ICYMI... HIBT Lab! OpenAI: Sam Altman

Hey, so you've probably been hearing a lot about this artificial intelligence tool called ChatGPT. It's Basically, a really powerful chatbot that can do things like write essays, summarize articles, conduct research, it can even generate computer code. And while chat GPT is still relatively new and still has a lot of room for improvement, It has sparked a lot of conversation about what this kind of AI technology could mean for the future of work and education. Some universities and schools have even banned or limited its use, including New York City Public Schools, the largest school district in the country. Anyway, back in the fall of twenty twenty two, I spoke with Sam Albman, the co founder of OpenAI. It's the company behind ChatGPT and other AI tools like the image generator Dali.

We talked about the enormous potential of these tools to Transform society. But also the company's approach to preventing some of the more sinister applications of this technology. With AI back in the news, we thought we'd bring you this conversation once again. Stick around, I think you're gonna want to hear what Sam has to say about the development and future of artificial intelligence. All right, here's our interview. Hello and welcome to how I built this lab.

I'm Guy Roz. So artificial intelligence is getting really good. Really fast. For many years, most of us assumed AI was gonna replace jobs in manufacturing in the service sector. But what's actually happening is that machine learning devices are becoming much more adept at generating things we often associate with human creativity. You might have seen that piece of digital art that recently won a competition in Colorado. Artwork completely drawn by a piece of software.

AI software is getting really good at composing original music, even writing newspaper articles. А не мізant фут. Machines might be able to diagnose diseases or answer any questions we have, or maybe even solve some of the world's biggest problems. One of the pioneers in AI technology is a nonprofit company called OpenAI. It was co-founded by Sam Altman in 2015.

Sam is the former president of the legendary tech incubator Y Combinator, the accelerator program that helped launch Airbnb, Stripe. Coinbase, StoreDash, and so many others. Today, Sam's on a mission to make artificial intelligence work in a way that has the maximum benefit for the maximum number of people. OpenAI has made headlines with some pretty impressive AI tools, including GPT-3. It's a language generator that's written op eds and scientific research papers.

And also Dali, which is an algorithm that can generate incredibly realistic images from any written description. But of course, there are lots of open questions about what happens if we can no longer control how this technology is used. Which we will hear about. Sam Altman grew up in the St. Louis area obsessed with computers from a pretty early age. I was a nerdy kid, that's true, but also like

The the just the power of this idea. That we were all gonna be connected to each other. Um, which I would say was evident by You know. Maybe like when I was like ten.

Yeah. That Like I was just like, this is like more important and more exciting and and more fun than than everything else going on. And this is just this is what I want to do. From what I gather, like a big a big kind of moment for you was Getting that your first Mac two.

Yeah. Mac L C two. That was a really big moment. And how did you use that computer? Did you play games on it? I played games, I uh used AOL, which was sort of what I thought was the internet at the time, and then a little bit after that figured out there was uh an actual internet out there, which was was was even cooler. Um I

read like most of the encyclopedia on a CD ROM. Yeah. Um I learned a program. Um What were you programming what?

In like basic or in basic. Yeah. And what were you what were you making? You know, like very little stuff, like print out all the prime numbers, like make a little interactive text game, stuff like that. But it didn't matter. It was just like the thrill of getting to make anything. Yeah. The excitement of like watching somebody else use something you made. Yeah, um was just unbelievably exciting. Uh Actually still is, but but especially as a as a little kid. And and so you describe yourself as a computer nerd in high school, but was it clear in your mind that this was what you were going to do? Or were your parents like, hey, you know, you're really good at math and science, you maybe you should go to med school or

Something like that. Um, it was totally clear in my mind. Uh my parents are are are are great. They they they never They never like pushed me to do something I didn't want to do. There was there was, you know, gentle suggestions of science or whatever else, but like It was very clear to me from a very young age and I'm thankful for this'cause I I know it doesn't happen to everybody that like Computers were my thing. Yeah. Uh or technology more broadly was my thing.

What was it about about computers as a teenager that you remember feeling So passionate about. I mean, was it was it a community of other people that you connected with locally or even beyond where you lived in Saint Louis? That was certainly part of it. Um I also was into like ham radio as a kid.

And the one thing that I noticed across both of them was the fact that you could like A there was interesting engineering and science problems and and and technology to figure out. There's like the fun of using a computer at all. Uh, especially back then when like they didn't work that often, you had to like upgrade parts of them yourself. That's just like a cool

Puzzle and technology is interesting. But definitely one thing that I felt across both of them is this ability to connect with anyone around the world instantaneously. Which was incredibly appealing to me. the ability to sort of like learn anything, talk to anyone.

W just just like And still is like pretty magical. I I think we've we've all just now accepted that as standard, but if you rewind the clock like thirty years things were very different. Yeah. Um I'm I I think the computer revolution is as big of a deal as the the the other great technological revolutions of the past.

Uh as big as the agricultural and industrial evolution. But we've kind of just lived through it so recently. that we don't talk about it it as historic as it's gonna turn out to be. Yeah, for sure.

So when you set out to to I know you studied at Stanford and you studied computer science, so it was clear you were very Focused on Entering this field. But presumably. you know, when you get to college, your th thought is

Eventually go work for one of these big companies. Yeah. I I picked Stanford because it was the best computer science program. I mean it turned out to be like a wonderful place in a lot of other ways. But that was what I wanted to study. And my dream job after school was to work at Google.

That didn't happen. That didn't happen. Um while you were while you were Stanford, you you basically ha had a business idea and And you dropped out. Yeah. Um, presumably with the intention of coming back Absolutely with the intention of going back.

Uh, one thing that's very cool about Stanford is they they let you stop out And return sort of no questions asked very easily. Yeah. So you drop out to pursue this idea. Business call looped.

This is like two thousand five, so this is pre iPhone, pre-apps. The idea was hey on your You could See where your friends were.

All at the same time on on a map. Yeah. And also you know, meet new people near you, find like interesting places to go or events to go to. And and so the idea was that like'cause I'm trying to go back to two thousand five and and I know I had I definitely had a Nokia.

at that time. Palm Trio was like the height of coolness in two thousand. Palm Trio, yeah, right, right, right. But most you know, the great, great majority of the world had like a Nokia something. But they were all of these like little mobile app stores. Uh there's something called J2ME at the time where you could like make a little mobile app on a on a flip phone or whatever. Um, it wasn't a very compelling experience. But uh It was sort of the idea was like as people lived more mobile lives.

Knowing everything in the context of location would be super powerful. Yeah. Um we turned out to be mostly wrong about that, by the way. I I think it didn't really work for a bunch of reasons. Um But one of them that is still somewhat surprising to me is even as the world has gotten very mobile. most people are sort of still at home or work all the time and there's just like far less spontaneity than it felt like to like a, you know, twenty year old or whatever I was at the time.

Yeah, I mean it's interesting because on the one hand, I think most people wouldn't want to do that with other people. You can do that on an iPhone. But what they do do is they post on social media all the time. So we do know where people are all the time. Exactly. So it's a different version of that. I'm not um I'm not giving away the plot here by saying that looped ultimately w did not sort of become Uh what you you envisioned. But it did. It did bring you into this world that would become a really big part of your life, Y Combinator. You were in the sort of inaugural class of Y Combinator. Um Paul Graham.

famously one of the main co founders, um I guess you met him and and he liked the idea and But what's the story? Yeah. Um I so I was like, you know, interested in this idea, playing around with it.

Uh a guy that lives in my freshman dorm. But name Blake Ross. posted something, I believe, on Facebook, but Memory could be wrong there now. that there was this new thing, uh, and it was called the Summer Founders program at the time, and you know, people should check it out.

The deadline was like the next day or something. I had followed Paul Graham online was like a big fanboy. Uh so I immediately applied with my co founders and They invited us out to interview. That was the first like meeting the Y s the four Y C founders, Paul, Jessica, Trevor.

And Robert. I that was like the first time I felt like all right, I have like finally found the kind of people I want to be around. I didn't really know about the startup thing, you know, you I heard about it as like a kid during the dot com boom, but I wasn't really paying attention. Um, I have met a few VCs on campus, but I could like tell I didn't like them. The world felt impenetrable.

Um But I met the the I was like, wow, these are my people and everything they say resonates. And even the other founders I met there interviewing. That was this immediate click for me of like this is awesome. This is what I wanna do. These are the people I wanna be around. This is so cool. What were you doing, um, at Loop? Like were you actually

Writing the the the software? I wrote the first version of it. Um Then I got pretty busy with other things. Uh, I think a lot of people assume they're gonna keep coding, but yeah, you know, hiring, managing the team, my job ended up being like a huge amount of business development. I spent a lot of the time on airplanes flying around to the mobile carriers that Work our partners. But it's I d I was not able to code for very long.

Yeah. So you're basically st this is a nineteen year old kid, right? It's a crash course. You've raised money um at a valuation of a hundred and seventy five million dollars, you raised almost forty million dollars. First of all, how did you learn how to become that person you had to become? to you know, to be in charge of a company with forty million dollars and

In capital. that was entrusted to you. I had really great advisors. Uh. Like I I think this is something that

is still underappreciated about Y Combinator. Uh People are capable of learning at a very fast rate. And one of the things that PG says that has always really resonated with me.

Is You can get tough really fast. And that's sort of like one of the skills that is important for young founders. Um But if you jump into the deep end.

And you are like surrounded by Supportive thought advisors. Um It was like interesting to me at the time how quickly I could learn. And one of the most fun things about running Y C later.

was helping other people learn very quickly. And just to clarify, when you say PG, you mean uh of course Paul Graham, one of the uh founders of Y Combinator. Yes. And I think this is one of the things that Y C still just like People on the outside don't see it. There's this assumption that like, oh, if you get into Y C, you're gold plated, you have this like you know, stamp of approval. That's why the companies do better. Like all the VCs are into it. But Y C does so much that no other program, as far as I know, has figured out.

about how to get people up the learning curve really quickly. Um That it's it's a very big deal, and it was super important to me personally. And I think to many of YC's biggest successes, it's been super important. And the difference that you can make there. Um It's huge.

Yeah. So So this was your life, looped was your life. Absolutely. With support from from Y Combinator for Several years but ultimately it just didn't it didn't Takeoff. It was acquired for uh I think for about forty three million dollars. Yeah, it was a acquired for like about what we raised. So the later investors didn't make much money. You you earned a little bit, actually more than a little bit. I earned some, but it you know, on like this is another thing that it's just sort of I try not to think about. I made like

orders of magnitude more money from angel investments that I made. Uh that I spent no time on the thing I poured my life into. Right. Right. Um, but after that happened you were

In twenty fourteen you were named. President of Y Combinator. already by that point why combinate our head. kind of become this sort of legendary Incubator.

Um. Tell me a little bit about sort of taking on that role. Did it feel scary? you know, to be'cause again, you were still pretty young. um leading this thing that that had gained this kind of Massive reputation.

You know, it it it This may be some sort of like weird flaw of mine. It didn't feel scary at all. Um, it felt a little Sad. Like it took PG a while to convince me because I really wanted to do another startup. Um, I wanted to like prove myself. I was yeah, twenty eight or something at the time. I didn't feel ready to like go retire and do a career adventure.

Which is really how I thought of it. Um I didn't like Have a ton of respect. Four. The career path? Um

And so there was like some sense in which it was An admission of defeat. About You know, I just I can't Run a company, so I'm gonna go

do the easy job or I'm gonna go do the retirement job or something. Um And it it took me a long time to get comfortable with the idea of doing it. Uh but

I can't overstate how much at the time and how much now still I love Y C. feel incredible gratitude towards it personally. Also, think it's just this incredibly good force in the world. And as I I I thought about the things that I wanted to do. Um

I I had this list from when I was like a college student of what I wanted to work on. It was like a long list. I I realized I couldn't start all of those companies. Um I also really was interested in pushing more investment in Hard tech, deep tech, whatever you want to call it, which I didn't think there was much of happening at the time. Right, because at the time Y C was focused mainly on like software, right? Yes. And you wanted you wanted them to focus more on things like

Nuclear energy. uh self driving cars like a like actual physical things that you use. Not necessarily physical things, but I would just say like hard technology. Like there's a thing about Silicon Valley, which is it's it had not been that well geared towards the time and capital intensive science projects.

Um So I wanted to do that. And I kind of was just like thinking about Yeah, I had like basically taken a year off. It had been really fun. I had like learned a lot about everything. I had met a ton of smart people. I had read like many, many dozens of textbooks.

And there was like all of this stuff that I wanted to do. And uh I was like, you know what? In some sense, yes, being a VC is like an admission of failure. But in some other sense. What an incredible platform that, you know, I could like do whatever I wanted here. Um and

And I love Y C so much. Alright, so you became president of Y Combinator. And and lots of uh Y C companies have b have been on how I built this in the past, as as I think you know, in including Stripe and Airbnb and Reddit and Coinbase um and and lots of others. Um I think. uh maybe like a year in to that job you co founded this non profit.

called open AI, which is Yeah. mainly what you're focused on now. Um t tell me what was the thinking behind it. What was the idea behind open AI? So so I studied AI as an undergrad, which was like dark times, nothing was working. And then in twenty twelve, uh Deep neural network started to work. And not only did they start to work, it appeared that the more compute you threw at them, the better they got.

Are you talking about like Watson and these these kinds of neural networks? The one that really kicked it off, I think, for the field, certainly for me personally. Was the ImageNet result? This is the um identifying images based on descriptions. Yes. So you type in the computer.

horse eating an apple or whatever and it g it could find them very quickly. Uh it it was more like you'd show it an image and it would correctly say this is a horse eating an apple. That's right. Or the time it would just be more like this is a horse. And that was twenty twelve. Yeah, it c it came out in my kind of like year off when I was just learning about things and I looked at it with like great interest. Um You know, but kind of like it was one of many things that I spent time on that year. But I kept watching it and kept paying attention.

And certainly like of all of the hard tech dreams. AGI is the top of the list. And just to clarify, Sam, when you talk about A G I artificial general intelligence. You're y y you're distinguishing that from From very narrow specific.

Types of artificial intelligence. Correct. So um You know, we already have made good progress with narrow AI that can Categorized images or or some one task like that. Yeah. But for me AGI is a system that can

learn and self improve and create and reason about new information like a human does. This amazing general ability. And I think if we're able to accomplish that. It will be m perhaps the most important invention in in human history, and it will be the culmination of this phenomenal amount of effort. All the way down the stack.

Um and like the collective Knowledge. accomplishment, moral progress, creativity of humanity into this one system? that I think will be an even bigger revolution than the computer revolution we talked about earlier. We're gonna take a quick break, but we'll be back with more from Sam Altman, co-founder and CEO of OpenAI. Stay with us, you're listening to How I Built This Lab.

Welcome back to how I built this lab. I'm Guy Roz, and my guest is Sam Altman, founder of the startup Looped, former president of Y Combinator, and now co-founder and CEO of the artificial intelligence company OpenAI. All right, so Sam, in twenty fifteen you and some some other pretty well known people including Elon Musk and and Reed Hoffman and others form this thing called OpenAI, which is a a nonprofit um designed to do research around artificial intelligence. Um but but beyond that, what was the purpose of OpenAI? What what what problem was it designed to address?

It it was to figure out how to it and still is to figure out how to build Deploy. And share the benefits of safe. general artificial intelligence. Um and it didn't seem like there was enough effort being put into this.

Given the stakes. And so the The purpose was looking at this thing saying This is as important as anything we can imagine. And

The upside is sort of unimaginable. The downside is catastrophic. And we we look around the world and we don't see people taking this seriously. We don't see people caring. We don't see really any good safety research. And so

The thought was really like Can we help nudge us in a good direction? Mm-hmm. Now AGI or at least powerful AI is like the hottest thing in the world. Everyone's interested in it.

But what I can't overstate enough is at the time. We really got started in January of twenty sixteen, so it's like, you know Six and a half years ago. Yeah. People thought we were crazy.

Like good researchers say you've totally discredited yourself by talking about AGI. Most other people were like, This is, you know, a hundred years away or more. There's no idea about how to do this. And We just kinda like got cracking on it. And

had a couple of huge discoveries. Some good engineering and now here we are. Why did you found it? As a nonprofit. I know that you have a for profit Um

Subsidiary now, but why did you originally make it non profit. I think this technology. really does deserve to belong to the world as a whole. It's gonna have such a profound impact on all of us that I think we deserve

Like we globally, all the people all All of humanity. deserve a say over how it's used, what happens, what the rules are. Um, we deserve to share in the benefits. We deserve to all have access to it. And

That was why. Yeah, I I I I'm like very pro capitalism, obviously. I think capitalism is great. Um But I think AGI is sort of an exception to that. In a well run society, I I think even if we rewound the US

You know, fifty plus years. This would absolutely happen by the government. Right. Because DARPA would have done it. Uh as they did with the internet and so much of the other technology we use every day. Yeah. Um I think most people, most of us have different views of what artificial intelligence means, right? And and I I guess ultimately it's it's whatever our imaginations can conjure up. But for some people it could be a version of like,

you know, humanoid robots from, you know, Westworld or or machines that can detect disease in humans, um based on you know hundreds of thousands of data points. Um, it can mean a machine that can I mean all of these things. But of course.

That's how we see it now. When you talk about artificial general intelligence, What do you imagine in your mind? What do you think it's Actually

could do. the first thing that I think it'll be and where where people will use something like wow that is that is real intelligence of some sort. What I imagine in my head is a gigantic skyscraper. Full of computers. And we all text it like we would text a

a co worker or a friend or whatever. And increasingly powerfully for whatever you want. It can answer it. So you can say like I need life advice or I need like great medical advice. Um

And it'll help you with that. Or or if you're just trying to like search for something to find information, it can it can help you with that. It can look at your email and say, you know, here are all the important emails that came in overnight. I've drafted a response to all of them. If these look good, hit send and you'll be happy with all of them. You want to hit send. Um already today you can say, I'd like this art, please. Um, can you generate an image that looks like this for me? Um, can you write this code for me?

Uh eventually. Now this'll require more compute. But if We society at allocate the compute to that, you can say like

Could you go off and find a cure for cancer? Mm-hmm. And it can spend a lot of its compute cycles doing amazing scientific progress like that. But but I imagine that we get to a world where uh each of us has sort of like a what feels like an AGI companion.

that we are talking to all day. that is helping us be the best version of ourselves, learn be efficient, be happier. And we'll experience that way. And then globally Uh we will say, Okay.

This is like very expensive, but we're gonna dedicate it to a few huge projects. You know, maybe we'll have it help us. figure out how to identify like better diplomacy between countries. Or the cancer example I mentioned. Or figure out how to do carbon capture.

Um, but when you have this like very superhuman intelligence. I think the harder challenge is not talking about limits of what it can do,'cause that's sort of like beyond our imagination. But how to conceptualize it um and how to like think about what it'll look like. At the beginning.

Um, I know that you went out uh originally and attracted a lot of sort of donors, right? A lot of capital to begin research and one of the first Really public things you essentially worked on was to train an AI to play the game, the video game uh Dota.

Um, and it's a really complex game. Right. And uh not to bury the lead here, but the You're Yeah, but one. I mean beat the reigning world championship team. Yeah.

Why was that? Um Why is it the sort of the ideal place to start in in AI development? You you need a really good environment. Um at the time.

W we kind of had a lot of Uh. people on the founding team and early employees. That were good at reinforcement learning. And reinforcement learning, we should explain. This is when you feed a lot of information to the AI and then the AI is basically taking random actions.

Yeah. And then you're giving it a signal. for okay, that worked or that didn't. Yeah. Um But the thing we learned with Dota is you can do it over a very long time horizon.

So there's like, you know, hundreds of action steps a minute. There's thousands of things you could choose for each action step. Um and games can last, you know The better part of an hour. No one thought that was within the realm of RL.

Uh That's just sort of too long to go without a reward signal and and we did experiment with some intermediate ones. Um But what it turned out is by playing Gigantic amounts of time.

Hundreds of thousands of hours of the game per day. And having the agent sort of play against itself. So it has to get better and better to keep winning. Just by trying random actions and reinforcing the ones that have a positive impact. the AI system can learn to to beat the best humans at this game.

Which was not obvious to us, uh quite remarkable given the size of the action space. How is it different than like Deep Blue beating Gary Kasperov? And chess. Um There's Sixteen pieces and chests.

The the search space is just not nearly as wide at any given, even if you do have to think pretty deeply. About each one? Um and so Yes. people were able to beat even without neural networks.

That just turned out. T Not be as hard. As as we thought. Um but but again, this is like the the complexity and the action space of this game.

Is just beyond what I myself did not think we were likely to succeed at the project all the way when we started. Mm. Sam, given that you guys are taking a big swing here, right, you are

You are looking to become the preeminent groundbreaking AI research organization in the world. I'm I'm curious how you go about like the the sort of the the series of steps you take in deciding what next to focus on. I know there are multiple things you're focusing on, but I'm st I'm talking about the things that we hear about. So for example that you know Dactyl was something that came out next, and this was basically and you could pe people listening can see it. It's a It's a video of it. It's a robotic

Uh hand or arm. And it c it solves a Rubik's Cube with one hand. Um I mean it's cool, it's very cool. But I'm wondering why that

challenge you want it to solve. How does that move the The needle. just slightly forward on AI technology. Well it turned out to be a mistake.

Hm okay. Um I mentioned that Dota was a great environment. It turns out that robotics are a very hard environment. Robotics is hard.

And it's not hard because the machine learning is hard. It's hard because the simulator is bad. the robot breaks, you can't run it that fast like you, you know, you don't have like hundreds of thousands of robots like you have hundreds of thousands of CPUs to run a game on. And so we thought It would be interesting. Uh Because robotic control.

seemed very interesting to us at the time and still does. But what happened Is It was too hard of an environment. And we ended up pausing our robotics work.

Someday we'll come back to it when the robots and the simulators get better. Um it's clearly You know, it would be great to have But it turned out to be hard. In the wrong kind of ways.

Hm. The way we pick projects to work on is like not as exciting as everybody would hope. You know, I think there's this belief that You have a bunch of people like sitting in a room picking this like brilliant secret strategy.

Um We just sort of like run our own reinforcement learning algorithm. Um we do more of what works, we follow the technology, we realize that it's like Very hard. To sit in a vacuum.

and predict where everything is gonna go. Hm. Um most people who have tried that have been catastrophically wrong. And it's very smart people trying it. the technology just kind of like

Surprises all of us. And so we pay close attention to what's working. We put more effort into what's working. we then put effort into figure out how we can deploy it safely and beneficially. And then we go do the next thing on top of that. But uh

It's very much like driving On a country road at night with headlights on. Mm. You can always like see Before you have to make a turn. But you'd have no idea what the next few turns look like.

Right. And that's the that's a question, right? Because if you are building uh a product Right. If you're building an autonomous vehicle. You know what you're building towards. But you're really not building towards anything in particular. You're building towards something that we humans can't even imagine, right? And that's the thing, like It's

I I guess it's a matter of just figuring out how to Technology that Can

Think? like a human beyond w how a human thinks can process information in highly complex ways. Yeah. Um You you you bring up a great point. The other company I'm involved with is this nuclear fusion company called Helion.

And There are a lot of similarities between the two companies in terms of like a very hard Scientific and engineering problem. But the biggest difference is like what to do. what success looks like. What's gonna happen on the other side of this.

what problem to go solve next. You can look at it backwards or forwards. It's very clear. It's all hard. But it's very clear there's no uncertainty about what to go after, what's gonna happen, what the company needs to do. Um, and another nice thing is like it's basically all upside. There's there's none of the harder issues to contend with.

At OpenAI, we we just don't have any of that certainty. And one of the things that has been like a little bit surprising to me. is how difficult it is to get advice about how to run a project or a company. In the face of such uncertainty, it it hasn't happened a lot of times. And

I am like wistfully envious of the the Helion world. Because It's you know, it's still super important and super great. But it's so much clearer. And we just have to like turn over one card as we go.

Yeah. Sam, one of the one of the things that you did um in twenty nineteen controversially was to form a for profit. wing of open AI. And originally it was kind of this heralded nonprofit, you know, hey, we're gonna make this technology available to everybody. We're not gonna compete. We're gonna collaborate. All of our research will be available

W wanna be a responsible in a steward of this technology and to encourage responsible use. Two thousand nineteen you form a for profit version. Plenty of people, including some people under you know, involved on your own board who weren't who were like, Wait a minute What? You know, w why?

A hundred percent. Um totally get why people Don't like that kind of change. What happened is really quite Simple, um, which is when we started OpenAI.

We did not expect massive scale to be as important as it was turned out to be. And by twenty nineteen. We Realized that. And that the amount of money we were going to need.

to succeed at the mission was beyond what we could raise as a nonprofit. Yeah. way more than ninety percent of that for compute. But also buy in expensive data sets. compensating people in competition with Google who can, you know, pay

very huge salaries. Yeah. And we we realized that like Either we had Some way. To like

Dip into the power of capitalism. And the ability to get the resources we needed. Or We were just gonna be irrelevant. Hm.

Um, we did you mentioned the government earlier. I had forgotten about this until right now. We did also just see if the government wanted to fund us. They definitely did not. Yeah. Um And there was no other source of capital that we could figure out. And so we wanted To preserve as much as we could of

The specialness of the nonprofit approach. the benefit sharing, the governance, what I consider maybe to be most important of all, which is the safety features and incentives. Um so like for example, we have this one thing called a profit cap. where our employees and investors can only make a certain fixed amount of money with their equity. And then beyond that, all other

Profit is is distributed as fairly as possible with the world. And I think, you know, at the time that was like another thing that seemed crazy to people. But I think that's gonna be really important because I think And you already see some people thinking this way. It it's gonna look at some point like wow. you can generate close to infinite wealth with AGI.

And people who have equity, even if they start out not wanting that, are like hmm. You know, maybe that sounds better than I originally thought. We also have something in our documents which says if we need to for safety, the board can just totally wipe out everyone's equity value to nothing. And we have something called the mergent assist clause, which says similar to that, if another effort's ahead. And we want to avoid a race condition.

Um, we can just shut down and merge with some other effort. Like these are things a normal company Wouldn't be able to agree to. Um while still being able to access the capital we needed. Which it was clear by that point would just be far, far greater than we ever thought.

Yeah. You did bring in a lot of money from Microsoft. And as a result, they get an exclusive license to use some of your technology in their software. also criticized by some, but you know, I mean again

You gotta bring in the money, so Was that, in your view, a compromise you had to make? And if it was, how does it impact the openness of open AI? I mean If Microsoft gets exclusive use of some of the technology you're developing. does that.

In some ways. Undermine the original. Vision. You you will see us open more technology over time. We just like to be pretty sure on the safety front. Yeah. Um First of all.

The most powerful model in the world, as far as we know, of an available language model. is still the one we created two and a half years ago. That is available in our API. And that that it just tell me what that is again? Oh, it's called GPT three.

It's like a powerful language model that people develop on top of. Right. And that's available, that's open source. Anybody can use that. It's not open source, but anyone can use it. I got you, okay.

We could open source it someday in the future. It's it's certainly something we'll consider. But uh there there is a thing about The API access, which we like, which is When we know Less than we'd like when we're in the fog of war.

If we just publish the weights of a model on the internet. And w and then we realize like there's actually a safety issue here. We can't take that back. It's done. It's like a one-way door. Um we also cannot put any usage restrictions on it after we we open source it. Right. When we deploy it via the API. We can take it back.

if we find out there's a real safety issue. But more importantly than that We can enforce usage restrictions. Um, we can make changes if unsafe things are happening. So let's let's kinda break down GPT three for people who don't. know this'cause obviously as you know how I built this is a

A generalist show. We talk about chocolate chip cookies and and and artificial intelligence on the same show. Um, and from what I understand I mean, G P T three can do a lot of things, but but there's a famous r example from twenty twenty In the Guardian newspaper. They publish an op ed They essentially asked GPT three to write an op-ed called uh Why We Shouldn't Be Afraid of AI and and published it. And it's pretty pretty interesting. I wasn't convinced by the argument the um the AI made, but that was it was pretty pretty cool.

Um, so this is one w because when you talk about safety Right. Um, you're talking about people misusing This technology to for example make Yeah, have AI

Creep. Fake information or Manipulative content. Um, you can imagine a lot of things, but the disinformation manipulative content. I think it's high on the list.

Um GPT three at this point we're we're more comfortable is relatively safe, although I think there are risks. related to misinformation in some cases. But we'd rather be too conservative than not like At some point there will come a time.

one, two, three orders of magnitude, who knows what how much more powerful than than GPT three. Where I think a language model can really have a huge disinformation effect on the world. And And again, kind of the traditional

stance of the tech industry is just like, Yeah, you know, launch the product and think about it later and deal with the problem later. But like We don't want to push a button through a one way door. Or at least if we do that, we want to be incredibly confident and careful about it. Yeah. We're talking about

five, ten years away. I think m more or less. Is that fair to say? That yeah, I would say you know. Sometimes these things take longer than you think, but let's say like in ten years, I think there will be powerful digital intelligence in the world. we're already seeing this with music and art. I think the a piece of art just won an award, um, and it was very controversial, but

We're already seeing relatively high quality. content uh produced. Yeah. I should say we did put in um we asked GPT three to write us an intro. An overview of Sam Altman's early life. For how I built this.

How was it? gave us two versions. The first one says um Sam Altman is the president of Y Combinator and co-chairman of OpenAI. He was born in 1985 in Cambridge, Massachusetts. He attended high school at the Phillips Academy, Andover, where he was a national merits scholar. The second one said uh Alman was born in nineteen eighty five in Berkeley, California, raised in the town of Los Altos, attended Los Altos High School.

Or he was president of the science Olympia team. Both of these are wrong. He grew up in St. Louis and did not go to either side schools. But but um it does have other information in there about open AI. I mean, you know, these are coherent sentences. Like you can you can imagine that A human could have written them just just got the facts wrong. Yeah, that's a big problem with the current version of language models is We haven't trained them yet to try to like

be helpful and to verify that what they're saying is accurate. They're just trying to sort of Sound like coherent text. Yeah. We have a lot of ideas about how to fix that, and my guess is in the next couple of years we'll make significant progress there. Um But that is I think that is the current

Biggest single problem with language models. Is As someone famously said in it once, they're very convincing bullshitters. Yeah. And it makes it like very hard to use for a case like this. Now, as you mentioned. An easier problem that has worked very well. Uh we have a system called Dolly that will take text and generate images.

Really cool. You could put in like a Giraffe a dancing ballet on top of a hypersonic Sharp. And it will generate that image. Yeah.

And people love it. Like people who had never cared about AI before that I went to high school with or whatever are like now totally obsessed and you know, I talk about it and it's it's very fun. Uh but it's like it you we talked about this idea of the technology gradient and kind of doing what works, like we still have more work to do on on language models and That's hard. But In a world where we don't need perfect accuracy and we want creativity,

Man, the image generation thing is so cool. Yeah. We're going to take another quick break, but when we come back, more from Sam Altman, co-founder and CEO of OpenAI. You're listening to how I built this lab. Welcome back to How I Built This Lab. I'm Guy Raz, and I'm talking with Sam Alman.

Former president of Y Combinator and co founder of OpenAI. Sam, I wonder I mean, we're talking about all these cool things, right? And we've talked about a lot of cool things. No. Here's the thing.

I remember Back in Must have been twenty ten. I interviewed Mark Zuckerberg on a at a public forum at the computer history museum. And the optimism about the future of social media was just so

unbelievably clear in his mind. That it was only for the benefits of the world, that it was going to be this amazing thing that will bring people together, that we're all gonna be part of this single global community. there's only gonna be one version of who you are, the same person at home and as you are online. And that it's gonna you know

open up all these channels of communication and cooperation and of course that vision was not realized. I know you're a really smart guy and you think about these things and so you know That all of these wonderful things, you hey it can answer questions for us, it could solve cancer maybe, it could it could give us a diagnosis, it could identify the best doctor. That will happen. And then there's gonna be other things too.

So let's talk about some of the other things and and w and what keeps you up at night about those things. You know, I've I've had three recurring bad dreams over the course of my life. When I was in high school and college, I had this one about like missing a test. It sort of evolved when I started my startup to like thinking in the dream that I was like still in school and I was missing stuff because I was running a startup. Mm-hmm. Um after the startup I had this like.

recurring bed dream for a while just about like failing, which was just incredibly shameful and Very, very tough. Uh, you know, startups are great when they're working, but when you're failing it's like quite brutal. And the current one is about like What if we're wrong about all of this?

Um Yeah, like Good and bad. with any new technology and and I can accept that as long as the good is order of magnitudes more than the bad. Um

And that is truly what I believe will be the case. Hm. I think the world is gonna be incredibly different, but there's no stopping that. Now or ever. And

I think we will be able to responsibly deploy current technologies. Uh and I think we've You know. One of the advantages of our nonprofit structure is we do a lot of things differently than a normal company would. But for me the real question is can we solve the existential issues?

And I am more optimistic about that than I've ever been before. Um, I think a lot of the the work we've done around alignment and and safety is like promising and we have and we have like a plan that I I believe in um But

You know, technology is is impossible. To predict. Uh we can make High confidence guesses, but A as you said, a lot of smart, well intentioned people. Thought that

social media was gonna be uh like an unequivocal good for the world. Yeah. And it is good in some ways, but it's it's come with some some real negatives. We try to learn every lesson we can about what's gone gone wrong there. And I am obsessed with this idea of incentives.

And if you can get the incentives right. in an organization. Then I think. You avoid a lot of the sort of things that cause well meaning people to do

things that maybe later they'll wish they hadn't. Yeah. Um but my fear is that there's just an unknown unknown that we totally missed. And technology or social pressure or or something else is gonna push us in a different way. Again, I think we're getting a lot of things right.

Right now. You know, we deploy our technology slowly. Cautious, like we're we're willing to piss users off to go slowly. But as the systems get much more powerful

the the challenges become more and more unprecedented. Yeah. There are a lot of intelligent people on planet Earth. And and some of those intelligent people are bad actors. I mean you look at how bad actors have deployed Social media. or digital technology to steal money from people's

bank accounts or crypto accounts or you know, just These are not dummies. These are intelligent criminal networks, right, that have figured out how to use a variety of tools that are now available to them. to do real harm.

And that's gonna happen with this technology, right? There there will be really intelligent people who figure out how to get an AI to create a new virus and deploy it very effectively. Like that could happen, or how to, you know build your own um very powerful explosive now. That being said Do we just accept that that is part and parcel of being human, that there are humans with bad intentions and there are humans with good intentions, and that

It doesn't matter. what we do with technology because some people will use it for good and some people will will use it for evil, and you just cannot prevent that. Um I mean what you said is true. Some people will use technology for good and some people will use it for bad.

But it doesn't mean we don't do anything. It means we work as hard as we can. to have the technology be as useful for good as possible. And as difficult to use for for evil as possible. And

It is in some sense like somewhat one dimensional, like more powerful tools. Light. Bad actors do worse stuff. But I think it's like very defeatist to just say and so there's nothing can that can be done. like our our strategy and it's uh it's you touched on been controversial.

has been to deploy the technology in a way that maximizes the good and Minimises the bad. Um And you know, there will be lots of technology where it's turned out we're too conservative and actually we can just make it more open to the world.

Mm. But I'm confident there will come a day where the world will say like It's good that OpenAI is on the conservative side with new stuff. Most of us interact with AI already multiple times on a daily basis. Like when I type an email and Gmail it It makes suggestions, which are often very helpful. And so there is some of that already that we use

Um you know a series of version of this. But um let's say in five years from now, based on what you are seeing and what you guys are working on, right? How how do you think the average kind of Person. might use.

the technology in five years from now. I I I think it is just gonna kinda be everywhere. You know When the App Store on the iPhone launched. It would be a big deal for companies to talk about being mobile companies.

Or there are mobile strategy at that point. And at this point now. It's just like Every company is mobile and you wouldn't think about it. It would just be like ridiculous not to have a mobile app for most companies. Um but no one goes around talking about being a mobile company. And I think the same thing will happen for AI. There will be a a layer of intelligence provided by us and others.

It's just everywhere. And as the systems continually get smarter, everything you use will get smarter and smarter as well. So there will be like you know, there will be d direct usage. Where We talked earlier about this idea that you'll you'll

chat back and forth with this like super intelligent assistant that'll help you with everything. But also in everything else you use. you'll just have a very high expectation. that it's really smart. So

You mentioned that you kind of like the Google suggestions in your email. Those will look quite trivial. And you will expect every morning to wake up and have for Google to have re d detailed thoughtful replies that you yourself would be proud to come up with. To every email in your inbox.

Which by the way, I'm thrilled for because if I never spent another minute in my inbox again, I'd be really delighted. Yeah. You know, It's interesting'cause I think for for a long time there was just This feeling that AI

And automation would replace like Menial work. Or you know, factory. Yeah. Um, which it still can. I mean that you know, you could make the argument that there's no need for a barista,'cause a machine could do.

Everything that uh that somebody making a cup of coffee not as beautifully. Doesn't say hi to you, doesn't smile to you, doesn't give you that minute of human connection. Or or isn't nasty to you either. Right. Depending on which coffee shop you go to. But but now it seems like something different is is happening, which is AI looks like it could replace more and more creative.

types of jobs, jobs that requ that are s quote unquote white collar jobs. Maybe even before it replaces So called blue collar jobs. Absolutely. The strong consensus Five, ten years ago.

was that first of all the AI was gonna come for the blue collar jobs. Second of all would be the less sophisticated white collar jobs. Third of all would be the very high cognitive load. white collar jobs like a computer programmer or a mathematician or whatever. And then last of all, and maybe never, because maybe this was like special and human only.

Would be the creative jobs. And If we look at it now, it appears relatively clear that it's gonna go in exactly the opposite direction. Hm. And

There's a lot of things one can take away from that, but one of the most important ones Is predictions about technology roadmaps are hard. And a lot of very confident experts get them wrong. So

My My observation is just that like This was hard to predict. Uh It seems fairly clear now.

With the benefit of hindsight, also seems fairly obvious now. But Everybody was confident and wrong in the other direction. Robotics are really hard, as we talked about earlier. Yeah.

So I mean what does that mean in practical terms? And right now we're talking here in twenty twenty two There's a labor shortage. in the United States. Right, there's just there are not enough. humans to fill all the jobs available. And there's a c big crisis in many companies across the board, not just in the service sector, the blue collar jobs, but also in technology companies. So so on the one hand you can you can imagine, oh, this is great because, you know, maybe some of these jobs will be taken over by, you know, artificial intelligence uh, you know, devices or or

But um this labor situation won't be around forever. There will be higher unemployment rates. And and so What happens? I mean, do you do you I mean If this technology is is gonna not become so good which it will be. What happens to the

To the humans. You know There's a huge amount of poll clutching about what what's gonna happen when AI replaces all of the jobs and what we're gonna do. Mm. A few thoughts about that.

Um One. I actually think we're in a humongous labor shortage, like much more than it seems. There are many people who love their jobs and derive great meaning from it, you know, but I think those people don't recognize it's a privilege that most people don't have. And most people

would love to work less or not or do something completely different. But We have sort of this like pay to play world right now where you have to pay to live. And I I get why some people think that's important, sort of, in theory. But I personally really don't think that's the society we should strive for.

Um I think like traditional work In the way we think of it. Should be optional. And you should be able to do less of it. I really love work. Uh it's my hobby. It's my passion. I I think it's great. But I know a lot of people don't and a lot of people would choose to spend their time

In a very different way if they didn't have to. I also think a lot of people who like work would clearly like to work less. We're we're seeing this. in this post pandemic world where You know, a lot of people are like, Oh, I go into the office two or three days. I'd really rather only work two or three days too. I

No, I really don't want to go back. And so I think the labor shortage is actually like Much bigger than it seems. I also think like a lot of things that we do. were understaffed even before the pandemic.

But certainly like going through an airport now or going to get medical care now, you like really feel You know, this is not what full employment would look like where I just like walk in and everything's ready to go all of the time. Or where like every student has like a one to one teacher to student. That could be amazing too. Who knows what? So Huge labor shortage now also.

We've seen this with every other technological revolution too. I don't know what the jobs of the future will look like, but I am confident that human creativity Desire for status. Desire to like do new things and and to like accomplish

That's not gonna go anywhere. Um, the economy and society will look super different, that's for sure. But we have always worried about this. We have always found something new to do. I do accept that this time it may be different, even though you're never supposed to say that. Like maybe if we figure out intelligence, that's just very unlike anything that's happened before. But I still would never bet against our

desire and ability to find very new things to be busy with. Sam. And I we're kinda g getting out of our lane here, which is fine. We should be. But our species, right, Homo sapiens, we've been around for three hundred thirty thousand years. More or less.

And really Only, you know, kind of in the last thirty thousand years have we Started to widely spread around the world. And previous our you know, our our our relatives, rel you know, the other hominin species lived a long time. Yeah. One point two million years One point three million or so.

And then another species replaced it. We replaced The other ones. uh to become the kind of the dominant uh hominin species on the planet. Um

If we're talking about Developing. Something that is. More advanced than humans. Are we talking about

Th you know, a form of evolution are we talking about The next species that replaces Homo sapiens? I get the analogy. But I think it's it's basically incomparable. Um, I hope.

that human homo sapiens are The end of the line for Evolution in a survival of the fittest sense. And that from here on Even as things change quite rapidly.

We are much more thought. And deliberative. And Unlike The previous process.

This time around. We can reflect, we can debate, we can build the system in a certain way. This is like very different. Than a random natural process. It's very important to me personally, and I assume to you and hope a lot of other almost everyone too.

that humanity uh is in charge of of the future of humanity. And The systems that we build, how we choose to deploy them. what we all collectively want out of the future. That is a discussion that I think we need to start now.

and have a lot of societal coordination around. Um, but I think the analogy, although it sounds very similar. is like different in almost all ways that matter. This is not gonna be just this like chaotic

stochastic process playing out. Um but we get to make all of the decisions about how this is gonna work. Yeah. While we're way out of our lane and talking about the very long term future.

Um I think if we end up in the us versus them framework. That's like some varying degree of bad. And the most positive long term futures that I can imagine. involves some degree of a merge.

It doesn't have to be the crazy sci fi like, you know, plug something into our brains or upload ourselves or whatever. But at a minimum, I think it needs to involve something like you know, this like AI alter ego that we're talking to all day that observes our whole life, that understands us, that its goal is to like extend our capability and will Um I think those are the easiest worlds to see it being very positive.

Yeah. Sam, you're not yet forty years old. What is my guarantee here? That's it. You don't

become like Robert Oppenheimer one day and say This that I was part of was terrible. I regret it. I have that book on my desk. I look at it every day. Um

I mean I can't give you a guarantee on that, right? But I can tell you I will work as hard as I possibly can and get the best people around us to make sure we don't end up there. This is gonna change almost everything. This is gonna just be a seismic shift. And the way we get to the best version of the world is to have as broad input

On Where we want to go, what we want to happen is possible. And the way to get there is for a lot of people to take this seriously, understand it. direct some of their like precious and limited attention towards it. Um and and we really want to drive that conversation forward.

That's Sam Altman, co founder and CEO of OpenAI. Sam, thanks so much. Thank you. I enjoyed that. Hey, thanks so much for listening to How I Built This Lab. Please do follow us on your podcast app so you always have the latest episode downloaded. If you want to follow us on Twitter, our account is at HowI Built This, and mine is at GuyRoz. And on Instagram, I'm at guy.Roz. If you want to contact the team, our email address is H I B T.

at id.wondery.com This episode was produced by Chris Massini with editing by John Isabella. Our audio engineer was Neil Rauch. Our music was composed by Ramteen Arablui. Our production team at How I Built This includes Alex Chung, Carla Esteves, Casey Herman, JC Howard, Liz Metzger, Sam Paulson, Carrie Thompson, Katherine Cypher, Josh Lash, and Elaine Coates. Neva Grant is our supervising editor, Beth Donovan is our executive producer.

I'm Guy Raz, and you've been listening. How I built this.

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