Y Combinator Startup Podcast - Sam Altman: The Future of OpenAI, ChatGPT's Origins, and Building AI Hardware

Episode Date: June 21, 2025

A fireside with Sam Altman at AI Startup School in San Francisco.Sam Altman grew up obsessed with technology, broke into the Stanford mainframe as a kid, and dropped out to start his first company before turning 20.In this conversation, he traces the path from early startup struggles to building OpenAI—sharing what he’s learned about ambition, the weight of responsibility, and how to keep building when the whole world is watching. He opens up about the hardest moments of his career, the limits of personal productivity, and why, in the end, it's all still about finding people you like working with and doing something that matters.

We said, okay, we're going to go for AGI. 99% of the world thought we were crazy. 1% of the world that really resonated with, you know, 10 or 20 years, unless something goes hugely wrong, we'll have like unimaginable superintelligence. This is the best fucking time ever in the history of technology ever, period, to start a company. Well, Sam, thank you so much for joining us.

And thanks for all the inspiration. I mean, Open AI itself is a true inspiration for any really, really ambitious person. maybe we just start with that. I mean, what were some of the decisions early that seemed small that turned out to be incredibly pivotal? I mean, just deciding to do it was a big one. Like, we got very close to not starting OpenAI. AGI sounded crazy.

I had Gary's job then, and there was like all of this other great stuff to do that would work, all these great startups. And AGI was like kind of a pipe dream. And also, even if it was possible, deep mind seemed like impossibly far ahead. And so we had this year over the course of 2015 where we were talking about starting it. And, you know, it was like kind of coin flippy. And I think this is the story of like many ambitious things where they seem so difficult. And there's such good reasons not to do them that it really takes a core of people that like sit in a room with each other in the eye and say, all right, let's do this.

And those are like very important moments. And I think when in doubt you should lean into them. So there were just a billion things, a billion reasons why people might say you shouldn't do it. I mean, off the bat, like even, you know, one of the things you figured out was the scaling laws. It's so hard to remember what it was like. Next year will be our 10-year anniversary. And so not, yeah, thank you. But to, like, remember what the vibes were like about AI 10 years ago, that was, like, way before the first language models that worked.

we were trying to like play video games and we had this little robotic hand that could sort of barely do a Rubik's cube and we had no ideas for products, no revenue, no really idea that we were ever going to have revenue. And we were like sitting around at conference tables and whiteboards trying to come up with ideas for papers to write. It was such, it's like hard to explain now, because it looks so obvious now, how improbable it seemed at the time and how the idea of chat GPT was like, complete. in the realm of science fiction. I mean, one of the things that really jumped out at me was you sort of, you know, rallied this idea that you should be working on AGI, and then simultaneously you found the smartest people in the world who were working on that thing. That second part was sort of easier than it sounds.

If you say we're going to like do this crazy thing and it's, and it's exciting and it's important if it works and other people aren't doing it, you can actually like get a lot of people together. And so we were, we said, okay, we're going to go for AGI. 99% of the world thought we were crazy. One percent of the world that really resonated with. Turned out there were a lot of smart people in that 1% and you couldn't get, like, there wasn't really anywhere else for them to go. So we were able to really concentrate the talent and it was a mission that people cared about. So even though it seemed unlikely, if it worked, it seemed super valuable. And we've observed this many times with startups. If you are doing the same thing as everyone else, it is very hard

to concentrate talent and it's very hard to get people to like really believe in a mission. And if you're doing like a one of one thing, you have a really nice tailwind there. Okay, so some people in this room might be thinking like, should I try to start an open AI scale thing off the bat? You know, you also worked on loop your first time around. You know, were there lessons from that? Open AI was not an open eye scale thing off the bat. Open AI was like eight people in a room and then it was 20 people in a room and it was very unclear what to do

and we were just like trying to write a good research paper. So the things that eventually become really big do not start off that way. I think it's important to dream that it could be big if it works. Nothing big starts that way and

Vinod Kostla has this quote that I've always liked which is there's a very big difference between a zero million startup and a zero billion a dollar startup, but they both have zero dollars of revenue. They're both like a few people sitting in a room, and they're both trying to, they're both just trying to get the first thing to work. So the only advice I have about trying to start something big is pick a market where it seems

like there's some version of the future where it could be big if it works. But other than that, it's like one dumb foot in front of the other for a long time. How people use chatyPD has changed a lot. how people use your API has changed a lot. What surprises you the most with the latest models like 03 and what emergent behaviors or use cases are standing out to you right now? I think we're in a really interesting time.

We haven't been in one of these for a while, but like right now we're in an interesting time where the product overhang relative to what the models are capable of is here. The products that people have figured out to build is way down here. there's a huge, even if the model's gotten no better, which of course they will. There's a huge amount of new stuff to build.

And also, like last week, 03 cost five times as much as it did this week. And that's going to keep going. I think people will be astonished at how much the price per performance falls. We have an open source model coming out soon. I think people are going to be, yeah. I don't want to like steal the team's glory and pre-announce this, but I think you all will be astonished. I think it will be like much better than you're hoping.

for and the ability to like use it to run incredibly powerful models locally is going to like really, really surprise people on what's possible. But so you have this world where like the model capability has gone into like a kind of like a very new realm. The cost of the APIs are going to keep falling quite dramatically. The open source models are going to be super great. And I think we have not yet seen the level of new product innovation that the reasoning models are capable of, which makes sense.

They're pretty new. But this is like an exceptional time to go build a company that takes advantage of this sort of new thing that exists, this sort of new square on a periodic table that no one has built with yet. So only in the last month, I think we really started to see startups that are saying, okay, like reasoning models are different. You know, the whole interaction model is different and really building for that. I mean, for me, even memory has turned into, it feels like I'm talking to someone who knows me, which is interesting.

Yeah, memory is my favorite feature that we've launched this year. I don't think most people at Open Air, I would say that because we've launched a lot of stuff, but I love memory in chat GPT. And I think it points to where we will hopefully go with the product, which is you will have this, like, entity that gets to know you, that connects to all your stuff and that is, like, proactively helping you. It won't just be like you send a message and it sends you one back, but it'll be running all the time.

It'll be looking at your stuff. It'll know when to send you a message. It'll know when to go do something on your behalf. You'll have special new devices and it'll be integrated on every other service you use and you'll just have this thing running with you throughout your life. I think memory is the first time where people can sort of see that coming. Back in the day, you tweeted a little bit about her. When is that coming?

Can you give us an alpha leak around that? I think gradually is the answer. No, no. If I had a date in mind, I would probably just be excited and tell you. But like, it's a little bit here with memory. It'll be a little more here when it's persistently running the background and sending you stuff. It'll be a lot more here when we ship the first new device. But I think the key of her is not the little piece of hardware.

It's that this thing got to a point where it could run in the background and feel like a sort of AI companion. I guess we're starting to see the power of LLMs with integrations into your real data. I've heard rumors that MCP is coming to OpenAI. I think today. Yeah. Oh, today? I think so.

Fantastic. What has been surprising about the actual integrations? Like, have you been seeing people actually operating on their core database? You know, at YC, we actually have that agent infrastructure internally and we use it all the time. Definitely people are starting to use chat GPD as this like operating system with everything, with their whole lives in it. And integrating into as many data sources as possible. is important. Devices that are always with you, like new kinds of web browsers, the connection to all data sources, memory, and then a model that's persistently running, put all that together.

I think you get to like a pretty powerful place. Do you think that'll be in the cloud in the future or will it be on our desktop or some mix of those? Some mix of all of that. Definitely people will run local models for some things. Like, man, if we could push like half the chat GPT workload onto your local devices, no one would be happier than us. Like our cloud, we, I think, I think, think we will run the largest and most expensive piece of infrastructure in the world pretty soon. So if we could push some of that off, that would be great. But a lot of it will run on the cloud.

Is it surprising to you how hard it is to get compute? I mean, we've gotten really good at it. But it is, we went from like a zero, like no chatchabit.com didn't exist two and a half years ago to like the fifth biggest website in the world. It'll be the third at some point, hopefully someday the first, if our current growth rates continue. I think doing that is just hard no matter what. You usually get longer than we've gotten to scale up infrastructure for a new company. But, you know, there's like a lot of people that want to help. Well, it's incredible work that you guys have been doing.

We're seeing reasoning models like 03 and 04 mini evolve in parallel with multimodal models like 4-0. What happens when these two threads converge? And what's the vision for GPD5 and beyond? I mean, we won't get all the way here with GPT5, but eventually we do want one integrated model that can reason really hard when it needs to and generate like real time video when it needs to do that. If you ask a question, you could imagine it thinking super hard, doing some research, writing a bunch of code just in time for like a brand new app only for you to use or kind of like rendering live video that you can interact with. So I think that will feel like a real new kind of computer interface. The AI sort of somewhat already does, but when we get to a model that has like true complete

multimodality, like perfect video, perfect coding, everything, and deep reasoning, that will, that will feel like quite powerful. It seems like that might be a hop step over to do the embodied aspect. You know, that's having vision, having speech, and having reasoning is a hop step. to, you know, basically the robot we want. Our strategy has been nailed at first and then make sure we can connect that to a robot. But the time for the robot is coming soon. I think I am very excited about a world where when you sign up for like the highest

tier of the Chachachapit subscription, we send you a free humanoid robot to. I mean, that future is going to be pretty wild. Being able to have robots that do real work in the real world. I think we're not that far away now. The mechanical engineering of robots has been quite difficult. And the sort of AI for the cognitive part has been quite difficult too, but it feels within grasp. And I think in a few years, robots will start to do super useful stuff. Making a billion robots is still going to take a while.

But I don't know. I'm interested in the question of how many robots do you need to fully automate the supply chain. Like if you make a million humanoid robots the old-fashioned way, can they run the entire supply chain? Drive the mining equipment, like drive the container ships, run the, you know, foundries and make the new robots. And then maybe like you actually can get a lot of robots in the world quickly. But the demand for humanoid robots in the world will be far more than we know how to think about with the current supply chain. I guess when you were sitting in my seat, one of the things you led was a lot more investment into hard tech at YC. sitting here where we are geopolitically, you know, what do we need to do to make sure that America

can actually have manufacturing and industrial capacity? You know, we can't even build precision screws and large sheet metal without crazy cost overruns. What can we do to make sure that happens here? There are all of these answers that people throw around and have thrown around the same things for a while and it clearly hasn't worked. So I think all of the policy is worth trying, but my instinct is we need to try something new. We shouldn't keep trying the same failed stuff. And, you know, like AI and robotics does give us a new possibility of a way to bring manufacturing back here and to bring sort of these complex industries here in a really important new way.

And I would say that's at least worth trying. Yeah. What does defensibility look like here? You know, one of the classic questions is, you know, how do I start a startup that doesn't get run over by Open AI? That's sort of the number one question that's in our chat. We don't want to run you over. Look, we're going to do our thing hopefully very well. We are going to try to make the best super assistant out of chat GPT that we can.

We're going to add the things that we think we need to add to that. But that is like one small part of the opportunity in front of us. And it makes us sad when people are like, I'm going to start a new startup and I'm going to like make a version of chat GPT. Because we think we're going to do that pretty well, and we have, like, you know, kind of a big head start. But there is so much more space to go after. And there are so many incredible other companies that have been built using our platform. We would like to make it easier for you all. We would like to do more things like finally now.

You can imagine that Chad ChGPT could drive a lot of traffic to new startups and that there's like a new kind of app or agent or whatever you want to call it store that we could do inside of Chat ChpT and drive traffic to new startups to help. You could imagine that we could do like a sign-in with open AI and people could bring their, you know, personalized model and easily connect it to a new startup. And that would probably help in a bunch of ways. So we want to be a platform for other people to build stuff. Our advice is like don't build our core, you know, chat assistant. But there is another problem, which is, and this is the same for every kind of like moment that I've seen in startup history.

people get excited about the same thing at the same time. And so rather than go build the thing that you have thought of that is not everybody what else is doing, we are like very social creatures and we get very influenced by what other people are doing. And I bet if Gary listed off the five ideas that he hears most often of what people want to build with AI, like half the room would raise their hands

for working on one of those five. And there is hopefully in this room the person who's going to start a company that is much bigger than Open AI someday. And I would bet that person is not working on any of the five. So it is hard to build something defensible if everybody else is trying to do the same thing. Sometimes it works. It's not impossible.

But the best, the most enduring companies are usually not doing the same thing as everybody else. And that gives you time to figure out what the great product is, how to build the technology, before you have to answer the defensibility question. It took us a long time to figure out to answer the defensibility question for Chachybt.

We had built this thing, and for a long time, the only defensibility was like, we had the only product out in the market, and then we kind of like a brand that started to be well known. And now we have things like memory and connections and a whole bunch of other stuff that really is defensible. But, you know, that was like a fair criticism of us

for a long time. We didn't have any defensibility strategy. We just, like, had the only good thing out there and then you have some window before which you have to build defensibility. One of the things we've talked about in the past is that we both are big followers of Peter Thiel in that he talks a lot about being contrarian but right. I think that you've... Peter is a genius.

Absolutely. And you've been contrarian in really fundamental ways. I mean, going back to beginning with the conversation, people thought, oh, this idea that the scaling laws are valuable. It's taken as basic truth, but it was exactly the opposite of ground truth not that many years ago. When you got that pushback, you know, what did you and your team feel? Did you say, you know, FUI won't do what you tell me? You know, I'm going to push back against, you know, getting pushback means that this is a contrarian area and we're going to bet here and we're going to be right.

It is hard to have conviction in the face of a lot of other people telling you you're right. wrong. And I think people who don't, who say it's easy are not being honest. It gets easier over time. But like I remember one time, I can say this one because it got publicized in early, not early, a few years into Open AI where Elon sent us this really mean email. We'd been working that for a while and said we had a 0% chance of success. Like not 0.10 that we were totally failing. We had showed him like GPT1 recently. He was like, this is crap. It's not going to work. That make sense. And I was really a hero of mine at the time. And I remember

going home that night and being like, what if he's right? Like this fucking sucks. You know, you're working so hard on this thing. Like, you're pulling your life force into it. And you have these people who are smart and that you look up to and they say you are totally wrong. Or, you know, this is just never going to work or

you don't have defense ability. Someone's going to kill you. This is going to happen. That's going to happen. And I don't have a magic answer other than It's really tough and it gets significantly easier over time. But it's going to happen to all of you. And you just like get knocked down and get back up and brush yourself off and try to keep going. Let's talk AI agents. You know, that's sort of level three AGI.

This is the year. I think Greg Brockman talked about recently, this is the year of the agent. With tools like operator, code interpreter, what kind of workflows do you think will disappear or appear that we just aren't ready for yet. For a long time, Chatjbc was like a Google replacement. You could ask it something that was about as long as a Google query, you know, maybe like half an hour worth of Google queries that could assemble together. And that was still pretty good, but it didn't, it still felt like a more advanced version

of search. But now you start to see things where you can like really give a task to Codex, for example, or to deep research. and you have this thing go off and do a bunch of stuff and come back to you with like a proposal. It's like a very junior employee that can work on something for like a short period of time. And if you think about how much of the work that the world does

is work that can be done in front of the computer in like few hour chunks where you then have someone say like, okay, that was good enough or not, it's quite a lot. So I think this is going to go, this is part of that overhead we were talking about earlier, but I think this is a lot of, I think this is going to go quite far. And I think with current O3, to say nothing of our next model, you can build a lot of experiences like this.

How do you see the future of human computer interaction and interfaces? And what are sort of the limitations of those interfaces that, you know, motivated you? You know, one of the things that I think sci-fi got right is the idea of the interface almost melts away. Like voice interfaces today we think of as something that is kind of, sucky because they don't work that well. But in theory, if you could say to a computer, this is exactly what I want to happen today. And if there's any changes, if like I'm delayed, if, you know, something happens, I trust you to, like, go off and do all those things, but, like, I don't want to be interrupted. I don't want to think about it. And it just did it all and you

trusted that it worked. That would be an interface that almost melted away, except when it, you know, was like a super great human assistant needed to talk to you. But you would be, like, really thrilled. When I, like, use my phone today, I feel like I am, like, walking down Times Square in New York, getting, like, bumped into by people. I love my phone. It's an incredible piece of technology. But it's, like, notification here, this thing happening, you know, this thing popping up, like, bright colors, like all kinds of flashing things in me. It's just stressful. And I can imagine an interface where the computer mostly melts away. It does the stuff I need. But I really trust that it's going to do a great job of, like,

surfacing information to me, making judgment calls about when I have to do, acting on my behalf when it should. And I'm quite excited for that. I'm not going to tell you what the new devices. Well, I'll tell you, like, one-on-one, but I'm not going to tell. But it's very, like, I hope we can sort of, like, show people a different way to have computers. Is that one of the reasons why you brought on one of the greatest living designers on the planet in Johnny Ivan I.O.? Yeah. Yeah, he is amazing.

He really lives up to all the hype. I think we've only had kind of two big revolutions in computer interfaces, really in the last like 50 years. We had the keyboard and mouse and screen, and then we had touch and phones. And the opportunity to do a new one doesn't come along that often. And I think AI really does totally open the playing field for something completely new. And I think if you've got to pick one person to bet on to figure that out,

he's the obvious bet. So one of the things that we've been debating at YC that, you know, don't know if this is good, might be scary for a lot of software engineers who want to create B2B SaaS is this idea that what if in the future you had your, you know, underlying database, you have an API layer that is, you know, your access control and enforces your business logic. And then the interface is the LLM. Like your computer is literally, you know, the agent. and you have just in time software.

They're like complex flows. You're just going to go in and it'll code gen an artifact or, you know, a pain for you that, like, does that thing you wanted. And it'll go in the file and it'll bring it back if you ever need it. That's going to happen. Yeah. Look, here, there are two ways you can look at this. First of all, I assume you all are like starting startups or have startup startups, think about starting startups. This is the best fucking time ever in the history of technology ever, period, to start a company.

Yeah, this is, but part of the reason it's the best is because, like, the ground is shaking. And it's true. There are a lot of these challenges. So on one hand, you can look at something like that and say, we have been a, you know, SaaS company and now, like, all of the code can just be generated right in time when someone needs it. And what does that mean for us? Or you can look at it and say, wow, this is going to happen, but it's going to happen to everybody. And the way startups win is when they can iterate faster than big companies, and they can do it at a much lower cost.

Like big companies have a lot of advantages, but they iterate very slowly. And they, you know, if something is like very cheap, then a lot of their big advantages go away. So you can look at all of these problems one way or another, but the way I would recommend looking at them is everybody is going to face the same challenges and opportunities. But when the clock cycle of the industry changes this much, startups almost always win. And we've probably never seen it change this much. Act on it from that direction. I think you'll be in incredible shape.

Maybe you can invite me sometime to do a talk about what the areas of defensibility that you can build over time are. Because I think that is the inherent question. People are like, oh, okay, you know, I'm a SaaS company. There's going to be just in time software. I think the question behind the question is like, what are actual defensibility strategies? So that would be a fun talk someday. I guess backstage at one of the last events we had, you know, we were talking about there's this book that's sort of like the classic McKinseyism, which is the Seven Powers.

And I was just thinking about that. Like I never would have thought like the two of us technologists sitting around actually citing a book that, you know, McKinsey consultants are known for. Feels so wrong. Yeah. I don't know. Aesthetically feels terrible. but yes, it's after there.

Seven powers, I guess. We're entering this age of intelligence. I love that essay of yours. What do you think this era will mean for, you know, how we live, how we work and how do we create value for each other as a society? You know, in some sense, the whole arc of technology is one story, which is we discover more science, build better tools, all of society, like, builds the scaffolding a little bit higher. And we have this more impressive tool chain. And the whole point of it is that one person can do way more than they could before. And this has been going on for a long, long time, each generation.

Certainly, I mean, if you compare a person today from a person 100 or 1,000 years ago, one person is incredibly more capable. And the kind of like social contract is that you put something, you know, you build the next layer of scaffolding. But what someone can do now with this new set of tools, with this, you know, this new layer that's been built in is pretty incredible. And I think the one of the things that will feel most different about these next 10 years versus these last 10 years is how much a single person or a small group of people with a lot of agency can get done. And that is a bigger deal than it sounds like. because coordination costs are huge.

And when we can empower people with more knowledge, more tools, more resources, whatever, I think we won't just see, like, a little bit more stuff get built, but because of these kind of coordination costs across people, we'll see, like, a real stuff change. So I think the amount that one person or a small team get done, the satisfaction in doing that,

and most importantly, like, the quality of stuff will all get for each other will be quite remarkable. When I think back about the open AI story, I often think about just the kind of key few tens of people that did the amazing work that led to what we all have now. But I try to remember that I always also have to think about like the tens of millions of people. Maybe it's more throughout history

that started like digging rocks out of the ground, figuring out how semiconductors work, building computers, building the internet and on and on and on that let this small group be able to work at such a high level of impact that they never would have been able to do without the collective output of society. Is it surprising to you, to what degree? I mean, this room is, you're preaching to the room of the converted.

This is awesome, by the way. I mean, this is like the collected set of people who are going to go create the future. But there's, you know, seven billion other people. There's maybe like never been a gathering like this in one place before. This is very cool to see. But at the same time, you know, we're in some ways, this is the leading, cutting edge of all of society because there are 7.5 billion people who probably have not even tried this stuff yet. And not only that, their main interaction with it is that it doesn't work, that it hallucinates.

You know, what do you have to say to the 3,000 people in front of you right now? Just this is the thin edge of the spear. We are literally teaching people and giving people this technology. First of all, that's like a great place to be in. One of the most fun things about working at YC is you get to live on the leading edge and you get to be around the people who are the advanced guard. And that's just like a fun way to live your life and you get to see what's coming and hopefully have some small amount of input into shaping it. But I don't know. I think AI is like somewhat mainstream right now.

The negative, the way that it's not is most people still think of AI as chat GPT. and a lot of people use chat GPT, but they use it like a chat bot, and they have not yet wrapped their head around what's coming next, and probably you all have. But I don't know, it's like a great privilege to get to live a little bit in the future and, you know, go build stuff for everybody else coming along. So you're sort of one of the best people in the world bringing together the smartest people.

What are some of the hardest lessons you've had to learn about hiring? A lot of the people in this room, like they have never managed a person before, let alone gotten someone to quit their six to seven figure job at some big company to come work on their revolution. Hiring really smart people who are clearly really driven and really productive and can work as part of the team, I think does get you 90% of the way there. And the degree to which people focus on other things to hire for always surprises me. So I think, you know, given that we can't do the full 45 minutes right now, really smart people, driven, curious, self-motivated, hardworking, like good track record of accomplishment and can work really well as part of the team and sort of aligned with the company's vision. and so everybody's at least going for the same direction, that works pretty well. I mean, by a strong track record,

do you mean the person who's like, you know, sort of been an administrator and had like the, the, you know, top name at the top institution for 20 years? Or do you mean, like, because you went the other way? I don't, especially early in a startup, I don't believe in hiring those people. Their experience is valuable. And there are times where you really need that.

But I have not had success. And to be frank, like YC has not had that much success trying to start with like the very senior eminent administrator as one of the like, you know, as the first hire in a startup. I would take like young, scrappy, but clearly like get stuff done over the person who has like the extremely polished track record. There will come a time where you need some of those people later. But I don't know how you do it. But when I was reading YC applications, I would like never look at the resume items. You know, you worked at like Google or went to this college. I never cared.

I would always go right to like what's the most impressive stuff you've done. And then sometimes I would like not be convinced by that and go look at the resume. But that was always like a backup to me as a secondary thing. So they sort of look at what they've actually, what they've coded, what they've built, like their velocity, how they think about. problems and solve them. I see P.B. back there, he has this quote. I hope it's his quote, because I've attributed to him a bunch of times of higher for slope, not Y Intercept. And I think that's just like unbelievably great advice. Let's talk about being CEO of Open AI. What are some of the hardest lessons there just overall? I don't recommend it. No one single challenge would be that hard,

but the number of things we have to do at the same time and the kind of like number of other big companies that are gunning for us in various ways. It's just like more context than I thought it was possible to handle it once and more sort of like switching from like big, big decision to like totally unrelated but also huge decision. Looking ahead 10 to 20 years, what are you sort of most personally excited about, you know, and what should people be building now to make that future possible? You know, there are people who are scientists, there are people who are software engineers,

There are people who are, I mean, this is an all technical crowd. Look, there's a lot. You know, in 10 or 20 years, unless something goes hugely wrong, we all have like unimaginable super intelligence and I'm very excited to see how that goes. Forced to pick one thing to just not leave it as like a vague answer. I think AI for science is what I'm personally most excited about. I am a believer that to a first order approximation all long-term, sustainable economic growth in the world, like everything that leads to people's lives getting better,

is basically discovering new science and having reasonably good governance and institutions so that that science can get developed and shared with the world. But if we could vastly increase the rate of new scientific discovery with AI, I believe that would compound to it and just incredible increases and wonders for everyone's lives. So I think I'd pick that on that time frame. I guess one of the things I've been always really impressed by, is, you know, you were, you know, personally recruited Helion to come do Wycombinator, and they're doing incredible things over on the fusion side.

Was that something that you were thinking about even all the way back then? Or, you know, obviously energy and climate was sort of a part of, you know, what everyone's worried about even back then, but... This is a little bit embarrassing. I've been obsessed with energy and AI as like the two, the things that I thought would be the two most important things, or at least the ones I was going to be most, that I felt most passionate about for a long time and really like the two areas that I knew I wanted to like concentrate time and capital towards. I cannot recall ever thinking until like after starting open

AI that they were going to be so obviously related that, you know, that energy would be the eventually the fundamental limiter on how much intelligence we could have. And I don't know how I miss that because I usually am good at thinking about things like that. But I really did think of them as like very independent. You know, we were going to need AI to have all the ideas, energy to make all the stuff happen in the world. And I obviously, right after starting to open AI, I got obsessed with meaning energy for AI. But like pre-2015, I think I thought of them as orthogonal vectors. I mean, I'm sure you've seen that chart that, you know, all the effective accelerationsists in the room have seen around basically having a high standard of living, like,

the sort of really it's I'm obsessed with this chart. I've been obsessed with that chart a long time. It's directly related to the amount of energy that any given person has access to. Yeah. I think this is one of the most amazing charts over a long, like long, long period of human history is the correlation of quality of life and abundance of energy and cost of energy. So that was, that chart and charts like that were a significant reason that I got obsessed with energy in the first place.

It is, it is just this like crazy high impact. It sounds like it wasn't entirely interdependent. It was more you had twin interests. Yeah. You've literally woven them together. I had like the one interest of like radical abundance and just like what what were the kind of technological leverage points to just like make the future like wildly different and better.

And these is the two kind of key things for that. But not as not as much as the same vector. Now I think a lot about like how much energy can we actually build on Earth before we just keep the planet too much from running the GPUs and like how long can we go before we to put all the GPUs in space. But at the time, yeah, I really thought of them differently. I mean, it seems like one of the defining beliefs that technologists uniquely ideally have,

that they believe that we can actually create that sort of abundance. You know, if you have intelligence on tap and then you have energy on tap, then how does that go? It's like, you know, all watched over by machines of loving grace. I've never been to one of those degrowth or conferences in Europe or whatever,

but I've always kind of wanted to go to one. This is the anti-de-growth conference. This is the anti-de-growth conference, totally. But I would like love to be like sitting, you know, in the dark and the cold with no one pulling out their phones and just like talking about how horrible everything was and there was no hope. Like I would love to experience that mindset once because I've never felt it. And I think it is like it is one of the movements that has been ever hardest for me to identify with. Obviously, this is like my crew in my world, but the sort of like the optimism

of startups of San Francisco, of the technology industry, of AI, of what all y'all y'all will do, like that is, that is like the natural space my brain abides. And it's very hard for me to really empathize with the other side of that. But I'm pretty sure we're right and they're wrong. How do we get there, though, right? This incredible vision of technology. actually creating for abundance for others. I mean, you've already done so much, but point us the way. Like, how else do we get there? How do we make it faster?

You know, does government play a role in this? Almost just about five years ago, like pretty much this week, we put GPT3 into an API, and people started playing with it. And it was barely usable. It was quite embarrassing. And in five years, we have gone from this, like, thing that could barely write a sentence to a thing that is like, you know, PhD level intelligence in most areas. Five more years, I think we'll be able to maintain the same rate of progress.

And I think if we do that, if we also build out the infrastructure to serve that to people, then everybody in this room will figure out how to take that technology and adapt it to what everybody needs. The analogy I like most for AI is the transistor, like the historical technical analogy. you know, some people figured out like a new, really important scientific discovery. And society, the economy, whatever you want to call it, just got the work. Just did its thing. The magic of that just figured out how to make incredible value for people. And really, over a fairly short period of decades, significantly ramp up quality of life.

I think this will be even faster and steeper than that, but I think it'll go in direction in the same way. You know, we need to make the great technology, figure out the remaining scientific stuff, which I don't think there's much left. We need to figure out how to build out the infrastructure that you all will need to be able to serve this. And then you all have got to go figure out what people in the world need with this new magic. So let's flashback to 2005.

The very first batch of white combinator. How did you hear about Paul Graham? You were reading his essays. I was reading his essays. So I'd heard about, like, he kind of had this cult following on the internet. But I heard about what was the, then called the Summer Founders Program, and that was just called Wycombinator, from Blake Ross, who I lived in the same freshman dorm with and posted about it on Facebook.

And then I think Paul said, oh, you're a freshman, you know, there's like another batch coming, and what did you reply to him by email with? You know, funny you bring that up. I just dug up the email, like a couple of days ago, because I felt I had been misquoted over time. I'm curious. and his telling of the story is I said like I'm a sophomore and I'm coming. But I wrote a much nicer thing. It was like, oh, maybe there was some misunderstanding.

You know, actually I'm a sophomore and I can still make it. And I would like love to if that's still okay to come the next day. So in some ways, you know, the wild thing is you're sitting in front of 3,000 people who kind of was, you know, they are sitting where you were back in 2005. What would you say to, you know, the Sam Altman from that time, you know, given what you know, all the things you've seen, all the things you've learned since? Like, what are the things that you're most surprised you didn't know that, I mean, it just took, I mean, you've been through it, you know, like, you've done it. I wish someone had, like, taught me the importance of, like, conviction and resilience over a long period of time. People don't really talk about how hard that is. It's like easy for a little while, but your reserves kind of like wear down on it and how to how to keep that going for a long period of time.

Also just sort of like trust that it's eventually going to work out like obviously my first startup didn't work that well. I think a lot of people kind of give up after one failed startup, but startups don't work out all the time. And learning how to keep going through that, keep working through that is I think, really important. Developing, like, trust in your own instincts and increasing that trust as you refine your decision-making and instincts over time, I think that's really important. Kind of courage to work on stuff that is out of fashion, but is what you believe and what you care about. I think that's really important. I had a kid recently, and the thing everyone tells you when you have a kid is that it is the best thing you will ever do, but also it is the hardest thing

you will ever do. Like, the good parts are much better than you can imagine. The hard parts are much harder. That is all totally true. And that is also basically what I feel like being an entrepreneur is like the good parts are really great, better than you think. And the hard parts are like shockingly much harder than anyone can can express in a way that makes any sense to you. And you have to just keep going. Sam Altman, everyone. Thank you. Thank you.