Sam Altman on Building OpenAI & Betting on the Impossible

I just brought up Tobi Lütke and the fact that I recorded with him previously. Why did you say that you think he's one of the most interesting CEOsright now?

One of the things that struck me the most about Tobi is in the very early days of AI, and then at every moment along the curve of its development, he has been the most forward-leaning CEO. He's in there, like, writing the software himself.

He is experimenting with it. He, like, sends us extremely detailed feedback on the product offering, on the capabilities of the models. He was, before anybody else was saying this, he was like, "We are not an NPC company, and thus we are going to adopt agents.

Otherwise, you know, we're totally screwed. We're going to build it ourself." Every time I talk to him, he is at the edge of what anyone, CEO or not, is doing. He builds himself. He understands it. He has, like, a great deep feel.

And he is always 6, 8 months ahead of, like, any other CEO.

Do you remember when he wrote—this is probably like a year and a half ago, maybe 2024—he wrote that letter saying that, like, the first thing you have to do is see if AI can solve your problem. And then, even back then, it was probably, I don't know, 18 months, 24 months ago, people went crazy.

They thought it was ridiculous.

That was ridiculous. This is my point. Like, he's just consistently been ahead. He has been correct. He's leaned in. He's very no-bullshit, so there's, like, no hype. There's nothing other than, like, "Here's what it can really doright now.

Here's what I think it'll be able to do soon. Here's how I'm going to push the company here." And just, like, extremely deep understanding of where it's at.

I never even thought of that, how much of a benefit it has to be for somebody in your position, where you have somebody like that giving you intense and very direct and clear product feedback.

A lot of people send product feedback. He is the only person at the intersection of, like, CEO of a large company and extremely accurate, detailed, on-the-cutting-edge feedback.

Yeah, he told me—I don't know if it was on the episode that we—if it was in the episode or if it was after—but he said he was very, um, adamant. He's like, "We're going to look back in 2026," and I want actually your opinion on this.

I didn't even think to talk to you about this. "We're going to look back on 2026 as the year that every business was up for grabs." He said, "Somebody is going to build the AI-Native version of Shopify," and he said, "and it's going to be me."

And so at night he is literally trying to rebuild. If you started from scratch, what would you do with the current technology?

That was the other thing I was going to say about him, is he does it himself. Like, he is using these tools himself. He is writing software himself. He's trying the models himself. He is trying to, like, reimagine his workflows himself.

Most CEOs, when you get to that level, have, like, teams of people that are managing teams of people that are trying to implement the thing, and they're trying to, like, make you happy, and they're trying to, like, you know, smooth the rough edges.

I think it's very hard to get the feel if you are not actually doing the thing. And he—he does it so hands-on all night long, as far as I can tell. I'm not sure if 2026 will be the year that every business feels up for grabs.

I might disagree with him a little bit there, but—but I get the spirit of that, and I do understand that it feels like that's happening.

Do you think that's even possible? Like, whether it's 2026 or 2046?

I mean, obviously not literally every business. I think there are some things that are very anti-AI. Like, the better AI gets, the more some businesses that have nothing to do with AI, I think, will be harder to compete with because we'll really want these, like, authentic non-technological experiences, or we'll care more about

sports teams or whatever. So, no, not everything. But I think there will be many software businesses that are very up for grabs.

Would you disagree that on the timeline, then? What do you mean?

Yeah, I disagree on the timeline. I think it's going to take a little bit longer.

Okay. Can you say more about that?

I love startups. Like, I think startups are the coolest thing in the economy, and I've spent my career trying to, like, really understand startups. And I thought when we got to GPT-4, which was back in 2023, I think, that very quickly after that, there was going to be much more disruption in software businesses being up for grabsright away than it turned out to be.

And the thing that I think I was wrong about a few things, but one of them, in terms of the speed, one of them is the economy just has so much inertia. People keep doing the same things they're doing.

They keep buying from the same, you know, company. They keep sort of wanting to use their tools in the same way. I think that's actually a positive in many ways, and it's going to make this big transition in front of us go smoother and slower, and I'm grateful for it.

But I think it means we've all been too ambitious on timelines, even with this incredible technology. I think AI is one of the most incredible technologies humanity has ever invented. Society and the economy will adapt more slowly.

Yeah, it's funny. We were talking before we started recording that there's all these parallels to history. Obviously, I read history for a living. When you were just talking, I wasn't even thinking about OpenAI and AI and Sam Altman.

I was thinking of, like, reading the biography of Larry Ellison in, like, the '80s. He was just like, "Guys, this isn't a software problem. It's a people problem. We have to convince them. Like, we can install software. They're not using it.

We have to change their behavior. The technology is there. It's like, we have to now adapt humans so they actually start using the technology."

My own example of this was, uh, after Netflix came out and started shipping DVDs, even before they started streaming, it was amazing to me that people still went to Blockbuster. It was incredible to me. Like, I would just watch this because I kind of—I drove by a Blockbuster on my way to and from school, and it was amazing to me that people still did it.

And, you know, like, that is an example that has stuck in my head of, like, force of habit and the way people do things and changing behavior is just much harder than, like, the tech nerds realize.

So if we go back to this uproar of Tobi writing that, you know, open letter or the letter to the people inside of his company, you're adopting this faster than anybody else because you're partially inventing them,right? So, like, is there something where you're actually shocked at your own behavior?

Where, like, "I know there's a better way to do this. I'm even creating the product that could be better, and yet I still can't get over this, like, habit, this force of habit?"

100%.

Okay.

I love that. I don't think anyone has ever asked me this before. I have been waiting for this question. The thing to me that feels most psychologically inconsistent about myself is that I have, for 20 years, been using computers the same way.

I now have a magic thing called Codex. So do you. So does everybody. That means I should completely be using my computer in a different way. I should not be clicking around, you know, pasting from one messaging app to another.

I should not be scrolling mindlessly through my emails and trying to figure out which one is, like, least painful for me to open and respond when I don't want to be dealing with it. I should not be, like, keeping a to-do list and doing sort of this, like, these rote computer tasks in the same way that I have for so long.

And yet there's, like, something in my mind that is encoded that, like, doing this kind of stuff is what it means to work and what it means to be productive. And if you asked me, I would never say I like doing it that way.

In fact, I would say the opposite, and I think I would mean it. But, like, by revealed preference, I have a better way to do it now. I can do it faster. I can be using Codex for more of just, like, my day-to-day, like, "Got to get through this stack of emails.

Got to do this stuff on my to-do list. Got to, you know, deal with all these things." And I still do it that way, and it makes no sense other than I must, like, secretly, like, like it or feel good about it.

What do you think is going to have to change for you to actually adopt your own product in a more deep way?

I don't really know. I—I mean, it's happening gradually, and this might be theright answer, which is these things have to happen gradually, and totally changing someone's, like, ingrained habits and workflows is difficult. I think there are better products we can build with this technology that will make it more seamless to do that.

Butright now, it feels like we're all kind of straddling these two worlds of, you know, we still have a computer we can use the old way, and we have Codex that can use our computer in this amazing new way, and we're, like, not sure which to use when for what.

And I think this is mostly a product failure. The phase that we're in now reminds me of, like, smartphones before the iPhone. I was, like, an early adopter. I had, like, a Palm Trio in, you know, 2003 or '04 or whatever that was.

Do you ever have a sidekick?

I never had a sidekick.

Do you remember the one?

I thought they were super cool. I wanted one. I never had one. And a lot of the technology was there. Like, it was missing multi-touch, but mostly it was missing, like, the product ideas that made the iPhone the iPhone.

And I feel like we are now in a world where we have all of the technological pieces, but we have not had the iPhone moment of, like, completely changing how someone interfaces with technology.

We were talking about Tobi. Like, Tobi's out here building these himself,right? We have a mutual friend in Josh Kushner. He says he's like, there's a big comparison to be made between the way that Steve Jobs thought and the way he ran his company to the way that he thinks that you do.

He wasn't the one, obviously, writing the code. He wasn't building the hardware, but he's like, "I am patient zero. I am making products that I myself want to use." And essentially, like, everything that we saw with Apple is just basically what he wanted.

There's this great story in one of the books where they—he—they were supposed to have a meeting on, I think, one of the MacBook, like, the new MacBook laptops, and the team prepares, like, this huge presentation for Steve, and they're, like, really nervous because of his commanding presence.

And he walks in, and they think it's going to be, like, an hour meeting. He walks in, and he shows them the laptop, and he's like, "On? Off." He presses the button. It comes on. He presses off, like, immediately.

And then he tries to open up the MacBook. There's, like, a delay. He goes, "Make this," meaning the MacBook, "like that," and then walks out the room. And that's the whole meeting. Like, there's a lot of examples in the history of Apple like that.

How do you approach it? Like, how do you improve the product? Like, are you just doing it through your own needs? Like, how do you think about this?

Most of my effortright now is on research and compute. I would love to be able to spend more time on product. We have great people thinking about the product here, but the most important thing that we can do is to create smart models and to be able to run them efficiently and abundantly for a lot of people.

If we can get thatright, I believe that everything else will follow. Philosophically, I'm very inclined to say, you know, try to find the, like, the high-leverage difficult problem that will continue the exponential. And for us, this is, like, models and compute.

I also just think those are, like, problems that naturally suit me.

Why do they naturally suit you?

To scale compute in the way that we're doing. This requires, like,

it's a complex supply chain. There's, like, a lot of interesting partnerships to figure out which I like doing. There's, like, interesting financial challenges of how you're going to

finance what is probably already, or at least rapidly becoming, the most expensive infrastructure project in history. The technology questions that go into building out compute at this scale, from, you know, designing your own chip to the supply chain of fabs and people that make racks to sort of the power systems for these things.

I've always been interested in energy. All come together. So there are a lot of problems that are interesting across technology, business, policy, supply chain, logistics altogether around building compute at this kind of scale. So I used to be a startup investor.

And the thing in my career that I have found closest to startup investing is managing a research program. There are all these ways in which they're really different too. Like, you know, the average researcher and the average founder have, I think, on the surface, look different for obvious reasons.

But there's, like, a lot of similarities about how you find the non-consensus bets, how you decide where to have conviction, how you understand what exponential growth looks like, how you manage, like, outlier talent, and how you identify it even more.

This is the research building that we're in, and it's where I sit.

Great. Say more about why the parallels between what you learned at startup investing with doing research.

One big one is the power law. So people talk about this all the time in investing, which is you have to kind of, like, reprogram your brain because we don't seem naturally well suited to think this way, where, you know, your best investment will outperform all of your other investments put together.

Your second best investment will outperform everything else put together after that. And AI research, at least, is like that as well. When we started, people thought it was totally unlikely or almost impossible that AGI was possible.

What year is this?

2015. End of 2015. I mean, we just got, you know, hammered in the

by, like, all of the intellectual giants of the field for saying that we were going after AGI. And then when we started really focusing on large language models, we got hammered again saying, "This is completely ridiculous." And we understood, or I understood, at least from my kind of, like, startup background, and I think other people understood in other ways, this point of high-risk bets are okay as long as you take the ones where, if they work, it's super valuable.

And research looks this way. The kind of people that make great researchers are sort of non-consensus, fresh approach, high-energy, sort of non-standard is the word that keeps coming to mind, people.

You got to say more about non-standard. Can you be more specific? Are they spiky?

You don't want to fund a founder who has

a very slightly different take on the same idea as the last thousand people you talked to has tried to convince you and maybe convince themselves that somehow they're completely different and doing something totally new, but is mostly, like, trying to, you know, fit in with the herd and be on the same track as everybody else and do what they're supposed to do, which is start a startup.

And, you know, they've heard Peter Thiel say enough times that, you know, there's, like, something that you're supposed to be doing different that they kind of try to emulate that, but they don't really mean it. Like, it's very clear to me when you have someone who just thinks differently than most other people and has is willing to stand by convictions that are very unpopular, may well be wrong, but ifright, like, at least they're going to be reallyright, versus someone who is, like, a, you know, thin veneer on the same idea that everybody else has.

In late 2015, when we were starting OpenAI, there were very few AGI efforts in the world. There's deep mind, one or two others that I can think of. It was, like, a very non-consensus thing to do. In that same year, there were probably, to pick on it just because it came to mind, but there are other categories too.

There were probably many, many thousands of founders starting photo sharing apps. You know, that was probably, like, not as good of a thing to do. Today, a lot of people want to start AI labs. There are some handful of people, you know, two, three, whatever, doing something completely new that actually wasn't possible until the AI got this good, but doesn't seem like a good idea yet.

And, like, that is the thing that, as a startup founder, I always wanted to fund and the thing that mostly worked for me. There's a similar thing for researchers. There were a lot of researchers that would chase whatever the last thing was that worked, and there were a small number of researchers that had high conviction towards a new idea.

And

I think we were and are the best research lab for those people.

I remember talking to Demis about this a few months ago, and he thought, you know, in terms of, like, chasing after the way that you guys are, it's like, you know, there's the big three players, the money, the capital required.

It's like, you're not there's not going to be, like, a fourth bigger player. But he's like, there's, like, this 10 I forgot what the number was. I'll just make it up. Say, 10% chance that there's just some monk researcher that's going to approach it in a way that just this angle we've never even considered.

Totally.

I don't know how to put a number on it, but there is some chance.

Yeah, I don't think you I'm making the number up, but it was, like, a small percentage.

There is some chance of that, for sure. And I love that. Like, I think that's why stuff stays exciting. I want to tell you about the presenting sponsor of this podcast, Ramp. I have been reading a lot about SpaceX lately.

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And you can do that by going to appleoven.com. That's appleoven.com. Okay, so what is confusing to me, you went from founder to investor to back to founder. But why in 2015? Like, what got you interested in artificial intelligence to begin with that you're saying, "Hey, this is such a non-consensus thing.

People think I'm fucking crazy. I'm going to do it anyways."

Well, I had been interested in AI my whole life. I was, like, a very nerdy kid. I, like, you know, I was, like, the kind of kid that spent Friday nights, like, playing on my computer and watching sci-fi, reading sci-fi.

And I always thought that AI would be, like, the most amazing, kind of craziest thing. I never thought I would actually get to work on it, but I always, I always, like, loved it. I even came to college sort of to study it.

I worked in the AI lab the summer between my freshman and sophomore year, and nothing was working. In fact, like, very memorably, a professor told me, "You can try all of these things. There's all these directions. The one thing we know doesn't work is deep learning."

We tried that for a long time. You know, it's the kind of most guaranteed way to have a bad career. And, you know, I was, like, an impressionable freshman in college, whatever. I, like, assumed that was true. So, you know, pursued these other things.

It was clear to me at the time, this is now, like, kind of 2005, that AI was not working. And

I happened to, like, accidentally get into startups, but then very much fell in love with it. And so I wouldn't even call it a career detour because it was super helpful. Looking back and becoming, like, a startup investor was great.

The sort of normal career path in Silicon Valley is you're, like, a or not the normal. A common path is that you're a founder, and then you, like, sort of semi-retire and you become an investor.

We don't want that. I heard you say that you're going to work on this for the rest of your career.

Yeah.

I'm hopefully we're starting you're going to come on the show multiple times. I'm going to hold you to this. We don't need more founders that retire and invest. We have enough investors.

No, I don't want we have too many investors.

Thank you.

We have too many investors.

I want to talk to you about this. But what I was going to say is, the fact that I got to go in the other direction, I was, like, an investor first, and then I ran a company, is pretty unusual.

Very unusual.

And I'm super grateful for it because the, like, you get this unbelievable set of learnings and pattern matching if you really study and watch companies as an investor that has been super helpful to me running OpenAI, but it's, it's like, it's the opposite normal direction.

So it's just, like, a very rare thing, and I strongly recommend it.

Why is it helpful?

If you're running a company, you have faced some number of similar decisions in your past, like some number of, like, crux decisions, and you've seen what works and what doesn't. And if you have to, like, you know, make a high-stakes strategy shift or, like, fire an executive in a really messy way, you have, like, whatever your own limited previous experience was over the last five or ten years that you've been doing it.

But as an investor, you kind of watch all the crux moments. So you don't get the kind of, like, operating practice that you do just day in and day out running a company, but you've seen a lot of the, like, big crux moments.

A lot. Kind of all day long, you see those. So just the wealth of the data set that I had, that was awesome.

So that plays in your head when you have a decision, Rick?

Yeah. I'm like, "Oh, this is what happened when this company had a similar thing," or, "I saw this founder make this mistake," or, "This founder got it reallyright." We were talking about that you studied the Industrial Revolution. We were talking about some great biographies that we both read earlier.

My friend Daniel Ek says this about me because, like, I think the benefit of me doing this project, my other podcast called Founders for Ten Years, is, like, so he's like, "You're like an LLM trained on history of greatest entrepreneurs."

Yeah.

But with the temperature turned up because you're fucking crazy. Because I'm, like, super passionate about it in a weird way to be, like, obsessed with dead entrepreneurs. But it is helpful. It's like, "Oh, like, I'll be talking to a founder, and they'll talk about something they dealt with."

I'm like, "Oh, well, like, Carnegie did this, and Rockefeller did this, and, like, you might want to try this." It's like.

And do you find that you have, like, one big insight out of all of that, or it's just, like, for any given scenario, you have, like, what all these people did and how it comes together?

I think it's dependent on the personality of the founder.

Yeah.

Right? And so, like, when I was reading I've read your blog for years. I think, like, you're a great writer. You're very succinct. Like, how.

Thank you.

The brevity is just really appealing to me. And I love numbered lists. It's weird that we both write in the same way. And I feel like I'm reading your blog, and I'm like, "This is the exact conclusion that I would come to based on all the reading."

And, like, there's just a handful of principles that could be applied, but it's, like, it really depends on, like, who the founder is and what they want to do.

Yeah.

This is what I'm trying to understand. It's like, let's go back to what we were just talking about. I still, like, like, you're making a big jump because, you know, you're one of the, from what I hear, one of the best investors of all time in Silicon Valley.

You could just be rich and not really have to work because investors are kind of lazy.

Oh, I thought I was lazy. Yeah.

I'm just kidding, by the way. Kind of not.

No, it's pretty having done both, I think I can say, it's, like, much, much, much harder to run a company than being an investor.

Exactly. And that's what people should be doing, in my opinion.

I agree.

So, Daniel, like, fuck that. I'm going to not take the easy route. I'm going to do the hardest thing ever, the thing that people think is impossible, the thing that you're made fun of.

Yeah.

I still need to understand, okay, so you're into a kid. Like, why would it appeal to a kid? You were living in St. Louis at the time.

Yeah, I was living in St. Louis.

Why would AI appeal to you back then?

Well, I think, like, it appealed to every kind of computer nerd. Like, I don't think it's that unusual about me. It just felt impossible. I think most people would say, like, "Of course, that'd be the coolest thing ever," but it's totally impossible.

I think the, like, the weird thing about me was, like, "Okay, let's try." But I think everybody thought it. Everybody would think it's awesome and something to go for.

So wait, that was a personality trait of yours as a kid that.

I think even as a kid.

People told you that you couldn't do something? Like, your initial response was resistance?

Not resistance, but, like, "Are you sure?" Like, "Why not? Let's try. Let's see what happens. Maybe I can. Maybe we can." I was, like, a very optimistic kid. And also, like, the more something seemed impossible, like, the more intrigued I was.

The idea that we could invent a technology that would let us do everything else, that would just empower people in this way that no other single technology could, that always seemed, like, innately incredibly appealing to me. It's like, "I want that thing.

I want to be able to do everything else." Like, I think another thing about that I kind of was, like, a personality trait as long as I remember is, like, you know, it's like, it is interesting to, like, really give people a lot more power, a lot more ability.

In some sense, this is the whole arc of technology, and I was for sure always a technology nerd. But, like, AI is the strongest version of that I can imagine.

What did you think that it would enable back then? Like, when you were a kid, like, "This seems like a cool technology. I want to do X. I can't do X unless AI is invented."

It's always hard to remember, like, how much of this is the stuff that I actually thought at the time versus, like, how much.

Or what you're trying to buildright now.

Yeah, like, how much my current work has, like, colored my memories of it. For sure. As a kid, I was, like, very into robots. You know, we had, like, a robots club in my school, and the robots at the time were, like, laughably I even remember at summer camp, we had this, like, little turtle that you could control with a computer on the floor or on the table, and I thought that was just the coolest thing.

There's something about, like, physical stuff moving controlled by a computer that I always thought was amazing. Now I am extremely interested in what AI can do to advance scientific discovery. In my memories as an adult, I think I thought that was cool as a kid too, but it feels just implausible.

And I assume that's an example of where, like, the memories have gotten more colored. But now the fact that we can have AI go discover new physics and cure diseases and what it's already doing for math, like, I think this is I think this will be one of the most important areas, even more important than automation of other tasks that AI can do, just to help us understand more things.

We were talking earlier about this book, The Beginning of Infinity, and rereading that book from today's vantage point, I'm like, "Man, AI is really going to help us do this, do this important thing of understanding everything or as much as we can."

I was definitely interested in the sort of, like, Star Trek version of, like, huge prosperity and abundance and what AI could do to drive that. Maybe the memory of being interested in science is more real. I also loved science and just this idea that we could, like, because we were smart, we could figure out how to understand the world and make predictions and do things that we couldn't without this deep understanding.

I don't know. That seems, like, innately awesome.

It's interesting how consistent over time what humans want from AI because, like, something you're describing is very similar. I just reread the biography of Claude Shannon for the second time, and I had forgotten because I hadn't read the book in maybe five years, and I forgot that him and Alan Turing used to meet every day for coffee when they were both at Bill Labs.

This is, like, the 1940s.

Yeah.

And they would just talk about AI.

Yeah.

And they were both obsessed with their they thought it was inevitable back then, and they thought it was going to happen, like, 15 years from there, so, like, 1955, that we're going to have computers, which didn't exist,right? They had the analog versions, that are going to be smarter than humans, and that anybody that thought that wasn't going to occur, they thought was absolutely ridiculous.

And they're like, "Well, what would you want the computer to do?" He's like, "Solve math problems, write poetry, like, cure diseases." Like, you hear this over and over again.

I have read a bunch of things that those guys wrote at the time, and I am so sad they are not here to see it because they were soright about everything. We're finally at the moment where AI is solving novel math problems.

It is discovering other stuff. It is, you know, you can argue about how good or not. I would say not very good, but it is writing poetry. It's here, like, to what these guys I think they would have said, "Allright, you've done it.

Like, this is it. We've got it." And that would have been so cool.

Yeah, this is the weird thing where everybody's just like, "Oh, it'll never do X." Like, I talk to people in the music industry. It's like, "It's never going to make great music." And then they're like, "Well, do you think it's going to, like, make a podcast?"

I was like, "Of course it's going to. It's going to do everything that we can do, at least to, I would say, better than, like, evenright now, like, better than what we can do." It's a very bizarre thing where it's like, "It will never surpass what is happening at this current point that I happen to be alive."

There's a deep human psychological flaw there. But here is, I think, a more interesting question. Let's say it does make a great podcast. You know, two AIs are having a more interesting conversation than you and I are. Do you think people will care, or will they want the one with the real people because we're all obsessed with people and the fact that it's not real people?

Yeah, for this, it's, like, more interesting. It's like, "Oh, these two people that I may be predisposed to like or dislike are having a conversation that's interesting to me." I think for, like, strict, like, reference, like, maybe my other podcasts where I'm just saying, "Hey, this is an interesting idea as I read in this book," that could maybe get disrupted or whatever the case is, but especially for people that were born before this happened, maybe it's different for your son, you know?

Maybe.

But for me, it's just like, I think humans are going to always be drawn to humans.

I really deeply believe that. I think there's, like, a lot of other jobs that could face significant transition, but stuff that's about people, stuff that's about people's connection, connection to people and people liking other people, that stuff feels like it gets more valuable in the post-data world, not less.

I may be actually the wrong person to talk about this, though, because I kind of deeply desire, even though my entire work is digital, it's, you know, broadcast all over the world, it's just like, I deeply desire, like, more, like, more of an analog life.

I like reading physical books. Like, when I was talking to Kelly, I was like, "I didn't want to get on Zoom. Call me." Or we'll talk in person. Like, I like physical shit. I don't like.

That's interesting. I'm like that too. I don't read ebooks.

Yeah.

I don't like Zoom meetings. I like to be, like, with people in the real world.

I definitely think there's a subset of weirdos, and there's probably a lot of them that live in this city that, you know, don't like humans and only want to communicate with computers, but it's just like, I think that's a tiny percentage of humanity.

I think it's a tiny percentage of humanity. This is why I think, like, the world is, on the whole, not going to be that different, even with superintelligence. Like, people are still going to be very fundamentally wired to care about other people, to want to be around other people, to interact with other people.

And, you know, there will be some people who just, like, get obsessed with the models and just think humans are in the way or, you know, danger to be contended with or whatever. And for most people, it'll be the whole point.

I think it's very important that when we do find people like that, to then be called out and make sure they don't acquire power.

I certainly agree with that. Maybe the two big risks that I'm most worried about with AI, which are a little bit in tension, one is, like, a loss of control where, you know, AI somehow just becomes too powerful in a way that we can't guarantee the control we want.

And the other is power gets too centralized, where you have, you know, one company or model or person with too much power. And in both of these, the fundamental thing is, like, I think it's a very anti-human position for either of these things to happen.

Theright approach is to say, like, "We want people deeply in control of the future. We want people deeply empowered." People are the whole point of this all. Like, this is we are not going to sit here and, you know, gradually hand over control to an AI model because we don't trust or like people.

And, you know, it's like a very misanthropic thing to say we're going to just, like, put all of our trust in this model and let it have all the power on decision-making over the world. But I think there are some people in the world who think that's theright outcome.

There's, like, another version of this, which is, "Because we don't trust people, we have to

limit who gets access to this technology and how they can use it," and all of these terrible things could happen. And, you know, out of fear of those, we are going to concentrate power in the hands of a few companies, and they're going to, you know, we're not going to let other people use this, but we'll give them some benefits.

Like, my caricature of this is, I think there are some people in the AI field who effectively say, "We're going to give the world a cure to all diseases, and we're going to make stuff really cheap in exchange for people giving up their autonomy and impact over the future and power and also in the name of safety and also, like, just absolutely rampant inequality."

Like, there will be people that have access to huge amounts of wealth and power, and other people just get a pretty good everything. And this is, like, a terrible sales pitch. This is a very anti-human sales pitch that somehow people feel willing to make.

Why do you think they feel willing to make that?

I think it's, like, fear and power. I think you can when people talk about the risks of AI, I think there are a lot of

people who are so nervous about the magnitude of those risks and get so taken by that and feel a need to protect the world from that, that they're like, "You know, we should trade off a lot of liberty for safety here because this is unlike other risks we've seen."

But then I think that also ends up kind of like a way to justify a lot of power-seeking behavior.

Everything when I read, like, when I was just saying what Claude Shannon was saying or Alan Turing, at least in the books I'd ever read, it's more of, like, an optimistic, like, "We're going to invent things that make our lives better and can do things for us."

You are totallyright that if you go back to the Claude Shannon, Alan Turing era, they talked about how wonderful AGI would be and all the things that it would do. And when we started, we really had a lot of pressure from the doomers.

Now, the part of the doomers that I agree with is that this is a powerful technology and we should err on the side of safety, and we should act with caution at each level of technology. The part of the doomers that I don't agree with is that it's an unsolvable problem.

If you go back to the beginning of OpenAI, I think there would have been two widely held opinions. Number one,

not at all and certainly not in 10 years were we going to build something that was very AGI-like and then conditioned on if we did, we certainly were not going to be able to make it safe. You know, if you had an AI that was smarter in many ways than a lot of the smartest people, most of the smartest people, then, you know, the doomers would say, "Surely at that point, the world would have been destroyed, the alignment thing would have failed," and there were just these very confidently held positions about what would have happened.

A decade on, we have built something that I think most people would say at the time would have said is very AGI-like. And, you know, a lot of good things have happened, and the kind of crazy bad predictions of the world ending have not happened.

So I think that should update people's predictions about the future. There are still higher stakes challenges in front of us to solve, but our approach this is another thing I learned from startups of the way you do things is to put things out into the world, get feedback from real customers, see where they break, see where they don't break.

That is the way you make a good product. That is also the way you make a safe product. And we have made way more progress on AI safety than I think most people thought we would when we started.

Why? Because so many people, there's billion people using your products on a weekly basis?

And each time we get a new level of model, we put it out in the world and we see what, you know, what works, what doesn't work, where people need us to relax the guardrails because they have good things they want to use it for, where we have alignment failures, where we have safety systems failures.

ChatGPT has only been out, like, less than four years. Billion people use it and sensitive, important stuff. And the fact that we can deliver something that is broadly considered safe, of course there's issues with it. Like, in that short of a time frame with such a powerful technology, I think there is no way we could have done that in an ivory tower.

And, you know, this is how I believe you build good, safe, robust, useful technology and products. And I think it's a great learning of Y Combinator. And it would have seemed to most of the AI safety people, you know, totally impossible to get to this stage and still have the level of safety guarantees we have.

Now, I do think it gets harder from here, but I don't think you're going to solve it by disconnecting yourself from reality.

Why does it get harder from here?

Because we're as about the smartest people in the world are as about as smart as the smartest models in the world, and that's going to flipright now.

Yeah, directionally, I think that'sright. I think that

the models are just so incredibly capable and improving on such a steep trajectory that the unknown unknowns,

maybe they don't get harder relatively, but from an absolute perspective, they seem harder. And I think we'll have to make a bunch of difficult decisions about, you know, when we delay development, when we sort of say, "Okay, you know what?

Let's have contact with reality now," or, "Let's wait longer to really study this more." Talking to someone recently,

and something that's stuck in my mind is that the, you know, the FAA, FAA has helped make flying incredibly safe. Flying on the surface seems like this extremely dangerous thing, and you probably get on an airplane without giving it much thought.

And this was certainly, you know, airplanes are not that old in the long trajectory of human history, and this was certainly not the case at the beginning of airplanes. They have extremely robust accident reporting, extremely clear-eyed. You're never they never try to, like, you know, hand-wave over something.

They want to extract as much information as possible. And in some sense,

I think with any new technology, an approach like that

works very well and is often underappreciated. So when we started deploying our models, when we said, "We're going to put ChatGPT out in the world," we know the model's imperfect. We know it hallucinates. We know it can do these other things.

But we also know that

the world has got to experience this technology. We've got to learn how to make it safe, and we've got to put the power in people's hands. We cannot just use this to impose our worldview. We cannot use this to go sit in a lab and try to think through all the impacts, which won't work anyway because society and the models are going to co-evolve.

Like, we have to all do this together as this joint product. And then we'll do very good accident reporting. We will study when something goes wrong. We will put out a very clear postmortem. We will learn as much as we can.

We will not only improve our own technology and product, but we'll try to share those learnings with other people building AI. I think that's worked surprisingly well so far. And that was, like, good examples from the history of technology, good examples from startups.

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One thing that has to be disorienting for you, because it doesn't make any sense to me. Like, you know, essentially what I focus on is just entrepreneurs and entrepreneurship,right? So all the podcasts I make are for the benefit of entrepreneurs.

I'm glad other people listen, but it's, like, heavily focused on just trying to serve, trying to find useful information, whether it's in a biography of a dead entrepreneur or talking to somebody like you, that other entrepreneurs can benefit from this conversation,right?

Or any of the podcasts that I make. So in that, there's not, like, a lot of entrepreneurs love AI, but, like, what I'm trying to figure out, like, if you can help me reconcile, it's like, everybody uses AI.

Everybody hates AI. What the hell is going on there?

Well, people are always afraid of, like, rapid socioeconomic change. We talked about the Industrial Revolution earlier. I love reading about previous technological revolutions too. And, like, people did not have universally warm and fuzzy feelings to the change that was happening throughout the Industrial Revolution.

I think it's probably a good feature of human society that we have some built-in inertia. We have some skepticism of rapid change. I think that probably helps society in times of turmoil or in times of, you know, localized craziness or whatever.

So some of it is probably good. And I think a feature of human biology, which I believe in never trying to fight too hard. I also think a lot of people building AI, you know, have been off saying, "There's a 25% chance we're going to destroy the world, and yet we're going to race ahead to do it because otherwise those bad guys will do it first."

Or, you know, it's like, "Man, this thing is going to be really terrible, and there's going to be 50% of the jobs are going to go away in the next year, and we hope you all are okay, but it seems really scary."

Like, we have not, as a field, done a very good job of explaining to people what the benefits are and how the downsides can be mitigated. And we certainly have not done a good job, even if people have had answers like, you know, saying, "Well, there's going to be universal basic income or work will be optional or whatever."

There's been very little discussion from people about how and why it's important that people have more power and personal freedom in the world, not less. And that matters a lot to most people. The ability of people to influence their own future and collectively to design kind of where society is going to go and the autonomy that comes with that, very important.

And I don't think a lot of people in the AI field, they feel it for themselves, but they don't spend much time thinking about it, reflecting on it, or acknowledging how important that is to other people. And so to go back to that, like, characterization of the sales pitch, characterization of the sales pitch, I don't even know if that's a word from earlier.

It is.

The, like, "Dear peasants, we will bequeath upon you these gifts of a cure for cancer and material wealth and some things and, you know, great entertainment, and you stop complaining and we'll make all the decisions about the future and just trust us, you know, we'll be benevolent dictators."

Not good. Not good. As a lover of entrepreneurs and sort of like a student of what has made this incredible economic miracle of recent centuries work, really empowering people to go do new stuff and to push on the things they believe in and to have the freedom to create companies and invent technology and sort of pursue ideas and, like, the system around that that makes that happen, there is, like, nothing I believe in more strongly.

And I think even if people don't see themselves as an entrepreneur ever, if they may never want to start a big company, they do understand how important that is. And then when you hear people kind of implicitly or explicitly saying there's going to be less of that with AI because a small number of people are going to have the power, but they're going to make great decisions and keep everybody safe, I think that's very scary to them.

I also think that even if maybe most people don't want to start really big companies, a lot of people want to start smaller companies. And that has been hard. That has been something that has required a fair amount of privilege and luck and resources to be able to do.

And we are about to see the greatest boom in people starting smaller businesses that we have ever seen. I think AI is empowering that. Now, for some reason, the field, including us, has not talked about that enough, even though we see all these signs of it, and it's great, and we have not built enough products to accelerate that.

But I think we're going to see a lot more of that. It's funny, we talked about Tobi Lütke earlier at the end of the conversation, and I think that's in the episode. He mentioned something I didn't think about.

He's like, "Oh, yeah, you and me are in the same business." He's like, "We're both trying to create more entrepreneurs. He's building infrastructure for entrepreneurs. I'm building educational and inspirational podcasts for them." And the crazy thing with what you just said, it's like, not only I think out of any new industry, it's not that as an industry, the AI industry, which it's doing the worst job I've probably ever seen.

And I think part of it is just the ability you guys have to get out there and talk about the stuff that you're seeing and educate. There's this great book called The Intel Trinity, and it talks the story of Intel.

And it's called The Trinity because the three main players are Bob Noyes, Andy Grove, and Gordon Moore. And when they there's a great story in the book I never forgot. They go from inventing, I think, the integrated circuits of the microprocessor, and they realize that that technology was so important and it would scare their potential customers.

So they went out, they stopped, those three people stopped doing what they're doing and went out and started educating potential customers, investors, the entire country. And they said at one time they were putting on more classes than, like, the local community college had in their entire course catalog.

That's how much they made it a top priority. It's like, "We're going to get out and educate about this new technology." And it's just like, "Well, why isn't anybody in AI doing that?"

I mean, no excuse that we should be doing more. I think we've, like, tried versions of this. We haven't gotten it quiteright.

Well, you're doing itright now. Like, this is one of the points of reason I wanted to talk to you about because, like, I use AI all the time. I think it's fascinating. But you have such, like, this you have a view that makes mine look like the view of an ant.

Like, you have just so much stuff in your head that I want to, like, get out and be like, "Hey, you have all this context." It's like, and you're inventing this incredible technology. I would love to know how other people are using it.

Actually, before we even get to other people, I heard you say something that was interesting that I think ties to what you just said about kind of, like, we're inventing technology, the technology's increasing rapidly, but the adoption should be slow and deliberate.

And I heard you on another podcast saying, "Hey, I'm even considering, like, how much should I let AI see every single thing that's on my computer?" You want to talk about that?

With the latest generation of models, I don't want to say that they feel smart enough because I think we should always aspire for them to get smarter, but they are pretty smart. And I feel more limited at this point by the amount of useful context the AI has on me.

Like, I want the AI to know as much as it can to help me. I want it to be doing things I can't or don't want to do on my own. Like, I'm not going to read every post on our internal Slack.

I'm not going to go read every story a customer has to tell about where ChatGPT worked for them or failed them. I can't. And then there's, like, other stuff of, like, I just, you know, I probably could read more research papers than I do, but, like, oh, it's, like, takes a lot of mental energy and, you know.

But I would love to have an AI agent that is constantly trying to be helpful to me and that can look at and understand more context than I can or that I have time for or energy for to do on my own and can help bring that context to bear and give me good advice when I have to make a decision.

So I think we've focused correctly so much on model intelligence that on the product side, we have not yet thought enough about what it means to give a model more context than any person could have and help advise that person on their big decisions.

My sense is we are

just on the precipice of being able to see

a very different way of working with AI on

an axis where people just can't get this good. There are plenty of, like, very smart people, but there is no one that can read, like, you know, tens of thousands of pages of context in some small number of seconds and really, like, use that accurately.

And this is something that AI can do that just is going to be very new and an incredible supplement. You've read all these biographies. There are probably times where you vaguely remember something that if you could remember a specific anecdote from one of them, it would, like, really help an entrepreneur in one moment for that particular entrepreneurright when you were talking to them.

But maybe you forgot it or maybe you don't remember it exactlyright.

I built my own AI tool.

Oh, you got it.

So I use it internally. So you know what it is? It's only trained on since 2018, I've kept every single note and highlight from every single book that I've ever used into this database, and I would search it for that.

And then when the work that you guys do came out, then I added, so I have it trained on that, then every note, every highlight, and then all the transcripts from my episodes of Founders. I use this thing every single day.

That's awesome.

To make every single episode. So I'm working on Claude Shannon,right? The Claude Shannon episode came out, I don't know, two, three weeks ago, whenever it was. And I'm asking questions about all, it's like, "Hey, what did Bob Noyes say about this?"

or, "What did Rockefeller do about this?" And I made the episode. I read the book. I took the note. I don't remember because it was, like, seven years ago. It's incredible. This is what I mean. I was like, "AI is fucking awesome."

That is so cool.

Let's get into, like, how you think about running the company,right? So you're spending your time. You said your main focus is getting more compute and then research,right?

Yeah.

Okay. So you want the models to be the best in the world. But how do you think about, like, do you have to build your own products? Now, you built Codex,right? I don't even know the fucking product lines.

Like, where is all the revenue coming from?

Actually, I think we should be more of a platform company than a product company.

Okay.

Like, we will build products, of course.

Well, how many products do you have? Let's back up. How many products do you have now?

We just merged ChatGPT and Codex together.

Okay.

So we used to have, like, ChatGPT, Codex, and the API.

Okay.

You know, and Codex sort of unfortunately named, but it was not just coding. It could kind of do any kind of work, which confused people.

I'm confused by that.

Yes.

Okay.

As were many other people. What I think most people want is the sort of, like, single interface to their own personal or their company's AGI that can kind of help them with whatever they need. And then the ability with an API to build anything they want on top of it.

And that is the platform that we should offer to the world. We're going to sell great AI at every point on the cost curve, cost performance curve, we will be the best. You know, you want really high-end AI to discover science, that's great.

You want really, like, inexpensive AI to do, like, you know, a massive amount of volume of work that maybe doesn't require genius-level intelligence. We got you covered there, too. And, you know, thinking about this as a sort of people talk about different ways in a utility and new commodity, whatever you want to call it, like, people want to use a lot of AI and they want it at a low cost and they want it to be fast and to work well and have their context and be smooth.

We got you. And then there's, like, a single product, which is I need to ask the AI something. Eventually, maybe it's the AI should proactively offer me things. But you will have this interface, which started as a chatbot and now also has cloud-native agents, and I think at some point will feel like a more persistent agent to this AI that is running on whatever you need it to run on.

But that's it. I don't think we should go build every product category. I don't think we should, like, go try to compete with all of our customers. I don't think we should try to, like, subsume the entire economy.

I think we should offer this platform and try to have 100 million new businesses and 8 billion people use it in all kinds of new ways. So one kind of direct interface to the product, one API for people to use it however they want.

Those eventually come more and more together, too. And then it's all about what people do with it, build on top of it, whatever else.

What mistakes did you make to have to learn that? I feel like you've had to kill some good ideas and sacrifice it going after the great with your full, like, intensity and focus.

Yeah. I think killing the good ideas, like, sacrificing the good ideas to go after the great ideas is kind of the hardest lesson for any entrepreneur or business to learn. It sucks to kill good ideas. And no matter how much you think you're going to do it, you people, everyone, like, kind of maybe just by the nature of who chooses to be an entrepreneur seems to do terrible at this.

I'm terrible at this. I know I'm bad at this. But last year, for example, we killed Sora, which was, you know, good product and fun and cool, but used a lot of compute and not as important as Codex where we put the compute.

We killed our web browser called Atlas, which, again, I think it was a great product and it was the best web browser, but not as important for us to focus on as somewhere else we could put that talent.

In a world of limited compute, limited people, limited resources, we thought really hard and we said, you know what? The general intelligence for knowledge work and eventually for science is the most important thing we can do. Anything that goes into making that upstream of generating that intelligence, building our own chip, building our own data centers, you know, writing good infrastructure software, certainly training models, obviously.

That's all really important. But then let's just offer this AI as a service and get people to

use it for intellectual pursuit, for work, for scientific discovery, to be more productive in their personal life. And let's have the kind of flexible general platform and not do a lot of other things.

Anybody engaged in complicated work, and you're got to be the, you know, top of sorts top of the list of anybody aliveright now, needs somebody to help organize their thoughts,right? It's extremely beneficial. You see this in every single biography.

You see this in histories. Like, you need somebody to talk to. There's actually a funny story of how extreme this can be. Charlie Munger has this thing called the orangutan theory. You've ever heard of this?

No.

Where he said a relatively smart human could go in, sit down with an orangutan, tell him all his problems, tell him everything he has in mind, and then, you know, the orangutan obviously says nothing else. The human leaves and the human's better off.

Just the idea of just being forced to put your thoughts into, you know, into some kind of structure. Now, obviously, with a very intelligent partner, Munger played this role for Buffett. Buffett's one of the most intelligent people who ever lived, greatest investor of all time, still needed to organize his thoughts for somebody else.

You are going through, I can't think, you have almost like a singular lived experience, especially for somebody as young as you are. So I'm curious, like, who plays this role in your life? Like, who do you go to that can even remotely empathize with what the hell you're dealing with on a day-to-day basis?

Kind of three categories here. One, a lot of the researchers that have been here forever, we've kind of, like, all been through it together and we've developed this set of, like, shared language, intuition, standards, whatever you want to call it.

And that I have not been able to replicate with anybody outside of the company when it comes to, like, the shape of what's happening and what might happen next and where the technology is likely to go. In terms of questions of just, like, you know, business and the world, for a long time in my career, Paul Graham and Peter Thiel have been two of the people that I have learned the most from about lots of different phases of my career and are still the two people that I go to if I really have, like, a very non-obvious problem that I'm stuck on.

And there, I have not found anyone else, after a lot of looking, that has the same kind of ability to just, to, like, think in a super non-linear way. Like, you know, if what LLMs do are predicting what word comes next, those are two of the people that I can predict the least what word is going to come next.

And that is a super valuable skill. You go with, like, "Oh, man, I feel really stuck and I've kind of thought through all these options." And someone that can tell you, like, "Eh, I think none of those options are good.

Here's this thing that now seems totally obvious and correct that you didn't think of, just a completely different view that you haven't heard anywhere else."

Is this, like, more of, like, a prompt for your own thinking as opposed to, like, explicit advice, "Do X," for example?

It's often like, "Here is a specific thing."

Really?

Yeah.

So what would be an example that you could share from, like, Peter? Peter's very fascinating to me.

He is very fascinating.

And it's actually who I thought of. The reason I thought of this question just now is because you're like, "We had to kill these good ideas for the great. We're cutting Atlas. We're compute-constrained. We have to focus, focus, focus."

It's like, that's something that is very obvious when you listen to him talk about the importance of focus and, like, if you have something that's working, making it work better and going down this line, like, taking an hour away from that to, like, explore something else is too expensive.

You should just go deeper on what's already working. Like, there's a lot of value at the extremes.

After we launched ChatGPT, sort of this weird thing because people didn't really know what to use it for and it was growing super fast, but it felt like very unstable or kind of almost like low-value growth. Like, people were using it to just because they were, like, interested in talking to it and what they could do.

And so there were a lot of people in the company who were like, "Uh, this, you know, we got to figure out something else. This is not sustainable value." And we were talking about this, like, list of five or six other things that we could focus on instead.

This is, like, maybe two months after ChatGPT launched, something like that. And he was like, "It's an obvious mistake to do anything but this, besides the fact that it's growing, which is rare and great." And it was not growing as fast than it did start after.

He's like, "The power of this is the power of the Google Textbox. It's like a textbox you can type anything into and it does theright thing." And the fact that it doesn't match the current Silicon Valley wisdom of, you know, you got to have, like, feeds and you have to have, like, a network effect and you have to have because we had none of these things and that's why everyone was worried.

You know, you have to have, like, a, you know, way that people are going to build up more con this before we had memory. People are going to build up more context. People are going to get locked in or people are going to have all the, he's like, "All that stuff, like, people have just been chasing the Google business model for 20 years and this is the first thing that's come up."

And, you know, clearly the empty textbox worked for Google. So why don't you just double down on that? It's growing. Like, it's very flexible and, you know, it has all of the signs other than it doesn't fit the current Silicon Valley wisdom.

And I was like, "Okay." And so we went super hard on ChatGPT and it was great.

There's, like, a simple genius to what you just said.

Sometimes there's, like, more complex genius, but that was an example of very important simple genius.

What about some advice that Paul Graham or some guidance or a direction he kind of pushed you in?

When you just said that, the thing, this is like a meme for many YC founders where you would go see him for office hours and he would say, "You know what you should do?" And he would, like, shake his hand, his finger like this.

"You know what you should do? You know what you should do?" And sometimes the thing that came after that was great. Sometimes the thing that came after that was terrible. But the important thing was

there was a kind of, there is a creativity and open landscape and just, you know, let's try a lot of things. We talked about the spirit of iterative deployment and we talked about, you know, what, how, like, in the same way startups, he really, I think, pushed the startup ecosystem into this world of you got to, like, get ship a V1 you're embarrassed of early.

And it doesn't matter if it's, you know, it could be much better. You'll get it much, much better because of the feedback to customers. I don't even think I asked him before we launched ChatGPT, like, "Hey, do you think we should launch this thing?"

But I knew what he would say. I knew it was still early. I knew it was still embarrassing and I knew theright thing was to, like, get it out and get it in front of people.

So wait, your mental model of Paul Graham is so complete you don't even have to call this one.

No, just in this one case.

That's the one where you would say, like, there's certainty.

Let me tell you something funny. Right before he died, a few months before he died, I went to Charlie Munger's house and had dinner with him and I was like, "How often do you talk to Buffet?" He goes, "Never."

I go, "What?" He goes, "We talk every day for hours and hours. Buffet can just pretend to pick up the phone call to call me and he already knows what I'm going to say." That obviously comes after 65 years of working closely together, but I thought it was hilarious.

That is hilarious. That is really a funny story.

No, there's many times that I couldn't predict what he's going to say, which is, that's why I think it's valuable. But in terms of the, like, launch when you're embarrassed of the product, I know what he's going to say there.

Like, that one,

that has been, like, I won't say the most valuable piece of tactical YC advice, but it's been up there. I'm astonished looking back at

all of my data points of YC founders over the years, how much the ability, the, like, moving fast and being iterative correlates with success.

Okay. You've mentioned YC way many, too many times in this conversation. I have to explore this because we talked before. It's like, listen, I'm not a journalist. I'm an enthusiast. I don't have a list of questions. I'm like, I have a world-class founder across from me.

I want to know what the hell is in this person's mind and I want to, like, extract information out selfishly for me. So, like, why, like, I just, I'm shocked at how much you reference it in conversations. Like, how impactful going through YC and then running YC, being, you know, affiliated with them is clearly on your life.

Can you, like, you expound on this?

There is some band that wasn't that successful. They didn't sell that many albums, I mean. But they, like, influenced all of the musicians that came after.

I think it's called The Band. Literally, Rick Rubin told this story.

I think it might be the Velvet Underground. But, but you know the idea I'm getting at.

Yes, Rick Rubin talked about this, yeah.

It's not fair to talk about YC in this way because YC measured by, like, traditional metrics and market cap created or whatever is one of the hand, would be one of the handful of most valuable tech companies. But the degree to which YC totally influenced everything that has happened in the last 20 years of the tech industry and startups, entrepreneurship, whatever, I think is only, like, sort of understood.

OpenAI is an example of that. Not just from, like, how we have shipped our products in the world, but, like, the philosophy of how we run our research lab. I think if you go talk to, like, many of the other people running, like, this generation of large tech companies, they would tell you similar stories, even if they didn't go through YC.

But what's happening there? Is it an operating system that YC is giving you because you hear, you know, do these five things or whatever, or is it more like a philosophy of building companies? This is the confusing part for me as an outsider.

I think it's two major things. I mean, there is some of the operating system of what to do, but I think it was the philosophy of how to run companies, the idea of iterative deployment and technical people in charge and sort of being willing to bet on young people with a lot of energy and ambition, but maybe less experience throughout all levels of the company.

And then it was also the change, the related change to the whole ecosystem that happened in the pre-YC tech ecosystem. So if we, you know, ran the clock back to 2004 and then projected technology forward to 2016, but not anything else about the shape of

the startup ecosystem, what it meant to be an entrepreneur, how capital flowed, all of the, you know, who got to run companies, all of those things, I do not think OpenAI would have been possible. I think the changes that YC induced in the whole ecosystem, you know, more leverage going to founders, young technical founders having the ability to raise lots of capital, the ability to sort of, like, work on ambitious things without a very proven resume, I don't think OpenAI would have been possible.

So this is kind of like a big change.

Is this all tied to the fact that you think there was a benefit in you going from founder to investor for a long period of time, back to founder?

There are all those benefits too. And I wouldn't say I really went from founder to investor to founder because, like, the first time I was founder didn't really work out that well. It's like, you know.

You learned some lessons from failure, but I think you learned way more from success.

Oh, you got to hold on. We're not moving on from that. You got to say more about that. There's some, I can't believe, I think it's Anna Karenina, some great Russian novel. I'm very embarrassed not to know this.

Starts with, like, all unhappy families are unhappy in their own way. All happy families are the same.

That's confusing. Yeah.

That explains why it jumped into my mind. But I think this is really true. Like, when I look at the lessons of where I have failed at something, I learned something generic about grit and determination and something not to do.

But there's, most things don't work. So there's, like, a lot of reasons why things don't work. And there's, I think it's, like, harder to put together the correct causation. And

when I've had something really work, when I understand, like, what parts of Y Combinator really worked or what parts of OpenAI really worked, trying to apply those lessons going forward has been much more helpful to me than trying to apply the anti-lessons of what didn't work.

And so, you know, you should, of course, learn as much as you can from every data point. So learn from the failures, learn from the successes. But in my own experience, I have, when I have tried to apply those lessons, the lessons I learned from success were very good and I should have applied those more.

And the lessons I learned from failure were either fairly generic and I kind of already knew them or got in the way of something else. And I think this is, like, generally true for a lot of people.

Yeah, but isn't it, like, we already kind of know what we should do or what we should avoid, but it's like the reminder, the constant reminder. So, like, the best description of my other podcast founders I ever heard is, like, it's church for entrepreneurs.

If you really think about it, I used to drop it on Sundays and I should go back to doing that. But it's like, really, I'm just telling the same, it's the same personality type has appeared throughout history. It's just like, now this person happens to be building ships and this person built technology, but, like, and they lived at different times.

It's the same personality. That's for sure.

I'm kind of obsessed with this idea of things that last for a long period of time. And, like, you know, companies, the best companies can last a long time, but not as long as cities. And cities don't last, cities and countries don't last as long as, like, religions.

And I'm like, so out of all the man-made things, what has lasted longer? I would say, I can't think of anything other than religion. It might be another answer. So then I started studying and I grew up, my mom was fundamentalist Christian, so I was forced to go to church my entire life.

And I just started analyzing, like, what do all the main religions in the world have in common? It's like, oh, we have a shared base of knowledge, usually some kind of book,right? We meet with like-minded fellow believers at regular intervals.

And it's not like I go to church on Sundays. It's like, okay, we talked about Jesus last week, but let's talk about this other guy. It's like, no, I go back to these same books and the same stories over and over again.

So I read your blog and you even said this, something about, like, when YC ended, it's like, you're repeating the same thing. You're telling it to them all the time and then they leave the church, you know, for this analogy, and then they stop doing the same thing.

It's like, it's not even the lessons. It's like the constant reminder that this is important.

I extremely strongly agree with that, but I think it is better to be reminded of the thing, like, talk to your users more, you know, ship products earlier, get more feedback, hold a higher bar for who you recruit and who you hire and more quickly.

This is what I mean.

But it's the positives that I think are good.

You have one of the greatest tweets I've saved on my phone. You're like, you know, skip the conferences, the fucking dinners, everything else. Just, like, essentially make the product or sell and sell the product. If you're not making it and you're not selling it, like, that's all you actually have to do.

And I think it's like, again, it goes back to, like, that simple genius. So then this is the other part that I find most fascinating because somebody asked me yesterday, they're like, what's your, usually there's some kind of historical equivalent for every founder I meet that I can be like, oh, that guy's kind of like Vanderbilt, that guy's kind of like Rockefeller, Ludwig, or any of these people.

And it's like, what's your historical equivalent for Sam? I was like, there isn't. I can't think of one because, like, I don't know him enough well. Like, I don't understand how he thinks yet.

What do you think now?

Well, this is the first of hopefully eight conversations, so I'll tell you in conversation seven. But this is very rare. I just talked to Doug Leone and he talked about one dude that he hired. It's the founder of Newbank.

And he was a fucking, like, an associate in VC and then leaves and founds one of the most successful companies. I'm like, I've never heard of that. Doug, have you? And he dedicated his life to this. He goes, no, that's the only one.

So again, very rare. Mostly people go from founder, sell their business, unfortunately, and then investor as opposed to run the business till you die, which is my preferred method of things. What I'm curious about this is, like, when you just said, I learned more from successes,right?

Well, it's the successes because you were exposed to, what, 10,000 different companies in that decade or decade and a half that you were doing this. And you saw, obviously, maybe the half a dozen or the dozen who were the best in the world.

So, like, are you taking their successes as well as, like, instructive?

No, no, totally. The, and I think people do, I mean, you're an incredible student, but there's a lot of pretty good students of entrepreneurialism and entrepreneurism. And people, I think, often try to go look for those lessons of the things that really worked.

And as you've said, it's kind of the same thing over and over again, like done in different industries, but you have to be reminded of it a lot and it's unglamorous.

I had no understanding going into this conversation. I think I have a slightly better understanding going into this. I told you before we started this just for my own edification. But, like, even the people that influence you or it's just like Peter Thiel saying, no, dummy.

He obviously wouldn't say, no, dummy, this is working. Why are you doing anything else but the thing that is working? So there's got to be examples where you're like, hey, I've given this advice to other founders a million times and then you catch yourself, oh shit, I'm not even applying my own advice at this point in time.

Totally. Yeah, I'll give many examples of that. I think it's also instructive to, like, what was the new thing? What didn't you have the advice for? And the thing that was really different about OpenAI than anything that I had pattern matching before is it was four and a half years from when we started the company to when we launched our first product.

The opposite of what I see advice,right?

Yes.

Okay.

Yes. And although there were all these ways which managing a research team was similar to selecting and advising founders,

learning what it, to whatever degree we learned it, because I don't think we did it perfectly, like, how you manage through this part of the world where you don't have the external signal from customers and you're just trying to, like,

you know, do what would normally be the catastrophic startup advice of not, not shipping a product for four and a half years. That was very difficult. And we tried all of these things about how we, like, had a, how we replaced the signal of do customers actually like the product for is our research actually working?

One of the things that worked actually is during the

DOTA 2 days when we were trying to use RL to beat this video, beat people at a video game, we put up like a leaderboard and people could just see how different ideas were performing and what was, you know, that was like objective and real and people wanted to, like, go up that.

But we had to try all of these things to

basically, like, simulate end users. And that was a totally interesting new problem I had no pattern matching for.

How did you work your way through that? What was your thinking? Like, how did you do this?

We asked a bunch of people who had been at great research labs of the past. And it had been, you know, OpenAI started at sort of a time when everybody in Silicon Valley, as their vanity project, including me, wanted to start a research lab.

And there were all these books about the heyday of Bell Labs or Xerox Park that were very popular. Everybody was talking about this. There was a huge amount of discussion. In fact, I even see one of the books over there about Bell Labs.

But there was not a ton of people that had, like, in living memory how to actually do it. So we talked a lot to Alan Kay, we talked to a handful of other people, and we got some advice from them about, you know, what made a really good research lab.

And some of it was really good. Some of it didn't translate as well to the current moment.

Well, you also didn't have this giant monopolistic profit printing machine. Like, Bell Labs was spun out independently. Polaroid did a lot more research when they had essentially like a monopoly on its photography. I just read the biography of the founder of Honda,right?

The guy created the most successful motor vehicle of all time, the Honda Cub. It sold uninterrupted for like 60 years, millions of fucking vehicles. And his whole thing, he arrived at the same conclusion that Bell Labs did, that he thought the research and development had to actually be separate.

It was spun out of the company and had separate ownership, just like Bell Labs did.

We did not have that.

No.

We did not have the, when I think back to those early days, I mostly feel like I was trying and failing to raise money. That's like my dominant memory of the early days of OpenAI. So much effort, so frustrating.

I wish we had some sort of cash machine like that. I remember, like, one of my clearest memories of all of OpenAI. So announced the company in 2015, but the first day wasright after New Year's in 2016. And 12 of us or 11 of us showed up at Greg Brockman's apartment, you know, like 9, 9:30 on a Monday or Tuesday morning, something like that.

Let's say it's January 4th and everybody's there and there's been this, like, big effort and everybody walks in with a lot of excitement. It feels like the first day of school, whatever. And then very quickly, people, like, look around the room and they're sort of like, well, what do we do now?

Someone says, okay, we should get a whiteboard. Greg, you know, gets someone to go off and find a whiteboard. Whiteboard comes. Look around again. You know, what are we supposed to do now? And you just feel the energy in the room collapse.

And none of us know what to do. Like, there's no, it was not like building a product startup. It was not like, let's build this product and let's talk to customers. It's like, okay, we've said we want to make AGI.

Maybe we should write some papers. Okay, let's write some papers. Maybe we should think about some ideas. Okay, let's think about some ideas. You know, everybody's got their, like, moments of, I have no idea what I'm doing. That was one of mine.

Like, I have, you know, we have just launched this thing. None of us have any idea what we're going to do. So we did what we know how to do and eventually we figured out, a lot of things didn't work.

Eventually we figured out a kind of like rhythm for

making and then evaluating research bets.

And

far from perfect, obviously, but we did find a gradient that we could kind of progress along. And we figured out how to get, you know, the resources that very smart people needed and how to make sure that we were, like, not completely getting lost in the wilderness.

And over

some number of years, mostly chaotic stumbling, we eventually made most of the big discoveries. You know, what started as the unsupervised sentiment on paper turned into GPT-1 and then eventually GPT-whatever. The scaling laws work that gave us the confidence, not only by the compute, but the understanding about how to scale up our models sort of came together.

And through, and many other things too, through this process, we learned things like that idea of leaderboards that worked. We also learned the incredible power of external demos for, like, an eminent person that the researchers really wanted to impress.

And then we learned a bunch of things that, like, didn't work, like fake deadlines.

That has to be so disorienting to live through that experience. You're at 12 people in an apartment, don't even have a whiteboard, don't know what to do. Fast forward a decade, you have a billion people using.

Very strange experience.

Do you keep a journal?

When my kid was born, my first kid, I would, like, you know, get home at the end of the day and be rocking in my sleep and just, like, talk to a kid or whatever. So I was just like, I need to come up with things to talk about.

So I would, like, just tell them about my day and what we were struggling with and kind of like what I was worried about and what was happening. It was kind of like fun for me to do. And I was like, this is sort of interesting and someday it'll be like interesting for him to have this.

So I started writing him, like, every Sunday I would, like, write him a letter. I would, like, talk just to talk and then I would, like, write it down. I only ever did like eight of them or something.

How many kids do you have?

Two.

Okay. Bezos has this great line about building Amazon. He's like, we're trying to do stuff that we can tell our grandkids about that we're proud of,right? And those things are hard. The fact that you were writing to your son.

Oh, man.

Keep writing the letters. And if you don't do that, this is real quick, just because I've read enough books about this. Most time, guess what? Founders don't write autobiographies when they're 40. They write them when they're 70 and they're looking back and they wish they could do it again.

And so much has been lost to the sands of time. They all repeat this. They're like, I wish I journaled. So even if you don't do it, you have enough resources. What I would do, write, have a book written, even if it's for internal purposes only.

You ever read The Little Kingdom by Michael Mors?

I never read it.

Oh, you have to. It's like the first six-year history of Apple written by Michael Morse.

Isn't it crazy that he wrote that book?

He's a phenomenal writer.

He's a great writer.

Like, crazy writer winds up being one of the best venture capitalists of all time, I guess. But like, the, you just, the point is that have, that book ends. Steve hasn't even kicked out of Apple yet. So you get like, what actually happened?

You're going to want this. You might not want it now, but you're damn sure you're going to want it when you're 60 or 70.

The thing that was so interesting was like the mindset of writing to your kid. Like, you really can't hide behind anything. Like, you're like, I really care what my kid's going to think about me. So like, this thing happened.

Like, you know, didn't feel great about it. Better do it differently next week. Like, it was a very interesting, extremely interesting mental framework. Maybe I'll find some way to do it again.

Not maybe. You're going to do it.

Okay.

Sam, thanks for taking the time. This was awesome, man.

Thank you.

Appreciate it. I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review. And make sure you listen to my other podcast founders. For almost a decade, I have obsessively read over 400 biographies of history's greatest entrepreneurs searching for ideas that you can use in your work.

Most of the guests you hear on this show first found me through founders.