and a lot of the things that plague us. I want to get into how you use AI a little bit. What do you know about prompting that most people miss?

Gary Tan (105:49)

I mean, I'm mainly a user. I spend a lot of time with people who spend a lot of time in prompts. Probably the person I would most point people to is Jake Heller. So he's the founder of Casetext. He was one of the first people to get access to GPT4. And we think of him at YC as the first man on the moon, in that he was the first to successfully commercialize GPT4 in the legal space. So what he said was that, you know, they had access to GPT 3.5, and it basically hallucinated too much to be used for actual, like, legal work. Like, lawyers would see one wrong thing and say, like, oh, I can't trust this GPT4. He found actually you know, with good evals would actually, you know, give. They could program the system in a way that it would actually work. And what he says he figured out was if GPT4 started hallucinating for them, they realized that they were doing too much work in one prompt. They needed to take that thing that they asked GPT4 to do and then break it up into smaller steps. And then they found that they could get deterministic output for GPT4 like a human if they broke it down into steps.

Shane Parrish (107:17)

Oh, interesting.

Gary Tan (107:18)

And what he needed to do, I mean I sort of, it's sort of equivalent to Taylor time and motion studies in factories. It feels like that's what he did for what a lawyer does. You know, let's say you have to put together a chronology of what happened in a case case and what a real, he's a real life lawyer. So which is he was like sort of unusually perfect to figure out this prompting step. Like he realized that he needed to look at what a real lawyer would do and literally replicate that like tailored time and motion style in the process and prompts and workflow. So for instance, doing this type of summarization, he would have to go through and read all the materials. And then this is why apparently lawyers have, you know, sort of their many, many different colored little flags and highlighters and things like that. They just get very good at doing a read through paragraph by paragraph, sentence by sentence and pulling out the things that are relevant and then sort of synthesizing it. And so, you know, early versions of case text and a lot of it today I think is still just doing that in. It's like what is a specific thing that a human does? Break it down into the very specific steps that a real human would do. And then actually basically if it breaks, you're just asking in that step to do too many things. So like break it down into even smaller steps and somehow that worked. And like basically this is the blueprint that I think a lot of YCE companies and AI vertical SaaS startups are doing across the whole industry right now. They literally are taking, you know, model out what a human would do in knowledge work and then break it down into steps and then have evaluations for each of those prompts. And then as the models get better, because you have, you know, what we call the golden evals. Basically you just run the golden evals against, you know, the new, the newest model, like you know, 4.0 comes out, cloud 3.5 comes out, Deepsea comes out, you know, uh, have evals which is basically a test set of prompt, context, window data and output. And you can actually, you know, what's funny is like, it's even fuzzy that way. Like, you can even use LLMs in the evals themselves to, you know, score them and figure out, you know, does it make sense?

Shane Parrish (109:43)

Can you give us an example of an eval, like, make it tangible for people?

Gary Tan (109:47)

Oh, yeah, it's really straightforward. It's just a test case. Right. So given this prompt and this data, evaluate the prompt to see if. And it usually maps directly to something that is true. False. Yes. No, something that is pretty clear. Let's say there's a deposition and someone makes a certain statement, you might have a prompt that is like, is what this person said in conflict with, you know, any of the other witnesses?

Shane Parrish (110:19)

Or.

Gary Tan (110:19)

I don't know, I'm. I'm totally making this example up. But, like, this is the kind of thing that you can do, you know, at a very granular level. You might have thousands of these. And then that's how, you know, Jake Heller figured out he could create something that would, you know, basically do the work of hundreds of, you know, lawyers and paralegals, and it would take, you know, a day or an afternoon instead of, you know, three months of discovery.

Shane Parrish (110:46)

That's fascinating. How do you use AI with your kids?

Gary Tan (110:50)

Oh, I love making stories with them. So, you know, what I find is 01 Pro is actually extra good now. So, yeah, actually there's like an interesting thing that's happening right now, and I saw it up close and personal this morning looking at some blog posts about DeepSeek R1, which is deep Seek's reasoning model. I was reading Simon Willison's blog post about. He got deep seek R1 running. It's the first one of the first open source versions of sort of the reasoning. And so what we just described with how Jake Heller broke it down into chain of thoughts to make case text work, it turns out that that maps to basically how the reasoning stuff works. And so the difference between what Jake did with GPT4 when it first came out and what 01 and 01 Pro maybe is doing, and what Deepseek R1 is doing, clearly because it's open source and you can see it, is that those steps, like breaking it down into steps, and the sort of metacognition of whether or not it makes sense at all of those microservices steps, that's what, in theory, this reasoning is actually happening. That's actually happening in the background for 01 and 03 and if you use ChatGPT, you'll see the steps, but it's like a summary of it.

Shane Parrish (112:24)

Right?

Gary Tan (112:24)

And so it's, you know, I just only saw it this morning. I mean, this is such new stuff. Like I was hoping that someone would do a open source reasoning model just so we could see it. And that's what it was. I think Simon's blog post this morning showed here's a prompt and then he could actually see, I think he said pages and pages of the model talking to itself, literally. You know, does this make sense? Like, can I break it down into steps? So what we just described as a totally manual action that a really good prompt engineer CEO like Jake Heller did, and he sold his company Case Text for almost half a billion dollars to Thomson Reuters. That is actually very similar to what the model is capable of doing on its own in a reasoning model. And that's what it's doing. When it's doing like test time, compute, it's actually just spending more time thinking before it spits out the final answer.

Shane Parrish (113:27)

So how do you create a competitive advantage in a world like that where perhaps that company had an advantage for a year or two and now all of a sudden it's like built into the model for free?

Gary Tan (113:39)

Yeah, I mean, I think, you know, ultimately the model itself is not the moat. Like I think that the evals themselves are the moat. I don't have the answer yet, basically, for now, maybe it's a toss up. If you're a very, very good prompt engineer, you will have far better golden evals and the outcomes will be much better than what O3 or DeepSeq R1 can do because it's specific to your data and it's much more in the details. I think that that remains to be seen. Like the classic thing that Sam Altman has told YC companies and told most startups, period, is you should count on the models getting better. So if that's true, then that might be a durable moat for this year, but it might not be past, I mean, 03 we haven't even seen yet. The results seem like fairly magical. So it's possible that advantage goes away even as soon as this year. But all the other advantages still apply. Like one thing that a lot of our founders who are getting the five to $10 million a year in revenue, revenue with five people in a single year are saying is, you know, yes, there's prompting, there's evals, like there's a lot of magic that, like it's sort of mind Blowing. But what doesn't go away is building a good user experience. Building something that a human being who does that for a job sees, that knows, that's for me, understands how to start, knows what to click on, how to, how to get the data in. In. And so, you know, one of the funnier quips is that, you know, the second best software in the world for everything is using ChatGPT because you can basically copy and paste, you know, almost any workflow or any data. And it's like the general purpose thing that, you know, you can just drop data into it. And it's the second best because the first best will be, be a really great UI made by a really good product designer who's a great engineer, who's a prompt engineer, who actually creates software that doesn't require copy paste. It's just like link this, link that. Okay, now this thing is now working. And so I think that that's. Those are the mo. Like the moats are not different actually, at the end of the day, it's still, you still have to build good software. You still have to be able to sell, you have to retain customers, you have to. But you just don't need like a thousand people for it anymore. You might only need six people.

Shane Parrish (116:20)

Okay, I want to play a game. I'm going to. You have 100% of your net worth. You have to invest it in three, three companies.

Gary Tan (116:27)

Oh, God.

Shane Parrish (116:27)

Okay, and so the first company, you have to invest half and then 30 and then 20. So altogether 100%. Which companies out of the big tech companies is. How would you allocate that between. Here's my biggest bet, my second biggest bet, my third from today going forward.

Gary Tan (116:46)

Okay. I guess, you know, is it cheating to say I'd put even more money into, into my, the, the YC funds that I already run? But that's a, that's a cop out.

Shane Parrish (116:55)

That's a cop out.

Gary Tan (116:56)

That goes without saying. I think that it's very unusual just because, you know, we end up like this is the commercialization arm of every AI lab is what I realize. But short of that, I mean, maybe Nvidia, Microsoft Meta, in that order, probably.

Shane Parrish (117:14)

Why?

Gary Tan (117:15)

I mean, Nvidia just, you know, has an out and out. Like for now, they're just so far ahead of everyone else. I mean, it can't last forever. But I think that the demand for building the infrastructure for intelligence in society is going to be absolutely massive and maybe on the order of the Manhattan Project, and we just haven't really thought about it. Enough, right. Like it's entirely conceivable, like if say like level four innovators turns out to work like, you know, it's sort of the Meta project because then it's like the Manhattan Project of instantiating more Manhattan Projects actually. Like, you know, you could imagine if we can, if more test time, computers or you could do the work of 10,200 IQ Einsteins working on bringing us basically unlimited clean energy. That alone will, I mean, if anything, that's probably the bigger problem right now. We know that the models will continue to get better. We know that, you know, the demand for intelligence will be unending. And then, you know, even going back to the robotics question, it's like if we end up making, you know, universal basic robotics, you know, the limit will still actually be, you know, sort of the climate crisis and the ability, the available energy available to human beings. Right. And you know, maybe solar can do it it, but maybe there are lots of other sort of solves. But you know, I think energy and access to energy is sort of the defining question at that point. Like everything else you could solve, like, and everything else you could sort of either, you know, if it's in the realm of science and engineering, like, you know, in theory, between robots and you know, more and more intelligence, like we could sort of figure these things out, but not if we run out of energy.

Shane Parrish (119:26)

Okay, why Microsoft and why Meta next?

Gary Tan (119:28)

I mean, I think Microsoft has just really, really deep access to OpenAI and I think OpenAI is probably. You said public companies, right? Yeah, yeah. So, you know, I think there's a non zero pretty large percentage of like the market cap of Microsoft that I think is pretty predicated on Sam Altman and the team at OpenAI continuing to be successful.

Shane Parrish (119:50)

Totally. And then why Meta?

Gary Tan (119:53)

I mean, I think Meta is sort of the dark horse because like they are amassing talent and then they have crazy distribution and I think, you know, I just would never count Zuck out. I think that he, you know, it's so crazy that it's super smart that he is on that. You know, he's always thinking about what is the next version of computing like, so much so that he probably put more money than he should have into ar and that was maybe premature. He might still end up being right there. But, you know, AI for a fraction of what he's put into AR is likely to push forward all of humanity and you know, and accelerate technological progress in a really profound way.

Shane Parrish (120:38)

I want to switch subjects a little bit. A few years ago you met with Mr. Beast.

Gary Tan (120:43)

Oh yeah.

Shane Parrish (120:44)

And talked about YouTube. What did you learn? Because your channel changed.

Gary Tan (120:48)

Oh yeah, he was great. I mean, he was very brusque with me. He said, you know, look man, your titles suck and your thumbnails are even worse. And you know, I think that he spent so much time trying to understand the YouTube algorithm and what people want that he just loaded it completely into his brain.

Shane Parrish (121:07)

And what makes a good title?

Gary Tan (121:10)

I think it's clickbait, unfortunately. You know, unfortunately. And this is the thing, like when you're trying to make smart content, it's actually kind of tricky because you don't want necessarily more clicks, you want more clicks from people who are smart.

Shane Parrish (121:27)

So we title our episodes differently on YouTube usually thank. Than on the actual audio feed. Because if you want YouTube to pay attention, you have to almost be more provocative intentionally.

Gary Tan (121:38)

That sounds right. Yeah.

Shane Parrish (121:39)

Like we could call this, you know, AI Ends the World or something.

Gary Tan (121:43)

Yeah, that's right.

Shane Parrish (121:44)

You know, get people to watch. But that's not actually what we're talking about at all. What makes a good thumbnail? What did you learn about thumbnails?

Gary Tan (121:50)

Oh, usually like a person looking into the camera seems to help a lot. And then you want it to be relatively recognizable. Like, you know, you want some sort of style that when someone sees it, you know, I mean, basically what I was doing at the time was just taking whatever frame that was, you know, sort of kind of representative and throwing it in there. But when you train someone to look at YouTube, you know, back to back to back, every time it shows up, like you sort of want to be highly recognizable, so you want to have.

Shane Parrish (122:25)

A distinct thumbnail like yours with the overlay, sort of like the red.

Gary Tan (122:30)

Yeah, but you know, once I stopped posting so regularly, you know, then it sort of didn't matter as much anymore. But if you're going to post very regularly, that's pretty important actually. So, yeah, unfortunately it's clickbait. And then there is an interesting interaction like, you know, yes, you can optimize for better thumbnails and better, better titles for the click through. But if it has absolutely nothing to do with the actual body, as you mentioned. Yeah. You will not get watch time.

Shane Parrish (123:02)

And then YouTube will be like, oh, people aren't watching this. We're not going to promote it because the big thing about YouTube is discovery.

Gary Tan (123:07)

Yep.

Shane Parrish (123:08)

And like we notice this all the time where it's sort of like you just get this audience, but you don't get to keep the audience as a creator, which is really interesting.

Gary Tan (123:16)

Well, you do if you are regular. And then the other hack is be very shameless about asking for subs. And then the funniest thing is like subs do very little actually. There's no guarantee that you show up in people's feeds. If someone subs it, like helps a little bit, liking helps more, Watch time helps the most. And then the extreme like, you know, over the top hack that you know, probably you should do here is you should ask for the like subscribe and hit the bell icon. Because if you hit the bell icon and they have notifications on, that's the only thing that is almost as good as having their email address and emailing them.

Shane Parrish (124:02)

You heard it here, people. Gary just told you.

Gary Tan (124:05)

You got to click like subscribe and hit the bell icon because. Because you want knowledge, you want to be smart and this is the place to get it.

Shane Parrish (124:14)

Oh, I love that. Thank you. Good advertising. I want to ask just a couple random questions before we wrap up here. What are some of the lessons that you learned from Paul Graham that you sort of apply or try to keep in mind all the time?

Gary Tan (124:28)

I think the number one thing that is very hard but is so, I mean you can see it and read it in his essays. It's to be plain spoken and to sort of be hyper aware of artifice, of kind of like bullshit, basically. Like don't let bullshit. You know, I think like it creeps in here and there. I'm like, oh yeah, you know, I, you know, I sometimes am in danger of like caring too much about like the number of followers I have and things like that, you know, whereas like, actually I shouldn't be worried about that. Like what I should be worried about is. And you know, I spend a lot of time with our YouTube team and our media team at YC talking about this. It's like if we get too focused on just view count, we're liable to just, yeah. Like optimize for the wrong audience. If we are not being authentic to ourselves or you know, if we're just trying to like follow true trends or you know, do things that get clicks. It's like that's not helpful to them either. Like then we're just on this treadmill, right? Yeah, basically like trying to be very, very high signal to noise ratio. You know, the thing that I probably struggle with most and you know, I don't know, maybe some of the listeners here might feel this. It's like sometimes I think out loud, out loud. And then, you know, really, really great ideas are not like thinking out loud. They're actually figuring out a very complex concept and then trying to say it in like as few words as possible. And you know, the amount of time that Paul spends on his essays is fascinating. It's, you know, sometimes days, like, sometimes weeks, like he'll just, you know, iterate and iterate and send it out to people for comment. And, you know, the amount of time he spends whittling down the words and trying to like combine concepts and say the, say the most with the least number of words, it would shock you. And then also that is actually thinking like writing is thinking. Like, one of the more surprising things that we do a lot of at YC is we help people spend time thinking about their two sentence pitch. So, you know, you would think that that's. Oh yeah, that's like something, you know, Startup 101. Like you're helping people with their pitch. That sounds so basic. Like, yeah, I guess that makes sense. Like that's what an incubator would do. But the reason why it's very important is that it's actually almost like a mantra. It's like a special incantation. Like you believe something that nobody else believes and you need to be able to breathe, breathe that belief into other people and you need to do it in as few words as possible. Like, so if you, the joke is like, oh yeah, like, what's your elevator pitch? But like, you might run into someone who could be your cto, who could introduce you to your lead investor, who could be your very best customer. And you will literally only have that time. You know, you will only have time to get two sentences in. And so, and even. And then, I mean, I guess it's kind of fractal. Like that's what I love about a really great interview. Like, you know, someone comes in and I'm like, oh yes, I get it. Like, I know what it is and I know why that's important. I know why I should spend more time with you. That's what a great two sentence pitch is. And you know, knowing what it is is very hard. Like that's all of Paul Graham's, you know, sort of editing down and whittling down. In a nutshell. It's like people do really complex things. How do you say what you do in one sentence? That's very hard, actually. And then, you know, the second sentence is like, why is it important? Why is it interesting? Why should I? You know, and then that may well change with like the person that you're talking to. So, yeah, to the degree that clear communication is clear thinking. You know, one of the things I did when I first joined yc, I had no intention of ever becoming an investor, ever being a partner, let alone running the place. Like I was just a designer in residence. And what I did was I did 30 minute, 45 minute office hours with companies in the YC winter 11 batch sitting in. Back then as an interaction designer, I used Omnigraffle a lot. And so we just sat there and designed their homepage. And it's like, this is what the call to action should say. Here's, you know, put the logo here, here's the tagline, then here's the. Maybe you have a video here or right below you have how it works. And then what's funny about it is some people would take the designs we did in those 30, 45 minute things and that would be their whole startup, sell those companies for hundreds of millions of dollars years later, which is just fascinating to think about. It's like clear communication, great design, creating experiences for other people. All of those are sort of exercising the same skill. And so that's what a founder really is. It's like, you know, a founder to me is a little bit less what you might expect. It's like, oh, this is someone with a firm handshake who looks like a certain way and like bends the will of the people. Like you might think of an SBF that's like, that's all artifice. Like, think about that guy. Like, that guy was like full of shakes and like the guy was like on meth, right? Like the guy was, you know, everything about it was an affectation, right. Like he was a caricature of like an autist. Right. Like we see very autistic, incredibly smart engineers all the time. But you know, for him it was like that was part of the act.

Shane Parrish (130:13)

Yeah.

Gary Tan (130:14)

Like, I remember he did a YouTube video with NAS Daily. And I love, you know, Nasir's great and I love Nas Daily, but I couldn't believe the videos that SBF went on. It was just like full of basically bullshit, right. And exact opposite of Brian Armstrong and. But we're always on the lookout for that.

Shane Parrish (130:34)

He wasn't trying to fool you.

Gary Tan (130:36)

What's that? Oh, yeah, I guess so. I mean, he was fooling the world.

Shane Parrish (130:39)

Because, you know, right? Like you, you know, it's hard to fool somebody who, who knows versus somebody who doesn't know. And he wasn't trying to appeal to you, he was trying to appeal to you. Other people who didn't know. It's the same as going back to Buffet, just tying a few of these conversations together. Everybody repeats what Buffett says, but the people who actually invest for a living or know Warren or Charlie or have spent time with them can recognize the frauds because they can't go a level deeper into it. They can't actually go into the weeds. Whereas those guys can go from like the 1 inch level to the 30,000 foot level and everything in between. And they don't get, get frustrated if you don't understand. Whereas a lot of the fraudsters, one of the tells is they can't go, they can't traverse the levels. And then they do tend to get defensive or sort of angry with you for not understanding what they're saying, which is really interesting. And then I just want to tie the writing back to what you said. You said. If you can't get it clear in two sentences, you might miss an opportunity that goes to the 10 minute interview you're looking for. Maybe it's not the perfect pitch, but you want that level of clarity with people and it's really the work of producing that that helps you hone in on your own ideas and discover new ideas.

Gary Tan (131:54)

Yeah, I mean, I feel like we're in like the idea fire hose. So we're just like hearing about all kinds of things that are very promising. And then I think the, the most unusual thing that, you know, I'm still getting used to is, I mean, in full transparency, I mean, probably, you know, the median YC startup still fails. Right. Like, you know, YC is, might be one of the most successful, you know, sort of, you know, institutions of its sort that has ever existed, you know, inclusive of venture capital firms. On the one hand.

Shane Parrish (132:29)

Yeah.

Gary Tan (132:30)

On the other hand, like the failure rate is absolutely insane. Right. Like, you know, it is still a very small proof percentage of the, the teams actually do go on and, you know, create these, you know, companies worth 50 or $100 billion. But the remarkable thing is not that, you know, it's that low. The remarkable thing is that it happens at all. Like, it's just unbelievable that I think.

Shane Parrish (132:56)

You have the coolest job in the world. Or at least like one.

Gary Tan (132:58)

Oh, I agree.

Shane Parrish (132:59)

If I had to pick like the top 10, like you'd be up there.

Gary Tan (133:02)

I agree. I mean, it's especially to have, you know, I pinch myself every day on the regular. Like in the morning I wake up and it's like, oh, this AI thing is happening. And then somehow I'm filling the shoes of the person who, like, I mean, Sam Altman probably brought forward the future by, you know, five years, 10 years, at least 10 years maybe. Like, all of the things that, you know, him and Greg Brockman and all the researchers he brought on, like, were working on, that happened, that was going to happen. Right. Like, I think there's a lot of the. The Sam Altman haters or the OpenAI haters out there love to point out, like, oh, you know what? Like, the transformer was made by all these teams. I mean, some of it's like these teams absolutely did incredible things. Like, you can't take away from that. Right? The researchers did, you know, Demis did incredible things, but at the same time, it's like they believed a thing that nobody else believed, and they brought the resources to bear. And so recently, Sam Altman came back to speak at our AI conference this past weekend. And I couldn't think of another way to start that conference than have Sam Altman and a bunch of his old. We had Bob McGrew there. We had Evan Morikawa, who was the ENG manager who released Chad Chatgpt. Bob McGrew actually worked with me at Palantir back in the day, but he's, you know, outgoing Chief Research Officer Jason Kwan was there. He actually worked at YC Legal before leaving to, you know, run a lot of things at OpenAI. And so I had them all stand up. And we had a room full of, you know, 290 founders, all of whom were working on things that happened essentially because OpenAI existed. And there was like a standing ovation. Oh, that's awesome. So. And, you know, Sam, to his credit, was like, you know, not just us, you know, these researchers did so many things as well. But all that being said, it's like we're in the middle of the revolution. Oh, this is just like. I mean, it's not even the middle. I think it's like just after the first pitch of the first inning of, like, what is about to be. Be a great, great time for humanity, for technology.

Shane Parrish (135:23)

I'm with you. So excited to be alive right now. So lucky, so blessed to be a witness to this. And I think we're going to make so much progress on so many things and go back to the haters. There's always people pulling you down, but they're never people that are in the trenches doing anything. I've rarely seen people who are working on the same problem, attacking their complex competition like that or undermining them or. No, it's just ignore it.

Gary Tan (135:49)

You know, on our end, we're just hoping to lift up the people who want to build. Yeah, this is the golden age of building.

Shane Parrish (135:55)

Amazing. I want to just end with the same question we always ask, which is, what is success for you?

Gary Tan (136:02)

I think looking back, I mean growing up, I always just looked, looked up to the people who made the things that I loved and you know, Steve Jobs, Bill Gates, like the people who really created something from nothing. And I just think of Steve saying, you know, we want to put a dent in the universe. And ultimately that's what I want. Like that's your success to me is how do we bring forward. You're actually, this is actually when Paul Graham came to recruit me to come back to YC, I had actually left and started my own VC firm. You know, got to $3 billion under management, like in.

Shane Parrish (136:47)

Yeah, you guys did Coinbase?

Gary Tan (136:48)

Yeah, totally. I mean, returned 6,700 million, $650 million on that investment alone. You know, I was sort of right at the pinnacle of my investing, you know, as a running my own VC firm. And Paul and Jessica came to me and said, Gary, we need you to come back and run yc. And it was really, really hard to walk away from that. Luckily I had very great partners. Brett Gibson, my partner, my multi time co founder, went through YC with me. He actually built a bunch of the software with me at YC before we left. He runs it now. They're off to the races and still doing great work. And I sat down with Paul right after we shook hands and he's like, Gary, do you understand what this means? It means that if we do this right, we kind of like I think what Sam did with OpenAI with pulling forward large language models and AI and bringing about AGI sooner. Like YC is sort of one of the defining institutions that is going to pull forward the future. And it's not more complicated than how do we get in front of optimistic, smart people who, you know, have been benevolent, you know, sort of goals for themselves and the people around them. How do we give them, you know, a small amount of money and a whole lot of know how and a whole lot of access to networks and you know, a 10 week program that hopefully reprograms them to be more formidable while simultaneously being more earnest and then the rest sort of takes care of itself. Like, you know, this thing has never existed before like this and it deserves to grow like it deserves to. You know, if we could find more people and fund them and have them be successful at even, you know, the same rate, we would do that all day. I mean, and I think, what are the alternatives? Right? Like, I think of all the people who, you know, they're locked away in companies, they're locked away in academia, you know, or heck, like, you know, these days, the wild thing about intelligence is like intelligence is on tap now, right? Like all of the impediments to being able to. All the impediments to fully realizing what you want to do in the world are starting to fall away. Like you, you know, there's always going to be something that stands in the way of any given person. And I'm not saying like those things are equal, but they, you know, through technology and through access to technology, those things are coming down. Like if there's the will, if there's the agency, if there's the taste, like that's what I want for society and I want them to achieve.

Shane Parrish (139:46)

In a lot of ways, we have more equality of opportunity now than we've ever had in the history of the world. But not equality of outcome.

Gary Tan (139:53)

That's right. Yeah. And that's sort of the quandary, right? Like you have to choose. Do you want the outcomes to be equal or do you want a rising tide to raise all boats?

Shane Parrish (140:05)

I'm a huge fan in equal opportunity but unequal outcome.

Gary Tan (140:09)

I'm with you.

Shane Parrish (140:12)

Thank you for listening and learning with me. If you've enjoyed this episode, consider leaving a five star rating or review. It's a small action on your part that helps us reach more curious minds. You can stay connected with Farnam street on social media and explore more insights at FS Blog where you'll find past episodes, our mental models and thought provoking articles. While you're there, check out my book Clear Thinking Through Engaging stories and Actionable Mental Models. It helps you bridge the gap between intention and action so your best decisions become your default decisions. Until next time.