Paul Graham On Startups, Ambition, and Great Founders

0:00 So, we're here in Mountain View today with PG himself at the original offices doing your talk that you do for the YC batch. I looked it up. This is actually the 47th YC batch. >> You know, I've been trying to calculate that number and I wasn't sure exactly what it was. >> No wonder I'm not too worried about this talk. >> I've done it that many times. It changes a little bit. >> Yes, it's the 21st year also of YC.

0:24 >> You know, the weird thing is I make the talk from scratch every time. >> Really? I probably say the same things over and over, >> but I always feel like I should like I think, oh my god, I you you know think a couple hours beforehand. Oops, I better I'm going talking to YC. I better think in advance a little bit about what to say. And so I open up a text file and start typing things. And that's how it always is.

0:43 >> Any tidbits from today that are new from winter? >> No, most of the things most of starting a startup here, this is an actually interesting point. Most of starting a startup is the same. Most of starting a startup is always the same, right? in, you know, microprocessors or AI or like in the internal combustion engine, it's always the same stuff. >> Good. Well, in that case, let's run through some questions. >> Okay. >> so you recently posted that the startups YC funds now are much more serious than they were in the supposedly good old days. Yeah.

1:14 >> What did you mean by that? Well, what I meant by supposedly good old days is people are always saying that YC has jumped the shark, you know, and like I remember when people started saying this, which is in about like 2008, right? YC is always this thing that was great, but now it's not what it was, right? Because they can't say it was always terrible. They have to admit it was once great. So, if they want to attack it, they have to say it's fallen, right? It was good, but now it's not.

1:38 It's always been. It's always not good anymore. and so that's what I meant by the supposedly good old days because the old days are what people claim used to be good. But like in the old days there was a lot of things like Reddit, right? Which valuable as it is, is not exactly intercontinental ballistic cargo, >> right? Which I did office hours with recently. How about that for an idea? >> Like it's like an ICBM except instead of exploding, it lands.

2:08 >> It's ambitious. >> And drops stuff off. Yeah, that's a that's serious, right? You don't get much more serious than an an intercontinental ballistic cargo. >> Yeah. What what were some of the other serious ones? >> Well, curing cancer. >> Yes. >> There's a startup in this batch. I mean, and there's all sorts of different approaches to curing cancer and I say fund many of them because like any one working would be good. So, like one way to approach curing cancer is vaccines.

2:34 That's one end, right? The opposite end is therapies, right? And so there's a startup in this batch that is basically doing on demand research for people with cancer, you know. and what I like about it is they're sort of inflicting on cancer the death of a thousand cuts, which may be the way to defeat it because, you know, it's notoriously it's notoriously not the sort of thing where you just like come up with a cure for cancer. I mean, maybe you do if these vaccines work. Maybe the maybe you do, right? But maybe or maybe the way you fight it is the death of a thousand cuts.

3:12 >> And they're actually really interesting because they helped another YC. >> Yeah. Right. It's part of the whole YC community. In they helped Sid from GitLab deal with his cancer which he has done very successfully. I mean he approached it like a startup, right? And these were essentially his co-founders in that de facto startup. And it's funny because I remember thinking after that, you know, I'm all I always had this process going of what would be a good startup idea, right? And I remember after that saying, you know, somebody should start a startup to do for everyone what Sid did for himself. And now, not only is this startup happening, it's the people who actually did it for Sid.

3:49 >> So, it couldn't be better. I was so delighted when I realized that one was happening. >> Yeah. And I think his essay that I went founder mode on my cancer, that's a very good way of describing what he did. >> Yeah. using another YC term to >> attack his cancer. so along that vein, I was rereading the essay you wrote in 2012 where you coined the term frighteningly ambitious. >> Frighteningly ambitious ideas. A lot of those ideas have actually gotten done since then.

4:14 >> Well, like which ones? >> Well, a new Google. That was one of them, right? And it turns out, look, the I I sort of predicted this, right? that the way you make a new Google is not by attacking them head-on. You have to wait for things to change so much that their model is is obsolete and that's when you can do it. And that's what Open AI is, >> right? Like I realized very early on like after using OpenAI like whoa, I don't use Google anymore. I don't use searches that what I really wanted from searches was not web pages. I wanted information. Why not just ask the AI for the information, right? And I'm sure everyone's now had this experience.

4:52 >> And it was incubated at YC. >> Yeah. Yeah. Yeah. Yeah. >> One of Sam's many side projects. >> Yes. and so along the same veins of around ambitious as a term, I am curious when you figured out why a founder needs to be not just unusually talented in technical ability or something else, but they actually have to be really ambitious as well. >> When did I figure out that founders had to be really ambitious? I think I already knew that before we started YC because when we started YC we already had all this experience of doing a startup ourselves, right? And so we knew that it was grueling and you need you need something to drive you through all those obstacles and honestly being beautiful is not enough. The obstacles are too fearsome for mere beautifulness to do it. So there's got to be something driving you and you you call it ambition.

5:48 >> And part of ambition, I assume, is wanting to be a billionaire someday. >> You'd be surprised actually. >> Tell me more. >> You know what actually motivates founders dayto-day? The fear of failure. Even though they're doing this thing that if they succeed, they'll be super rich. that does motivate them. But the thing that motivates them at that at any given moment is the fear of like disaster, looking like a fool, the server crashing, right? Like if you're running some online service and your server's crashing, you're not thinking, I got to deal with this because if I deal with it, I'll become a billionaire. You're thinking, oh no, the server is crashing. It will be a disaster. You know what? My the engine of my model train set is falling off the edge of the table. I need to go and save it, right? That's what you're thinking at any given time. You just don't want your model train set to break. You put your head down and work on the model train set for 10 years and then you lift your head up and like, holy the like if I add up the value the value of all my shares at the current the last round valuation, I'm a billionaire. But it takes people by surprise when it happens.

6:54 >> Yeah, that's great. >> Sometimes I'm the first to tell them. Like I do the math. I'm like, "Wait, according to your last round, you're a billionaire." And like, "Oh, yeah, I am. >> That's the way we want it." and I'm curious, can you tell me a story about a founder where when they started YC, they were maybe not ambitious enough and then they became very ambitious during the batch? >> Those stories are rare because most of this quality is inborn. And so like you know Sam Alman famously was extremely formidable the first time I met him before we even accepted him. and that's more the rule than the exception.

7:30 So the one case where you see founders it's not that they lack ambition. It's more like they've been trained not to show it. Because when you're young you're often in situations where you're supposed to be obedient. You're not supposed to like go off and do what you want. You're supposed to do what people tell you. Maybe you have pushy parents >> who tell you to do what they want, right? And so you've never you've never let yourself go. That's the one case.

7:56 But even then, I would argue that the person was ambitious. They just been trained not to show it. >> Yeah. Actually, that's a good point because I think nowadays a lot of people think about startups like another badge that they should collect along the resume. >> only if it fails. If it works, it's like your life's work. you don't you don't have any more badges, right? And so what kind of badge is a failed startup? Not much of a badge. I think if people think that, they're not thinking it through, right? Like honestly, we would get it was quite early on in YC's history when people started applying to YC for the prestige, right? Like they would apply to Harvard hoping they would get in. But the thing is, if you apply to Harvard and get in, you should do it because no matter how stupid you are, there's easy majors you can choose and sort of slide through. You'll get a Harvard degree and everybody will be impressed with you. With startups, there's no easy major, right? It's like applying to Harvard and being forced to study theoretical physics, right? It's really rough if you're not very hardworking and clever and determined.

9:01 And so these people trying to get Y trying to do YC as a credential, they had no idea what they were asking for. They didn't realize like this is not this is a really really unsuitable credential. >> There are many easier ways to get credentials. >> Yeah. >> Yeah. If you want to seem cool, like starting a startup is just about the least efficient way to do it. >> Yeah. >> It's brutally hard. >> Yep. >> and you won't seem cool for years of years of brutal hardness before anyone will think you're cool.

9:29 >> 100%. when you were describing some of the founders before you used the word formidable. Yeah. Can you tell us a little bit more about how that word came to be, what it means to you as well? >> Well, a lot of the language of Y Combinator is is Maya and Jessica's private language, right? And so, we already had this word before we started YC like for a certain a certain kind of person. And I described it in an essay.

9:53 I said, I think that it's someone who who gets what they want. That's that's the test, right? Do you get what you want? Because how formidable are you if you don't get what you want, right? so it's someone who gets what they want in any situation. And so if you invest in them and you have stock in their company and they have stock in this company, your interests are aligned. If they get what they want, you get what you want. So that's why investors want people who are formidable.

10:19 >> Pretty good. >> Yeah. Because they get a part of what the founder gets. So, changing tracks a little bit at startup school, Patrick Collison recently said that lean startups may be dead. >> Did he? >> And part of that is because he says, you know, it's really easy to raise money these days. AI lets you work on many things in parallel. You could, you know, spin out a bunch of agents and work on a bunch of things. Do you agree with him or would you have a rebuttal?

10:46 >> I've never really been clear what what a lean startup is. I mean, we already had a kind of startup that we funded in YC and maybe it has a lot of overlap with what was described in this book, The Lean Startup, but I haven't actually read it. And so, I mean, why would I, right? Tolken didn't read all those other fantasy books people wrote. >> and so I don't exactly know what a lean startup is, but if you tell me what qualities you might think might be obsolete, maybe I could answer that.

11:13 >> Let's say starting with just a little bit of money on a very focused area. You mean it might be dead starting with a little bit of money? >> Mhm. >> I don't think so. I think there's a lot of things you can do for cheap. I mean, tokens are very expensive at the moment, but that's just because there's a shortage of GPUs. Token prices will certainly inference prices at any given any given level of inference go down dramatically like 30x a year or something like that. So, even token prices are somewhat misleading because they're you're getting higher quality tokens over time. So I think no everything technology always gets cheaper. I don't think I think you can still start a startup on not much money.

11:53 >> Even if it's a rocket startup or any of the >> it's hard to it's harder to start a rocket startup on no money. But even a rocket startup you can start on not much money because what you do is you adjust for how much money you've got. Like you can't build an actual rocket. What you can build is you can build a design for a rocket and show that to and you can simulate it, right? or show it to experts. If it's convincing enough, you can go to investors and get a bit of money to do the new the next stage.

12:20 You're always able to raise another round, right? So, you just do what you can on the money you got. As long as you can get to some kind of milestone, then you can convince investors to give you more. >> Yeah. I think Philip from StarCloud was saying that they basically just wrote a white paper and booked a launch and they raised the money for StarCloud. >> Yeah. Nothing as convincing as booking a launch, right? It's got to happen if you booked a launch. But he's a huge famous expert >> in the stuff he works on, right? So he comes with automatic cred, >> right?

12:50 >> It's easier for him to do that than some kid who just graduated from college. >> So let's transition to talk a little bit about AI more broadly. I'm curious what surprised you about AI. >> Well, I was studying AI back in the 1980s. A very different kind of AI that would never have worked. I mean, it was a joke. So, I've been thinking about this a lot and I had a lot I spent a lot of time thinking about what AI would eventually be like. And the way we thought it was all going to play out was you start out with like a perfect fly, right? You know, it doesn't do much.

13:27 It's like a fly, like a fly's brain, right? It can only do what a fly does, but it does it as well as a fly, right? And then you work your way up from flies to mice, you know, to cats and eventually you get monkeys and then humans, right? But at every point you're you're perfect. You're doing what they do well. And instead, what we got was basically a full-on human but full of right? Like the first the first AIS, the first versions of Chat GPT were like an undergrad trying to his way through a paper. something plausible sounding, full human stuff, like not even this fly stuff, full human but like terrible, right? And so you start and so instead of starting with perfect and then working your way up to human, you start with human and then work your way towards perfect. So it's sort of the opposite the opposite quality ended up ended up being the one that got optimized. That nobody expected that.

14:20 Nobody expected when the first plausible AI turned out turned up it would be like a bullshitting undergraduate, right? Just a slot slot machine. >> Yeah. And it still you it still has a lot of that flavor, doesn't it? >> Yes. >> I hear all these stories about how AI is solving famous open problems in math and I can't even get it to answer questions about when restaurants are open. >> That's what's called the jagged frontier. >> Yes.

14:43 >> Yeah. >> They must know things I don't know. Or they have research versions. Maybe they can find out when restaurants are open. The other thing you are you are famously very good at taking complex topics and making them sound very simple. So what is the official PG definition of AGI? >> Oh well honestly I mean could you do better than the Turing test? The Turing I think Turing was pretty good right? One of the reasons one of the ways I knew we were getting close to AGI was I actually had to go and look up the definition of the Turing test. That in itself is a piece of evidence like what was the touring test again? Are we there already? I think what's happened now is we thought back in the day that AI would be sort of like this finish line and we a agi would be this finish line. We cross it. It would be very clear. But when you're standing on the finish line, you realize it has width. From back in the 1980s, it looks like it's just a line going across the horizon. But here you realize, okay, some bits of AI are way across the finish line. Some bits are like maybe in the middle of it. Who knew the line had dimension? The finish line is actually this sort of smear. And I think the best answer you can give is like we're on the smear. It turns out to be a smear and we're on it.

15:57 >> And that's why you can't get your restaurant times. >> Yeah. Cuz they're in a bad part of the smear. Whereas the Ryman hypothesis maybe we're already there. >> Okay. Ch changing back to talk about startups. So the other thing you've said is that the best predictor of success for a startup is the pace that they ship new stuff. >> Yeah. with AI. Does that change? Do you think there's a new metric? >> No. No, there's I mean, we have all these powerful AI tools and there's still a lot of startups in this batch that are not shipping fast enough.

16:27 And so, obviously, there's variation in shipping speed. It's not just the rate at which you can produce things. You have to think of these ideas first, right? Everyone always asks me like with all this AI, is anything still the same? And the answer is so far almost everything is exactly the same. The only weird the only weird new thing is the companies have had these giant AI bills. It used to be startups big bill big cost was salaries. That was all that mattered salaries, right? Everything else was cheap guy by comparison because it was just laptops and people. And now suddenly you got to pay for GPUs, you know, giant giant I mean tens of thousands of dollars a day in tokens.

17:05 >> I'm also curious, how does being in a batch like YC give founders an advantage? >> There's a bunch of reasons. One is starting a startup is normally a very lonely business. What you know you have all this upside. You have complete control over what you're doing. The downside is you don't have any colleagues and so this is like working in an office with all these colleagues except you're all working on your own startup. So there's that you have colleagues. You can also learn from what the other startups are doing at the same time you are. So you have some technical problem. Probably somebody in the batch had that problem. You can just ask them how they solved it. Right? And another advantage is what we call the YC GDP which is you have some sort of product you can sell it to the startups in your batch no almost no matter what it is you can find some and they're the exact kind of users you want early adopters who decide quickly right and they sort of have to at least listen to your pitch.

17:58 >> How did you come up with the idea for YC? gradually initially the idea was just going to be we were we we didn't have the batch idea at first I the part we had at first was to do was to be an angel firm because at that point there were VC firms who would do these giant latestage round and there were angel investors who were individual people investing their own money but there was no there were no angel firms that was the original idea to be an angel firm like a VC firm but invest small amounts of money earlier and use standardized paperwork. And then having decided that, we thought, all right, we need to learn how to be investors since we don't know how to be investors. We'll just fund a whole bunch of startups all at once. And so we thought to ourselves, well, all these college students get summer jobs working at Microsoft. Wouldn't they like to startups instead? We'll create this alternative to getting a summer job.

18:54 That's why the batch takes place during the summer. It turns out to be the perfect length, but that was just an accident. And so we thought we'll fund a whole bunch of startups. We'll replace summer jobs. It'll be for current undergrads. They won't we they won't mind if we don't if we're not real investors because they're not real founders, right? Just a bunch of college students. And it turned out in the course of that we became real investors and they became real founders very quickly. And this batch thing was so great. This thing that we just discovered by accident. We thought, "All right, we're going to always do investing that way."

19:25 >> How has YC changed or not changed since it's grown? It's changed very little. It's basically like the same thing but more of it. You know, like the problems the startups have are pretty much the same. There's a lot more of them, but but they, you know, they live in small groups that are just like individual bits of YC. So, one of the things people saying Y when people say YC has jumped the shark, one of the things they always say is, "Oh, it's so big now." Well, people used to say that back when we had 40 startups in the batch, right? They would say, "Oh, come on. The old days are gone. There's 40 startups in the batch, right? People are always saying that." And if you if you divide the YC up into little bits where each startup is part of a pod of 70 startups, then it's just like they were back in 2012 when there were 70 startups.

20:15 >> Where do you think the next trillion dollar company comes from? What are the founders of it like? >> The founders of it are formidable. In fact, that is the answer to your question. The answer to your question of where the next giant company comes from is it comes from the right founders, right? It's not some particular idea. I mean, it probably is some idea rather than others. It's probably not dog walking, right? Maybe, who knows? Maybe I mean startups ideas are very mutable, but it it comes from the next trillion dollar founders is where it comes from.

20:47 And they probably have good ideas, >> right? Whatever they're working on is probably promising. >> Do you think the founders in the future are going to look very different from the ones in the past? >> They're going to be robots. >> I hope not. >> No, no, no. Founders, they still I mean, we now have 20 years of data. They still look exactly like they used to 20 years ago. Why should they change 20 years from now?

21:11 >> Great. Thank you so much. >> Thank you. >>