Y Combinator CEO Garry Tan: Turning Ambitious Misfits into Founders

0:00 The world is full of problems, like why are people sort of retired in place? Pulling down You know, insane. by average American standards, absolutely insane salaries to build software that, you know, doesn't change, doesn't get better. You know, sometimes I sit there and I run into a bug, whether it's a Google product or an Apple product or, you know, 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.

0:32 and it will never get fixed because people don't care, no one's paying attention. That's just one symptom out of uh a great many that is, you know, the result of basically treating people like, you know, hoarded resources. The world is full of problems. Let's go solve those things. Welcome to the Knowledge Project. I'm your host, Shane Parish. In a world where knowledge is power, this podcast is your toolkit for mastering the best of what other people have already figured out.

1:08 If you want to take your learning to the next level, consider joining our membership program at fs.blog slash membership. As a member, you'll get my personal reflections at the end of every episode, early access to episodes, no ads including this, exclusive content, hand edited transcripts, and so much more. Check out the link in the show notes for more. Today We're pulling back the curtain on one of the most powerful forces in the tech and venture capital world, why combinator? With less than a one percent acceptance rate and a track record that includes sixty percent of the last decade's unicorn startups, Y C has shaped the startup world as we know it.

1:47 Gary Tan, president of Y Combinator, joins us to break down what separates transformative founders from the rest and why so many ambitious entrepreneurs still get it wrong. We'll explore the traits that matter the most, the numbers behind billion dollar companies, and why earnestness often beats raw ambition. But there's a seismic shift happening in venture capital, and AI is at the center of it. We'll dig into how artificial intelligence is reshaping startups from idea generation to regulation and what it means for the next wave of innovation. If you're curious about Silicon Valley's secrets, the present and the future of AI, or how true innovation gets funded, this conversation is for you. It's time to listen.

2:34 and learn. I want to start with What makes Y Combinator so successful? I guess I can't talk about Y C without talking about Paul Graham and Jessica Livingston. Um I mean, it started because

2:51 They're remarkable people. And uh Yeah. Paul when he started his company. Um, I don't think he ever had the idea that

3:00 Um Yeah, he would ever become a Someone who created a thing like Y C, he was just trying to help people and uh sort of Follow his own.

3:11 interests I think. He just said Uh I know how to Your make. products and make software and make them in a way that people can use them. And then after he actually sold that

3:24 Company Via Web is one of the first, um you're say we have Shopify. uh Veb was sort of like the very first version of it. He actually basically created the first Uh web browser based. Uh program.

3:39 So he was one of the first people to hook up uh a web request to an actual program in Unix. You know, today we call it CGI bin or you know the all these Different things, but you know, he he was so early on the web that um Yeah, it was a a new idea to make software.

3:59 For Uh the web that didn't require like some desktop thing that you had to use to configure the website. And so I think he's just always been

4:10 Um An autodidact. uh a really great engineer and then just a polymath. So I think that that's what really made Y C. I mean, he wrote essays, he sort of attracted all the people in the world who wanted to do the thing that he wanted to do. Um and so I think Paul Graham

4:29 And his essays became a shelling point. for people who this new thing that could really happen. uh in the world. And you know

4:38 That started very early. I mean I I think it started literally with The web itself. And you know, that's why in two thousand five he was able to get uh hundreds to thousands of really amazing applications from people who wanted to do what he did. And then the magic is it's only a 10 week program. Uh I think he had Yeah, only a dozen people in that very first program in two thousand five.

5:03 And then um out of that very first program. Sam Altman went through it. Um and Sam, you I I guess it's interesting. I mean If you have A draw that is very profound, і will draw out of the world.

5:17 uh the people who you that speaks to those people. And so you end up needing in society these like sort of shelling points. for uh certain ideas and then that you know the idea that someone could sit down in front of a computer and create a piece of software that a billion people could use. uh turned out to be very contrarian and very right. And so um

5:42 Yeah, today I think of Y C as Really Uh It's actually Yeah. Software

5:49 uh events and media. And Yeah, I think you've had Nival Ravakant on. before and uh you know I think I remember distinctly Naval talking about like Those are the few forms of extreme leverage you have in the world.

6:05 And so you're I think Y Combinator is this crazy thing. It's like When people realize they could start a startup. They went on Google. And they searched and they found Paul's essays. And then through at his essays he f they found White Combinator.

6:22 And then YC started funding people like you know Steve Huffman, who ended up uh creating Reddit in that very first batch and selling that to Condemast. And um Yeah. Dropbox, then Airbnb, then you know, today your coinbase

6:38 Um There are just so many companies. That you know. are incredible. I mean Airbnb is this insane

6:47 marketplace that houses way more people on any given night than you know the biggest h hotel chains in the world. And it's like on the one hand unimaginable, on the other hand like that's the kind of thing that you can do. Like you can just Yeah. Do things. Which is wild.

7:03 And so I think that that's why it works. It's uh We attract people who want to create those things and then we give them money. And then more importantly, I think the know how is Uh we give it away for free.

7:17 Actually, go deeper on the Yeah. Uh We earlier just now we were chatting about this uh podcast setup, but Um We spend a lot of time writing essays and putting out content on our YouTube channels and Uh

7:32 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 uh You know, on the other hand, I think of Y s doing Y C, being in the program. It's a ten week program. We make everyone come to San Francisco now. Uh at the end of it, it culminates in

7:58 Um people raising Yeah, 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 uh you know at at the beginning. That's the demo day. Is that the yeah. And yeah, we have you know, I think we have about a billion dollars a year in

8:19 uh you know funding that comes into Y C companies, and that's because uh the acceptance rate to get into Y C is only one percent. So let me get this straight. You have I think I read somewhere forty thousand applications a year. Yeah, I think it's close to seventy, eighty thousand at this point. How do you filter those?

8:37 Uh well, we ourselves use software, but we also um have Thirteen. uh general partners who actually read applications and we watch the one minute video you post. Um, and you know, the most important thing to me is that I want us to Try the products, right? Um, you know, sure we can use the resume and you know, people's careers and uh where they went to school.

9:02 Yeah, we're not gonna throw that out. Like it's a factor in anything, but the most important thing to me is not necessarily uh the biography. It's actually You know, what have you built? What can you build? Go deeper on the software thing. I don't think I've heard that before that you guys uh obviously you have to use software, but what does the software do? How does it filter? Yeah, I mean ultimately the best thing that we can do is actually brute force read.

9:29 Uh and On average, I think. the a group partner will read something like a thousand to fifteen hundred applications. uh for that cycle that they're working. So the best thing we can do is like not

9:42 Uh It is basically like humans trying to make decisions, you know, which is maybe a little antithetical to Um Yeah, the broader thing right now. And now it's Yeah, let's just use you use AI for everything. But I think that the human element is still very important.

9:58 And then at the end you sort of like I guess the last filters like this 10 minute interview you used. So what do you ask in 10 minutes to determine If somebody's gonna be part of a combinator. I guess The the surprising thing that um has worked over and over again ultimately is um in those ten minutes Either you learn a lot about both the founders and the market, or you don't.

10:23 So we're looking for incredibly crisp communication. So I wanna know You know, what is it? Um, and you know, often w the first thing I ask is not just what is it, but why are you working on it? Like I wanna sort of understand Where did this come from? Did you just read about it on the internet? Or a much better answer is

10:43 Yeah, 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 Yeah. What's cool about

10:53 You know, the bi biographical is that then it Uh invites more questions, right? It's um the best interviews in ten minutes. Like you learn about an entire market. You learn about um A set of people that

11:07 You know. normally you might not ever hear of the Um it's like you're traveling. It's like you're traveling the idea maze with uh the people you're talking to. This is all over Zoom. And um You know, at the end of those ten minutes, like sometimes the ten minutes becomes fifteen. Like you want to talk to people longer,'cause that's what a great interview feels like to me. It feels like uh 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 because

11:37 It's like this you're there's something here. This person understands something about the world that um You actually make sense to me. And um I I think what we're looking for is actual signal.

11:50 There's s there's a there, there there's a real problem to be solved. uh there are people on that end who are willing to pay. And then you're working backwards, what a great startup ultimately is. Is

12:03 Something real. that people are willing to pay for Uh that probably has durable motes that you it it doesn't mean that Yeah, 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 wanna start

12:23 uh you know something like Airbnb that has network effects or um that can really scale. Exactly. Or in you know, in AI today, one of the more important things is um Yeah, are people willing to pay? And uh today because people are not selling software, they're increasingly actually selling

12:43 uh intelligence. Like these are things that Um you could not buy before like Yeah. probably the most vulnerable things in the world today are things that you could you know, a farm out to an overseas call center. That's sort of like the low hanging fruit today.

13:02 And um Yeah, basically how do you find Things that people want. And uh 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 ten minutes. Um

13:16 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 uh Google or Facebook or all these really famous places Uh they had an impressive resume. Um, or they had the credentials of someone who I felt like, you know Should be able to do it. But then they had a mess of an interview.

13:46 Um, 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, ten minutes is too short, we need more time. And uh one of the things I feel like I learned from Paul was that If in ten minutes you cannot actually understand what's going on. uh 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.

14:14 That's a really good point. I bet you that holds true. Do you 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? Oh, definitely all the time. I mean Um I think that's the trickiest thing, you know, I think the system itself will always produce Um You know, both false positives and false negatives.

14:37 'Cause it is only ten minutes. But you have the highest batting average. Like Y Combinator, my understanding is It's like five percent of the companies become billion dollar companies. Yeah, about two and a half percent end up becoming dec decorns, uh sooner or later. So but that would be the highest batting average of any B C 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

15:02 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. Yeah, I mean, what's great is I you know, I don't want to compete with Sequoia or Benchmark or Andrees and Horowitz or uh you know, they're our friends, honestly. Done right, like we're much earlier than everyone else because we wanna actually give them half a million dollars when they have Uh just an idea. Or maybe they don't even know their co vendor yet. 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 stock in terms of funding. Yeah. You know what it is, though? Um I spent five year uh seven years actually away from Y C before coming back a couple of years ago. So I ended up, I think, in the top ten of the Forbes Midas list as my

15:54 final year uh before coming back to YC. And um Why has why haven't other people we ask this all the time, why haven't other people Uh Come for us.

16:04 You know, I I think there are lots of people who are doing various things that might work. And uh I guess so far. people sort of lose interest or you know float off and go do higher status things. Right. Working with founders when they're just

16:21 Right at the beginning and just an idea. Is actually Yeah. relatively low status work because Yeah, it's very high status to work with a a company that

16:31 Is uh You know? worth fifty or a hundred billion dollars now. But guess what? Like that's ten years from now, or sometimes fifteen or twenty years from now. Um, you know the it it all starts out very low status and all the way in the weeds. Like you're asked you're answering sort of relatively simple questions and you're giving relatively small amounts of money.

16:54 Well, you were giving twenty at the start, right? Now you give five hundred. Is that the half a million dollars today, yeah. H has that changed The ratio of success. I think some of it is um well, I we find out in ten years. Uh if anything, I think that the unicorn rate has gone up over time. You know, uh ten, fifteen years ago, I think it was close to maybe three and a half to four percent. And now we're around five and a half percent. Some batches from

17:21 Um You're the maybe twenty seventeen, twenty eighteen or you know, pushing Eight to ten percent. Oh wow. Um Some of those companies in that

17:30 area in that vintage, uh, about fifty percent of companies end up raising what looks like a series A Uh and then the wild thing about it is is it is it actually takes a long time for people to get there. Um So I you know I think the Y C has actually flipped a lot of the I guess myths of venture. You know, th one of the myths of venture maybe ten, fifteen years ago, was that uh, you know, within

17:55 nine months of funding a company. you will know whether or not that company was good or bad. And um you know, going back to that stat, you know, about half of companies that go through Y C will end up raising a series A that's you know much higher than any other pre-seed or seed sort of situation that I know of.

18:15 Um 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

18:26 twenty two year olds, you know, nineteen year olds, twenty-four year olds. I mean, we're funding people who are so young that sometimes they've never uh shipped software before. Sometimes, you know, they're fresh off of an internship, you know, let alone you know it takes three to five years to To mature. to um 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,

18:57 Sometimes it takes five years for those things to come together. In my head. Uh 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

19:22 um better success or better outcomes. Oh that was definitely true for me. I mean um 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 um you know, my fellow Stanford grads, but I guess the weird thing To say is that like

19:41 being around people who are really earnestly trying to build. Uh helps. Uh yeah. 10xmore. Um, 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

20:03 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 what we really try to do at Y C 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.

20:27 market. Uh actually Building. And then iterating on that, getting customers uh, you know. long term retention, all of those things are the fundamentals and everything else is like the trappings of success or

20:41 And those will always feel I I what's funny is like in other communities. uh 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. Um 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 uh a startup founder. And uh suddenly some you know

21:13 People have heard of you. And uh people try to add you as a scout. Like people kill their startups all the time by that, just by taking their eye off the ball. Go deeper on that a little bit in terms of focus and 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?

21:36 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. 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? I think that that's Probably the biggest danger to people who uh want to be founders. I mean

22:02 I think I've seen Peter Thiel talk about this. Like he doesn't really want people who want to start startups. It. 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 uh if it if you look at

22:23 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, um You know, I never meant to start a company or ever wanted to make money. All I wanted to do was uh 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. It's like the company was a reluctant side effect on post.

22:54 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 tends to be localized and then spread over the world, if if I understand it correctly. Why Silicon Valley, why San Francisco? And why haven't other countries been able to replicate that success inside? Well, um At Y C what we hope is that people actually come to San Francisco and uh I you know

23:24 We do strongly advocate that they stay, but it's no requirement. Um And then what we hope is that if they do leave they they end up bringing the networks and uh know how and culture and you know, frankly vibes and they bring it back to all the other um

23:44 And I think that that's Some of some of the stuff that has actually come about. I mean Um Monzo was started by now my partner Tom Blomfield. Uh he's a partner at YC now, but I 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 uh San Francisco becomes sort of

24:08 really Athens or Rome in anti antiquity. You know, send us your best and the brightest. You know. ideally you stay here. One one thing we spotted is that uh the teams that come to San Francisco and then stay in San Francisco or the Bay Area.

24:24 they actually double their chance of becoming a unicorn. Oh wow. So if it's one. one thing that you could do it's be around people. And be in the place where

24:34 Uh making something brand new is in the water. So if hypothetically you created a new country tomorrow and you wanted to spur on innovation What sort of policy you've got to compete with San Francisco, no. 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? I think

25:00 What I want for San Francisco, for instance, is I think that 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 was I think I was looking at it for the entire last calendar year. Mm-hmm. I think so. You know, maybe Scott Wiener had just posted this on X that

25:24 literally there were no new housing starts in all of s you know San Francisco proper. for the last year. So how are we supposed to actually bring down the rents and make this place You know. actually livable.

25:38 You know, if San Francisco is the microcosm where You know, people build the future. And it is sort of the siren song for you know, hundred and fifty IQ people who are very, very ambitious and have

25:52 Uh you know, techno optimistic ideology. Um and it's also where they are most likely to succeed. uh society and certainly І до Америка із на сервing.

26:04 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. Um, 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 gonna be successful, they just scare you. And you're like, No, that's too scary.

26:23 I mean If it's scary, but Might might or probably will be good. I think we want to fund them. And certainly there are things that

26:31 Um would be bad for society, but are likely to make money and Um, you know, the history is our our partners are everyone's independent. You know, we have um 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. Um, you know, we run it by each other to make sure you sort of double check like the thinking, but Um, 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, uh ADHD meds. And I distinctly remember one of our partners, um, Gus Gustav Alstromer.

27:12 Yeah, he met that team. And he said, You know what? Um, we're not gonna fund these guys. You know, it's gonna make money. But

27:20 I don't want to live in a world where Uh it is that easy to get you know, people on these drugs. Like they're ultimately uh methamphetamines and you know these are controlled substances and this is the wrong vibe. Like we didn't not like the vibe that we got from the founders of that company So

27:39 Yeah. I hope that Y C continues that way and I think it will. Um ultimately we want people we want Uh People who are I mean, ultimately trying to be benevolent, at least, you know.

27:52 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. I guess some of it is like are you only gonna sell to five eyes?'Cause you know, uh I really liked what MIT put out recently. Um They were very clear. They said, you know, MIT is a uh

28:13 An institution And that institution is an American institution. And so um being very clear about that, I thought was totally the right move for MIT and Uh you know.

28:26 I think that Y C needs to be a similar you know, an an institution of similar character. I like that. W what do you wish founders knew about sales coming in? Oh, how hard it is and I mean You know, like it or not. Yeah, the the ideal founder is someone who has lived like

28:42 twenty lifetimes and has the skills twenty people. And uh The thing is, you know, you can't get that. And so um Probably the first conference that we have the first mini conference we have when we welcome the batch in is the sales mini conference. And um essentially

29:01 It is. Don't run away from the no. Spencer Skates of Amplitude has this great analogy that he uh told You know, uh some companies when he came by to speak recently that I've been thinking a lot about, which is sales is about um

29:17 you know, having a hundred boxes in front of you and maybe five or six of those boxes has uh a gold nugget in them. And if you haven't done sales before. You think. I really I'm gonna gingerly in in a very gingerly way open that first box and hope, hope, hope that

29:36 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 um High school and family. And uh you know, the ten thousand hours of human training people get from their childhoods comes up in Paul Graham's essays. I would always think about that because

29:58 I think that most people's backgrounds just don't prepare them for uh sale. It's a very unnatural thing to do sales. But then the sooner that 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 s

30:21 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 um 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

30:39 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. Um And I think that

30:54 Yeah, maybe that's actually one of the reasons why Y C startups uh are much more successful. Like other people give as much money or, you know. As you said, like venture capital. Uh V C firms tend to give you know, a lot more money. I mean there are clones of Y C right now that give like twice as much money, for instance, but I don't think that they're they're gonna see this level of success because

31:16 Um they're not going to have as earnest people who become as formidable around you. Like it's it's actually a process. It 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.

31:36 Yeah. Um and'cause 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 with Charlie Moogger 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 that That's really interesting. And it's not like you're doing something that's super complicated. Yeah. It doesn't sound like it unless I'm missing something. Like it's it's a very simple sort of process to bring the people together. And obviously there's filtering and And you guys are really good at at doing that, but

32:12 I mean what my hope is I I feel like When Paul and Jessica created Y C for my I I went through the program myself in two thousand eight and Uh I came out transformed. And then that's very explicitly what I want to happen for people um who go through the batch today. It's you know it it isn't just like show up to a bunch of dinners and network with some people who happen to be are you know it it's

32:39 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 um a di a very radically different world view. I want someone who is Much more earnest.

32:55 Someone who is not necessarily trying to sort of like hack the hack. They're trying to the you know And I think this mirrors what you were saying from You know what um you know, rest in peace, Charlie Munger talks about and what uh Warren Buffett talks about.

33:11 around all of these things are in the short term um 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 fet 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

33:49 You know, create something that You. When all is said and done. Uh, solve some real problem, put people together. Um

33:58 You know, is there real enterprise value and that's the weighing machine. And you know, the way that Y C makes money the way that Um you know the founders make money. Uh. It's all aligned at at that point. Like

34:12 Yeah, there's like a way to hack the hack. And the I don't 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 five, ten, fifteen year basis, like if you are nose to the grindstone, earnestly working on the thing. Um 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

34:39 uh twenty five percent of the people who reach some form of product market fit at Y C do it in year five or later. It's like they don't quit year one, they don't quit year two. Like Yeah, they are learning and growing. Um I have one other really crazy stat that like I'm thinking about all the time right now. Uh there's a founder uh Or uh there's a V C actually. Uh his name is Ali Tamasup, he works at Data Collective. He wrote a book called Uh Super Founders. And I get this email from him uh out of the blue. He says, Did you know that um about forty percent of the unicorns from the last ten years in the world

35:14 Or started by multi time. serial founders. And it was like okay, that's a cool stat. Like makes sense. Like multi-time founders are Uh you know, they know a lot more, they have networks, they have access to capital, like that's not a surprising stat. You know, if anything, it's a little surprising that it's only forty percent. Like you would have guessed maybe that was eighty, but Uh the the thing he said after that really shocked me. He said, Did you know that forty th you know, of those forty percent Sixty percent of those people

35:42 The people who created unicorns. The last ten years. uh our Y C alumni. Oh wow. So I'm like, that's crazy. Like I'm really glad that Y C exists now because you know, even if Yeah, w Y C today is basically a thing that is for first timers.

35:59 Um, 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 um, you know, it really is for people who are starting out. And it's kinda hilarious. Like I have no product right now for people who are uh, you know, for for my YC alums. Um And maybe that's okay, you know? It's uh Yeah, that's our gift to the rest of Sand Hill Road because you know, they're the ones who are gonna be the fund returners for all of the rest of Sand Hill Road. Would you say like w in terms of personal characteristics, it sounded like determination was definitely One of the most important

36:37 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 the the not only the successful first time, but second, third, fourth. Yeah. I mean the the number one thing that I want um that comes to mind for me is Uh I mean maybe it's even surprising because that's not a word that you might

37:02 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

37:18 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

37:32 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 Uh Sam Bankman Freed is sort of the opposite of that. Right.

37:51 You know, Brian Armstrong is an incredibly earnest founder who literally read the Satoshi Nakamoto white paper and said, This is gonna be the future and let me work backwards from uh that future like you know when you talk when you talk to him like the reason why he wanted these things like comes directly out of his own experience. I mean, at Airbnb, they were dealing with the financial systems of

38:17 you know, myriad countries and it's like international just Sending money from one country to another was totally fraud. And Totally not. you know something that was accessible to normal people, like remittance is this crazy scam. It's it's it's insane, like how many fees that people have to pay just to like send money home.

38:36 Um 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 uh you know, Buenos Aires and Argentina. And he saw hyperinflation and he said Yeah.

38:55 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 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 um someone like Sam Bankman Freed and just wondering like What's going on over there? His speed ran.

39:25 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's that's anti Ernest is the definition of he was a crook. He's in jail now. And um Yeah.

39:42 My hope is that Uh, people who look you know, if if you just look at Brian Armstrong versus SPF. I'm hoping that You know, young people listening to this right now, take that to heart.

39:53 It's like the things that actually win. You know, I mean I and going back to Buffett, I you know, I went to um They're uh you know, sort of conclave and Omaha. Oh, you went to the Woodstock for capitalists. Yeah. I mean, amazing. And uh

40:09 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.

40:22 What is it does it it it's their thing, right? It's um You know. Work on high class problems with high class people. I mean it's that's very, very simple. You gotta just do it the right way, right? Yeah. Um

40:34 And so that's what I want. I think that if Y C is the shelling point for Ernest. friendly, ambitious nerds. uh to steal something from uh

40:45 Yeah, I I I have a uh A friend on Twitter go who goes by Visa. Uh VisaCon. Um And he you know, he has a whole book on it. I think it's called Friendly Ambitious Nerd. If you look it up. I mean, um

40:59 I think that that's what YC by definition should be attracting. And uh 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 um 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

41:23 Yeah, chop wood, carry water, that's it. He also took a big stand before it became popular. uh that the workplace is like a performance place it's not You don't bring all of the 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 first people. Yep out of the gate. And he took so much flack for that. Yeah.

41:47 And he's vindicated now. 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. That's the media environment, right? I I thought it was interesting anyway that that he came out and did that. And I 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. Yeah, that's true leadership. Yeah. What's the biggest unexpected change you've seen?

42:23 would in building companies in the AI world. I think the biggest thing that is um increasingly true and we're seeing a lot of examples of it in the last year. is uh split scaling for AI might not be a thing. It would spit. So I think Reed Hoffman wrote a whole book about it. Um it was definitely true in the time of Uber. So

42:46 You know, that was sort of a moment when um interest rates were descending and then uh these sort of international increasingly international marketplaces, these sort of you know, offline to online marketplaces like Uber in cars or delivery, or you could say inst car door dash, you could throw in, you know, lift. There was sort of this whole wave Of um you know, sort of the top startups were um marketplace startups, um, but also in software too, this idea that, you know, scale could be used as a bludgeon. That

43:19 you know, the network effects grow um you know, sort of exponentially and then uh 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 um in that era, sort of the 2010s. And then in the twenty twenties, especially by you know, we're in the mid twenty twenties now. I think that uh we are seeing incredible revenue growth with Way fewer people. And that's very remarkable. Um, we have companies basically

43:49 you know, going from zero to six million dollars in revenue in six months. We have companies going from Zero to twelve million dollars a year in revenue. in uh twelve months, right? And uh with under a dozen people. Like are you usually five or six people.

44:06 And so that's brand new. Like this is Uh the the result of large language models and intelligence on tap. Um and so that's a big change. Like Yeah.

44:18 I think we are seeing companies that in the next year or two We'll get two fifty, a hundred million dollars a year in revenue. Um Really with under ten you know, maybe ten people, maybe fifteen people tops.

44:31 Um And so that was relatively rare and my prediction would be this becomes quite common.

44:39 Um and My hope is that's actually a really good thing. Like this is sort of the silver lining to um You know. What has been really a decade of big tech. Right. Like it's more and more centralized power.

44:54 Um, you know, what might happen here is that you know and what we're actively trying to do at Y C 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. Um and We we hope that that will be very different than sort of this.

45:15 Yeah, the opposite, I think, was increasingly true. Like we have fewer and fewer choices in operating systems, in um, you know, web browsers, and you know, across the board, like just more and more concentration of power in tech. Two thoughts here. One, like How much do you think that cloud computing plays into that? Because now I don't have to buy six billion dollars in infrastructure to be that, you know, five person company. I can rent it based on demand. So that's enabled me not to compete on a capital basis. Yeah, that was true. That was even why Y Combinator in two thousand five could exist. You know, I remember working um at a startup in 1999, 2000, or at like uh internet consulting firms. And these were like million dollar projects because you had to actually pay

46:03 a hundred thousand dollars 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. So the cost of even starting a company was just huge. Yeah. I mean um I remember Jeff Bezos actually launched um AWS at a YC startup school uh at Stanford campus in 2008, right when I was starting my first company. So Um, I think you know, cloud really opened it up and that you know, that's part of the reason why Um

46:34 Startups could be successful. You know, you didn't need to raise five, ten million dollars just to rack your server. Um and uh you know, that's the other big shift. Like I think in the past it was very, very common to have.

46:48 uh, 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 um Yeah, th there was sort of that split. And then now what we're seeing is you know what, like The the CEO of The majority of Y C companies, they are technical.

47:11 Is this the first revolution, like technological revolution, where the incumbents have a huge advantage? You know, I think They have an advantage, but it's not clear to me that they are um conscious and aware and like at the wheel enough to take real advantage of it because they have too many people.

47:34 Right. And then it's all I mean, I think This is what founder mode is actually about. Um, so last year we had a conference with Brian Chesky. We invited our uh top YC alums there, we brought Paul and Jessica back from England and Um

47:51 We had this one talk that wasn't even s on the agenda, but I managed to text um Brian Cheske of Airbnb and I got him to come and speak um very openly and honestly in front of You know, a a crowd of about two hundred of our absolute top um uh alumni founders. And he spoke very

48:11 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

48:24 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 uh as much rope as you possibly can. And then somehow that's going to result in you know, good outcomes. And then

48:49 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. Um, it's sort of a shade of what you were saying earlier with uh Brian Armstrong, like You know, you can sit back and allow your executives to sort of run amok. And um Yeah.

49:13 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.

49:25 And the leader is not there. then you enter the situation where Uh neither the leader Nor the executives. have power or control or agency.

49:37 And then you have you're everyone's disempowered. Everyone is making the wrong choice. uh, 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. I think uh A pretty crazy dysfunction that took hold across Arguably every Silicon Valley company, period.

50:02 And it's still taken it's still you know, mainly in power at uh, 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. Are are the bigger companies sort of like shaping up or no? The way that I think about this analogy is sort of like

50:18 If I'm the young skinny kid and I'm competing against the fat, bloated company. I wanna run upstairs. It's gonna suck for me, but it's w gonna suck way more for them. Right. I I think this is maybe a function of um you know, blitz scaling and uh using capital as a bludgeon like gone wrong. Um

50:40 Yeah, you can look at Um you know, almost any of these companies, um they probably hired way too many people and at some point they were viewing smart people as Uh you know.

50:52 maybe a a hoarded resource that you know, if you were playing um some sort of adversarial uh you know starcraft. And you didn't want Yeah, the ironic thing is like they themselves Верна юзін

51:06 um the resources properly either. Right. They just didn't want somebody else to have the Exactly. I guess it felt like a little bit of a prisoner's dilemma,'cause I think the result is that um you know, tech progress itself decelerated. You have like the smartest people of a generation. basically retired. In place.

51:26 working at places that Yeah, the world is actually full of problems. Like why Why are people sort of retired in place? Pulling down. You know, insane.

51:36 Fine. average American standards absolutely insane salaries to build software that, you know, doesn't change, doesn't get better. You know, I mean, sometimes I sit there and I run into a bug into in, you know, whether it's a Google product or an Apple product or, you know, 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. Yeah. To make some of the worst software. And it will never get fixed because there's no way

52:08 Like you people don't care, no one's paying attention. Yeah, tha that's just one symptom out of uh a great many that is, you know, the result of Um Yeah, 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.

52:25 When it comes to AI, the the raw inputs I guess if you think about it that way, or sort of the L M. 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 uh can

52:42 successfully compete. Yeah, I mean I we had a little bit of a scare, I think, last year with uh AI regulation that was potentially Um 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 uh sort of

53:03 You know. past a certain amount of uh, 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 um the number of different players.

53:30 Mm. You know, sitting here a year after a lot of those attempts, um I feel pretty good because it feels like there are five, maybe six labs. All of whom are competing in a fair market.

53:43 trying to deliver models that you know Honestly, any startup, anyone Yeah. Any of us could just Y you know, pick and choose and you know, there's no

53:54 um monopoly danger. There's no uh you know crazy pricing power that one person one entity uh wields over the whole market. And so I think that that's actually really, really good. Um I think it's a much fairer playing field today. And then I think it's

54:12 Interesting because It's an interesting moment. I think that um You know, basically there's a new Google style, um sort of oligopoly that's emerging around like who provides the AI models, but because

54:28 Um it won't be It probably won't be a monopoly. that's probably the best thing for uh the consumer and for actually every citizen of the world. Because You know, you're going to have choice.

54:42 Let's go deeper on the regulation and then c 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? It's a great question. Um I guess there are a bunch of different models that I could see happening. Um Yeah, I think What what's emerging for me is that um the two things that I think

55:03 I think the first wave of people who are really worried about AI safety. not to be flippant, but like my concern is that they basically watch Terminator Two, you know, and I'm like, I like that movie too, but Um Yeah, right now, you know, th there's sort of that moment in the movie where they say suddenly the uh

55:23 The AI becomes um self aware and it becomes an you know it it be it takes agency, right? And um I think the funny thing, at least as of today Yeah.

55:36 These systems are It's just matrix math. And There is no agency yet. Like there's basically

55:45 They're 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. Um because That means that there's still going to be a you know clear role for humans. Um in the coming decades.

56:04 And uh you know I think it Takes the form of Two very important things. One is uh 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

56:18 a school system that is not designed for agency. It is literally designed to take agency away from our children. Um, 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. Um that's why I'm actually Uh personally pretty pro screens and pro Minecraft and Roblox and

56:41 You know, giving children like this sort of playground where they can exercise their own agency. Have you tried synthesis tutor? 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 um how education will actually change. But that's a great example. Like those Synthesis is like designed around trying to help people have H help children like

57:05 you actively be in these games that increase instead of decrease agency. 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 five math with With synthesis tutor and the results are like astounding. Incredible. Yeah, it's way better. I mean the kids get involved and and they're obviously invested in it. And uh the the regulation question is really interesting too because it it begs the question of it's a worldwide industry. Yeah. And so regulating something in one country, be it the United States or or another country, doesn't change what people can do in other countries. And yet you're competing on this global

57:46 Level. Yeah, I think the biggest question around it is of course I mean the the existential fear is like where are all the jobs going to go? And then um My hope is that it's actually um two things. One is like I think that robotics will play a big key role here.

58:03 Where um I think that's If we can actually provide robots two people that um do real work for people.

58:13 Um that will actually change people's sort of standards of living in like fairly real ways. So I think u universal basic robot is relatively important. You know, I think some of the studies coming back about UVI have not, you know, universal basic income where you just give money to people, it's just not really resulting in A different um I think they've never read a psychology textbook. I mean, just go going away from the economics of it. People need to feel like they're part of something larger than themselves. Yeah.

58:43 uh and if they don't feel like they're part of larger s than 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. uh then it leads to all these problems. Yeah, exactly. And then you know, I think that we really need to actually Give

59:02 Everyone. You know, on the planet some real reason why this stuff is actually Good for them, right? Like I think it if if I'm not sure if I'm If there is only sort of a realignment without a material increase in people's

59:18 uh day to day livelihoods and you know their quality of life. Like maybe Or 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

59:32 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 do. the nature of work changing.

59:44 Um that would be the outcome that Yeah. 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 I I think that that's Uh one of the criteria, I don't have the answer, but I think that that's sort of one of the things I'd be on the lookout for.

1:00:04 At what point do you think the models start replacing the humans in terms of developing the models? So like at what point are the models doing the work of the humans in open AI right now? And they're actually better than the humans at improving the model. Yeah. We're not there yet. So there's some ev evidence that um synthetic data is working. And so some people believe that synthetic data is You know, where the models are like sort of self bootstrapping. Um

1:00:30 So just to explain to people, synthetic data is when the model creates data that it trains itself on. That's right. And so Um I guess the other really big shift is actually test time compute. Like literally O one Pro is this thing that you can pay two hundred dollars a month for. And um It

1:00:47 actually just spends more time at the sort of quarry level. It might come back, you know, five minutes, ten minutes later. Um But it will be much more correct. Then sort of the you know. predict next token version that you might get out of standard chat GPT. Um

1:01:06 Yeah, from from what I can tell. that's where a lot of the wilder things might come out. Um Yeah, level four AGI as defined by OpenAI is uh 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 um the latest reasoning models from OpenAI that are about to come out, you know, like O three.

1:01:33 and O three mini and uh they're trying to apply them to actually you know scientific and engineering use cases. So Yeah. There's a um a cancer uh vaccine biotech company called Helix that did YC uh a great many years ago, but

1:01:51 um what they've figured out is they can actually hook up some of these models to Um actual wet lab tests. And Yeah. that's something that I'd be keeping track of, like over the next couple of years. Like if only by applying, you know, dollars to energy that then goes into these models will there be

1:02:12 uh real breakthroughs in, you know, biological sciences, like being able to do new processes or come to a deeper understanding of Um you know, whether it's uh you know cancer or cancer treatment or you know anything in biotech, um you the the first experiments of those of that sort um that's happening in the next year.

1:02:34 Um even Yeah, in computer aid uh design and manufacturing. I mean, there's a YC company called Campher that is trying to apply Uh they actually were one of the winners of the recent um Y C uh O one hackathon we hosted with OpenAI. And uh their winning entry was literally hooking up O one to um airfoil.

1:02:57 Design. So being able to increase the uh sort of lift ratio just by applying Yeah. Ex spend more time thinking about this. And it's able to create uh a better and better airfoil given a certain number of constraints. So

1:03:14 You know, obviously these are like relatively early in toy examples, but I think it's a real Um sort of optimistic point. around how do we increase Uh

1:03:26 the standard of living and push out like sort of the light cone of all human knowledge, right? Like you're that That is like a fundamental good for AI. Um Yeah, between that and uh the inroads it might make in education.

1:03:42 Um, these are like some real, you know, white pill things that I think are going to happen over the next ten years. And these are the ways that AI becomes not You know, sort of. Terminator two. 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

1:04:04 increase the amount of knowledge and know-how and science and technology in the world that solves real problems. Um And you know, I don't think it's gonna happen on its own. Like you know, each of these examples are there's you know, frankly a a Y C startup like right there on the edge trying to take these models and then apply them to domains that

1:04:26 You know. K it's kinda like You know, Google probably could have done what Airbnb did, but it didn't because Google's Google, right? And so in the same way, I think that whether it's open AI or anthropic or Meta's lab or Deep Seek or some other lab that wins. Like I think that we're gonna have a bunch of different labs and they're gonna 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

1:04:54 Uh one where You know? our kids or really any kid. Um with agency. can get access to uh a world class education can get all the way to the edge of

1:05:07 you know, what humans know about and are able to do or are able to like sort of affect. Um And then you know, sort of empowered by these agents, empowered by chat GPT or Perplexity or you know, whatever agent, you know, uh it's gonna look like her from the movie, right? Like we're going to have these you know, basically super intelligent.

1:05:28 Entities that we talk to Um, I'm hoping that they don't have that much agency, you know? I 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 wanna be in. Like I don't want to be Um

1:05:48 I don't think anyone wants to be Uh sort of You know, to to borrow a term from Van Kateshrao. Like I don't think any of us want to be under the API line of you know, these AIs, right? Like I in and I think That really passes through agency.

1:06:05 The minute a robot can do laundry, I'm in, I'll be the first customer. Yeah. There are YC companies and many startups out there that are uh actively trying to build that right now. It my intuition is that it strikes me as immediate progress could come from just ingesting. All of the academic papers. that have been done on a certain topic and either disproving ones that people think are are still correct. Uh 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? Like not the quantum leap, but like the next logical step that

1:06:47 Who's doing that? 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 uh we should do a joint uh request for startup for this next YC batch. I like it. I want equity there. All right. 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 and just 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 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 L M quality point of view?

1:07:31 Hm, 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 I don't actually run uh an AI lab, even though um Yeah, and I I was not actually. Yeah, that's right. Not the meta AI label. Not meta the company, but like meta as in all of them. Yeah. Um That's a good question. I guess.

1:07:55 The funniest thing, m my main response to all of that, um 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 the yeah. Um Well here's another interesting twist on this then. Like so the airfoil

1:08:13 uh they they designed this new airfoil. Is that patentable? I mean At least in terms of like generated images. Uh my understanding is generated images are not copyrightable. But if AI generates not only the the science behind it, maybe like we're at a point where

1:08:29 You know, maybe in the next couple of years AI is doing more science than we've done. Like is is that going to be copyrightable or patentable or sort of like withheld, or is that public access, public knowledge? Well, my intuition would say people are just going to take the outputs of you know, these AI systems and Um 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 so I wouldn't here's another start up idea for anybody listening that we we both went in on

1:09:01 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 attempt to just patent it. Yeah. Like a one person company could literally like 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 got two ideas there. I love those.

1:09:30 I don't know. I think these are all uh totally open and fair fair game. And then I guess maybe th going back to regulation, that's one of the stranger things that is happening right now. Um You know one of the uh pieces of discourse out there during the AI safety uh debates like in the last year, for instance, are about um bioterror. And uh you know, the wild thing is

1:09:52 You know, basically possessing um 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.

1:10:07 That I mean, that's just like my sort of rhetorical question back when people are really, really worried about uh bioterror. You know, I think there's this uh funny example where uh AI safety think tanks were in Congress uh And um They were sort of Yeah, going to chat GPT and

1:10:26 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'd need to do this, you'd have to acquire this. You know, here's this thing you would do in the lab. Um and you know, of course, like those steps are illegal. Uh and then I think Um

1:10:42 uh a a cooler had prevailed Uh in that you know. Uh 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 Two as well. You know, am I worried about it? You know, my P Doom score is one percent. Like I'm not totally

1:11:03 You know, unwried, right? Um it it would be a mistake to completely dismiss all um worries. It would also potentially be worse to uh prematurely optimize Uh and

1:11:17 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 gonna stand in the way of it until we have a very clear sense like that is actually what we need to do.

1:11:47 What does scare you about AI? I mean It's brand new, right? So the the the risk is always there. You know, it it's so funny though. I mean I'm

1:11:57 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.

1:12:10 Like Maybe you would go and like Let's shut down the power systems. Let's shut down uh the data centers themselves, like why wouldn't people try to do that, right? And they might do that. Uh and Yeah. I think that's people try to do that every day now. Right. Before AI. Right.

1:12:27 If it became that bad, like You know, I'm sure There would be some sort of human solution to try to fix this. Um But

1:12:36 You know. Just because I read about the Butlerian jihad in the Dune series. uh doesn't mean that I need to live like that's what's going to happen. So you don't believe there's gonna be one winner that dominates like Open AI or Anthropic or It might still happen, right? Um

1:12:54 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. Like I know but you're the person to ask. I guess we'll Windows or MacWin 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. Totally. And then to me though, like working backwards, the best scenario is actually one where uh we have lots of marble vendors and you get choice and nobody has

1:13:30 sort of too much control or, you know, uh cornering of all the resources. What what's your read on Facebook? Almost doing a public good here. and spending, you know, I think it's over fifty billion at this point. And just releasing everything open source. Yeah, I think that you know, what Zak and Ahmad and the team over there are doing is

1:13:51 uh frankly God's work. I think it's great that they're doing what they're doing. Um and I hope they continue. W what would you guess is the strategy behind that? It's kind of funny because uh my critique on meta would be You know, um they very openly make every 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 um 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 could 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

1:14:41 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. Um so I 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

1:15:09 You know, I think that um 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 the sp uh, you know The um you know, runtime that you run on your computer, like there's going to be a layer of intelligence. Like you 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 uh 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.

1:15:48 Um You know, the rate of improvement in the models. uh as of today all the smartest things that you might want to do, there's still actually

1:15:59 uh things that you have to go to the cloud for and then that opens a whole c can of worms. But there's some evidence that um you know in the frontier research of Yeah, the best AI labs. Uh, it's pretty clear that Uh there's sort of parent models and child models. And so there's distillation

1:16:18 uh happening from the frontier very largest models with the most data and the most intelligence down into smarter and smarter tiny models. Uh there's a claim this morning that a one point five billion parameter model I think got eighty four percent on the AI M E math test. Oh wow. Um which is like One point five billion parameters is like so small that it could fit on anyone's phone. Yeah. So um Yeah, and that was like Deep Seek R one just got released this morning. So

1:16:49 uh hasn't been verified yet, but I think it's super interesting. Like we are literally day to day, week to week. uh learning more that You these intelligent models are going to be on our desktops in our phones and You know, we're right at that moment.

1:17:05 So is the model better? Is the L L M better? Like what makes that model so successful with So few parameters. Oh, I don't know. I haven't tried it yet. But you know, I mean some of it is you can be um very uh specific about what parts of the domain you keep. Okay. And then you know, I I guess you know math might be one of those things that just isn't Yeah, it doesn't require.

1:17:29 you know, one point five trillion parameters. It takes one point five billion to do An eighty four percent job of it, which is pretty wild. Um I mean, that's another weird thing of AI regulation, you know. Um, I think Biden, for instance, um, his last EO was sort of this export ban, and uh Deep Seek is a Chinese company. uh releasing these models open source and I believe that they only have access to uh last generation NVIDIA chips. And so

1:17:59 You know, some of it is like Why are we doing these? Like measures that like may not actually even matter. It it's interesting, right? Because you think of constraint being the key contributors to innovation. Yeah. 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. That sounds right. I mean I think the uh awkward thing about Um

1:18:24 AI regulation is there's something like four billion dollars of money sloshing around. Think tanks and AI safety organizations and You know, uh someone was telling me recently, like, if you looked at on LinkedIn for um some of the people in these sort of Giant uh the giant NGO morass of think tanks. Sorry if people are part of that and getting mad at me right now hearing this, but uh, you know, there's

1:18:49 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 AI bioterror safety experts. And I'm not saying that's a bad thing. But it's just, you know, very telling, right? Like any time you have billions of dollars. going into you know a thing maybe prematurely

1:19:14 Um, you know, people have to justify what they're doing day to day. And I get it. So many rent seekers. I I I want Tim. foster an environment of more competition. uh within sort of like general safety constraints. But I don't 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

1:19:35 Other people might not think the same way about safety that we do. And then it's almost irrelevant what we think i in a world where other people aren't thinking that way. And it can be used against us. I think we're going into a very interesting moment right now with um You know, the AI Czar is uh Sri Ram Krishan, who, you know, used to be a general partner at Andrees and 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. Um

1:20:05 you know, as close to that as possible and Yeah, I think that um It is actually a real concern. The uh the space is moving so quickly, 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 totally in the executive branch, at least in the United States, like they should be able to

1:20:30 uh respond quickly, whether it's through an EO or other means. Uh I don't know if 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 sixty in the Senate kind of thing? Like does anybody understand technology or do they all grow up in the world where, you know, Google became a thing after they were already adults? And it strikes me that there there's a difference, you know, the pace of technology improvement versus the pace of law, but also or regulation, but also the people that are enacting those laws don't tend to all they have a different pace as well, right? Like they our kids are in a different world. Like my kids don't know what a world without AI looks like. Neither do yours. Yeah. Uh, but we do, you know, because we're 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.

1:21:21 And and it strikes me that those people shouldn't maybe not be regulating Uh you know, AI. 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 um It's I I what comes to mind is that meme on the internet where like there's the guy at this dance, it's just like you know, that uh everyone else is dancing and they're in the corner and it's like they don't know. If you go any almost anywhere in the world Um

1:21:52 I you know, people maybe have heard of Chat GPT. They definitely haven't heard of Anthropic or Claude. Yeah. Um 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 they're using Google and you know, they're addicted to TikTok and things like that. 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 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 watch before. Well my my hope is that there's such a flowering of choice that you know, it's gonna be your choice, actually. I mean the d the difficulty is like

1:22:42 Well, then you have a filter bubble, but you know that exists today with social media today. Yeah. Um Okay, so here's a white pill that I don't know if it's going to happen, but I hope it happens. Um You know, one of the reasons why it's so opaque today is literally that Um

1:23:04 You know, X has Yeah. Or X or tw you know, before it was called Twitter. And Twitter had Yeah.

1:23:10 Thousands of people working at that place and um You know, you needed thousands of people, maybe, right? Or I guess the tricky thing is like Elon came in and quickly asked like eighty or ninety percent of the people, and it turns out you didn't need eighty or ninety percent of the people. So that's like another, you know, form of founder mode taking hold. But um, like it or not, you know, I can't go into uh Twitter today and tool around with my four you like my four you was written for me, right? It's in some server some place and there's a whole infrastructure thing. Yeah. But you don't control it.

1:23:46 But it's conceivable, um, you know, today. with code gen, you know, uh today Engineers are basically you know, writing code about five or ten X faster than they would before. Um

1:23:58 And that sort of capability is only getting faster and better. Like it's sort of conceivable that uh you should be able to just write your own algorithm and maybe you'll be able to, you know, run it on your own Yeah, and and you'll want choice. And so you know. The kind of um 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 the best version of that is actually like I want to see an exp you know, I I w maybe want to see

1:24:29 uh my four you I'll go like very plainly and then I wanna be able to see see if I can convert that into the one that I want, or I can choose from twenty different ones. Two ideas here, you know, as you're mentioning that one, like your list could 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. Uh you know, you could consider it political as one parameter from left to right. Right. Um, but you can you could be like happy sad, like you you could sort of filter in that way. I know that'd be super

1:25:03 Yeah. 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. Um, and then the opposite is clearly what's been happening, right? Like Uh Apple You know, closing off the iMessage protocol so that you know it's literally emote. Like, oh no, like that person has uh an Android, so they're gonna turn our really cool blue chat into a green chat. We don't talk to those people, do we? I know, right? Uh I I mean that's just a pure example of

1:25:36 Um Apple. Even today still Yeah, they're opening it up a little bit more with RCS, but You know, it's uh those are actually in reaction to the work of Jonathan Cantor and the DOJ. So there are efforts out there that are very very much worth our

1:25:55 Uh attention. around reining in big tech and reining in Um the ways in which Like these sort of subtle product decisions. only make money for big tech and they reduce choice and you know ultimately reduce liberty.

1:26:10 It'd be super interesting to be able to have an advantage if you're big tech and you you are a company and you come up with this, but have that advantage erode automatically over time in the sense that you might have a twelve month lead, but what you're really trying to do is foster continuous innovative 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 and you can't be complacent. So you have to earn it every day. And so yeah, maybe you have like a a two year window on this blue bubbles and and then you have to open it up. But now you gotta come up with the next thing. You gotta you push forward instead of just coasting. Like Apple really hasn't come up with a ton lately. Yeah. And then I think uh the reason why it's so broken is actually that uh government ultimately is

1:26:57 You know. very manipulatable by uh total by money. Yeah. And Yeah, that's sort of the world we live in. D do you think that'll be different under Trump? I I don't tend to get into politics here, but so many people in the administration are already incredibly wealthy that That's the hope. I mean

1:27:16 Uh, 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 we're hoping that really great things come back. And You know, uh in full transparency, like I think I was too naive and didn't understand how anything worked in twenty sixteen. That's not what I was saying in twenty sixteen. I was fully, you know, an NPC in the system. Um but you know, also that being said, I'm a San Francisco Democrat, so I really have uh very, very little uh

1:27:45 Yeah. I have very little special knowledge about how the new administration is going to run. Um, except that I you know, 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 a hundred percent, you know, even though I didn't vote for Trump. Uh, I am a hundred and ten percent, you know, down for making America truly awesome.

1:28:09 What do you believe about AI that few people would agree with you on? It might be that point that I just gave you. Like I think that um A lot of people are hoping that uh the AI become self aware or you know have agency. And um

1:28:26 From here, the kind of world we live in will be very different. If somehow. the you know literally AI entities are given ca you know Maybe the line is actually will we have an uh an AI? CEO.

1:28:42 Like will we have a company that just like literally gives in Two You know, whatever the central entity says, like that's what we're going to do. Every problem you know You know, it's sort of the exact um 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 a trillion token top context window.

1:29:10 and like won't be able to do what we wanted to do. So we would trust an AI and you know an LLM conscious based con consciousness more than a human being. Like I'd be worried about that. I was thinking about this last night, watching the football game, actually, and I was like, Why are humans still calling players? Like yes for coaching. Yeah. But like calling players in the game. Th an AI, I feel like at this point with like O one Pro or something, we'd be ahead of

1:29:37 Where we are is human. Team should try that. That'd be super interesting. Oh, that's gonna be the next level of money ball then. We'll just try it in preseason, yeah. Right. Like or try it in a regular season game. I don't know, but it 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 sixteen 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 fascinating. Yeah. I mean What's funny about it is um I think in in in those sort of scenarios you might just see a crazy speed up because um

1:30:22 of human effects. I mean when you look at organizations and how they make decisions Um So many of them. Yeah. There's sort of like

1:30:32 A uh a Straussian reading of them. There's sort of like at the a surface level, you're like, I wanna do X. But like right below that is actually something that is not about acts, you know, uh on 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. Um Yeah, corporations or you know

1:30:55 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. Um Yeah. The people who are really in power, uh, you know, don't like that person or you know, they love them the wrong way or or human. Yeah, exactly. Right. It's like these are like extremely influenzable uh systems. Your idea might be best, but I'm gonna disagree because it's your idea, not my idea. Right. And then

1:31:21 I think that's why in general we really hate politics inside um companies. Um Because You know, it sort of works against the collective. Do you think we we'd ever see a city, like a mayor, then first, before even a CEO? As like an AI mayor.

1:31:38 You know, I 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. Kinda like a plane, right? Like we want a physical pilot, even though the plane is probably better off by itself. Yeah, that's right. And that that might be what what ends up happening. Like even if Ninety percent of the time you're using the autopilot. Like you always need a human in the loop. And

1:32:05 Yeah, I'd be curious If that turns out to be one of the things that society learns. uh one of the crazier ideas uh 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 Um

1:32:21 You know, sort of how this interacts with nation states. Um like You know, China obviously is run by uh a central committee, and arguably Xi Jinping. Um You know, seemingly if you had ASI you would only want You know, sort of the central

1:32:38 committee to have it. And so that might turn into like a very specific form of you know it's you know, China might end up having one ASI that is totally centrally c controlled and then everything else about it. Yeah, sort of comes out of that. And then you might end up with

1:32:56 Yeah, 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

1:33:14 uh argue one way or another um what what it's like to be in China. But Um That's an interesting idea. It's like Yeah, that society probably, you know, unless there's other changes there, like That's you can sort of count on a single

1:33:30 uh 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 discussions about what do we do with this ASI and who gets to you know, where where does the agency, the ultimate agency of that nation come from? Going back to something you said earlier, I think the ultimate combination, at least for right now, is

1:33:55 human and machine intelligence working in concert where the machine intelligence might be the default and then the human opts out. Right. Uh and that's exercising judgment. It's like no, we're not gonna and when you look at chess, it that tends to be the case where the best players are using computers, but they know when, oh, there's something the computer can't see here or uh there's an opportunity that it it just doesn't recognize. I think it was Tyler Cowan who said that like the I had a word for it, mixing the technology. Fascinating. Yeah.

1:34:25 And then yeah, the question is like, well, what how does America approach it? Like potentially it's much more laissez faire. And then in that case, like my argument would be like 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, d we should all be superman in that in that sense. And that would be like the most um empowering version of

1:34:55 uh a society that of like free and uh you know free people created equal, right? And then you know, there might be other versions in your I mean I 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

1:35:14 uh you know, was this AI assisted or not? And like let's check the provenance on like you know how that that AI was like trained. And I mean, I don't know. There are all these different there's like A billion ways all of these different um governments are going to sort of approach this. technology. W what are the smartest people at the leading edge of AI talking about right now? I mean,

1:35:36 I you know, the hard part is like I spend most of my time not with those people. I spend most of my time with people who are um commercialising it. So So the very, very smartest people are clearly the people who are uh in the AI labs actually actively Doing. You've sort of creating these models.

1:35:54 Um But you know sort of the Uh the people who I know who are in those rooms. I mean, sounds like test time compute compute is really it. Um

1:36:04 Yeah, that's the reasoning models are sort of the thing that Will really come to Com come to bear this year. Like we're sort of under you know understanding that right now.

1:36:14 Um Yeah, for now it sounds like pre-training might have hit some sort of scaling limit, you know, the nature of which I don't understand yet. Um Yeah, there's a lot of debate about it. You know, will will there be uh new four style models that have more data or more compute and seemingly you know there's just rumors of

1:36:34 um, you know, training runs gone awry that you know, basically the scaling loss may have petered out, but I don't know. So we have sort of like the LLM, we have the reasoning the LM and the reasoning model are different, correct? The way OpenAI talks about O one, they're sort of uh connected, but like different steps. Okay. And so so we have progress there. Yeah then we have progress with the data. And then we have progress with inference. Yep. Well, we just have don't have enough GPUs really. Like I you know, I think

1:37:05 What's funny is like I'm still pretty bull on NVIDIA and that they more or less have the monopoly on, you know, sort of the best um price performance and so you think this is gonna continue like demand for trillions of dollars of investments in in AI Basically I you know I think You can live in two different worlds. One world says like

1:37:30 Uh, all of this is hype. We've seen AI hype before, like, it's not going to pan out. Ah, and then I think the world that we're spending a lot of time in Like the world really wants intelligence. And yeah. 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 desk a computer on every desk. Like everyone needs a smartphone. You know, we're selling you Microsoft Office, we're selling you package software, we're selling you uh

1:38:02 Oracle, SQL Server, like you know, we're selling Uh you know, SaaS apps like Salesforce, like you know, it's ten thousand dollars, you know, per seat per year, that kind of thing. Uh, or we're selling

1:38:17 Yeah. Classically Palantir was selling you know, million dollar or ten million dollar ACV. uh you know, very specific vertical apps, right? Um

1:38:28 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 Yeah. Especially the bleeding edges probably customer support and all of the things that you would use Um

1:38:46 For a call center. Like those are sort of the things that are already so well defined and sp you specified. And there's a whole training process for people in you know, usually overseas to do these jobs. And uh AI Now is just coming in and Like it's you know the

1:39:06 Spee to text and text to speech. Those things are indistinguishable from human beings now. And um, you can train these things, the evals are good, the prompting is good. Um Yeah, I You know, going back to what we were saying earlier, like what we're seeing is like

1:39:24 Yeah, like it or not, is actually Uh Replacing labor. Has anybody created an AI call center from scratch and now is ingesting customers? Uh yes. I mean I

1:39:37 I funded a company in this very current batch that um Yeah. uh it's called leaping AI. They are uh they are working with Some of the biggest wine merchants in Germany, which is fascinating. Um

1:39:51 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 certain distinguishable. And uh, you know, I think eighty percent of uh the ordering volume for some of their customers is entirely No human in the loop. I would love to see government call centers go to this. Yeah. It would scale so much better.

1:40:15 Uh I was on the hold for like three hours the other day for like a fifteen 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. Uh and probably more securely and reliably. Yeah and more consistent. Uh, regardless of who's on the other end or how they're talking.

1:40:34 How would you define AGI? Um I guess the funniest thing is uh Microsoft I think is defining it when it gets its hundred billion dollars back. But uh I You know, I'm sort of skeptical of that because Uh you know, I think

1:40:50 basically only Elon Musk then would, you know, qualify as a uh human general intelligence, I think. Um Like AGI uh the thing is like in limp in a lot of domains it feels like it's here. Actually. I mean. You know can it

1:41:05 have a conversation with someone and take uh 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 uh sort of interaction and also like take orders for very expensive wine and have that just work. Yes, like that's happening right now. Yeah. So I think in a lot of domains, and this is sort of the year where like maybe there's like five or ten percent of things that like

1:41:36 It's you know, sort of hitting the Turing test and Yeah, really satisfying that. But You know. I think maybe this is the year where it goes from like ten to thirty percent and the year after that it doubles again and

1:41:48 You know, the next few years are like actually the golden age of building AI. Totally. I I think li I'm super optimistic, at least for the next like five years, about the things we'll discover. uh the progress we'll make, uh the impact we'll have on humanity and a lot of the things that plague us. What do you I wanna get into how you use AI a little bit. What do you know about prompting that most people miss? I mean, I'm mainly a user. Um

1:42:15 Yeah, I spend a lot of time with people who spend a lot of time in prompts. Um Probably the person I would most point people to is uh Jake Heller. So he's the founder of Case Text. He was one of the first people to get access to GPT four, and uh we think of him at Y C as the uh first man on the moon, and that he was the first to successfully commercialize GPT four.

1:42:38 Um in the legal space. So um what he said was that Yeah, they had access to G GPT three point five. And uh it basically hallucinated too much to be used for Um actual

1:42:52 Like legal work. Like lawyers would see one wrong thing and say, like, oh, I can't trust this. Uh GPT four, he found. Actually You know, with good evals. would actually you know give the they could program the system in a way that

1:43:08 it would actually work. And what he says he figured out was If Um GPT four started hallucinating for them. They realize that They were doing too much work in one prompt.

1:43:22 They needed to take that thing that they uh asked. GPT four to do, and then break it up into smaller steps. And then they found that they could get um deterministic output. For um

1:43:36 for GPT four like a human if they broke it down into steps. Oh interesting. And what he needed to do, I mean, I sort of um It's sort of equivalent to uh Taylor time and motion studies in factories. It feels like that's what he did for what a lawyer does. Um You know, let's say you have to put together a chronology of what happened in a case. And uh what a real like he's a real life lawyer, so which is he's 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.

1:44:15 like tailored time and motion style in the process and prompts and workflow. So uh for instance doing uh This type of uh 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 uh little flags and highlighters and things like that. They just get very good at

1:44:39 um, you know, doing a read through uh 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. 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 uh evalu evaluations for each of those prompts. Um, 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

1:45:44 Uh, you know, 40 comes out, cloud three point five comes out, deep seed comes out, you know, you have evals, which is basically a test set of prompt. uh 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. And um can you give us an example of an eval, like make it tangible for people to Oh yes, I mean it's really straightforward. It's just a test case, right? And so given this prompt and this data, you know Evaluate the prompt to see if you And it it usually maps directly to like something that is

1:46:23 You know, true, false, yes, no, like something that is pretty clear, like You know, let's say there's a deposition and you know, someone makes a certain statement, right? You might have a prompt that is like You know, is this uh You know, is what this person said. Um

1:46:39 in conflict with uh you know, any of the other witnesses. Or 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. Um Yeah, at a very granular level, you might have thousands of these. And then that's how uh you know, Jake Heller figured out he could

1:46:56 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

1:47:08 You know, three months of discovery. That's fascinating. How do you use AI with your kids? Oh, um I love um making stories with them. So uh you know what I find is O One Pro is actually extra good now. Um

1:47:23 So yeah, th that actually there's like an interesting thing that's happening right now. Um And it I saw it. up close and personal uh this morning looking at some blog posts about Deep Seek R one, which is uh Deep Seek's uh reasoning model.

1:47:38 Um I was reading Simon Willison's blog post about um he got Deep Seek R one running. It's uh the first one of the first open source versions of uh sort of The reasoning and so Uh what we just described with um how Jake Heller broke it down into chain of thoughts to make case text work. Um, it turns out that that maps to basically how the reasoning stuff works. And so, you know, the difference between what Jake did with uh GPT four when it first came out. And

1:48:13 uh what O One and O One Pro maybe is doing and what Deep Seek R One 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 like А ведер Like it makes sense at all of those m micro steps. That's what um In theory, this reasoning is actually happening, that that's actually happening in the background for O one. And uh O three.

1:48:41 And uh if you use chat GPT, you'll see the steps, but it's like a summary of it. Right. Um and so it's Yeah, I just only saw it this morning. I mean it's this is such new stuff. Like I was Hoping that someone would uh do a open source reasoning model just so we could see it. And that's what it was. I think um Simon's blog post this morning showed, here's a prompt. And then uh you he could actually see.

1:49:07 I think he said pages and pages of the model talking to itself. Literally. Uh, 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 uh his company Case Text for almost half a billion dollars to Thomson Reuters. Um

1:49:32 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 taking doing like test time compute. It's actually just spending more time Yeah, thinking. before it spits out the final answer. So how do you create a competitive advantage in a world like that where uh perhaps that company had an advantage for a year or two.

1:49:58 And now all of a sudden it's like built into the model. For free. Yeah, I mean I I think uh You know, ultimately the model itself, um, is not the moat. Like I think that the evals themselves are the moat. Um

1:50:13 I don't have the answer yet. I basically for now. Uh 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 O three uh or you know deep seek R one can do because it's specific to your data and it's much more in the details. Um

1:50:37 I I think that that remains to be seen. Like the classic thing that Sam Altman has uh told YC companies and you know told most startups, period, is you should count on the uh models getting better. So if that's true, then you know, that might be a durable mode for this year. But it might not be past. You know? I mean O three we haven't even seen yet the

1:50:58 The results seem like fairly magical. Um So it's possible that advantage goes away even as as soon as this year. Uh, but all the other advantages still apply, like Yeah. One thing that a lot of our founders who are getting the five to ten million dollars a year in revenue with five people in a single year are saying is

1:51:19 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 um what doesn't go away is Building a good user experience. Building uh 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. Um

1:51:43 And so, you know, one of the funnier quips is that uh the second best software in the world for everything. Is uh using chat GPT. Because you can basically copy and paste

1:51:57 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. Um And it's the second best because the first best will 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. Um

1:52:25 And so I think that that's Those are the mo like the motes are not different. actually at the end of the day, it's still you ha still have to build good software, you s still have to be able to sell, you have to retain customers, you have to um But you you just don't need like a thousand people for it anymore. You might only need six people.

1:52:44 Okay, I wanna play a game. I'm gonna y you have a hundred percent of your net worth, you have to invest it in three Three companies. Oh God. Okay. And so Uh the first company you have to invest half and then thirty and then twenty. So altogether a hundred percent. Which companies

1:52:59 out of the big tech companies. How would you allocate that between Here's here's my biggest bet, my second biggest bet, my third from today going forward. Okay, I guess you know. Is it cheating to say I'd put uh even more money into into my

1:53:14 the the Y C funds that I already run, but that's a that's a cop out. That goes without uh saying. Um 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. Um but short of that, I mean Maybe NVIDIA, Microsoft Meta.

1:53:35 In that order. Probably. Why? 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 it can't last forever, but Um

1:53:47 I think that Yeah. The demand for building uh the infrastructure for uh intelligence in society is going to be absolutely massive.

1:53:57 And uh 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

1:54:11 You know, it's sort of the meta project because then it's like the Manhattan project of instantiating more Manhattan projects. Yeah actually like You know, you could imagine If we can if if more test time compute or you know, you could do the work of

1:54:28 You know. Ten thousand. Two hundred IQ Einsteins working on uh bringing us You know.

1:54:37 Basically unlimited clean energy. Yeah. Like That That alone will that I mean, if anything, like that's probably the bigger problem right now. Like we know that

1:54:47 uh the models will continue to get better. We know that um you know, the the demand for intelligence will be unending. And then uh, you know, even going back to the robotics question, it's like if we end up making you yeah, universal basic Robotics. you know, the limit will still actually be uh you know sort of the climate crisis and um the ability the available energy

1:55:13 uh available to human beings, right? And You know. Maybe solar can do it. But uh maybe there are lots of other sort of solves, but Yeah.

1:55:23 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 uh in the realm of science and engineering, like You in the between robots and

1:55:40 um you know more and more intelligence. Like we could sort of figure these things out. Um But not if we run out of energy. Okay, why Microsoft and why Meta next? I mean I think Microsoft has um

1:55:54 you've just really, really deep access to open AI, and I think open AI is probably You said public companies, right? So you know, I think Yeah, there's a non zero pretty large percentage of like the market cap of Microsoft that I think is pretty predicated on

1:56:09 Sam Altman and the team at OpenAI continuing to be successful. Totally. Um And then why I met a I mean, I think Meta's sort of the dark horse because like they are amassing talent. And then they have crazy distribution.

1:56:24 And I think um You know, I just would never count suck out. I think that he you know, the it's It's so crazy that it's super smart that he is On that. He's always thinking about what is

1:56:38 the next version of computing, like so much so that he'd probably put more money than he should have into AR and that was maybe premature. He might still end up being right there. but uh 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.

1:57:01 I wanna switch subjects a little bit. A few years ago you met with Mr. Beast. Oh yeah. And talked about you two. What did you learn? Because your your channel changed. Oh yeah, he was great. I mean, uh he was very uh brusque with me. He said, you know, look, man, your titles suck and your thumbnails are even worse. And um Yeah, I think that he spent so much time trying to understand the YouTube algorithm and what people want.

1:57:28 that he just loaded it completely into his brain and um what makes a good title? I think it's clickbait. Unfortunately. Yeah, unfortunately and this is the thing, like Um When you're trying to make smart content, uh it's actually kinda tricky because you don't want necessarily

1:57:46 More clicks. You want more clicks from people who are smart. So we we title our episodes differently on YouTube usually than on the actual audio feed because if you want YouTube to pay attention, you have to almost be more provocative intentionally. That sounds right. Yeah. Like we can call this, you know, AI ends the world or something. Yeah. That's right. 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? Oh um Usually like a person looking into the camera seems to help a lot. Okay

1:58:21 Um And then you want it to be relatively recognizable. Like you know, you want Um some sort of style that When someone sees it.

1:58:30 Yeah, I mean, basically what I was doing at the time was just taking Whatever frame that was Yeah, sort of. kind of representative and throwing it in there. Um

1:58:40 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 a distinct thumbnail, like yours with the the overlay sort of like the the red. Yeah. But you know, I once I uh stopped posting so regularly. Uh 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.

1:59:05 So yeah, unfortunately it's clickbait. And then there is an interesting interaction, like Um You know, yes, you can optimize for better thumbnails and better Titles for the click through. But if it has absolutely nothing to do with the actual body, as you mentioned, um you will not get watch time.

1:59:25 And then YouTube will be like, Oh, people aren't watching this, so we're not gonna promote it'cause the the big thing about YouTube is discovery. Yep. 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. Well, you do if you are uh regular and then the other tack is uh be very shameless about asking for subs. And then the the funniest thing is like subs do very little, actually. Um, there's no guarantee that

1:59:52 You show up and Um people's feeds if someone subs, it like helps a little bit. Um liking helps more, watch time helps the most. And then uh the extreme like

2:00:05 you know, uh over the top hack that Uh you know, probably you should do here is um you should ask for the uh like, subscribe, and hit the bell icon. 'Cause if you hit the bell icon

2:00:19 and they have notifications on. That's the only thing that is almost as good as having their email address and emailing them. You heard it here, people. Yeah. Gary Gary just told you. You got to click like, subscribe, and uh hit the bell icon because You want knowledge. You want to be smart and this is the place to get it. Oh, I love that. Thank you. Good advertising. Yeah. Uh I want to ask just a couple of random questions before we wrap up here. What 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. I think the number one thing that is very hard, um but

2:00:55 Is so I mean you can see it and read it in his essays. It's um to be plain spoken. And to sort of um be hyper aware of uh artifice of um Kinda like bullshit, basically. Like don't let bullshit you know, I think

2:01:13 Um Like it creeps in here and there. I'm like, Oh yeah, I you know, I um 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

2:01:31 Um, and you know, I spend a lot of time with uh our YouTube team and our media team at YC talking about this. It's like If we get too focused on just view count. Um We're liable to just Yeah, like optimized for the wrong audience.

2:01:47 Um if we're not being authentic to ourselves or you know, if we're just trying to like follow 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? Um Yeah, basically like Trying to be very, very high. signal to noise ratio.

2:02:07 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 l out loud. And then, you know, really, really great ideas are not like thinking out loud. They're actually uh figuring out a very complex concept and then trying to say it in like as few words as possible. And um You know, the amount of time that Paul spends on his essays is fascinating. It's you know

2:02:34 Sometimes days, like sometimes weeks, like he'll just you know, iterate and iterate and send send it out to people for comment and Yeah. The amount of time he spends, um Whittling down the words.

2:02:47 And uh trying to like combine concepts and say the the Say the most with the least number of words. Um It it it would shock you and then Also that is actually thinking, like writing is thinking.

2:03:02 Like um 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 Um You know, you would think that that's oh yeah, that's like something, you know, startup one oh one.

2:03:17 Like uh You're helping people with their pitch that sounds so basic. Like, yeah, I guess that makes sense. Like that's what a incubator would do. But um, 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 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

2:03:56 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 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

2:04:21 You know. Knowing what it is is very hard. Like that's all of Paul Graham's um 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

2:04:35 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.

2:04:47 So yeah, to to the degree that Uh clear communication is clear thinking. You know, um one of the things I did when I first joined Y C. 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 thirty minute, forty five minute um office hours with companies in the YC Winter Eleven batch sitting in um back then as an interaction designer, I used uh Omni Graphle 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

2:05:27 Here's the um you know maybe you have a video here or You know, right below you have a how it works. And then, you know, what's funny about it is like some people, you know would take the designs we did in those like thirty, forty five minute things and like that would be their whole startup. Yeah. I sell those companies for hundreds of millions of dollars years later, which is just like fascinating to think about. It's like cleanation, great design, you know, 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 I, you know, to a founder to me.

2:06:02 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 SPF. 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 math, right? Like the guy was You know, everything about it was an affectation, right? Like he was a caricature of Like an artist.

2:06:29 Right. Like we see very autistic, incredibly smart engineers all the time. Type. You know, for him it was like that was part of the act. Yeah. Like I remember he uh did a YouTube video with Nas Daly and I love I you know, Nasir's great and I I love Nas Daly, but I couldn't believe the video that SBF went on. It's just like full of basically bullshit, right? And um

2:06:52 Exact opposite of Brian Armstrong. And um Yeah, we're always on the lookout for that. He wasn't trying to fool you. What's that? Oh yeah, I guess so. I mean he was fooling the world. 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. Yeah.

2:07:12 other people who didn't know. It's the same as the going back to Buffett, just tying a few of these conversations together. Right. Like 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. Uh, 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 one inch level to the thirty thousand foot level and everything in between and they don't get frustrated. If you don't understand. Uh, whereas a lot of the the fraudsters, one of the tells is they they can't go, they can't traverse the levels. And then they do tend to get defensive or sort of uh angry with you for not understanding what they're saying, which is really interesting. And then

2:07:55 I just want to tie the writing back to what you said. You said If you can't get it clear in like two sentences, you might miss an opportunity. That goes to the 10 minute interview. Where you're 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 the your own ideas and discover new ideas. 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 Um I think the the most unusual thing that

2:08:29 you know, I'm still getting used to Is uh I mean in full transparency, I mean probably, you know, the median Y C startup still fails, right? Like You know, Y C is might be one of the most successful, you know, sort of I you know, institutions of its sort that has ever existed, incl you know, inclusive of uh venture capital firms on the one hand. Yeah. On the other hand, like the failure rate is absolutely insane. Right. Like you know, it is still a very small percentage of

2:09:00 the the teams actually do go on and, you know, create these you know, companies worth fifty or a hundred billion dollars. Uh but the remarkable thing Is not that uh you know it's that low, the remarkable thing.

2:09:14 Is that it happens at all. Like it's just unbelievable that um I think you have the coolest job in the world, or at least like warn out. I agree. If I had to pick like the top ten, like you'd be up there. I agree. I mean it's uh especially to d 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.

2:09:35 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, ten years, at least ten years, maybe like all of the things that

2:09:50 you know, him and Greg Brockman and all the researchers he brought on. Like we're working on That happened Like I I think there's a lot of the the Sam Altman haters or the open AI haters out there.

2:10:05 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 and But At the same time, it's like they believed a thing that nobody else believed and they brought the resources to bear.

2:10:27 And so recently, um you know, Sam Altman came back to speak at our AI conference this past weekend. And uh we you know I couldn't think of another way to start that conference than have Sam Ollman and uh you know a bunch of his Uh you know, old

2:10:44 Uh we had Bob McGrew there, we had Evan Morikawa, who was the end manager who released ChatGPT. Bob McGrew actually worked with me at Palantir back in the day, but he's you know outgoing chief research officer. Um Jason Quan was there. He actually worked at YC Legal before leaving to uh, you know, run a lot of things at OpenAI. And so I had them all stand up. And uh we had a room full of, you know, two hundred ninety founders, all of whom

2:11:12 were working on things that happened it Essentially because open AI existed and there was like a standing ovation. Oh, that's awesome. So And uh, you know, to Sam to his credit was like, you know. Not just us. You know, these researchers did so many things as well. But

2:11:30 All that being said, it's like we're in the middle of the revolution. This is just like I mean, it's not even the middle. I think it's like like just after the first pitch of the first inning of like what is about to be Like. A great, great time for humanity, for for technology. I'm with you. Like so excited to be alive right now, so lucky, so blessed to like be a witness to this. And I think we're gonna make so much progress on so many things and go back to the haters. Like there's always people pulling you down, but the they're never people that are in the trenches doing anything. I've rarely seen, you know, people who are working on the same problem attacking their competition like that or undermining them, or no, it's

2:12:11 Yeah. So I'm you know, on our end we're just hoping to uh lift up the people who Want to build. Yeah. This is the golden age of building. Amazing. Uh I wanna just end with the same question we always uh Ask which is what is success for you? I think looking back

2:12:27 I mean Growing up, um I Always just looked up 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.

2:12:42 And um I just think of Steve saying Uh You know. we want to put a dent in the universe.

2:12:51 And um Ultimately that's what I want. Like that's you know, success to me is How do we bring forward You actually th this is actually when Paul Graham. Uh.

2:13:01 came to recruit me to come back to YC. I had actually left and started my own VC firm. uh you know got to three billion dollars under management like in the coinbase. Yeah, totally. I mean returned six, seven hundred million six hundred fifty million dollars on uh that investment alone. Um Yeah, I was sort of

2:13:21 right at the pinnacle of my uh investing, you know, as a you know 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 uh It was really, really hard to walk away from that. Um, luckily I had very great partners. Brett Gibson, my partner, my uh multi-time co-founder, went through Y C with me. Uh he actually built a bunch of the software with me at YC, you know, before we left.

2:13:49 Uh, he runs it now, they're off to the races and still doing great work. And Uh you know, I had sat down with Paul and you know, right after we shook hands and you know, he's like Gary, do you understand what this means?

2:14:02 Um It means that You know, if we do this right. We You know, kind of like I think what Sam did with OpenAI with you know pulling forward large language models and AI and bringing about AGI sooner. Like YC is sort of one of the defining institutions.

2:14:21 That is going to pull forward the future. Um and it's not more complicated than How do we get in front of Optimistic.

2:14:30 Smart. people who, you know, have been benevolent. Uh you know, sort of. goals for themselves and the people around them. How do we give them

2:14:40 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 ten week program that hopefully reprograms them to be more formidable while simultaneously being more earnest. Uh And then the rest sort of takes care of itself. Like you know, this thing has never existed before like this. And um

2:15:02 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

2:15:13 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

2:15:29 The wild thing about intelligence is like intelligence is on tap now, right? Like all of the impediments to being able to all of the impediments to fulfing what you want to do in the world. uh are starting to fall away. Like you they you know, there's always going to be something that stands in the way of um any given person. And I'm not saying like those things are equal.

2:15:53 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 a achieve that.

2:16:09 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 a quality of okay. That's right. Yeah, and I that you know, that's sort of the quandary, right? Like you You have to choose do you want the the outcomes to be equal or do you want um a rising tide to uh raise all boats?

2:16:29 I'm a huge fan in in equal opportunity but not unequal outcome. I'm with you. Yeah. 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 Furnham Street on social media and explore more insights at fs.blog, where you'll find past episodes.

2:16:53 our mental models and thought provoking articles. While you're there, check out my book Clear Think You. 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.

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