How To Build The Future: Sam Altman

• The pursuit of AGI has been a long and challenging journey for OpenAI, driven by a unique focus on deep learning and scaling.
• Sam Altman emphasizes the transformative potential of technology startups in today's evolving landscape.
• Personal experiences in early startups and research labs have shaped a grounded understanding of perseverance and conviction in the tech industry.
• The development of models like GPT-3 and GPT-4 showcases the rapid advancements in AI capabilities and their real-world applications.
• A strong peer network and community support is critical for startup success, encouraging ambition and innovation.

We said from the very beginning we were going to go after AGI at a time when in the field you weren't allowed to say that because that just seemed impossibly crazy. I remember a rash of criticism for you guys at that moment.

We really wanted to push on that and we were far less resourced than DeepMind and others. So we said, okay, they're going to try a lot of things and we've just got to pick one and really concentrate. And that's how we can win here.

Most of the world still does not understand the value of like a fairly extreme level of conviction on one bet. That's why I'm so excited for startups right now. It is because the world is still sleeping and all this is just such an astonishing degree.

We have a real treat for you today. Sam Altman, thanks for joining us. Thanks, Gary. This is actually a reboot of your series, How to Build the Future. And so welcome back to the series that you started years ago.

I was just trying to think about that. Something like that. That's wild. I'm glad it's being rebooted. Let's talk about your newest essay on the age of intelligence. You know, is this the best time ever to be starting a technology company?

Let's at least say it's the best time yet. Hopefully, there will be even better times in the future. I sort of think with each successive major technological revolution, you've been able to do more than you could before. And I would expect the companies to be more amazing and impactful and everything else.

So, yeah, I think it's the best time yet. Big companies have the edge when things are like moving slowly and not that dynamic. When something like this or mobile or the Internet or semiconductor revolution happens, that is probably when upstarts have their edge.

And it's been a while since we've had one of these, so this is like pretty exciting. In the essay, you actually say a really big thing, which is super intelligence is actually thousands of days away. Maybe. I mean, that's our hope, our guess, whatever. But that's a very wild statement.

I can see a path where the work we are doing just keeps compounding. The rate of progress we've made over the last three years, continuous for the next three, six, or nine years would be like 3,500 days or whatever.

If we can keep this rate of improvement or even increase it, that system will be quite capable of doing a lot of things. I think already, even a system like Zero1 is capable of doing quite a lot of things. From just like a raw cognitive IQ on a closed-end, well-defined task in a certain area.

I'm like, 01 is like a very smart thing. And I think we're nowhere near the limit of progress. I mean that was an architecture shift that sort of unlocked a lot. What I'm sort of hearing is that these things are going to compound.

We could hit some unexpected wall or we could be missing something. But it looks to us like there's a lot of compounding in front of us still to happen. This essay is probably the most techno optimist of almost anything I've seen out there.

Some of the things we get to look forward to include fixing the climate, establishing a space colony, the discovery of all of physics, near limitless intelligence, and abundant energy. I do think all of those things, and probably a lot more we can't even imagine, are maybe not that far away.

And one of I, I think it's like tremendously exciting that we can talk about this even semi-seriously now. One of the things that I always have loved the most about YC is it encourages slightly implausible degrees of techno optimism and just a belief that like, ah, you can figure this out.

In a world that I think is like sort of consistently telling people, this is not going to work, you can't do this thing, you can't do that. I think the kind of early PG spirit of just encouraging founders to like think a little bit bigger is like it is a special thing in the world.

The abundant energy thing seems like a pretty big deal. If we do achieve abundant energy, it seems like this is a real unlock. Almost any work, not just knowledge work, but actually like real physical work could be unlocked with robotics and with language and intelligence on tap.

Like there's a real age of abundance. I think these are like the two key inputs to everything else that we want. There's a lot of other stuff, of course, that matters, but the unlock that would happen if we could just get truly abundant intelligence, truly abundant energy, what we'd be able to make happen in the world, like both like come up with better ideas more quickly and then also like make them happen in the physical world.

It'd be nice to be able to run lots of AI. And that takes energy too. I think that would be a huge unlock. And the fact that it's. I'm not sure whether to be surprised that it's all happening at the same time or if this is just like the natural effect of an increasing rate of technological progress, but it's certainly a very exciting time to be alive and a great time to do a startup well.

So we sort of walk through this age of abundance. You know, maybe robots can actually manufacture, do anything. Almost all physical labor can then result in material progress, not just for the most wealthy, but for everyone.

You know, what happens if we don't unleash unlimited energy? If there's some physical law that prevents us from exactly that. Solar plus storage is on a good enough trajectory that even if we don't get a big nuclear breakthrough, we would be like, okay, ish.

But for sure. It seems that driving the cost of energy down, the abundance of it up, has like a very direct impact on quality of life. And eventually, we'll solve every problem in physics. So, we're going to figure this out. It's just a question of when.

And we deserve it. There's, you know, someday we'll be talking not about fusion or whatever, but about the Dyson sphere. And that'll be awesome too. Yeah, this is a point in time. Whatever feels like abundant energy to us will feel like not nearly enough to our great-grandchildren.

And there's a big universe out there with a lot of matter. Yeah, wanted to switch gears a little bit to sort of your. Earlier you were mentioning Paul Graham, who brought us all together, really created Y Combinator.

He likes to tell the story of how you got into YC — you were a Stanford freshman. He said, you know what? This is the very first YC batch in 2005. He said, you know what, you're a freshman and YC will still be here next time, you should just wait.

And you said, I'm a sophomore and I'm coming. You're widely known in our community as one of the most formidable people. Where do you think that came from, that one story? I think I would be happy if that like, drifted off the industry. Well, now it's purely immortalized.

My memory of that is that like, I needed to reschedule an interview one day or something and PG tried to like, say like, just do it next year or whatever. I think I said, some nicer version of I'm a sophomore and I'm coming.

But yeah, you know, these things get slightly apocryphal. It's funny, I don't, and I say this with no false modesty, I don't like identify as a formidable person at all. In fact, I think there's a lot of ways in which I'm really not.

I do have a little bit of a just like I don't see why things have to be the way they are. And so I'm just going to like do this thing that from first principles seems like fine. I always felt a little bit weird about that.

Then I remember one of the things I thought was so great about YC and still that I care so much about YC is it was like a collection of the weird people who are just like, I'm just going to do my thing.

The part of this that does resonate as a like accurate self-identity thing is I do think you can just do stuff or try stuff a surprising amount of the time. I think more of that is a good thing. I think one of the things that both of us found at YC was a bunch of people who all believed that you could just do stuff.

For a long time when I was trying to figure out what made YC so special, I thought that it was like, okay, you have this like very amazing person telling you you can do stuff. I believe in you.

And as a young founder, that felt so special and inspiring. And of course it is. But the thing that I didn't understand until much later was it was the peer group of other people doing that.

One of the biggest pieces of advice I would give to young people now is finding that peer group as early as you can was so important to me. And I didn't realize it was something that mattered. I kind of thought, ah, like I have, you know, I'll figure it out on my own.

But man, being around like inspiring peers, so, so valuable. What's funny is both of us did spend time at Stanford. I actually did graduate, which I probably shouldn't have done, but I did. Stanford's great.

You pursued the path of, you know, far greater return by dropping out. But, you know, that was a community that purportedly had a lot of these characteristics. But I was still beyond surprised at how much more potent it was with a room full of founders.

It was. I was just going to say the same thing, actually. I like Stanford a lot. But I did not feel surrounded by people that made me, like, want to be better and more ambitious and whatever else.

And to the degree I did, the thing you were competing with your peers on was like, who was gonna get the internship at which investment bank, which, I'm embarrassed to say I fell into that trap.

This is like how powerful peer groups are. It was a very easy decision to not go back to school after seeing what the like YC vibe was like. Yeah. There's a powerful quote by Carl Jung that I really love.

It's, you know, the world will come and ask you who you are, and if you don't know, it will tell you. It sounds like being very intentional about who you want to be and who you want to be around as early as possible is very important.

Yeah. This was definitely one of my takeaways, at least for myself, is no one is immune to peer pressure. And so all you can do is, like, pick good peers.

Yeah. Obviously, you know, you went on to create Looped, sell that, go to Green Dot, and then we ended up getting to work together at YC. Talk to me about, like, the early days of YC Research.

One of the really cool things that you brought to YC was this experimentation. I remember you coming back to partner rooms and talking about some of the rooms that you were getting to sit in with, like, the Larry and Sergeys of the world.

AI was at the tip of everyone's tongue because it felt so close, and yet it was. You know, that was 10 years ago. The thing I always thought would be the coolest retirement job was to get to run a research lab.

It was not specific to AI at that time when we started talking about YC Research. Not only was it going to fund a bunch of different efforts, but I wish I could tell the story of, like, oh, it was obvious that AI wasn't going to work and be the thing.

But we tried a lot of bad things too. Around that time, I read a few books on the history of Xerox PARC and Bell Labs and stuff. There were a lot of people. Like, it was in the air of Silicon Valley at the time that we need to, like, have good research labs again.

I just thought it would be so cool to do. It was sort of similar to what YC does, in that you're going to, like, allocate capital to smart people. Sometimes it's going to work and sometimes it's not going to.

I just wanted to try it. AI for sure was having a mini moment. This was kind of late 2014, 2015, early 2016 was like the superintelligence discussion. Like the book Superintelligence was happening. Bo Strum y up.

Yeah, DeepMind had a few impressive results, but a little bit of a different direction. I had been an AI nerd forever, so I was like, oh, it would be so cool to try to do something. But it was very hard to say, was ImageNet out yet?

Imagenet was out. Yeah, for a while at that point. So you could tell if it was a hot dog or not. You could sometimes. That was getting there.

You know, how did you identify the initial people you wanted involved in, you know, YC Research and OpenAI?

I mean, Greg Brockman was early; in retrospect, it feels like this movie montage. There were like all of these, you know, at the beginning of like the bank heist movie, when you're like driving around to find the people and whatever, and they're like, you son of a bitch, I'm in.

Ilya, I heard he was really smart. Then I watched some video of his, and he’s also, he's extremely smart. True, true, genuine genius and visionary. But also he has this incredible presence.

I watched this video of his on YouTube or something. I was like, I gotta meet that guy. I emailed him. He didn't respond. So I just went to some conference he was speaking at, and we met up.

After that, we started talking a bunch. Like Greg, I had known a little bit from the early Stripe days. What was that conversation like, though? It’s like, I really like your ideas about AI, and I want to start a lab?

Yes. One of the things that worked really well in retrospect was we said from the very beginning we were going to go after AGI at a time when in the field you weren't allowed to say that because that seemed impossibly crazy and, you know, borderline irresponsible to.

So that got his attention immediately. It got all of the good young people’s attention and the derision of the mediocre old people. I felt like somehow that was like a really good sign and really powerful.

We were like this ragtag group of people. I was the oldest by a decent amount. I was like, I guess I was 30 then. You had like these people who were like, those are these irresponsible young kids who don't know anything by anything, and they're like saying these ridiculous things.

That was really appealing for the same kind of people who would have said, like, it's a, you know, I'm a sophomore and I'm coming or whatever. They were like, let's just do this thing, let's take a run at it.

We kind of went around and met people one by one and then in different configurations of groups, and it kind of came together over the course of. It fits and starts, but over the course of like nine months.

Then it started, it started happening. One of my favorite memories of all of OpenAI was Ilya had some reason that Google or something that we couldn't start.

We announced in December of 2015, but we couldn't start until January of 2016. Like January 3rd, something like that of 2016, or very early in the month, people came back from the holidays, and we go to Greg's apartment, maybe there's 10 of us, something like that.

We sit around and it felt like we had done this monumental thing to get it started. Everyone's like, so what do we do now? And what a great moment. It reminded me of when startup founders work really hard to like, raise a round and they think like, oh, I accomplished this. We did it, we did it.

Then you sit down and say, like, fuck, now we gotta like figure out what we're gonna do. It's not time for popping champagne. That was actually the starting gun, and now we gotta run.

Yeah. And you have no idea how hard the race is going to be. It took us a long time to figure out what we're going to do. One of the things that I'm really amazingly impressed by, Ilya in particular, but really all of the early people about is although it took a lot of twists and turns to get here, the big picture of the original ideas was just so incredibly right.

They were like up on one of those flip charts or whiteboards, I don't remember which, in Greg's apartment. Then we went off and, you know, did some other things that worked or didn't work or whatever some of them did.

Eventually now we have this like, system. It feels very crazy and very improbable looking backwards that we went from there to here with so many detours on the way, but got where we were.

Point was deep learning. Even on that flip chart initially. Yeah, I mean more specifically than that, like do a big unsupervised model and then solve RL was on that flip chart. One of the flip charts from a very early offsite.

I believe there were three goals for the effort at the time. It was like, figure out how to do unsupervised learning, solve RL and never get more than 120 people missed on the third one.

That's right. The predictive direction of the first two was pretty good. So deep learning. Then the second big one sounded like scaling. The idea that you could scale. That was another heretical idea that people actually found even offensive.

I remember a rash of criticism for you guys at that moment when we started. The core beliefs were deep learning works and it gets better with scale.

I think those were both somewhat heretical beliefs at the time. We didn't know how predictably better it got with scale. That didn't come for a few years later. It was a hunch first, then you got the data to show how predictable it was.

People already knew that if you made these neural networks bigger, they got better. We were sure of that before we started. What took the like word that keeps coming to mind is like a religious level of belief was that that wasn't going to stop.

Everybody had some reason of, oh, it's not really learning, it's not really reasoning, it can't really do this. this, you know, it's like a parlor trick. These were like the eminent leaders of the field.

More than just saying you're wrong, they were like, you're wrong. This is like a bad thing to believe or a bad thing to say. They said, you're going to perpetuate an AI winter. You're going to do this, you're going to do that.

We were just looking at these results and saying they keep getting better. We got the scaling results. It just kind of breaks my intuition even now. At some point you have to just look at the scaling loss and say, we're going to keep doing this and this is what we think it'll do.

It was starting to feel like something about deep learning was this emergent phenomenon that was really important. Even if we didn't understand all of the details in practice here, which obviously we didn't, and still don't, there was something really fundamental going on.

It was the PGM for this; we had discovered a new square in the periodic table. We really wanted to push on that, and we were far less resourced than DeepMind and others.

So we said okay, they're going to try a lot of things and we've just got to pick one and really concentrate and that's how we can win here. Which is totally the right startup takeaway.

We said, well, we don't know what we don't know. We do know this one thing works. So we're going to really concentrate on that. Some of the other efforts were trying to outsmart themselves in too many ways.

We just said we; we'll do the thing in front of us and keep pushing on it. Scale is this thing that I've always been interested in, it turns out, for deep learning models for a lot of other things.

I think it’s a very underappreciated property and thing to go after. I think when in doubt, if you have something that seems like it's getting better with scale, I think you should scale it up.

I think people want things to be, you know, less is more, but actually more is more. We believed in that. We wanted to push on it. One thing that is not maybe that well understood about OpenAI is we had just this.

Even when we were like pretty unknown, we had a crazy talented team of researchers. If you have like the smartest people in the world, you can push on something really hard. Yeah.

And they’re motivated or you created sort of one of the sole places in the world where they could do that. Like one of the stories I heard is just even getting access to compute resources even today is this crazy thing.

Embedded in some of the criticism from maybe the elders of the industry at the moment was that you're going to waste a lot of resources and somehow that’s going to result in an AI winter.

It's funny; people were never sure if we were going to waste resources or if we were doing something kind of vaguely immoral by putting in too much resources, and you were supposed to spread it across.

Lots of bets rather than conviction on one. Most of the world still does not understand the value of like a fairly extreme level of conviction on one bet.

Did we have this evidence, we believe in this thing. We’re going to, at a time when the normal thing was to spread against this bet and that bet. In that bet, you're definitely optimistic.

I think across many of the successful YC startups, you see a version of that again and again. Yeah, that sounds right. When the world gives you sort of pushback and the pushback doesn't make sense to you, you should do it anyway.

Totally. One of the many things that I'm very grateful about getting exposure to from the world of startups is how many times you see that again and again and again.

Before YC, I really had this deep belief that somewhere in the world there were adults in charge, and they knew what was going on and someone had all the answers. If someone was pushing back on you, they probably knew what was going on.

The degree to which I now understand that, to pick up the earlier phrase, you can just do stuff, you can just try stuff; no one has all the answers. There are no adults in the room that are going to magically tell you exactly what to do.

You kind of have to like iterate quickly and find your way. That was like a big unlock in life for me to understand. There is a difference between being high conviction just for the sake of it, and if you're wrong and you don't adapt and you don't try to be like truth-seeking, it still is really not that effective.

The thing that we tried to do was really just believe whatever the results told us and really kind of tried to go do the thing in front of us. There were a lot of things that we were high conviction and wrong on, but as soon as we realized we were wrong, we tried to like fully embrace it.

Conviction is great until the moment you have data one way or the other. There are a lot of people who hold onto it past the moment of data. So it's iterative. It's not just they're wrong and I'm right; you have to go show your work.

But there is a long moment where you have to be willing to operate without data. At that point you do have to just sort of run on conviction. Yeah, it sounds like there's a focusing aspect there too.

You had to make a choice and that choice had better be; you didn't have infinite choices. The prioritization itself was an exercise that made it much more likely for you to succeed.

I wish I could go tell you, like, oh, we knew exactly what was going to happen. We had this idea for language models from the beginning and, you know, we kind of went right to this.

But obviously the story of OpenAI is that we did a lot of things that helped us develop some scientific understanding, but we're not on the short path. If we knew then what we know now, we could have speedrun this thing to like an incredible degree.

It doesn't work that way. You don't get to be right at every guess. We started off with a lot of assumptions about the direction of technology, what kind of company we were going to be, how we were going to be structured, how AGI was going to go, and all these things.

We have been humbled and badly wrong many, many, many times. One of our strengths is the ability to get punched in the face and get back up and keep going. This happens for scientific bets for, you know, being willing to be wrong about a bunch of other things.

We thought about how the world was going to work and what the sort of shape of the product was going to be. Again, we had no idea, or I at least had no idea. Maybe Alec Radford did. I had no idea that language models were going to be the thing.

We started working on robots and agents, playing video games and all these other things. A few years later, GPT-3 happened; that was not so obvious at the time.

It sounded like there was a key insight around positive or negative sentiment around INGP1. Even before GPT-1, the paper was called the unsupervised sentiment neuron. I think Alec did it alone, by the way; Alec is this unbelievable outlier of a human.

He did this incredible work, which was just looking at. He noticed there was one neuron that was flipping positive or negative sentiment as it was doing these generative Amazon reviews.

I think other researchers might have hyped it up or made a bigger deal out of it or whatever, but you. It was Alex. So it took people a while to, I think, fully internalize what a big deal it was.

He then did GPT-1, and somebody else scaled it up into GPT-2. But it was off of this insight that there was something amazing happening.

At the time, unsupervised learning wasn't really working. So he noticed this one really interesting property: there was a neuron that was flipping positive or negative with sentiment.

That led to the GPT series. I guess one of the things that Jake Heller, from Case Text, I think of him as maybe, I mean, not surprisingly, a YC alum who got access to both 3, 3.5, and 4.

He described getting 4 as sort of the big moment revelation. Because 3.5 would still do; I mean, it would hallucinate more than he could use in a legal setting.

With 4, it reached the point where if he chopped the prompts down small enough into workflow, he could get it to do exactly what he wanted. He built huge test cases around it and then sold that company for $650 million.

I think of him as like one of the first to commercialize GPT-4 in a relatively grand fashion. I remember that conversation with him; that was one of the few moments in that thing where I was like, okay, we have something really great on our hands.

When we first started trying to sell GPT-3 to founders, they would be like, it's cool. It's doing something amazing. It's an incredible demo. But with the possible exception of copywriting, no great businesses were built on GPT-3.

Then 3.5 came along, and people, startups, like YC startups in particular, started to do interesting things. It no longer felt like we were pushing a boulder uphill. People actually wanted to buy the thing we were selling totally.

Then 4, we kind of got the, like, just how many GPUs can you give me? Oh yeah. Moment, like very quickly after giving people access. We felt like, okay, we got something like really good on our hands. So you knew actually from your users then?

Totally. When the model dropped itself and you got your hands on it, it was like, well, this is better. We were totally impressed then too.

We had all of these tests that we did on it that were very. It looked great. It could do these things that we were all super impressed by. Also, when we were all just playing around with it and getting samples back, I was like, wow, it could do this now.

It could rhyme and it could tell a funny joke, slightly funny joke. It could, you know, do this and that. It felt really great, but you never really know if you ever hit product on your hands until you like put it in customer hands.

You're always too impressed with your own work. So we were all excited about it. We were like, oh, this is really quite good. But until like the test happens, it’s like the real test is. Yeah, yeah. The real test is users.

So there's some anxiety until that moment happens. Yeah, I wanted to switch gears a little bit. Before you created obviously one of the craziest AI labs ever to be created, you started at 19 at YC with a company called Looped which was basically Find my friend's geolocation, probably what, 15 years before Apple ended up making it too early in any case.

What drew you to that particular idea? I was like interested in mobile phones and I wanted to do something that got to like use mobile phone. This was when mobile was just starting. It was still three years or two years before the iPhone.

It was clear that carrying around computers in our pockets was somehow a very big deal. I mean, that's hard to believe now that there was a moment when phones were literally just a phone.

I think I try not to use it as an actual phone ever. I still remember the first phone I got that had Internet on it and it was this horrible, mostly text-based browser. It was really slow. You could painfully and so slowly check your email.

I was like in high school sometime in high school when I got a phone that could do that versus just texting or calling, and I was hooked right then. This is not a phone. This is like a computer we can carry, and we’re stuck with a dial pad for this accident of history.

But this is going to be awesome. Now you have billions of people who don’t have a computer. To us growing up, this is your first computer. Not physically a replica or like another copy of my first computer which islc too.

Yeah. So this is what a computer was to us growing up. The idea that you would carry this little black mirror, like kind of. We've come a long way.

Unconscionable back then. So you even then like technology and what was going to come was sort of in your brain. Yeah, I was like a real.

I mean I still am a real tech nerd but I always; that was what I spent my Friday nights thinking about. One of the harder parts of it was we didn't have the App Store; the iPhone didn't exist.

You ended up being a big part of that launch. I think a small part, but yes, we did get to be a little part of it. It was a great experience for me to have been through because I understood what it is like to go through a platform shift and how messy the beginning is.

How much like little things you do can shape the direction it all goes. I was definitely on the other side of it then. I was watching somebody else create the platform shift.

But it was a super valuable experience to get to go through and sort of just see how it happens and how quickly things change and how you adapt through it. What was that experience like?

You ended up selling that company. That was probably the first time you were managing people and doing enterprise sales. All of these things were useful lessons from that first experience.

I mean it obviously was not a successful company. It was very painful to go through. The rate of experience and education was incredible.

Another thing that PG said or quoted somebody else saying but always stuck with me is your twenties are always an apprenticeship, but you don't know for what and then you do your real work later.

I did learn quite a lot and I'm very grateful for it. It was like a difficult experience. We never found product market fit really, and we also never really found a way to get to Escape Velocity, which is just always hard to do.

There is nothing that I have ever heard of that has a higher rate of generalized learning than doing a startup. So it was great in that sense. When you're 19 and 20, riding the wave of some other platform shift, this shift from dumb cell phones to smartphones.

Here we are many years later and your next act was you. I mean, I guess two acts later literally spawning one of the major platform shows we all get old.

Yeah. But that's really what's happening. 18-20 year olds are deciding that they could get their degree, but they're going to miss the wave because all of this stuff that's great.

Everything's happening right now. I am proud of that. Do you have an intuitive sense like speaking to even a lot of the really great billion-dollar company founders?

Some of them are just not that aware of what's happening. It's astonishing to me. It's wild, right? Yeah.

I think that's why I'm so excited for startups right now. It is because the world is still sleeping and all of this is just to such an astonishing degree.

Then you have like the YC founders being like, no, no, I'm going to do this amazing thing and do it very quickly.

It reminds me of when Facebook almost missed mobile because they were making web software and they were really good at it. They had to buy Instagram, like Snapchat right up.

Yeah. So it's interesting. The platform shift is always built by the people who are young, with no prior knowledge. I think it's great.

So there's this other aspect that's interesting in that I think you, you and Elon and Bezos and a bunch of people out there, like, they sort of start their journey as founders, you know, really, whether it's Looped or Zip2 or really in maybe pure software, like, it's just a different thing that they start and then later they sort of get to level up.

Is there a path that you recommend at this point? If people are thinking, I want to work on the craziest hard tech thing first, should they just run towards that to the extent they can, or is there value in you sort of solving the money problem first, being able to invest your own money, like very deeply into the next thing?

It's a really interesting question. It was definitely helpful that I could just write the early checks for OpenAI. I think it would have been hard to get somebody else to do that at the very beginning.

Elon did it at a much higher scale, which I'm very grateful for. Other people did after that. There are other things that I've invested in that I’m really happy to have been able to support.

I don't think it would have been hard to get other people to do it. That's great for sure. I did, like, we were talking about earlier, learn these extremely valuable lessons.

But I also feel like I kind of like was wasting my time, for lack of a better phrase, working on Looped. I don't regret it. It's all part of the tapestry of life, and I learned a ton.

But what would you have done differently, or what would you tell yourself from like now to a time, time travel capsule that would show up on your desk at Stanford when you were 19?

Well, it's hard because AI was always the thing I most wanted to do. I went to school to study AI. When I was working in an AI lab, the one thing they told you is definitely don't work on neural networks; we tried that.

A long time ago, I think I could have picked a much better thing to work on than Looped. I don't know exactly what it would have been, but it all works out; it's fine.

There's this long history of people building more technology to help improve other people's lives, and I actually think about this a lot. I think about the people that made that computer and I don’t know them.

Many of them are probably long retired, but I am so grateful to them. Some people worked super hard to make this thing at the limits of technology. I got a copy of that on my eighth birthday, and it totally changed my life and the lives of a lot of other people too.

They worked super hard. They never like got a thank you for me, but I feel grateful to them very deeply, and it's really nice to get to like add our brick to that long road of progress.

Yeah, it's been a great year for OpenAI. Not without some drama. Always. We'll be good at that. What did you learn from sort of the ouster last fall, and how do you feel about some of the departures?

I mean, teams do evolve, but how are you doing? Man, I tire but good. Yeah, we've kind of like Speedrun like medium-sized or even kind of pretty big-sized tech company arc that would normally take like a decade in two years.

Yeah. There is a lot of painful stuff that comes with that, and there’s any company as it scales goes through management teams at some rate, and you have to sort of realize the people who are really good at the 0 to 1 phase are not necessarily people that are good at the 1 to 10 or the 10 to the 100 phase.

We've also kind of changed what we're going to be, made plenty of mistakes along the way, done a few things really right, and that comes with a lot of change.

I hope that we are heading towards a period now of more calm, but I'm sure there will be other periods in the future where things are very dynamic again. So I guess how does OpenAI actually work right now?

The quality and like the pace that you're pushing right now I think is beyond world-class compared to a lot of the other really established software players that came before.

This is the first time ever where I felt like we actually know what to do. I think from here to building an AGI will still take a huge amount of work. There are some known unknowns, but I think we basically know what to go do.

It'll take a while; it'll be hard, but that's tremendously exciting. I also think on the product side there's more to figure out, but roughly we know what to shoot at and what we want to optimize for.

That's a really exciting time. When you have that clarity, I think you can go pretty fast. Yeah, if you're willing to say we're going to do these few things, we're going to try to do them very well.

And our research path is fairly clear, our infrastructure path is fairly clear, our product path is getting clearer. You can orient around that super well. For a long time, we did not have that.

We were a true research lab. Even when you know that, it's hard to act with the conviction on it because there are so many other good things you'd like to do.

But the degree to which you can get everybody aligned and pointed at the same thing is a significant determinant in how fast you can move. I mean it sounds like we went from level one to level two very recently and that was really powerful.

Then we actually just had our O1 hackathon at YC that was so impressive. That was super fun. Then weirdly, one of the people who won, I think they came in third, was Camphor.

So CADCAM startup did YC recently, last year or two, and they were able to, during the hackathon, build something that would iteratively improve an airfoil from something that wouldn't fly to literally something that had, yeah, that was awesome, a competitive amount of lift.

That sort of sounds like level four, which is the innovator stage. It's very funny you say that. I had been telling people for a while, I thought that the level 2 to level 3 jump was going to happen, but then the level 3 to level 4 jump, level 2 to level 3 was going to happen quickly.

Then the level 3 to level 4 Jump was somehow going to be much harder and require some medium-sized or larger new ideas. That demo and a few others have convinced me that you can get a huge amount of innovation just by using these current models in really creative ways.

Well, yeah, I mean, what's interesting is Camphor already built sort of the underlying software for CADCAM; language is sort of the interface to the large language model which can then use the software like tool use.

If you combine that with the idea of code generation, that's kind of a scary crazy idea. Not only can the large language model code, but it can create tools for itself and then compose those tools.

Similar to chain of thoughts with 01. Yeah, I think things are going to go a lot faster than people are appreciating right now.

It's an exciting time to be alive honestly. You mentioned earlier that thing about discover all of physics. I always want to be a physicist, wasn’t smart enough to be a good one.

Had to like contribute this other way. But the fact that somebody else I really believe is now going to go solve all the physics with this stuff like I'm so excited to be alive for that.

Let's get to level four. I'm so happy for whoever that person is. Do you want to talk about levels three, four, and five briefly?

We realized that AGI had become this badly overloaded word and people mean all kinds of different things. We tried to just say, okay, here's our best guess roughly of the order of things.

You have these level one systems, which are these chatbots. Level two would come, which would be these reasoners. We think we got there earlier this year with the 01 release.

Level three is agents’ ability to go off and do these longer-term tasks. Maybe like multiple interactions with an environment, asking people for help when they need it, working together.

I think we're going to get there faster than people expect, as innovators like that's like a scientist. The ability to explore like a not well understood phenomena over a long period of time and understand what's to just kind of go just figure it out.

Level five, this is the sort of slightly amorphous; do that but at the scale of the whole company or a whole organization or whatever that's going to be a pretty powerful thing.

Yeah. It feels kind of fractal, right? Like even the things you had to do to get to two sort of rhyme with level five in that you have multiple agents that then self-correct that work together.

That kind of sounds like an organization to me, just at like a very micro level. Do you think that we’ll have, I mean, you famously talked about it. I think Jake talks about it.

You will have companies that make billions of dollars per year and have like less than 100 employees, maybe 50, maybe 20 employees, maybe one. It does seem like that.

I don't know what to make of that other than it’s a great time to be a startup founder. Yeah, but it does feel like that's happening to me.

Yeah. You know, it's like one person plus 10,000 GPUs. That could happen. Pretty powerful. Sam, what advice do you have for people watching who you either are about to start or just started their startup?

Bet on this tech trend. Like bet on this trend. We are not near the saturation point. The models are going to get so much better so quickly.

What you can do as a startup founder with this versus what you could do without it is so wildly different. The big companies, even the medium-sized companies, even the startups that are a few years old, they're already on quarterly planning cycles and Google is on a year-decade planning cycle.

I don’t know how they even do it anymore. But your advantage with speed and focus and conviction and the ability to react to how fast the technology is moving is the number one edge of a startup kind of ever, but especially right now.

I would definitely like to build something with AI and I would definitely like to take advantage of the ability to see a new thing and build something that day rather than, like, put it into a quarterly planning cycle.

I guess the other thing I would say is it is easy when there's a new technology platform to say, well, because I'm doing some of the AI, the laws of business don't apply to me. I have this magic technology and so I don't have to build a moat or a competitive edge or better products.

Because I'm doing AI and you're not. So that's all I need, and that's obviously not true. But what you can get are these short-term explosions of growth by embracing a new technology more quickly than somebody else and remembering not to fall for that.

You still have to build something of enduring value. I think that's a good thing to keep in mind too. Yeah, everyone can build an absolutely incredible demo right now.

Everyone can build an incredible demo, but building a business, then that's the brass rules still apply. You can do it faster than ever before and better than ever before, but you still have to build a business.

What are you excited about in 2025? What's to come? AGI, yeah, excited for that. What am I excited for?

When a kid. I'm more excited for that than I've ever been. Incredible. Yeah, probably that. That's by far. That's gonna be really, that's the thing I've like most excited for ever in life.

It changes your life completely. So I cannot wait. Well, here's to building that better world for our kids and really, hopefully the whole world.

This was a lot of fun. Thanks for hanging out, Sam. Thank you.