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 Deep Mind and others. And 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 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 on all this to such an astonishing degree. We have a real treat for you today, Sam Altman. Thanks for joining us. Thanks. 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 trying to think about that, something like that. That's

wild. I'm glad it's being rebooted. That's right, 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'll 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 in 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, and then when something like this or mobile or the internet or semiconductor revolution happens, or probably like back in the days of the Industrial Revolution, that was when upstarts had their edge. So yeah, this is like, 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 ASI superintelligence is

actually thousands of days away, maybe. I mean, that's our hope, our guess, whatever, but that's a very wild statement. Yeah, um, tell us about it. I mean, that's big. That is really big. I can see a path where the work we are doing just keeps compounding, and the rate of progress we've made over the last three years continues for the next three or six or nine or whatever. Um, you know, nine years would be like 3500 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. Uh, even a system like A1 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, oh, one 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, yeah, a lot. And what I'm sort of hearing is that these things are going to

compound we could hit some like unexpected Ed 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. I mean, 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: 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 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. And you know, 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, you know, there's sort of path A and path B, you know, if we do achieve abundant energy, seems like this is a real unlock almost any work, not just, you know, knowledge work, but actually like real physical work, yeah, 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 key to in 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, like to say nothing of it'd be

nice to be able to run lots of AI, and that takes energy too. Uh, I think that would be a huge unlock, and the fact that it's... I'm not sure to be whether, like, whether it 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 you 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, you know, 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 okayish, 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. Someday, we'll be talking not about Fusion or whatever, but about the dys feere, 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. It was actually you were a Stanford freshman, and he said, you know what, this is the very first YY batch in 2005, and he said, you know what, you're a freshman, and we will still be here next time. You should just wait, and you said, I'm a sophomore, and I'm

coming and widely known in our community as you know 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 history. Well now it's purely immortalized here. Here it is. My memory of that is that like I needed to reschedule an interview one day or something um and PG tried to like say like I just do it next year or whatever. And then 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 seem fine, and I always felt a little bit weird about that. And then I remember one of the things I thought was so great about YC, and still that I care so much about YC about, 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 an accurate self-identity thing is I do think you can just do stuff or try stuff a surprising amount of the time, and I think more of that is a good thing. And then 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 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, and 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. Um, 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 is I probably shouldn't have done that, but I did. Sford, it's great. You pursued the path of, uh, you know, far greater return, uh, 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 liked Samford a lot, yeah, but I was, I did not feel surrounded by

people that made me want to be better and more ambitious and whatever else. And to the degree did the thing you were competing with your peers on was like who was going to get the internship at which Investment Bank, which I'm embarrassed to say I fell on 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 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 pick good peers. Yeah, obviously, you know, you went on to create looped.

know sell that go to Green Dot, and then we ended up getting to work together at YC. Talk to me about the early days of YC research. One of the really cool things that you brought to YC was this experimentation, and you sort of—I mean, I remember you coming back to partner rooms and talking about some of the rooms that you were getting to sit in with the Laran Sur gaze of the world, and that AI was some sort of 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, and it was not specifically to AI at that time when we started talking about YC research. Well, not only was it going to—it did end up funding a bunch of different efforts, and I wish I could tell the story of like, Oh, it was obvious that AI was going to work and be the thing, but like, we tried a lot.

of bad things too. Around that time, I read a few books on the history of Xerox Park and Bell Labs and stuff, and I think there were a lot of people like it was in the air of Silicon Valley at the time that we need to have good research labs again. And I just thought it would be so cool to do, and it was sort of similar to what YC does in that you're going to allocate capital to smart people, and sometimes it's going to work, and sometimes it's not going to. And I just wanted to try it. AI for sure was having a mini moment. This was like kind of late 2014, 2015, early 2016. It was like the superintelligence discussion, like the book Superintelligence was happening. Bo, yep, yeah. DeepMind had a few impressive results, but a little bit of a different direction. You know, I had been an AI nerd forever, so I was like, Oh, it'd be so cool to try to do something, but it's very hard to say was.

imet out yet. imag net was out yeah yeah for a while at that point so you could tell if it was a hot dog or not. you could sometimes yeah that was getting there yeah you know. how did you identify the initial people you wanted involved in you know YC research and open AI. I mean Greg. Greg Brockman was early. in retrospect it feels like this movie montage and there were like all of these like you know at the beginning of like the Bai movie when you're like driving around to find the people and whatever and and they're like you son of a I'm in right right like Ilia. I like heard he was really smart and then I watched some video of his and he's ALS now he's extremely smart like true true genuine genius and Visionary but also he has this incredible presence and so I watched this video of his on YouTube or something I was like I got to meet that guy and I emailed him and he didn't respond so I just like went to some con

conference he was speaking at, and we met up, and then after that, we started talking a bunch, and then 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 what your ideas about AI, and I want to start a lab. Yes, and 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 FI you weren't allowed to say that because that just seemed possibly crazy and, you know, borderline irresponsible to talk. So, that got his attention immediately. It got all of the good young people's attention and the derision, whatever that word is, of the mediocre old people. And I felt like somehow that was like a really good sign and really powerful, and we were like this ragtag group of people. I mean, I was the oldest by a decent amount. I was like, I guess I was 30 then. And so, you had like these people who were like

those are these irresponsible young kids who don't know anything about anything, and they're like saying these ridiculous things. And the people who that was really appealing to, I guess, are 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. And they were like, Let's just do this thing. Let's take a run at it. And so 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, in fits and starts, but over the course of, like, nine months. And then it started, I mean, and then it started, it started happening. And one of my favorite, like, memories of all of Open Eye was Ilia had some reason that with Google or something that we couldn't start in. We announced in December of 2015, but we couldn't start until January of 2016. So, like, January 3rd, something like that, of 2016, like very early in the month, people come back from the holidays.

and we go to Greg's apartment, maybe there's 10 of us, something like that, and we sit around, and it felt like we had done this monumental thing to get it started, and 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 raise a round, and they think like, oh, I accomplished this great, we did it, and then you sit down and say like, now we got to figure out what we're going to do. It's not time for popping champagne, that was actually the starting gun, and now we got to 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, um, but one of the things I'm really amazingly impressed by Ilia in particular, but really all of the early people about, is although it took a lot of twist and turns to get here, the big picture of the original ideas was just so incredibly right, and

so they were like up on like one of those flip charts or whiteboards. I don't remember which in Greg's apartment. And then we went off and you know did some other things that worked or didn't work or whatever. Some of them did and eventually now we have this like system and 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 pointing was deep learning even on that flip chart initially. Yeah uh 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. This is before Greg's apartment but from a very early offsite. I think this is right. I believe there were three goals for the 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 but right the like d the

predictive direction of the first two is pretty good, so deep learning. Then the second big one sounded like scaling, like the idea that you could scale. That was another heretical idea that people actually found even offensive. You know, I remember a rash of criticism for you guys at that moment when we started. Yeah, the core beliefs were deep learning works and it gets better with scale, and I think those were both somewhat heretical beliefs at the time. We didn't know how predictably better a got with scale. That didn't come for a few years later. It was a hunch first, and then you got the data to show how predictable it was. But people already knew that if you made these neural networks bigger, they got better. Yeah, um, like that was, we were sure of that, um, before we started. And what took the, like, where that keeps coming to mind is like 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, I can't really do this. It's

you know it's like a parlor trick, and these were like the eminent leaders of the field. And more than just saying you're wrong, they were like, You're wrong, and this is like a bad thing to believe or a bad thing to say. It was that there's got to you, you know, this is like, You're going to perpetuate an AI winter, you're going to do this, you're going to do that. And we were just like looking at these results and saying, They keep getting better. Then we got the scaling results. It just kind of breaks my intuition even now. And 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. And it also, it was starting to feel at that time like something about learning was just this emergent phenomenon that was really important. And even if we didn't understand all of the details in practice here, which obviously we didn't and still don't, that there was something really fundamental going on. It was the PG ISM.

for this is we had like discovered a new Square in the periodic table. Yeah, and so it we just we really wanted to push on that, and we were far less resourced than Deep Mind and others. And 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. And so 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. And I think some of the other efforts were trying to outsmart themselves in too many ways, and we just said, We'll just do the thing in front of us and keep pushing on it. Scale is this thing that I've always been interested in, um, at kind of just the emergent properties of scale for everything, for startups, 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, and I think it's, you know, 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, uh, you know, less is more, but actually, more is more. More is more. We believed in that. We wanted to push on it. I think 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. You know, if you have like the smartest people in the world, you can push on something really hard. Yeah, and they're motivated. And/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. And embedded in some of the criticism from maybe the Elders of the industry at the moment was sort of that, you know, know you're going to waste a lot of resources, and somehow that's going to

result in an AI winter, like people won't give resources anymore. 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 like conviction on one. Most of the world still does not understand the value of like a fairly extreme level of conviction on one bet, and so we said, Okay, we have this evidence, we believe in this, we're going to... At a time when like the normal thing was, We're going to spread against this bet and that bet and that bet, definite Optimist, you're a definite Optimist. And I think across like 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 and before I think before YC I I really had this deep belief that somewhere in the world there were adults in charge adults in the room and they knew what was going on and someone had all the answers and you know if someone was pushing back on you they probably knew what was going on and the degree to which I Now understand that you know 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 like adults in the room that are going to magically tell you exactly what to do um and you just 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 uh 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 fully embrace it. Conviction is great until the moment you have data one way or the other, and there are a lot of people who hold on to 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, and at that point, you do have to just sort of run on conviction. Yeah, it sounds like there's a focusing aspect there too. Like, you had to make a choice, and that choice had better, you know, you didn't have infinite choices. And so, you know, 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, and it was, you know, 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 opening eyes 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 whole thing to like an incredible degree. Doesn't work that way. Like, you don't get to be right at every guess. And so, we started off with a lot of assumptions both about the direction of technology but also what kind of company we were going to be and how we were going to be structured and how AGI was going to go and all of these things. And we have been like humbled and badly wrong many, many, many times. And 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. You know, we started working on robots and agents, PL video games, and all these other things. Then a few years later, GPT-3 happened. That was not so obvious at the time. Yeah, it sounded like there was a key insight around positive or negative sentiment around GPT-1, even before GPT-1. Oh, before he. I think the paper was called The Unsupervised Sentiment. And I think Alec did it alone, by the way. Alec is this unbelievable outlier of a human. And so 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 more, made a bigger deal out of it or whatever, but you know, it was Alex, so

It took people a while to, I think, fully internalize what a big deal it was. And 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 where, and at the time, unsupervised learning was just not really working. So, he noticed this one really interesting property, which is there was a neuron that was flipping positive or negative with sentiment. And yeah, that led to the GPT series. I guess one of the things that Jake Heller from Case Text, we think of him as maybe, I mean, not surprisingly, a YC alum who got access to both 3, 3.5, and 4. And he described getting 4 as sort of the big moment of revelation because 3.5 would still do, yeah, I mean, it would hallucinate more than he could use in a legal setting. And then 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, and he built huge test cases around it, and then sold that company for $650 million. So, it's, you know, 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, yeah, with one GPT-4. Like, 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 copyrighting, no great businesses were built on GPT-3. And then GPT-3.5 came along, and people, startups like YC startups in particular, started to do interest. Like, it no longer felt like we were pushing a boulder uphill. So, like, people actually wanted to buy the thing we were selling totally. And then GPT-4, we kind of like got the like just

how many GPUs can you give me? Oh yeah, moment like very quickly after giving people access. So, we felt like, okay, we got something like really good on our hands. So, you knew actually from your users that totally like 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 and it could just do these things that we were all super impressed by. Also, like when we were all just playing around with it and getting samples back. I was like, wow, it's like it can do this now. And they were, it can rhyme and it can tell a funny joke, slightly funny joke. And it can do this and that. And so, it felt really great. But you know, you never really know if you have a hit product on your hands until you put it in customer hands. Yeah, you're always too impressed with your own work. Yeah, and

and so we were all excited about it. We were like, Oh, this is really quite good. But until the test happens, it's like the real test is, yeah, the real test is users. So there's some anxiety until that moment happens. Yeah, I wanted to switch gears a little bit. So 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, you know, probably what, 15 years before Apple ended up making it too early in any case. Yeah, yeah. What drew you to that particular idea? I was interested in mobile phones, and I wanted to do something that got to use mobile phone. This was when mobile was just starting, was like, you know, still 3 years or years before the iPhone, but 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 actually literally. You just, they were just a phone. They were an actual phone. Yeah, yeah. I mean, I try not to use it as an actual phone ever really. I still remember the first phone I got that had internet on it, and it was this horrible, like, text-based, mostly text-based browser. It was really slow. You could, like, you know, do, like, you could so painfully and so slowly check your email. But I was, like, a, I don't know, in high school, sometime in high school, and I got a phone that could do that versus, like, just text and call, and I was, like, hooked right then. Yeah, I was, like, ah, this is, 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. And I mean, now you have billions of people who, they don't have a computer. Like, to us growing up, you know, that actually was your first computer, not physically, is a replica or, like, another copy of my first computer.

which is LC2. Yeah, so this is what a computer was to us growing up, and the idea that you would carry this little black mirror, like, kind of, we've come a long way, unconscionable back then. Yeah, so, you know, even then, you liked 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. And then, uh, one of the harder parts of it was we didn't have the App Store, the iPhone didn't exist. Uh, 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, I kind of like understood what it is like to go through a platform shift and how messy the beginning is and how much like little things you do can shape the direction it all goes. I, I was definitely on the other

side of it then. Like 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 what 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. It was probably the first time you were managing people and, you know, 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, and so it's a very painful thing to go through, but 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 20s are always an apprenticeship, but you don't know for what, and then you do your real work later. And I did learn quite a lot, and I'm very grateful for it. It was like a difficult experience, and we never found product-market fit really, and we also never like 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. You know, when you're 19 and 20, like riding the wave of some other platform shift, this shift from, you know, dumb cell phones to smartphones and mobile, and you know, here we are many years later, and your next act was actually, you know, I mean, I guess two acts later, literally spawning one of the major platform shifts. We all get old, yeah, but that's really what's happening, you know. Uh, 18-20 year olds are deciding that they could get their degree, but they're going to miss the wave, like 'cause all of the stuff that's great, everything's happening right now, like proud. Do you have an intuitive sense, like speaking to even a lot of the, you know, really great billion-dollar company founders, some of them are just not that aware of what's happening, like there are C to it's wild. I think that's

why I'm so excited for startups right now is because the world is still sleeping on all of this to such an astonishing degree. Yeah, and then you have like the YC Founders being like, No, no, I'm going to like do this amazing thing and do it very quickly. Yeah, it reminds me of when um Facebook almost missed mobile because they were making web software and they were really good at it. Yeah, and um like they, they, I mean they had to buy Instagram, like Snapchat right up. Yeah, and WhatsApp. So um, it's interesting. The platform shift is always built by the people who are young with no prior knowledge. It's, it is, I think it's great. So there's this other aspect that's interesting in that I think you're, you know, you and Elon and uh Bezos and a bunch of people out there, like they sort of start their journey as Founders, you know, really, you know, whether it's looped or zip to or you know, really in maybe pure soft software. Like it's just a different thing that

they start, and then later they, you know, sort of get to level up, you know. Is there a path that you recommend at this point if people are thinking, you know, 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 know, 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 like write the early checks for OpenAI, and I think it would have been hard to get somebody else to do that at the very beginning. Um, and then Elon did it a lot at much higher scale, which I'm very grateful for, and then other people did after that, and there's other things that I've invested in that I'm really happy to have been able to support, and I don't, I think it would have been hard to get other people to do it. Um, so that's great for sure. And 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, I definitely don't regret it. It's like all part of the tapestry of life, and I learned a ton and whatever else. What would you have done differently, or what would you tell yourself from like now to in a time 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, and AI just like, I went to school to study AI, but at the time I was working in the AI lab. The one thing that I, they told you is definitely don't work on neural networks. We tried that. It doesn't work a long time ago. I think I could have picked a much better thing to work on than loped. I don't know exactly what it would have been, but it all works out. It's fine. Yeah, 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, like I think about the people that made that computer, and I don't know them. You know, many of them probably long retired, but I am so grateful to them. Yeah, and some people worked super hard to make this thing at the limits of technology. I got a copy of that on my ETH birthday, and it totally changed my life. Yeah, and the lives of a lot of other people too. They worked super hard, they never got to thank you for me, but I feel it to them very deeply, and it's really nice to get to add our brick to that long road of progress. Yeah, um, it's been a great year for OpenAI, not without some drama, uh, always. Yeah, we're good at that. Uh, what did you learn from, you know, sort of the ouster last fall, and how do you feel about some of the, you know, departures? I mean, teams do evolve, but how are you doing, man? Tired.

but good, yeah. Uh, it's we've kind of like speedrun, uh, like medium size or even kind of like pretty big size tech company Arc that would normally take like a decade and two years. Like chpt is less than two years old, yeah. And there's like a lot of painful stuff that comes with that, um, and there are, you know, any company as it scales goes through management teams at some rate, uh, and you have to sort of the people who are really good at the zero to one 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 like changed what were going to be, um, made plenty of mistakes along the way, done a few things really right, and that comes with a lot of change. And I think the goal of the company, uh, the emerging AGI or whatever, however you want to think about it, is like just keep making the best decisions we can at every stage, but it does lead to 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? You know, I mean, the quality and like the pace that you're pushing right now, I think, is like beyond world-class compared to a lot of the other, you know, really established software players like who came before. This is the first time ever where I felt like we actually know what to do. Like, 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. And 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, and 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. We, for a long time, did not have that. We were a true research lab, and even when you know that, it's hard to act with the conviction on it because there's so many other good things you'd like to do. Yeah, 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, sounds like we went from level one to level two very recently, and that was really powerful. Um, and then we actually just had our 01 hackathon at YC. That was so impressive. That was super fun. Um, and then weirdly, one of the people who won, I think they came in third, uh, was Camper, and so CAD cam startup, you know, did YC recently, last year or two, and uh, 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. And I mean, that sort of sounds like level four, which is, you know, the innovator stage. It's very funny you say that. I had been telling people for a while I thought that the level two to level three jump was going to happen, but then the level three to level four jump was, level two to level three was going to happen quickly, and then the level three to level four jump was somehow going to be much harder and require some medium-sized or larger new ideas. And 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, it's, uh, what's interesting is basically camper already built sort of the underlying software for CAD/CAM, and then, you know, language is sort of the interface to the large language model that then, which then can use the

software-like tool use, and then if you combine that with the idea of code gen, that's kind of a scary, crazy idea, right? Like, not only can the large language model code, but it can create tools for itself and then compose those tools similar to, you know, chain of thoughts with O1. Yeah, I think things are going to go a lot faster than people are appreciating right now. Yeah, well, it's an exciting time to be alive, honestly. You know, we mentioned earlier that thing about discovering all of physics. I was wanting to be a physicist, wasn't smart enough to be a good one, had to contribute in this other way, but the fact that somebody else I really believe is now going to go solve all the physics with the stuff, like, I'm so excited to be alive for that. Let's get to level four, so happy for whoever that person is. Yeah, do you want to talk about level three, four, and five briefly? Yeah, so we realized that AGI had become this like

badly overloaded word and people meant all kinds of different things, and 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. There'd be level two that would come, which would be these reasoners. We think we got there earlier this year with the O1 release. Three is agents' ability to go off and do these longer-term tasks, you know, maybe like multiple interactions with an environment, asking people for help when they need it, working together, all of that. And I think we're going to get there faster than people expect for as innovators. Like, that's like a scientist and, you know, that's the ability to go explore like a not well-understood phenomena over like a long period of time and understand what's just kind of go just figure it out. And then level five, this is the sort of slightly amorphous like do that but at the scale of the whole company or, you know, a whole

organization or whatever that's going to be a pretty powerful thing. Yeah, and it feels kind of fractal, right? Like even the things you had to do to get to two sort of rhyme with level five, and that you have multiple agents that then self-correct, that work together. I mean, 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. It's like you will have companies that make, you know, 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, um, you know, it's like one person plus 10,000 GPUs, pretty, pretty powerful. Sam, what advice do you have for people watching who, you know, either about to start or

just started their startup. Bet on this Tech trend. Bet on this trend. It's this. 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. And the big companies, even the medium-sized companies, even the startups that are a few years old, they're already unlike 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, that is the number one edge of a startup kind of ever, but especially right now. So 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 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 something with AI. The rules, 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 a better product. It's because, you know, 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 and that you still have to build something of value. That's, I think, a good thing to keep in mind too. Yeah, everyone can build an absolutely incredible demo right now, but everyone can build an incredible demo. But building a business, man, that's the brass ring. The 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. Uh, what am I excited for? Um, we a kid. I'm more excited for that than congratulations. Ever been incredible. Yeah, probably that. That's going to be. That's the thing I've like most excited for ever in life. Yeah, it uh, changes your life completely. So, I cannot wait. Well, here's to building that better world for, you know, our kids and really hopefully the whole world. This is a lot of fun. Thanks for hanging out, Sam. Thank you. [Music]

How To Build The Future: Sam Altman

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Y Combinator

샘 알트만은 AI 기술 발전과 관련된 낙관적인 전망을 공유하며, 특히 에너지 문제 해결과 융합 기술 발전에 대한 기대감을 드러낸다.

OpenAI는 AGI를 향해 나아가며, 스타트업은 AI 기술을 활용해 혁신적인 사업을 빠르게 구축할 수 있는 유리한 위치에 있다.

알트만은 젊은 세대에게 기술 트렌드에 적극적으로 참여하고, 긍정적인 동료 집단을 형성하는 것이 중요하다고 조언한다.

샘 알트만과의 인터뷰 시리즈 재개 및 환영.

기술 회사 시작하기 가장 좋은 시기

ASI 초지능이 수천 일 안에 올 수 있다는 발언.

AI 기술 발전 속도가 매우 빠르며, A1과 같은 시스템도 상당한 능력을 갖추고 있음.

기후 변화 해결, 우주 식민지 건설, 물리학 발견, 무한한 지능과 에너지 등 긍정적 미래 전망.

풍부한 에너지와 지능이 있다면 더 나은 아이디어를 빠르게 떠올리고 실현할 수 있음.

로봇이 거의 모든 육체 노동을 수행하고, 물질적 진보가 모든 사람에게 가능해지는 시대.

폴 그레이엄과의 초기 YC 이야기 언급.

샘 알트만의 초기 YC 합류와 관련된 에피소드

폴 그레이엄과의 인터뷰 재조정 관련 일화.

YC의 특별함은 '무언가를 할 수 있다'는 믿음을 주는 것.

영감을 주는 동료 그룹의 중요성 강조.

동료 압력에 대한 인식과 좋은 동료를 선택하는 것의 중요성.

YC 리서치에서 다양한 시도를 했으며, AI가 성공할 것이라고 확신하지 못했음.

Xerox Park 및 Bell Labs의 역사에 대한 책을 읽고 좋은 연구소를 다시 만들고 싶어함.

YC 리서치와 OpenAI에 참여할 초기 인물 식별 과정.

초기에 AGI를 추구하겠다고 선언한 것이 좋은 젊은 인재들의 관심을 끌었음.

OpenAI의 시작 시기 및 초기 멤버 구성.

초기 아이디어의 큰 그림이 매우 정확했으며, 특히 일리야의 공헌이 컸음.

초기 오프사이트에서 딥러닝과 강화 학습 목표 설정.

딥러닝이 효과가 있고 규모가 커질수록 더 좋아진다는 핵심 신념.

규모가 커질수록 성능이 향상되는 것에 대한 확신과 비판에 대한 대처.

규모의 중요성에 대한 강조와 OpenAI의 연구 집중 전략.

OpenAI의 뛰어난 연구팀과 컴퓨팅 자원 접근의 어려움.

세상이 반대하더라도 신념을 가지고 밀어붙이는 것의 중요성.

세상에 모든 답을 가진 어른은 없으며, 빠르게 반복하고 길을 찾아야 함.

데이터가 나오기 전까지는 확신을 가지고 운영해야 하며, 데이터가 나오면 빠르게 수용해야 함.

OpenAI가 기술 방향과 회사 구조에 대해 많은 가정을 했고, 여러 번 틀렸음.

GPT-1 이전의 핵심 인사이트와 알렉 래드포드의 공헌.

케이스 텍스트의 제이크 헬러가 GPT-4를 통해 얻은 깨달음.

GPT-3, GPT-3.5, GPT-4의 발전에 따른 스타트업들의 관심 증가.

모델 출시 후 사용자 피드백을 통해 제품의 성공 여부를 확인.

모바일폰에 대한 관심과 초기 스마트폰 시대에 대한 경험.

초기 인터넷폰 경험과 컴퓨터의 진화에 대한 깨달음.

플랫폼 변화를 경험하고 작은 변화가 방향을 바꿀 수 있다는 것을 깨달음.

기업 판매, 인력 관리, 기업 판매 등 유용한 교훈을 얻음.

스타트업 경험을 통해 일반화된 학습을 얻을 수 있음.

세상이 AI를 간과하고 있으며, YC 창업가들이 빠르게 혁신을 주도할 것임.

OpenAI 초기 투자 결정에 대한 회고.

AI 연구에 대한 초기 경험과 19세 때의 조언.

OpenAI의 지난 한 해에 대한 회고와 앞으로의 방향성에 대한 언급.

OpenAI의 빠른 성장과 변화, 그리고 미래를 위한 최선의 결정.

OpenAI의 현재 운영 방식과 미래 전망

OpenAI가 AGI 구축에 필요한 단계를 알고 있으며, 제품 측면에서도 최적화 방향을 알고 있음.

OpenAI의 연구, 인프라, 제품 개발 방향이 명확해지고 있음.

최근 YC에서 열린 01 해커톤에서 CAD/CAM 스타트업 Camper가 혁신적인 결과를 보여줌.

AGI의 레벨 3, 4, 5에 대한 간략한 설명.

AGI의 레벨 분류 및 미래 발전 가능성.

AGI가 조직 규모로 확장될 가능성 및 소규모 인력으로 운영되는 기업의 등장 가능성.

스타트업 창업자를 위한 조언: AI 기술을 활용하고 빠른 변화에 적응하며 사업 구축에 집중.

2025년에 대한 기대와 AGI에 대한 열정.

새로운 가족에 대한 기대와 더 나은 미래 건설에 대한 희망.

👶 미래에 대한 기대: 아이와 더 나은 세상

샘 알트만은 앞으로 태어날 아이에 대한 기대감을 표현하며, **인생에서 가장 기대되는 일**이라고 밝혔습니다. 그는 아이를 위해, 그리고 전 세계를 위해 더 나은 세상을 만들고자 하는 바람을 드러냈습니다.

💪 Y Combinator 합류 비하인드 스토리

폴 그레이엄과의 일화를 소개하며 Y Combinator에 합류하게 된 계기를 밝혔습니다. 그는 당시 상황에 대해 "**나는 2학년이고, 곧 갈 것이다**"라는 더 부드러운 표현을 썼다고 회상했습니다.

🤔 과거의 후회와 현재의 깨달음

과거 스타트업 '루프드'에 대한 아쉬움을 드러내며, **인공지능(AI) 연구에 집중하지 못했던 점**을 후회했습니다. 하지만 모든 경험이 삶의 일부이며, 그 과정에서 많은 것을 배웠다고 덧붙였습니다.

🚀 OpenAI의 여정: 초기 멤버들과의 만남

OpenAI 초기 멤버인 그렉 브록먼과의 만남을 회상하며, 일리야 수츠케버와의 첫 만남에 대해 "**그의 유튜브 영상을 보고 천재임을 확신했다**"고 밝혔습니다.

📈 GPT 모델의 성공적인 상용화

GPT 모델 개발 후 사용자들의 긍정적인 반응을 통해 성공을 예감했다고 밝혔습니다. 특히 GPT-4에 대해 "**우리 손에 정말 좋은 것이 있구나**"라고 느꼈다고 언급했습니다.

🌱 Y Combinator가 준 중요한 교훈

Y Combinator에서 얻은 가장 큰 깨달음은 "**어른들은 없고, 스스로 시도해야 한다**"는 점이라고 강조했습니다. 그는 기존의 방식에 얽매이지 않고 빠르게 시도하며 길을 찾는 것이 중요하다고 설명했습니다.

💡 풍요로운 미래를 위한 에너지의 중요성

에너지 비용을 낮추고 풍부하게 만드는 것이 삶의 질에 직접적인 영향을 미치며, **궁극적으로 모든 물리 문제를 해결할 수 있을 것**이라고 전망했습니다. 그는 언젠가 핵융합을 넘어 다이슨 스피어에 대해 이야기하게 될 것이라고 덧붙였습니다.

🤝 기술 낙관주의와 신념의 중요성

세상의 반대에도 불구하고, **자신의 신념을 밀고 나가는 것**이 중요하다고 강조했습니다. 그는 성공적인 YC 스타트업들이 이러한 모습을 보여줬다고 설명했습니다.

🗺️ OpenAI 초기 목표와 현재의 발전

OpenAI의 초기 목표는 비지도 학습, 강화 학습 해결, 그리고 120명 이하의 규모 유지였다고 밝혔습니다. 그는 현재 시스템이 초기 아이디어에서 크게 벗어나지 않았다는 점에 감탄했습니다.

AGI 레벨 3, 4, 5에 대한 설명을 통해 **소프트웨어 도구 사용과 코드 생성을 결합**한 미래 기술의 잠재력을 강조했습니다. 그는 AGI가 기업 규모로 확장될 수 있다고 전망했습니다.

🌟 AI 시대, 스타트업 창업자를 위한 조언

AI 기술을 활용하여 빠르게 변화하는 기술 트렌드에 적응하고, **새로운 것을 즉시 구축하는 능력**이 스타트업의 가장 큰 강점이라고 조언했습니다. 그는 AI를 활용하되, 경쟁 우위와 가치 창출에 집중해야 한다고 강조했습니다.

✨ 2025년, 그리고 그 이후의 기대

2025년에 대한 기대감을 표현하며, **AGI 개발에 대한 희망**을 나타냈습니다. 그는 앞으로 3~6년 동안 현재의 발전 속도를 유지한다면 AGI가 많은 것을 할 수 있을 것이라고 믿고 있습니다.

🤝 기술 발전의 역사에 기여하는 것에 대한 감사

기술 발전에 기여한 과거 사람들에 대한 감사를 표하며, **자신도 그 역사에 벽돌 하나를 더하는 것**에 대한 기쁨을 표현했습니다.

OpenAI가 겪은 변화와 함께 앞으로 **AGI 구축이라는 명확한 목표**를 가지고 나아갈 것이라고 밝혔습니다. 그는 제품 측면에서도 최적화할 방향을 알고 있다고 덧붙였습니다.

💸 초기 투자와 지원의 중요성

OpenAI 초기 투자자로서의 경험을 공유하며, **자신이 직접 초기 자금을 지원**할 수 있었던 점이 큰 도움이 되었다고 밝혔습니다. 그는 다른 사람들의 지원을 받기 어려웠을 초기 단계에서 자신의 투자가 중요했다고 설명했습니다.

초기 모바일 플랫폼 변화를 겪으면서 얻은 경험이 **변화에 적응하는 데 큰 도움**이 되었다고 밝혔습니다. 그는 당시 경험을 통해 플랫폼 변화의 시작이 얼마나 혼란스럽고, 작은 요소들이 방향을 어떻게 바꿀 수 있는지 알게 되었다고 설명했습니다.

🧑‍🤝‍🧑 훌륭한 동료 집단의 중요성

젊은 시절 **영감을 주는 동료 집단을 찾는 것**이 매우 중요하다고 강조했습니다. 그는 Stanford에서의 경험보다 YC에서의 경험이 훨씬 더 강력한 동료 의식을 느끼게 해주었다고 밝혔습니다.

🧠 OpenAI의 핵심 가치와 연구 방향

OpenAI의 핵심 가치는 **딥러닝이 효과적이며, 규모가 커질수록 더 좋아진다**는 믿음이었다고 밝혔습니다. 그는 이러한 믿음이 OpenAI를 이끌어가는 원동력이 되었다고 설명했습니다.

초기 모바일 시대에 인터넷이 되는 휴대폰을 처음 사용했을 때의 경험을 회상하며, **휴대폰이 단순한 전화기가 아닌 컴퓨터**가 될 것이라는 확신을 가졌다고 밝혔습니다.

🎯 OpenAI의 초기 목표와 방향 설정

OpenAI의 초기에는 언어 모델이 주력이 아니었으며, 로봇, 에이전트, 비디오 게임 등 다양한 분야를 연구했다고 밝혔습니다. 그는 **언어 모델의 잠재력을 발견**한 것이 중요한 전환점이 되었다고 설명했습니다.

규모가 커질수록 더 좋아지는 것을 발견하면, **주저하지 말고 규모를 확장**해야 한다고 강조했습니다. 그는 OpenAI가 규모 확장을 통해 성공할 수 있었다고 설명했습니다.

🙌 기술 낙관주의와 과감한 시도의 중요성

세상이 불가능하다고 말할 때에도, **기술 낙관주의를 가지고 과감하게 시도**하는 것이 중요하다고 강조했습니다. 그는 Y Combinator가 이러한 정신을 장려하는 곳이라고 설명했습니다.

⚖️ 확신과 데이터 사이의 균형

데이터가 없을 때는 확신을 가지고 밀어붙이되, **데이터가 나오면 즉시 수용**하고 방향을 수정해야 한다고 강조했습니다. 그는 맹목적인 확신은 위험하며, 데이터 기반의 의사결정이 중요하다고 설명했습니다.

OpenAI가 빠르게 성장하면서 많은 변화를 겪었지만, **AGI 개발이라는 목표**를 향해 최선을 다하고 있다고 밝혔습니다. 그는 회사가 계속해서 최고의 결정을 내릴 수 있도록 노력할 것이라고 덧붙였습니다.

초지능(ASI) 시대가 **수천 일 안에 도래할 수 있다**는 예측을 제시하며, 이는 매우 놀라운 일이라고 언급했습니다. 그는 현재의 발전 속도가 유지된다면 초지능 시스템이 많은 것을 할 수 있을 것이라고 믿고 있습니다.