Transcript
0:04 Good afternoon, everyone, and welcome to another View from the Top. It's my pleasure today to introduce our guest, Sam Altman, who is a co-founder and the CEO of OpenAI, and one of the most prominent voices right now shaping how the world thinks about artificial intelligence. As many of you know, Sam began his career as an entrepreneur, co-founding a company called Loopt before becoming an investor and an entrepreneur.
0:37 He went on to serve as president of Y Combinator, where he worked closely with many early-stage founders and helped shape the next generation of startups. OpenAI has become one of the most closely watched companies in the world, and Sam's views on where this technology is headed continue to influence conversations across business, policy, and culture. Today, Sam is going to share his perspective, share the perspective he has built at the center of one of the most consequential technological shifts of our time.
1:15 We could not be more happy to have him with us today. Please join me in welcoming Sam Altman. (applause) Hi, Sam. Hello. Welcome to the GSB. Thank you for having me. We are all so excited to have you here. You know, one of the things I've been doing to get ready is I've been talking to an AI version of you, built by Codex, of course.
1:49 And it's been very helpful, answered most of my questions, but I have to say, I still don't think anything beats an in-person conversation, so thank you. Excited to be here. Sam, many in this audience know you left Stanford to build your first startup, but fewer probably know that you almost did an internship in investment banking instead. I did. That's true. Could you take us back to that decision and your mindset at the time?
2:19 You know, I never wanted to be an investment banker, but it was what all the cool kids wanted to do, and I fell victim to peer pressure. No, if any of you want to be investment bankers or are, more power to you. It was just not my organic thing. I was a nerd, but I got to Stanford and all of the smartest kids were trying to get to be investment bankers, and that was the cool thing.
2:41 And I was like, well, I want to be a smart kid. Maybe I should want to be going into investment banking too. So for no reason other than peer pressure, literally, I... Did the whole thing and got an internship and felt super cool. At the time, they would like fly you out to New York, and there was this interview, and then they had this like super nice dinner, and it was, to like a 19 or 18, whatever I was, very impressive.
3:07 I'd like never experienced anything like that, and that made me even more think, like, this must be the right path for me. And then I had this like increasing feeling of dread, and I just didn't think I was gonna like it. Meanwhile, I had been building this sort of... Not even a startup at the time, but this project with some friends. And then Y Combinator was like, "We will give you money for the summer to live on, and you can try to do a startup."
3:36 And I was like, "Okay, this is a sign from the universe. I should try to go do that." And you joined the very first batch of Y Combinator Yeah. and eventually become president of YC. What did that experience teach you going from running a startup to running the world's most famous startup accelerator? So I was sad to drop out of school because I loved school, and I was also worried I just was not going to keep learning stuff.
4:06 And one of the coolest things for me about doing a startup is it was, and still is, is the highest rate of learning environment I had ever been in. This is maybe the wrong room to say this in, but a thing that someone said to me, I think it was Perfect. maybe Paul Graham, maybe someone like that said to me around then is, you will get an entire MBA in one month of doing a startup. (audience laughter) And, you know, I don't have an MBA, so I can't say whether or not that's true, but I did learn a lot very quickly about business and product and customers and sales and all of this other stuff that has been very important to the rest of my career that I think I just wouldn't have been able to learn in school.
4:52 It was one of these things that you can just learn by doing, and maybe there's no other good way to learn it. So the rate of learning for those That first period of a startup was like unbelievable. I definitely have never learned more in any other period of my life. Running YC was its own kind of learning. That's like almost like a government job. You don't have like the power of these companies, but you're trying to like build a healthy community and make sure everyone thrives.
5:26 So that was another interesting and very different learning experience. And I like everything that comes before, and you'll all find this out in your own lives. You build on everything, no matter how unrelated they seem. So I learned a lot of things about running a startup that were helpful for YC, but it was a very different job. How did you go about learning all of those skills that were so different than running a startup? Do you feel like you learned them on the job or you had mentors?
5:51 How did you actually go about that? Hmm. I mean, definitely had great mentors, but there's no... I think if a job has been done many times before, you can maybe learn it in a classroom. Maybe. You can learn a lot of skills that are helpful and a lot of sort of knowledge that's helpful. But I think things like any specific startup or running Y Combinator or any other kind of, like, not that many people have done it jobs, you're just gonna learn as you go.
6:27 And I I am a big believer that people spend too much time trying to prepare to do the thing than just doing the thing and learning as they go. So, you're gonna make a lot of mistakes. You're gonna learn very quickly, as long as you really approach it with a spirit of, I think people call it deliberate practice, where you are running a lot of experiments and then actually reflecting on the learnings, positive or negative, and trying to update your model, and not just sort of like...
6:55 Random flailing. I think the rate of learning is super high, and people almost always surprise themselves on, if you just jump in and do the thing, how quickly you can get good at it. YC gave you a front row seat to all kinds of ambitious ideas, yet in 2015, when you co-founded OpenAI, most people told you artificial general intelligence was impossible, at least for the foreseeable future. What do you think you and your team saw then that other people were missing?
7:26 Deep learning was working. I actually, when I was a student at Stanford, I worked in the AI lab, and I was planning to work on AI for my career. And I remember very memorably in 2005, a Stanford CS professor telling me that the one thing we knew for sure— there were a lot of things about AI. It was probably gonna take another century at least. But the one thing we knew for sure is deep learning was not gonna work.
7:55 And I was like, okay. I believe you. You're like, you know, impressive. And then in 2012, there was the famous AlexNet result. And then by 2014, we had seen that deep learning seemed to get better with scale in all these different ways. And the world didn't accept this at all. It's actually hard to overstate how much the world was just like, there is no way this works.
8:26 You know, there are still a few people online that you can find that will spend all their time talking about how deep learning is not going to work and it's a parlor trick and this and that. At this point, that's sort of the radical view. At the time, the radical view was there is no way this is going to get to any serious level of intelligence. This is a ridiculous thing. But I had kind of learned, and this has been, to the degree there is, has been a central sort of insight of my career, that there are surprising effects to scale, and that when you see a beautifully scaling exponential of some sort, you are supposed to keep going.
9:06 And something about human intuition does not do a good job with that. And in all of these different things, the scaling returns of individual companies, networks like Y Combinator, neural networks, lots of other examples I could give, but we'll skip in the interest of time. I have just always found this, and it is something that I have developed a High personal conviction to continue to bet on. And so when we saw that this was working and we saw that it got better with scale, we didn't know at the time how predictably better it got with scale.
9:34 That came a few years later. It seemed like we should keep pushing on it. And, you know, maybe it would break at some point. We weren't sure. Like, there are various times where we almost lost conviction and thought we were hitting some sort of wall, but we just kept going. And it was so non-obvious to pretty much anyone that was not inside the walls of OpenAI at that time didn't seem to believe it. That's not literally true.
9:57 There are obviously people who did, but it was like, it was a very unpopular opinion. OpenAI starts as a nonprofit research lab with no intention of becoming a product company. And today, over a billion people use ChatGPT every week. How has your own leadership had to evolve as OpenAI has gained tremendous influence? Like many other tech people in the mid-2010s of Silicon Valley, I got enamored with the idea of research labs.
10:30 This is a time when everybody was talking about how Silicon Valley had lost the plot on research, and we needed to get back to Xerox PARC, or Bell Labs, or whatever your favorite example was. And it was kind of a cool thing to talk about and do. And I always thought research was awesome. I always kind of wanted to run a research lab, and I was always interested in AI. So... This was kind of supposed to be my retirement project.
11:05 And for the first, like, five years, I mean, truly the coolest job I could imagine. It was like there was a small set of us. It was a research lab. We were right about many things, but we were wrong about the way that we thought AI was going to go once developed. And so it truly hadn't occurred to us that the most likely path was that we were going to have to build a product company. We mostly thought that we were going to develop this unbelievable superintelligence, and I realize this sounds ridiculous now, and that we wouldn't then have to do all of the hard work of figuring out how to get it usefully into people's hands and for society to benefit.
11:45 It was like, there's that meme from a long time ago of, do the first thing, do the second thing, question mark, question mark, question mark, question mark, profit, about how startups were going to work. Ours was like, do research, build superintelligence, many question marks, people are benefit and happy. And... And the technology went in a very different path than we originally thought it was going to. I think it's like a much better one, but it means we did have to productize this.
12:13 And it also means, and this is why I think it's one reason I think it's a good direction, is that this is much more like a normal economic force than we imagined at the time. This is like electricity or transistors or some other major scientific Discovery that enables many other companies, technologies, scientific discoveries to happen, and we have to put infrastructure, which is building products like ChatGPT, it's also building an amount of compute bigger than any project humanity's ever taken on in history, into people's hands.
12:48 So it became clear by I would say 2020, late 2020, early 2021, that we were gonna have to build a big company. Didn't think it would happen so quickly, and then when we launched ChatGPT, which was like almost four years ago, then overnight everyone was like, okay, we're gonna be a big company very fast. Very big company you are. I'm curious to know what sorts of decisions are hardest for you to make today?
13:22 I think that... That would take the rest of our time. I'd be happy to talk about it, but the number one that comes to mind is... People call it different things: pausing, pacing, intelligence speed limits. But fundamentally, if you believe, which I very much do, that people always should be in control of the future and we should not either intentionally hand over irreversibly decision-making power to AI or accidentally lose control, and also you believe that power needs to be pretty distributed in the future, Both of which are saying a different version of, we want the kind of democratic, pluralistic ideals that have gotten us this far to continue, and we don't want to see too much power concentration either in an AI with IQ 10 billion or in a small number of people that have AI and can do whatever they want, everybody else can't.
14:18 If you believe both of those, then I think you get to very difficult questions about How much smarter than the smartest people can we let an AI system get? How quickly? And how much do we need to enhance people's ability to be smarter or to understand these systems? And I think this is solvable. It just takes time. And then if you believe that, there's like a limited number of ways that I think the world can come together and agree on how we're gonna say what those limits are and how we're gonna handle them.
14:51 And these are very tricky stuff because you have extremely strong opinions on all sides. You know, you have people saying like, Any regulation is bad, which I think is wrong. People saying that we have to absolutely stop all progress right now, which I think is absolutely wrong. And in many other of society's highest-stakes debates, trying to be the pragmatic centrist in the middle of this is a very difficult place to be in. How is your own thinking about being a pragmatic centrist and AI safety evolved over the past six months as you've seen capabilities accelerate?
15:27 I still believe in a lot of our earliest tenets about how we think AI should get deployed in society. I still believe in iterative deployment. I still believe that society has got to contend with this technology as we develop it, even though it's difficult and an incredible rate of change. And I still believe that People are incredibly resilient and adaptable and honestly don't care that much about the work of machines.
15:57 They're just so focused on people. Even if the technological landscape of the future looks very different, that the sort of societal shape of the future is going to be much more similar today than people think. All that said, watching the rate of progress the last six months has tested the strength of my convictions on some of those things. In some narrow senses, like certainly mathematical reasoning, as far as I'm concerned, we have superintelligence now, and I didn't think it was going to happen this fast.
16:28 And If I play out another year or two of a similar rate of progress, which I think without any changes to the current landscape in the world and sort of the alignment at the level of companies and governments and societies we will have, then it seems to me like that's not going to work to make no changes to how we do things now. Like, the systems are just Getting so capable so quickly that if we want to hold on to those two things I mentioned a little bit ago, we're going to have to make some changes to how we think about pacing progress from here.
17:08 People have very strong opinions about this. I'm curious, like, how does that debate play out inside of OpenAI today? I'd say the more people see and feel how good the models are getting, the less spread there is in the debate, and the more people are like, okay, we're going to have to think about the strategy for the future and what it takes to make the safety guarantees we want to make somewhat differently than we did in the past.
17:38 I realized that a lot of the debate inside of OpenAI, or a lot of the difference in opinion inside of OpenAI, and I think it's also a good learning for the difference in opinion in society, is there was a lot of debate about how good the models were going to get, how quickly that Got reframed as a debate about what the right strategy was. Once that debate is answered, and it's like, okay, the models are about to get extraordinary, then there's less space on the "what should we do about it" questions.
18:10 Still some. You recently spoke at the UN about AI safety, and you emphasized the importance of the government when it comes to regulating AI. But traditionally, policy and technological change move at pretty different paces. So what do you think are some ways that AI labs and the government can better work together? I'm actually pretty happy with how it's trending. I was not...
18:40 Recently, I didn't think most people in most governments took it seriously enough. Last few months it's changed, and now I think the conversation is being— We'll see if something actually happens, but I didn't think anything good was on a trajectory likely to happen, and now I think probably yes. There's also this question about AI safety at an individual or organizational level. What can people or business leaders do today to better prepare themselves for AI's potential risks?
19:16 There's so many levels of that. I think the one, just so that I don't go through a laundry list of 30 things, This cybersecurity... I don't even know what a dramatic enough word to use, but the problem coming towards us, I think is still something that we have not, as organizational leaders or countries or individuals, taken seriously. There was like a moment earlier this year where it was like the Mythos freakout or whatever people called it, and I think things did get somewhat better.
19:54 Like, people definitely patched some bugs, but it was another one of these cases where people didn't really intuit the exponential. So it's like, okay, there are these models, they can find a bunch of security vulnerabilities. Those are probably all of the security vulnerabilities that exist, or all the security vulnerabilities that models are ever going to find. And I don't think most people sort of said, okay, the models are going to get much smarter. They can probably find bugs, like crazy bugs, bugs that are outside of the normal threat model of what we think a hacker can do, hardware-level insane stuff, whatever.
20:32 And so people fix some bugs, and security professionals still freaking out, CISOs still freaking out. But largely speaking, I think most organizational leaders have somewhat moved on. And the models have kept going. And I no longer think we're going to be able to just patch all the bugs. I think we're going to have to completely switch the defense model to sort of using the same technology for sort of a new kind of defense shield.
21:05 And I think it's a huge safety and security issue, and I am somewhat astonished at how relatively little attention it's getting. And to bring it back to the people in this room, as business school students at Stanford, we're right by the people building the future of AI, but it's not always clear where we fit in. So what advice would you give us on how we can shape what comes next?
21:36 My honest belief is that it's not... It's not going to be as different as quickly as people think. Like, it is totally true that huge numbers of tasks that are done today by people will be done better, cheaper, faster. You know, completely differently by AI. And it's also true that wide swaths of current jobs will just— the way they're done today, at least— will be entirely done by AI.
22:09 It's actually been interesting to watch over the last 10 days the reaction of the mathematical community. You know, the day zero reaction was like, "This is fake." The day one reaction was like, "We're cooked, and our field is over, and we're telling our grad students to switch majors." The day three reaction was maybe like, "It's horrible that OpenAI did this, and no one should be allowed to— If this is the price of understanding more mathematics, we don't want it."
22:43 But then by kind of maybe day five, it shifted a lot, and I started hearing from mathematicians who were like, "Yes, there is one kind of math job that is over, but mostly what we did was not Solve Millennium Prize problems. Most of us never did that. Most of what we did was figure out how to make sense of knowledge, how to communicate it, how to present it in a human-intelligible way. And we've now seen a lot of people say, actually, I'm going to have a different but hopefully better kind of career as a mathematician.
23:17 I get that AI is going to drive mathematical discovery from here to a significant extent, but in terms of figuring out what to work on, what it means, how to present it, how to explain it, how to write a great proof, how to Do all these other things, you know, I think there's going to be a whole new set of stuff. And that was the fastest that I think we've all gotten to watch a field go through this cycle, but versions of it happen more slowly in other fields.
23:46 You know, I'm sure many of you watched what happened the last two years of computer programmers saying, "Our job is over," to actually, looks like we're going to keep paying ever higher salaries to people that can get computers to do things. It's just the day-to-day of the job is going to be different. Consultants went through a period where they were like, "This is totally over," and then they found out that actually, no matter how good the AI models are at making nice-looking PowerPoint slides, which they are good at, people really want to interact with another person, and actually almost no one wants the AI consultant to show up and tell them what to do.
24:21 And... You get the point. This is going to keep happening. So I don't think there's going to be like a job apocalypse in the way that many people talk about. There will be huge amounts of short-term changeover in terms of the tasks people do and some kinds of jobs. But if you are like heading into the world right now to go work on something, I think you want to be starting your career at a time of the highest degree of change, and you want the most open playing field.
24:57 I think people with high potential and drive and ideas and sort of a new way of looking at things and new skills do relatively the best when the ground is shaking the most. My chronic disappointment in MBA students is that they always want to just go do the thing that was cool like five years ago and not do the new thing. And I think this is a great time to go do the new thing. So go do the new thing, even if AI is the cool thing right now.
25:28 Yeah, I mean, I don't think this is a good time to start another copy of OpenAI. But I think there are many companies that can be built on top of technology that OpenAI has put out into the world that will be much, I hope, bigger and more important than OpenAI. I want to shift now to AI's impact on who we are as humans and future potential. AI makes it possible for people to build almost anything without prior experience.
26:03 In this world, what should people still learn to do for themselves, even if AI can do it for them? So an example of a thing that someone promised me was important to learn to do for myself, even though there were computers, is my second grade teacher. Was absolutely convinced I had to learn to write cursive. I was like the last kind of generation of students that had to learn to write cursive.
26:33 I was like, "I am never gonna use this again. I am so sure." I don't even sign my name in cursive. I was like, "I am just never..." I couldn't write cursive today probably if I had to. And I was like, "If you're making me learn this because, you know, I have like bad handwriting and I need to like learn fine motor skills or, you know, however a seven-year-old would articulate that, I can believe it. But if you think I'm actually going to need to learn cursive for the sake of cursive, then I don't believe you.
27:04 And my teacher was like, "Nope, you're going to need this as like a functioning member of society. You have to be able to write cursive." Okay. And then I had another teacher a couple years later, who was like, "You have to learn to write." And I was like, "I have no desire to be a writer." And that teacher was like, "Well, that's fine, but writing is mostly an important tool to learn how to think." I was like, "I'm gonna be a mathematician."
27:39 And the teacher was like, "Well, you still have to learn how to think, and you still have to learn how to structure ideas. And even if you never write as a grown-up for anyone else to read, it is an important skill to learn how to think." And My takeaway is that there was an important learning from both of those. Still learning skills that help you think is incredibly valuable. Learning skills that are clearly going to be in the realm of machines and not something that people value are clearly not valuable.
28:17 And those are kind of obvious, right? So I think most people would agree directionally that we should still learn these kind of general things. We should not learn things that AI is going to do really well. But then there's a question of: we develop our general skills, we do our exercise as people, what remains in the domain of people in the future? Number one, I think obvious but worth stating, human values have inherent values because they are human.
28:43 We really do not seem to care, and I think this is great that we don't, about what machines do for their own purposes. And I think no matter how good AI gets, this will continue to be the case. You know, in some sense, the history of science is this history of successive disappointments to the main character energy of humanity. You know, we used to think that the sun rotated around the Earth. And that we were really at the center of that, and then we realized there was this whole solar system, and then there was this whole galaxy, and this goes on and on, and you get to this multiverse, and there's really... You have to squint...
29:23 why people are at the center of it. But we feel like we are very much at the center of what matters, and we're at the center of our own stories for sure. And I think this is great, and I think this will keep going. And I think that no matter how far AI goes from a capability perspective, as long as we get the safety right, as long as we don't have one of these loss of control incidents, human values will matter to all of us because there is a person behind them.
29:46 Work will matter because a person does it, and we're just always gonna care that way. So figuring out How to be there, how to be useful to other people, how to create stuff of central importance to other people, that's going to go on forever. And I think learning how to do that is a surprisingly learnable skill. And let's say we get the distribution and development of AI right. What do you think is a problem facing the future today that people still dramatically underestimate our ability to solve?
30:19 Well... It's like a funny time to ask that because my conversations with people the last few days, even the people who are skeptics are like, "You know what? At this point I get it. Like AI is going to solve every piece of knowledge." And... I don't quite agree with that, but I directionally kind of feel that way. You know, like what it means to have a great life and enable everybody to have the choice, freedom, flexibility, To still make mistakes, but when people look back and zoom out and say, "Yeah, I had like a great life."
30:58 I think that will still be hard. I think that will always be hard in some sense. Maybe it's important that that's hard. I am hopeful that as these next few years roll on, we push much more towards what feels like a new renaissance than a new industrial revolution. But I think a lot of the energy in AI has been towards the industrial revolution. We're going to automate everything. We're just going to perfectly take humans out of the equation and make the economy run faster and better, and we're going to give everybody some money, and the AI will decide what's important.
31:33 And people will be this just cog running more smoothly in a machine, and the machine is going to just be this incredible Moloch thing. And I hope we get that one right. What do you think is the biggest thing standing in the way between this Renaissance version of the future versus this Industrial Revolution style version of the future?
32:06 I actually... I think people want the Renaissance version. I think people want the creativity, the ability to explore stuff, the ability to discover knowledge, the ability to sort of have this like wide open space of New freedom and directions and exploration. I think a lot of the pressures of society and the economy are so just positioned to ruthless efficiency that probably there will have to be some force against that from the companies or the governments or people as a whole.
32:47 We're going to turn it over to our audience questions now. Okay. Hey Sam, my name is David Lee. I'm part of the MBA class of 2027, and I'm originally from the Bay Area. My question for you is, OpenAI had the foresight to invest heavily in compute in 2024 and 2025, and I think that investment is paying off with Astra and the speed of ChatGPT. What infrastructure are you all betting on now to further the mission of OpenAI?
33:18 Robots? So I think people talk a lot about recursive self-improvement for AI models. There is another loop, which is if you have robots that can build more robots and then data centers that can effectively build more data centers, including operating chip fab facilities and such, you can make a lot more compute all the way down to building more power plants. But I think this is as big of a deal, or almost as big of a deal, let's say, as the kind of potential recursive loop in AI research.
33:50 It gets much less talk, so that's one. Another is we made our own chip called Jalapeño, and we have another one coming after it that are huge wins from a sort of intelligence per watt perspective relative to anything else out in the market. And we will continue to push there. And as the models get smarter, they'll be able to help us design smarter and smarter chips. So that's like another kind of one of these loops. We're investing a lot in the whole supply chain, like fabs, power, all the equipment in data centers, So we'll do a lot of that, but those are probably the three big areas.
34:28 Hi, Sam. I'm Shruti, MBA class of '27 from India. On your 30th birthday, you wrote a list of life lessons called "The Days Are Long But the Decades Are Short." And now that you're 41 and a father, I was curious, what would you change about that list? Are there any that you would rewrite? I remember that I wrote that. I don't remember everything in there, but I could do a very short version right now. I think it was pretty long.
34:54 It would be like, have a family, build something that is important to you, spend time having fun and doing the things that you find fulfilling, stay healthy. It would be very short. I kind of think this is a case where you should just listen to old people when they look like they were all right. Hello, Sam. Thank you for the time. My name is Yerko. I'm from the class of 2028. I'm from Peru. And I have a question on what is a strong belief you held back in 2020 about how AI will unfold that has come to be wrong, and how it changed how you run OpenAI?
35:32 From when did you say? From 2020. Late 2020. Okay. Yeah. I didn't think that we were going to be able to get to models that were I guess a better version of saying this, I thought the alignment problem was gonna be harder. It may still turn out to be very hard from now. In fact, I suspect it will get much harder, and I also think it is really incredibly important from where we go from here.
36:04 But the fact that we got to models that are superhuman in some important— very superhuman in some important ways, and alignment has so far been easier than expected, I would have said one of those two was wrong with like a high degree of certainty. Now, A thing I'm worried about is that some people look at where we've gotten and say for n plus one it's going to keep going and alignment's going to stay easy. And I think it's actually not at all obvious to me that that's the case.
36:33 And I also think that the potential cost of alignment being wrong has gone up enormously. I increasingly believe, for a while we can sandbox these systems, and I think we can make some safety guarantees based off the sandbox, and then when that fails, because the models get so smart, we can't really understand what a sandbox would even mean, then I think we'll have monitorability that'll work for a while. But eventually—and the reason that I was talking about that speed limit earlier— nothing but alignment, I think, actually will work.
37:09 and alignment that we're smart enough to understand and can really say with confidence, "It's okay to run this model and people will stay in control of the future." And I worry that... Some AI efforts around the world are so confident in alignment, given how easy it's been so far, that they're too willing to run ahead. Hey Sam, thanks for being here. I'm Josh Island, MBA class of '27. You said recently on the Decoded podcast that you think that the world should accept some bad things happening in exchange for the benefits of AI.
37:46 As you think about the world that your son's going to be growing up in, what are the bad things you think we should be okay with specifically versus where you draw the line? Yeah. I wish I had phrased that answer differently. The way I meant it seemed like so obvious in my head that it was non-controversial, but I see why it didn't come across that way. People are gonna have open-source models that are gonna output a bunch of hate speech and troll internet forums and that are gonna hack into some systems and that are...
38:23 Try to commit fraud in some sorts of ways on behalf of their user. There will be a new generation of phone scams, for example, that will be much worse than the previous generation. And I think there are people who would say, if that's going to happen, let's ban all open-source models. The point I was trying to make in that podcast, but I think got clipped out, was banning all open-source models. would be an extremely high cost to innovation and liberty in society, and we shouldn't do it.
38:51 But if you are going to have open source models, and if you're going to let people run these locally on their computer, no matter how good you are at, global agent monitoring, there's going to be some stuff that you wish someone hadn't done. And I wouldn't take the trade of saying no open source models whatsoever. Now, there is some threshold, and this is where I think society needs a debate, above which the models can do sufficient harm that we don't give people the liberty to use them however they want.
39:22 And obvious examples are no one should get to take down the internet. No one should be able to cause physical harm to another person. No one should be able to make a new kind of bioterror attack that they put out into the world. One of my learnings from that thing is If you tell people to accept any amount of bad things happening, they'll just say, "I don't want anything bad happening at all," and they will lose the subtlety of, like, liberty is important too, and what really matters is where we draw the line.
39:58 Like, if we go back to the example of, like, scams on phones, you could just take away all phones and no one would ever have a phone scam, and we decide that's not worth it. So that's the point I was trying to make. I don't think we should take big risks as a society, and in fact, that's why I think AI is different than any technology that has come before it, is it is a really new step in the amount of harm one person can cause.
40:20 And there are a bunch of uncomfortable consequences that are gonna follow from that about how society has to adapt, and some freedoms we do have to give up. But... I don't think it means we shouldn't get to run neural networks on our computers. Hi, Sam. I'm Shiraz, MBA class of 2028. Thanks for being here with us. So I'm coming in as a software developer. So one of my questions was, as model capabilities get better over time, one of the problems we're seeing is that software moats are increasingly being swallowed by better and better AI models.
40:52 So for the next generation of budding entrepreneurs, what advice would you have on what kind of moats within software, or generally, for people that are software developers, what kind of technologies or what kind of software can we kind of work on to build like a startup or an entrepreneurial venture in the future? To a first approximation, I think there haven't been that many software moats in history. There's other kind of moats. There's like network effects, like Facebook or something.
41:26 There are scale effects. Like Google, there are data moats, like a lot of enterprise software company has. But software itself has never been a super strong moat. You use it to get a temporary advantage to build up one of the traditional moats of a business. And I kind of think that's just gonna keep going the same way. I'm Adam. I'm an MBA 2. I'm also a chief of staff at the OpenAI Foundation. Cool. (audience laughter) Just throwing that out there.
41:56 (audience laughter) Yeah, so I've kind of got a bit of a related or follow-up question to this one. As AI becomes more powerful, there's a growing tension between making the technology broadly accessible to all of humanity while also ensuring that it's developed and deployed safely. So I wonder, how do you think about the trade-off between open-weight and closed-weight models, and what would have to be true for you to feel comfortable open-weighting the very most capable models?
42:25 Well, I think open weight models are very important for society to have up to some fairly high level of capability. And the question of exactly how much damage will society accept for the benefits of open weight models is something that is gonna get very heavily debated in the next couple of years. It's also a moving target because the more that society builds up resilience, like OpenAI Foundation is, I think, hopefully going to do a lot of work to help prevent against future pandemics.
42:56 and then maybe the world can have more capable biomodels because we can respond better than we could today. So it's not a static question. It'll evolve as the world builds up more resilience with AI. But I don't... I don't think we could responsibly open source our current best internal model. Anytime soon.
43:26 I'm trying to think what would have to happen to the world, like... The total cyber defense model of the way the internet works would have to change. We'd have to build up this bio layer and a long list of other stuff too. Now, the reason that this is an interesting and live question is how far ahead Does anyone really believe that model is from someone who will open source a model of that level? Not super. And so I think this, like, whatever this rolling advantage is of closed-source, closed-weight models to help the world build the resilience and defend against the open models that I think the world does deserve is super important and part of— I did not know you worked for the OpenAI Foundation.
44:15 This is not a plant, but I'll take it. Part of why I think it's cool that we have now the biggest foundation in the world is we can try to sprint at helping the world build a defense shield. Name is Serim. First of all, thank you for being with us today. Last year when you joined David Perell's How I Write podcast, you shared the analog note-taking system you have where you carry a notebook with you all day, write down your thoughts.
44:43 Are there any areas in your life, both personal and professional, where you try to stay more low-tech and more analog to preserve some skills you have as a person and as a professional? And what would be your suggestions for us to follow the same path? Like the notepad, I think I'm just wrong there, and I probably should switch to a computer, but it's like an old force of habit.
45:15 I think the analog stuff that matters is like all of the stuff with Speaking of old people are always right, it's like spend a lot of time with other people, have meals together, go out into nature a lot, exercise. There's a long list of analog stuff that has worked for a long time, but I think it's all the obvious stuff. And trying to be like, oh, someone writes with a notepad, they're probably just wrong. That one you can probably just stop.
45:44 Sam, I have one final question for you today. At the GSB, we talk about changing lives, changing organizations, and changing the world. As you look at the world today, what do you think future leaders need to focus on to drive change? I mean, my very obvious answer is AI. Like, if we pick the easiest one of those maybe, or the most tractable, when you talk about like changing organizations. The degree to which I think it's possible to design a different kind of organization now with a lot of AI relative to Each person in it, and...
46:28 I think it's just like an underexplored sort of area of study, like what it means to set up the best possible organization today. I think it's like very, very different. This is pure speculation. I've never studied to know if this is true or not, but my sense is that the way, like most organizations are designed Came from copying the military, and there was a way that we set up this very hierarchical command and control thing. And to scale beyond a certain point, that was the one example we had of something that could do it.
46:59 Organizations seem to have some of that. And then over the last few decades, the tech industry and other industries too have innovated on that and somewhat improved it, but it's still inspired by that same basic principle. And one of the things that's been extremely interesting to me about watching These AI systems is, if you look at, I think it was 10,000, the 10,000 agents that solved Navier-Stokes, they worked together in a way that is unlike any organization I could imagine.
47:31 There was no leader. There were no credit squabbles. There was perfect alignment. There was no bickering. Everyone was just trying to solve this goal. There's beautiful flaws in humans that are gonna mean we never do that. People are gonna work in many of the same ways that they've worked in the past. And I wouldn't want to just be that kind of emotionless world, obviously. But I think there is still something to learn from that. And as we have watched inside of OpenAI teams that really embrace AI and be much smaller change the way that they work, I'm sure you all will be the people that go figure out how to design new organizations, and I think the world will really benefit from it.
48:16 Sam, this has been a pleasure. Thank you. Please join me in thanking Sam Altman. (applause)