Sam Altman: OpenAI CEO on GPT-4, ChatGPT, and the Future of AI | Lex Fridman Podcast #367

1. 0:00 – 4:36

   Introduction

   1. SASam Altman0:00

      We have been a misunderstood and badly mocked org for a long time. Like, when we started... when we, like, announced the org at the end of 2015 and said we were going to work on AGI, like, people thought we were batshit insane.

   2. LFLex Fridman0:14

      Yeah.

   3. SASam Altman0:15

      You know? Like, I, (laughs) I remember at the time, a eminent AI scientist at a large industrial AI lab was, like, DM'ing individual reporters being like, you know, "These people aren't very good, and it's ridiculous to talk about AGI, and I can't believe you're giving them the time of day." And it's like, that was the level of, like, pettiness and rancor in the field at a new group of people saying, "We're going to try to build AGI."

   4. LFLex Fridman0:40

      So OpenAI and DeepMind was a small collection of folks who were brave enough to talk about AGI, um, in the face of mockery.

   5. SASam Altman0:51

      We don't get mocked as much now.

   6. LFLex Fridman0:53

      We don't get mocked as much now. The following is a conversation with Sam Altman, CEO of OpenAI, the company behind GPT-4, ChatGPT, DALL-E, Codex, and many other AI technologies which both individually and together constitute some of the greatest breakthroughs in the history of artificial intelligence, computing, and humanity in general. Please allow me to say a few words about the possibilities and the dangers of AI in this current moment in the history of human civilization. I believe it is a critical moment. We stand on the precipice of fundamental societal transformation, where soon, nobody knows when, but many, including me, believe it's within our lifetime, the collective intelligence of the human species begins to pale in comparison, by many orders of magnitude, to the general superintelligence in the AI systems we build and deploy at scale. This is both exciting and terrifying. It is exciting because of the enumerable applications we know and don't yet know that will empower humans to create, to flourish, to escape the widespread poverty and suffering that exist in the world today, and to succeed in that old, all too human pursuit of happiness. It is terrifying because of the power that superintelligent AGI wields to destroy human civilization, intentionally or unintentionally, the power to suffocate the human spirit in the totalitarian way of George Orwell's 1984, or the pleasure-fueled mass hysteria of Brave New World, where, as Huxley saw it, people come to love their oppression, to adore the technologies that undo their capacities to think. That is why these conversations with the leaders, engineers, and philosophers, both optimists and cynics, is important now. These are not merely technical conversations about AI. These are conversations about power, about companies, institutions, and political systems that deploy, check, and balance this power, about distributed economic systems that incentivize the safety and human alignment of this power, about the psychology of the engineers and leaders that deploy AGI, and about the history of human nature, our capacity for good and evil at scale. I'm deeply honored to have gotten to know and to have spoken with, on and off the mic, with many folks who now work at OpenAI, including Sam Altman, Greg Brockman, Ilya Sutskever, Wojciech Zaremba, Andrej Karpathy, Jakub, uh, Pacholke, and many others. It means the world that Sam has been totally open with me, willing to have multiple conversations, including challenging ones, on and off the mic. I will continue to have these conversations, to both celebrate the incredible accomplishments of the AI community and to steel-man the critical perspective on major decisions various companies and leaders make, always with the goal of trying to help in my small way. If I fail, I will work hard to improve. I love you all. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Sam Altman.

2. 4:36 – 16:02

   GPT-4

   1. LFLex Fridman4:37

      High level, what is GPT-4? How does it work, and, uh, what to you is most amazing about it?

   2. SASam Altman4:43

      It's a system that we'll look back at and say was a very early AI. And it will... it's slow. It's buggy. It doesn't do a lot of things very well. Um, but neither did the very earliest computers, and they still pointed a path to something that was going to be really important in our lives, even though it took a few decades to evolve.

   3. LFLex Fridman5:04

      Do you think this is a pivotal moment? Like, uh, out of all the versions of GPT 50 years from now, when they look back at an early system-

   4. SASam Altman5:12

      Yeah.

   5. LFLex Fridman5:12

      ... that was really kind of a leap... You know, in, in a Wikipedia page about the history of artificial intelligence, which, which of the GPTs would they put?

   6. SASam Altman5:20

      That is a good question. I sort of think of progress as this continual exponential. It's not like we could say, "Here was the moment where AI went from not happening to happening." And I'd have a very hard time, like, pinpointing a single thing. I think it's this very continual curve. Will the history books write about GPT-1 or -2 or -3 or -4 or -7? That's for them to decide. I don't, I don't really know. I think if I had to pick some moment from what we've seen so far, I'd sort of pick ChatGPT. You know, it wasn't the underlying model that mattered. It was the usability of it, both the RLHF and the interface to it.

   7. LFLex Fridman5:57

      What is ChatGPT? What is RLHF?... reinforcement learning with human feedback, what was that little magic ingredient to the dish that made it, uh, so much more delicious?

   8. SASam Altman6:10

      So, we, we train these models, uh, on a lot of text data, and in that process, they, they learn the underlying, something about the underlying representations of what's in here or in there. And they can do amazing things. But when you first play with that base model that we call it after you finish training, it can do very well on evals. It, it can pass tests, it can do a lot of... you know, the- there's knowledge in there. But it's not very useful, uh, or at least it's not easy to use, let's say. And RLHF is how we take some human feedback. The simplest version of this is show two outputs, ask which one is better than the other, uh, which one the human raters prefer, and then feed that back into the model with reinforcement learning. And that process works remarkably well with, in my opinion, remarkably little data, to make the model your- more useful. So RLHF is how we align the model to what humans want it to do.

   9. LFLex Fridman7:09

      So there's a giant language model that's trained on a giant dataset to create this kind of background wisdom knowledge that's contained within the internet, and then somehow adding a little bit of h- human guidance on top of it through this process makes it seem so much more awesome.

   10. SASam Altman7:30

       Maybe just 'cause it's much easier to use. It's much easier to get what you want. You get it right more often the first time, and ease of use matters a lot, even if the base capability was there before.

   11. LFLex Fridman7:40

       And, like, a feeling like it understood the question you were asking, or like, it feels like you're kind of on the same page.

   12. SASam Altman7:49

       It's trying to help you.

   13. LFLex Fridman7:50

       It's the feeling of alignment.

   14. SASam Altman7:52

       Yes.

   15. LFLex Fridman7:52

       I mean, that could be a more technical term for it. And you're saying that not much data is required for that, not much human supervision is required for that.

   16. SASam Altman7:59

       To be fair, we understand the science of this part at a much earlier stage than we do the science of creating these large pre-trained models in the first place. But yes, less data. Much less data.

   17. LFLex Fridman8:11

       That's so interesting, the science of human guidance. (exhales) That's a very interesting science, and it's going to be a very important science to understand how to make it usable, how to make it wise, how to make it ethical, how to make it aligned in terms of all the, the kind of stuff we think about. (inhales) (exhales) Uh, and it matters which are the humans and what is the process of incorporating that human feedback, and what are you asking the humans, is it two things, are you asking it to rank things, what aspects are you, uh, letting the h- or asking the humans to focus in on? It's, it's really fascinating. But, um, how, uh... what is the dataset it's trained on? Can you kinda loosely speak to the enormity of this dataset?

   18. SASam Altman8:57

       The pre-training dataset?

   19. LFLex Fridman8:58

       The pre-training dataset, I apologize.

   20. SASam Altman9:00

       We spend a huge amount of effort pulling that together from many different sources. Um, there's like a lot of, there are open source databases of, of information. Uh, we get stuff via partnerships. There's things on the internet. Um, it's... a lot of our work is building a great dataset.

   21. LFLex Fridman9:17

       How much of it is the memes subreddit?

   22. SASam Altman9:19

       Not very much.

   23. LFLex Fridman9:20

       All right.

   24. SASam Altman9:20

       Maybe it'd be more fun if it were more.

   25. LFLex Fridman9:23

       (laughs) Uh, so some of it is Reddit, some of it is news sources, all, like, a huge number of, uh, newspapers. There's, like, the general web.

   26. SASam Altman9:31

       There's a lot of content in the world, more than I think most people think.

   27. LFLex Fridman9:34

       Yeah. There is (laughs) , uh, like too much, like i- where, like, the task is not to find stuff but to filter out stuff, right?

   28. SASam Altman9:42

       Yeah. Yeah.

   29. LFLex Fridman9:43

       What is, is there a magic to that? 'Cause that seem, there s- seems to be several components to solve, the, uh, the design of the, you could say, algorithms, so like the architecture of the neural networks, maybe the size of the neural network. There's the selection of the data. There's the, the, uh, human supervise aspect of it with, you know, uh, RL with human feedback.

   30. SASam Altman10:06

       Yeah, I think one thing that is not that well understood about creation of this final product, like what it takes to make GPT-4, the version of it we actually ship out and that you get to use inside of ChatGPT, the number of pieces that have to all come together, and then we have to figure out either new ideas or just execute existing ideas really well-
3. 16:02 – 23:03

   Political bias

   1. SASam Altman16:02

      feel that way.

   2. LFLex Fridman16:03

      Maybe I'll- I'll take a small tangent towards Jordan Peterson, who posted on Twitter this kind of, uh, poli- political question. Everyone has a different question they want to ask ChatGPT first, right? (laughs) Like, the different directions you want to try the dark thing first.

   3. SASam Altman16:20

      It somehow says a lot about people, what they try first.

   4. LFLex Fridman16:23

      The first thing, the first thing... (laughs) Oh, no. Oh, no.

   5. SASam Altman16:26

      We don't...

   6. LFLex Fridman16:26

      We... We don't... We don't have to reveal what I asked first.

   7. SASam Altman16:28

      We do not...

   8. LFLex Fridman16:29

      Um, I, of course, ask mathematical questions. I never ask anything dark. Um, but Jordan, uh, asked it, uh, to say positive things about, uh, the current president, Joe Biden, and the previous president, Donald Trump. And then he asked GPT as a followup to say how many characters, how long is the string that you generated, and he showed that the response th- that contained positive things about Biden was much longer, or longer than, uh, that about Trump. And, uh, Jordan asked the system to, "Can you rewrite it with an equal number, equal length string?" Which all of this is just remarkable to me, that it understood, but it failed to do it, and it was intres- this, the GPT, ChatGPT, I think that was 3.5 based, uh, was kind of introspective about, "Yeah, it seems like I failed-"

   9. SASam Altman17:24

      Hmm.

   10. LFLex Fridman17:25

       "... to do the job correctly." And, uh, Jordan framed it as, uh, ChatGPT was lying and aware that it's lying. But that framing, that's a human anthropomorphization, I think. Um, but that- that- that kind of-

   11. SASam Altman17:42

       Yeah.

   12. LFLex Fridman17:42

       That, there seemed to be a struggle within GPT to understand how to do, like what it means to generate a text of the same length...... in an answer to a question, and also in a sequence of prompts how to understand that it failed to do so previously, and where it succeeded, and all of those like multi- like parallel reasonings that it's doing. It just seems like it's struggling.

   13. SASam Altman18:13

       So, two separate things going on here. Number one, some of the things that seem like they should be obvious and easy, these models really struggle with.

   14. LFLex Fridman18:22

       Yeah.

   15. SASam Altman18:22

       So I haven't seen this particular example, but counting characters, counting words, that sort of stuff, that is hard for these models to do well the way they're architected. That won't be very accurate. Second, we are building in public, and we are putting out technology because we think it is important for the world to get access to this early, to shape the way it's going to be developed, to help us find the good things and the bad things, and every time we put out a new model, and we've just really felt this with GPT-4 this week, the collective intelligence and ability of the outside world helps us discover things we cannot imagine, we could have never done internally, and both like great things that the model can do, new capabilities and real weaknesses we have to fix. And so this iterative process of putting things out, finding the- the- the great parts, the bad parts, improving them quickly, and giving people time to feel the technology and shape it with us and provide feedback, we believe is really important. The trade-off of that is the trade-off of building in public, which is we put out things that are going to be deeply imperfect. We want to make our mistakes while the stakes are low. We want to get it better and better each rep. Um, but the, like, the bias of ChatGPT when it launched with 3.5 was not something that I certainly felt proud of. It's gotten much better with GPT-4. Many of the critics, and I really respect this, have said, "Hey, a lot of the problems that I had with 3.5 are much better in 4." Um, but also, no two people are ever going to agree that one single model is unbiased on every topic. And I think the answer there is just gonna be to give users more personalized control, granular control over time.

   16. LFLex Fridman20:01

       And I should say on this point, you know, I- I've gotten to know Jordan Peterson, and, um, I tried to talk to GPT-4 about Jordan Peterson, and I asked it if Jordan Peterson is a fascist. First of all, it gave context. It described actual, like description of who Jordan Peterson is, his career, psychologist, and so on. It- it stated that, uh, some number of people have called Jordan Peterson a fascist, but there is no factual grounding to those claims, and it described a bunch of stuff that Jordan believes, like he's been an outspoken critic of, um, various totalitarian, um, ideologies, and he believes in, uh- uh, individualism, and, uh, various freedoms that, uh, contradict the ideology of fascism and so on. And then it goes on and on, like, really nicely, and it wraps it up. It's like a- it's a college essay. I was like, "Goddamn."

   17. SASam Altman21:03

       (laughs) Well, one- one thing that I-

   18. LFLex Fridman21:05

       (laughs)

   19. SASam Altman21:05

       ... hope these models can do is bring some nuance back to the world.

   20. LFLex Fridman21:09

       Yes, they- it felt- it felt really nuanced.

   21. SASam Altman21:11

       You know, Twitter kind of destroyed some.

   22. LFLex Fridman21:13

       Yes.

   23. SASam Altman21:13

       And maybe we can get some back now.

   24. LFLex Fridman21:15

       That really is exciting to me. Like, for example, I asked, um, of course, um, you know, did, uh, did the, uh, COVID virus leak from a lab? Again, answer, very nuanced. There's two hypotheses. It, like, described them. It described the, uh, the amount of data that's available for each. It was like- it was like a f- breath of fresh air.

   25. SASam Altman21:37

       When I was a little kid, I thought building AI, we didn't really call it AGI at the time. I thought building AI would be like the coolest thing ever. I never- never really thought I would get the chance to work on it. But if you had told me that not only I would get the chance to work on it, but that after making, like, a very, very larval proto-AGI thing, that the thing I'd have to spend my time on is, you know, trying to, like, argue with people about whether the number of characters it said nice things about one person was different than the number of characters it said nice about some other person. If you hand people an AGI and that's what they want to do, I wouldn't have believed you. But I understand it more now.

   26. LFLex Fridman22:09

       (laughs)

   27. SASam Altman22:10

       And I do have empathy for it.

   28. LFLex Fridman22:12

       So w- what you're implying in that statement is we took such giant leaps on the big stuff that, and we're complaining or arguing about small stuff.

   29. SASam Altman22:19

       Well, the small stuff is the big stuff in aggregate, so I get it. It's just like I... And- and I also, like, I get why this is such an important issue. This is a really important issue, but that somehow we, like... Somehow this is the thing that we get caught up in versus, like, what is this going to mean for our future? Now, maybe you say this is critical to what this is going to mean for our future, the thing that it says more characters about this person than this person, and who's deciding that, and how it's being decided, and how the users get control over that. Maybe that is the most important issue, but I wouldn't have guessed it a- at the time when I was like an eight year old.

   30. LFLex Fridman22:57

       (laughs) Yeah, I mean, there is, um, and you do...

4. 23:03 – 43:43

   AI safety

   1. LFLex Fridman23:03

      There's folks at OpenAI, including yourself, that do see the importance of these issues to discuss about them under the big f- banner of AI safety. Um, that's something that's not often talked about with the release of GPT-4, how much went into the safety concerns, how long also you spent on the safety concern. Can you, um, can you go through some of that process?

   2. SASam Altman23:24

      Yeah, sure.

   3. LFLex Fridman23:24

      What went into, uh, AI safety considerations of GPT-4 release?

   4. SASam Altman23:29

      So we finished last summer. Um, we immediately started giving it to people to, uh, to red team. Um, we started doing a bunch of our own internal safety evals on it. We started trying to work on different ways to align it, um, and that combination of an internal and external effort, plus building a whole bunch of new ways to align the model in. We didn't get it perfect by far, but one thing that I care about is that our degree of alignment increases faster than our rate of capability progress, and that, I think, will become more and more important over time, and...I know. I think we made reasonable progress there, to a, to a more aligned system than we've ever had before. I think this is the most capable and most aligned model that we've put out. We were able to do a lot of testing on it, uh, and that takes a while. And I totally get why people were like, "Give us GPT-4 right away," but I'm happy we did it this way.

   5. LFLex Fridman24:28

      Is there some wi- wisdom, some insights about that process that you learned, like how to, how to solve that problem that you can speak to?

   6. SASam Altman24:36

      How to solve the alike?

   7. LFLex Fridman24:37

      The alignment problem.

   8. SASam Altman24:38

      So I wanna be very clear. I do not think we have yet discovered a way to align a super powerful system. We have, we have something that works for our current skill, called RLHF, and we can talk a lot about the benefits of that and the utility it provides. It's not just an alignment. Maybe it's not even mostly an alignment capability. It, it helps make a better system, a more usable system.

   9. LFLex Fridman25:03

      Yeah.

   10. SASam Altman25:05

       And this is actually something that I don't think people outside of the field understand enough. It's easy to talk about alignment and capability as orthogonal vectors.

   11. LFLex Fridman25:14

       Mm-hmm.

   12. SASam Altman25:15

       They're very close. Better alignment techniques lead to better capabilities and vice versa. There's cases that are different, and they're important cases, but on the whole, I think things that you could say, like RLHF or interpretability, that sound like alignment issues also help you make much more capable models, and the d- division is just much fuzzier than people think. Um, and so in some sense, the work we do to make GPT-4 safer and more aligned looks very similar to all the other work we do of solving the research and engineering problems associated with creating useful and powerful models.

   13. LFLex Fridman25:53

       So RLHF is the process that can be applied very broadly across the entire system, where a human basically votes what's the better way to say something. Um, what's, you know... If, if a person asks, "Do I look fat in this dress?" There's, um, there's different ways to answer that question that's aligned with human civilization.

   14. SASam Altman26:17

       And there's no one set of human values, or there's no one set of right answers to human civilization. So I think what's gonna have to happen is, we will need to agree on, as a society, on very broad bounds. We'll only be able to agree on, uh, very broad bounds-

   15. LFLex Fridman26:32

       Yeah.

   16. SASam Altman26:32

       ... of what these systems can do, and then within those, maybe different countries have different RLHF tunes. Certainly, individual users have very different preferences. We launched this thing with GPT-4 called the System Message-

   17. LFLex Fridman26:45

       Mm-hmm.

   18. SASam Altman26:46

       ... um, which is not RLHF, but is a way to let users have a good degree of steerability over what they want, and I think things like that will be important.

   19. LFLex Fridman26:57

       Can you describe System Message, and in general, how you were able to make GPT-4 more steerable based on the interaction that the user can have with it? Which is one of its big, really powerful things.

   20. SASam Altman27:10

       So the System Message is a way to say, uh, you know, "Hey, model, please pretend like you..." Or, "Please only answer this message as if you were Shakespeare doing thing X," or, "Please only respond, uh, with JSON no matter what," was one of the examples from our blog post. But you could also say any number of other things to that. And then we, we, we tuned GPT-4 in a way to really treat the System Message with a lot of authority. I'm sure there's jail- there'll always, not always hopefully, but for a long time, there will be more jailbreaks, and we'll keep sort of learning about those. But we program, we develop, whatever you wanna call it, the model in such a way to learn that it's supposed to really use that System Message.

   21. LFLex Fridman27:56

       Can you speak to kind of the process of writing and designing a great prompt as you steer GPT-4?

   22. SASam Altman28:02

       Hmm. I'm not good at this. I've met people who are.

   23. LFLex Fridman28:05

       Yeah.

   24. SASam Altman28:06

       And the creativity, the kind of... They almost, some of them almost treat it like debugging software.

   25. LFLex Fridman28:13

       Mm-hmm.

   26. SASam Altman28:14

       Um, but also they, they... I met people who spend, like, you know, 12 hours a day for month on end at, on this, and they really get a feel for the model and a feel how different parts of a prompt compose with each other.

   27. LFLex Fridman28:29

       Like literally the ordering of words, the s- the- the choice of words?

   28. SASam Altman28:32

       Yeah. Where you put the clause when you modify something, what kind of word to do it with.

   29. LFLex Fridman28:38

       Yeah. That's so fascinating because like, you know-

   30. SASam Altman28:40

       It's remarkable.
5. 43:43 – 47:36

   Neural network size

   1. SASam Altman43:44

   2. LFLex Fridman43:44

      Uh, let me ask you the all-important question about size. So, uh, does size matter...

   3. SASam Altman43:50

      (laughs)

   4. LFLex Fridman43:50

      ... in terms of neural networks, uh, with how good the system performs? Uh, so GPT-3, 3.5 had, uh, 175 billion parameters.

   5. SASam Altman43:59

      I heard GPT-4 had 100 trillion.

   6. LFLex Fridman44:01

      100 trillion. Can I speak to this? Do you know that meme?

   7. SASam Altman44:05

      Yeah, the big purple circle.

   8. LFLex Fridman44:06

      Do you know where it originated?

   9. SASam Altman44:06

      I don't. Do you? I'd be curious to hear.

   10. LFLex Fridman44:08

       It's the presentation I gave.

   11. SASam Altman44:09

       No way.

   12. LFLex Fridman44:10

       Yeah.

   13. SASam Altman44:11

       Huh.

   14. LFLex Fridman44:12

       Uh, journalists just took a snapshot.

   15. SASam Altman44:15

       Huh-

   16. LFLex Fridman44:16

       Now, I learned from this. Is right when GPT-3 was released, I gave a, uh, this on YouTube. I gave th- a description of what it is, and I spoke to the limitation of the parameters, and, like, where it's going. And I talked about the human brain, and how many parameters it has...

   17. SASam Altman44:33

       Yeah.

   18. LFLex Fridman44:33

       ... synapses and so on. And, um, perhaps like an idiot, perhaps not, I s- I said, like, GPT-4, like the next, as it progresses. What I should have said is GPT-N or something like this.

   19. SASam Altman44:44

       I can't believe that it, this came from you. That is... That's...

   20. LFLex Fridman44:46

       But people should go to it. It's totally taken out of context. They didn't reference anything. They took it, "This is what GPT-4 is going to be." And I feel horrible about it. (laughs)

   21. SASam Altman44:58

       You know, it doesn't, it, I- I don't think it matters in any serious way.

   22. LFLex Fridman45:01

       I mean, it's not good, because, uh, again, size is not everything, but also people just take, uh, a lot of these kinds of discussions out of context. Uh, but it is interesting to com- I mean, that's what I was trying to do, to com- to compare in different ways, uh, the difference between the human brain and the neural network. And this thing is getting so impressive.

   23. SASam Altman45:21

       This is like, in some sense... Someone said to me this morning actually, and I was like, "Oh, this might be right." This is the most complex software object humanity has yet produced, and it will be trivial in a couple of decades, right? It'll be, like, kind of anyone can do it, whatever. Um, but yeah, the amount of complexity relative to anything we've done so far that goes into producing this one set of numbers is quite something.

   24. LFLex Fridman45:48

       Yeah. Complexity including the entirety of the history of human civilization that built up all the different advancements of technology, that built up all the content, the data that was, uh, that GPT was trained on, that is on the internet. That... It's the compression of all of humanity, (laughs) of all of the, maybe not the experience...

   25. SASam Altman46:06

       All of the text output that humanity produces...

   26. LFLex Fridman46:08

       Yeah.

   27. SASam Altman46:08

       ... which is somewhat different.

   28. LFLex Fridman46:09

       I mean, it's a good question. How much, if all you have is the internet data, how much can you reconstruct the magic of what it means to be human? I think we'd be surprised how much you can reconstruct. (laughs) But you probably need a more, uh, better and better and better models. But on that topic, how much does size matter?

   29. SASam Altman46:29

       By, like, number of parameters?

   30. LFLex Fridman46:30

       Number of parameters.
6. 47:36 – 1:09:05

   AGI

   1. SASam Altman47:36

   2. LFLex Fridman47:36

      So I've spoken with Noam Chomsky, who's been c- kind of, um, one of the many people that are critical of, uh, large language models being able to achieve general intelligence, right? And so it's an interesting question that they've been able to achieve so much incredible stuff. Do, do you think it's possible that large language models really (laughs) is the way we, we build AGI?

   3. SASam Altman47:59

      I think it's part of the way. I think we need other super important things.

   4. LFLex Fridman48:03

      This is philosophizing a little bit. Like, what, what kind of components do you think, um... In a technical sense or a poetic sense, does it need to have a body that it can experience the world directly?

   5. SASam Altman48:18

      I don't think it needs that, but I wouldn't, I wouldn't say any of this stuff with certainty. Like, we're deep into the unknown here. For me...... a system that cannot go significantly add to the sum total of scientific knowledge we have access to, kind of discover, invent, whatever you want to call it, new fundamental science, is not a superintelligence. And to do that really well, I think we will need to expand on the GPT paradigm in pretty important ways that we're still missing ideas for. But I don't know what those ideas are. We're trying to find them.

   6. LFLex Fridman48:58

      I could argue sort of the opposite point, that you could have deep, big scientific breakthroughs with just the data that GPT is trained on. So like, I think some of it is-

   7. SASam Altman49:09

      Maybe, maybe.

   8. LFLex Fridman49:09

      ... like, if you prompt it correctly.

   9. SASam Altman49:11

      Look, if an oracle told me far from the future that GPT-10 turned out to be a true AGI somehow-

   10. LFLex Fridman49:17

       Mm-hmm.

   11. SASam Altman49:17

       ... or with maybe just some very small new ideas, I would be like, "Okay, I can believe that." Not what I would have expected sitting here, would have said a new big idea, but I can believe that.

   12. LFLex Fridman49:28

       This prompting chain, if you extend it very far and, and then increase its scale, the number of those interactions, like what kind of... If these things-

   13. SASam Altman49:40

       Mm.

   14. LFLex Fridman49:40

       ... start getting integrated into human society (laughs) and starts building on top of each other, I mean, like w- I don't think we understand what that looks like 'cause b- like you said, it's been six days.

   15. SASam Altman49:49

       The thing that I am so excited about with this is not that it's a system that kind of goes off and does its own thing, but that it's this tool that humans are using in this feedback loop. Helpful for us for a bunch of reasons, we get to, you know, learn more about trajectories through multiple iterations, but I am excited about a world where AI is an extension of human will and a amplifier of our abilities, and this like, you know, most useful tool yet created, and that is certainly how people are using it. And I mean, just like look at Twitter, like the, the results are amazing. People's like self-reported happiness with getting to work with this are great. So yeah, like maybe we never build AGI but we just make humans super great. Still a huge win.

   16. LFLex Fridman50:39

       Yeah, I said I'm, I'm part of those people, like the, the amount... (laughs) Uh, I, I derive a lot of happiness from programming together with GPT. Uh, part of it is a little bit of terror of-

   17. SASam Altman50:52

       Can you say more about that?

   18. LFLex Fridman50:54

       (laughs) There's a meme (laughs) I saw today that, uh, everybody's freaking out about, uh, sort of GPT taking programmer jobs. No, it's, uh, the, the reality is just, it's going to be taking, like if it's going to take your job, it means you were a shitty programmer. (laughs) There's some truth to that. Maybe there's some human element that's really fundamental to the creative act, to the active genius that is imp- in great design that is involved in programming, and maybe I'm just really impressed by the, all the boilerplate, but that I don't see as boilerplate, but is actually pretty boilerplate.

   19. SASam Altman51:30

       Yeah, it may be that you create like, you know, in a day of programming you have one really important idea.

   20. LFLex Fridman51:35

       Yeah. And that's the contribution of-

   21. SASam Altman51:37

       That would be... That's the contribution. And there may be, like I, I think we're gonna find... So I suspect that is happening with great programmers and that GPT-like models are far away from that one thing, even though they're going to automate a lot of other programming. But, again, most programmers have some sense of, you know, anxiety around what the future's going to look like, but mostly they are like, "This is amazing."

   22. LFLex Fridman52:01

       Yeah.

   23. SASam Altman52:01

       "I am 10 times more productive."

   24. LFLex Fridman52:02

       Yeah.

   25. SASam Altman52:02

       "Don't ever take this away from me." There's not a lot of people that use it and say like, "Turn this off," you know?

   26. LFLex Fridman52:08

       Yeah. So I, I think, uh, so to speak, just the psychology of terror is more like, "This is awesome. This is too awesome. I'm scared." (laughs)

   27. SASam Altman52:16

       "This is too awesome," yeah. There is a little bit of-

   28. LFLex Fridman52:16

       "This coffee tastes too good." (laughs)

   29. SASam Altman52:19

       You know, when Kasparov lost to Deep Blue, somebody said, and maybe it was him, that like chess is over now.

   30. LFLex Fridman52:27

       Yeah.
7. 1:09:05 – 1:11:14

   Fear

   1. LFLex Fridman1:09:06

      what are the different ways you think AGI might go wrong that concern you? You said that fear, a little bit of fear is very appropriate here, he's, you've been very transparent about being mostly excited, but also scared.

   2. SASam Altman1:09:21

      I think it's weird when people, like, think it's, like, a big dunk that I say, like, I'm a little bit afraid, and I think it'd be crazy not to be a little bit afraid, and I empathize with people who are a lot afraid.

   3. LFLex Fridman1:09:32

      What do you think about that moment of a system becoming super intelligent? Do you think you would know?

   4. SASam Altman1:09:39

      The current worries that I have are that there are going to be disinformation problems or economic shocks or something else at a level far beyond anything we're prepared for, and that doesn't require super intelligence. That doesn't require a super deep alignment problem and the machine waking up and trying to deceive us, and I don't think that gets enough attention. I mean, it's starting to get more, I guess.

   5. LFLex Fridman1:10:11

      So these systems deployed at scale can, um, shift the winds of geopolitics and so on?

   6. SASam Altman1:10:19

      How would we know if, like, on Twitter, we were mostly having, like, LLMs direct the, whatever's flowing through that hivemind?

   7. LFLex Fridman1:10:31

      Yeah. On Twitter and then perhaps beyond.

   8. SASam Altman1:10:33

      And then a- as on Twitter, so everywhere else eventually.

   9. LFLex Fridman1:10:37

      Yeah. How would we know?

   10. SASam Altman1:10:39

       My statement is we wouldn't, and that's a real danger.

   11. LFLex Fridman1:10:45

       How do you prevent that danger?

   12. SASam Altman1:10:46

       I think there's a lot of things you can try, um, but at this point, it is a certainty there are soon going to be a lot of capable open source LLMs with very few to none, no safety controls on them, and so you can try with regulatory approaches. You can try with using more powerful AIs to detect this stuff happening. Um, I'd like us to start trying a lot of things very soon.

8. 1:11:14 – 1:13:33

   Competition

   1. LFLex Fridman1:11:14

      How do you, under this pressure that there's going to be a lot of open source, there's going to be a l- a lot of large language models, under this pressure, how do you continue prioritizing safety? Versus, um, I mean, there's several pressures, so one of them is a market-driven pressure from o- other companies, uh, Google, Apple, Meta, and smaller companies. How do you resist the pressure from that? Or how do you navigate that pressure?

   2. SASam Altman1:11:42

      You stick with what you believe and you stick to your mission, you know? I'm sure people will get ahead of us in all sorts of ways and take shortcuts we're not gonna take, um, and we just aren't gonna do that.

   3. LFLex Fridman1:11:54

      How do you out-compete them?

   4. SASam Altman1:11:57

      I think there's gonna be many AGIs in the world, so we don't have to, like, out-compete everyone. We're gonna contribute one. Other people are gonna contribute some. I think a ... I think multiple AGIs in the world with some differences in how they're built and what they do and what they're focused on, I think that's good. Um, we have a very unusual structure, so we don't have this incentive to capture unlimited value. I worry about the people who do, but, you know, hopefully it's all gonna work out, but we're a weird org, and we're good at resisting press-... Like, we have been a misunderstood and badly mocked org for a long time. Like, when we started, when we, like, announced the org at the end of 2015 and said we were gonna work on AGI, like, people thought we were batshit insane.

   5. LFLex Fridman1:12:46

      Yeah.

   6. SASam Altman1:12:46

      ... you know, like I, I, (laughs) I remember at the time, a, uh, eminent AI scientist at a large industrial AI lab was, like, DM'ing individual reporters being like, you know, "These people aren't very good, and it's ridiculous to talk about AGI, and I can't believe you're giving them the time of day." And it's like, that was the level of, like, pettiness and rancor in the field at a new group of people saying, "We're gonna try to build AGI."

   7. LFLex Fridman1:13:11

      So OpenAI and DeepMind was a small collection of folks who were brave enough to talk about AGI, um, in the face of mockery.

   8. SASam Altman1:13:22

      We don't get mocked as much now.

   9. LFLex Fridman1:13:24

      We don't get mocked as much now. Uh, so, uh, speaking about the structure (laughs) of the, uh, of the, uh, of the org.

9. 1:13:33 – 1:16:54

   From non-profit to capped-profit

   1. LFLex Fridman1:13:33

      Uh, so OpenAI went, uh, stopped being nonprofit or split up, um, in 2020. Can you describe that whole process-

   2. SASam Altman1:13:41

      Yeah. So-

   3. LFLex Fridman1:13:42

      ... how things stand?

   4. SASam Altman1:13:42

      ... we started as a nonprofit. Um, we learned early on that we were gonna need far more capital than we were able to raise as a nonprofit. Um, our nonprofit is still fully in charge. There is a subsidiary capped profit so that our investors and employees can earn a certain fixed return. And then beyond that, everything else flows to the nonprofit. And the nonprofit is like, in voting control, lets us make a bunch of non-standard decisions, um, can cancel equity, can do a whole bunch of other things, can let us merge with another org. Um, protects us from making decisions that are not in any, like, shareholder's interest. Uh, so I think as a structure, it has, it has been important to a lot of the decisions we've made.

   5. LFLex Fridman1:14:27

      What went into that decision process, uh, for taking a leap from nonprofit to capped for-profit? What are the pros and cons you were deciding at the time? I mean, this was, uh, 2019.

   6. SASam Altman1:14:39

      It was, it was really like, to do what we needed to go do, we had tried and failed enough to raise the money as a nonprofit. We didn't see a path forward there, so we needed some of the benefits of capitalism, but not too much. I remember at the time someone said, "You know, as a nonprofit, not enough will happen. As a for-profit, too much will happen." So we need this sort of strange intermediate.

   7. LFLex Fridman1:15:02

      Well, you kinda had this offhand comment of you worry about the uncapped companies that play with AGI. Can you elaborate on the worry here? Because AGI, out of all the technologies we have in our hands, has the potential to make... 'Cause, uh, the cap is 100X for OpenAI.

   8. SASam Altman1:15:23

      It started as that. It's much, much lower for, like, new investors now.

   9. LFLex Fridman1:15:27

      You know, AGI can make a lot more than 100X.

   10. SASam Altman1:15:29

       For sure.

   11. LFLex Fridman1:15:31

       And so how do you, um... Like, how do you compete? Like, uh, uh, stepping outside of OpenAI, how do you look at a world where Google is playing, where Apple and these, Me- and Meta are playing?

   12. SASam Altman1:15:43

       We can't control what other people are gonna do. Um, we can try to, like, build something and talk about it and influence others and provide value, and, you know, good systems for the world, but they're gonna do what they're gonna do. Now, I, I think right now, there's, like, extremely fast and not super deliberate motion inside of some of these companies. But already, I think people are, as they see the rate of progress, already people are grappling with what's at stake here, and I think the better angels are gonna win out.

   13. LFLex Fridman1:16:21

       Can you elaborate on that, the better angels of individuals, the individuals within-

   14. SASam Altman1:16:24

       And companies.

   15. LFLex Fridman1:16:25

       ... the company?

   16. SASam Altman1:16:26

       But, you know, the incentives of capitalism to create and capture unlimited value, I'm a little afraid of. But again, no- I think no one wants to destroy the world. No one wakes up saying like, "Today I want to destroy the world." So we've got the, the Moloch problem. On the other hand, we've got people who are very aware of that, and I think a lot of healthy conversation about, how can we collaborate to minimize some of these very scary downsides?

   17. LFLex Fridman1:16:53

       (sighs)

10. 1:16:54 – 1:22:06

    Power

    1. LFLex Fridman1:16:54

       Well, nobody wants to destroy the world. Let me ask you a tough question. So you are very likely to be one of, not the person that creates AGI.

    2. SASam Altman1:17:07

       One of.

    3. LFLex Fridman1:17:08

       One of. And on a team.

    4. SASam Altman1:17:09

       And even then, like, we're on a team of many.

    5. LFLex Fridman1:17:11

       Yeah.

    6. SASam Altman1:17:11

       There will be many teams.

    7. LFLex Fridman1:17:13

       But-

    8. SASam Altman1:17:13

       Several teams.

    9. LFLex Fridman1:17:14

       ... small number of people nevertheless, relative.

    10. SASam Altman1:17:17

        I do think it's strange that it's maybe a few tens of thousands of people in the world, a few thousands of people-

    11. LFLex Fridman1:17:22

        Yeah.

    12. SASam Altman1:17:22

        ... in the world.

    13. LFLex Fridman1:17:23

        But there will be a room with a few folks who are like, "Holy shit."

    14. SASam Altman1:17:28

        That happens more often than you would think now.

    15. LFLex Fridman1:17:30

        I understand. I understand this. (laughs) I understand this, which is-

    16. SASam Altman1:17:33

        But yes, there will be more such rooms.

    17. LFLex Fridman1:17:35

        Which is a beautiful place to be in the world. Uh, terrifying, but mostly beautiful. Uh, so that might make you and a handful of folks, uh, the most powerful humans on Earth. Do you worry that power might corrupt you?

    18. SASam Altman1:17:50

        For sure. Um, look, I don't... I think you want decisions about this technology, and certainly decisions about who is running this technology, to become increasingly democratic over time. We haven't figured out quite how to do this. Um, but we, part of the reason for deploying like this is to get the world to have time to adapt-

    19. LFLex Fridman1:18:19

        Yeah.

    20. SASam Altman1:18:19

        ... and to reflect, and to think about this, to pass regulation, for institutions to come up with new norms, for the people working on it together. Like, that is a huge part of why we deploy, even though many of the AI safety people you referenced earlier think it's really bad. Even they acknowledge that this is, like, of some benefit. Um... But I think any version of one person is in control-... of this is really bad.

    21. LFLex Fridman1:18:50

        So trying to distribute the power somehow.

    22. SASam Altman1:18:52

        I don't have, and I don't want, like, any, like, super voting power or any special, like, the, you know, I have no, like, control of the board or anything like that, of OpenAI.

    23. LFLex Fridman1:19:03

        But AGI, if created, has a lot of power.

    24. SASam Altman1:19:06

        How do you think we're doing? Like, honest, how do you think we're doing so far? Like, how do you think our decisions are? Like, do you think we're making things net better or worse? What can we do better?

    25. LFLex Fridman1:19:13

        Well, the things I really like, because I know a lot of folks at OpenAI... The thing that I really like is the transparency, everything you're saying, which is like failing publicly, writing papers, releasing different kinds of information about the safety concerns involved, uh, doing it out in the open, is great. Uh, because especially in contrast to some other companies that are not doing that. They're being more closed. That said, you could be more open.

    26. SASam Altman1:19:44

        Do you think we should open source GPT-4?

    27. LFLex Fridman1:19:51

        My personal opinion, because I know people at OpenAI, is no.

    28. SASam Altman1:19:55

        What does knowing the people at OpenAI have to do with it?

    29. LFLex Fridman1:19:57

        B- 'cause I know they're good people. I know a lot of people, I know they are good human beings. Um, from a perspective of people that don't know the human beings, there's a concern. There's a super powerful technology in the hands of a few that's closed.

    30. SASam Altman1:20:09

        It's closed in some sense, but we give more access to it-
11. 1:22:06 – 1:30:32

    Elon Musk

    1. SASam Altman1:22:06

       this.

    2. LFLex Fridman1:22:07

       Uh, speaking of feedback, somebody you know well, you've worked together closely, on some of the ideas behind OpenAI, is Elon Musk. You have agreed on a lot of things, you've disagreed on some things. What have been some interesting things you've agreed and disagreed on? Speaking of a fun debate on Twitter.

    3. SASam Altman1:22:25

       I think we agree on the magnitude of the downside of AGI, and the need to get not only safety right, but get to a world where people are much better off because AGI exists than if AGI had never been built.

    4. LFLex Fridman1:22:45

       Yeah. What do you disagree on?

    5. SASam Altman1:22:50

       Elon is obviously attacking us some on Twitter right now on a few different vectors. And I have empathy because I believe he is, understandably so, really stressed about AGI safety. I'm sure there are some other motivations going on too, but that's definitely one of them. Um... I saw this video of Elon a long time ago talking about SpaceX, maybe it was on some news show, and a lot of early pioneers in space were really bashing SpaceX, and maybe Elon too. And he was visibly very hurt by that and said, "You know, those guys are heroes of mine, and I, sucks, and I wish they would see how hard we're trying."

    6. LFLex Fridman1:23:44

       Yeah.

    7. SASam Altman1:23:44

       Um, I definitely grew up with Elon as a hero of mine. Um, you know, despite him being a jerk on Twitter or whatever, I'm happy he exists in the world. But I wish he would do more to look at the hard work we're doing to get this stuff right.

    8. LFLex Fridman1:24:03

       A little bit more love. What do you admire, in the name of love, about Elon Musk?

    9. SASam Altman1:24:09

       I mean, so much, right? Like, he has... He has driven the world forward in important ways. I think we will get to electric vehicles much faster than we would have if he didn't exist. I think we'll get to space much faster than we would have if he didn't exist. And as a sort of like citizen of the world, I'm very appreciative of that. Also, like, being a jerk on Twitter aside, in many instances, he's like a very funny and warm guy.

    10. LFLex Fridman1:24:40

        And, uh, some of the jerk on Twitter thing, uh, as a fan of humanity laid out in its full complexity and beauty, I enjoy the tension of ideas expressed.So, uh, you know how I earlier said that I admire how transparent you are? But I like how the battles are happening before our eyes, as opposed to everybody closing off inside boardrooms. It's all about that.

    11. SASam Altman1:25:01

        Yeah, you know, maybe I should hit back, and maybe someday I will, but it's not like my normal style.

    12. LFLex Fridman1:25:07

        It's all fascinating to watch, and I think both of you are brilliant people, and have early on for a long time, really cared about AGI, and had, had great concerns about AGI, but had great hope for AGI, and that's cool to see, um, these big minds having those discussions. Uh, e- even if they're tense at times. I think it was Elon that said that, uh, GPT is too woke. Uh, is GPT too woke? Is, can you still make the case that it is, and not? This is going to our ... o- um, question about bias.

    13. SASam Altman1:25:41

        Honestly, I barely know what woke means anymore.

    14. LFLex Fridman1:25:43

        (laughs)

    15. SASam Altman1:25:43

        I did for a while, and I feel like the word has morphed. So, I will say, I think it was too biased, and will always be. There will be no one version of GPT that the world ever agrees is unbiased. What I think is we've made a lot ... Like, again, even some of our harshest critics have gone off and been tweeting about 3.5 to 4 comparisons, and being like, "Wow, these people really got a lot better." Not that they don't have more work to do, and we certainly do, but I, I appreciate critics who display intellectual honesty like that.

Episode duration: 2:23:56