Sam Altman: Getting Fired (and Re-Hired) by OpenAI, Agents, AI Copyright issues

Jason Calacanis 0:00 I first met our next guest, Sam Altman, almost 20 years ago when he was working on a local mobile app called Loopt. We were both backed by Sequoia Capital, and in fact, we were both in the first class of Sequoia Scouts. He did investment in a little unknown fintech company called Stripe, I did Uber, and in that tiny experimental fund-

Sam Altman 0:19 You did Uber? I've never heard that before. (laughs)

Chamath Palihapitiya 0:20 Yeah. (laughs) I think so. Possible.

David Sacks 0:22 Cool.

Chamath Palihapitiya 0:22 I've got the starting ready. (laughs)

David Friedberg 0:24 You should write a book, Jacob. (laughs)

Chamath Palihapitiya 0:26 Maybe. What's going on? Let your winners ride.

Jason Calacanis 0:31 Rain Man, David Sa-

Chamath Palihapitiya 0:35 What's going on?

David Sacks 0:36 And it's sad. We open sourced it to the fans, and they've just gone crazy with it.

Chamath Palihapitiya 0:40 Love you Betsy.

David Sacks 0:40 What's his... queen of quinoa.

Chamath Palihapitiya 0:42 Going all in.

Jason Calacanis 0:45 That tiny experimental fund that Sam and I were part of as Scouts is Sequoia's highest multiple returning fund. Couple of low digit millions turned into over 200 million, I'm told.

Sam Altman 0:54 Really?

Jason Calacanis 0:54 And then he did... Yeah, that's what I was telling you-

Sam Altman 0:56 Wow.

Jason Calacanis 0:56 ... about Rudolph, yeah. And he did a stint at Y Combinator, where he was president from 2014 to 2019. In 2016, he co-founded OpenAI with the goal of ensuring that artificial general intelligence benefits all of humanity. In 2019, he left YC to join OpenAI full-time as CEO. Things got really interesting on November 30th of 2022. That's the day OpenAI launched ChatGPT. In January 2023, Microsoft invested $10 billion in November 2023. Over a crazy five-day span, Sam was fired from OpenAI, everybody was gonna go work at Microsoft, a bunch of heart emojis went viral on X/Twitter, and people started speculating that the team had reached artificial general intelligence, the world was gonna end, and suddenly, a couple days later, he was back to being the CEO of OpenAI. In February, Sam was reportedly looking to raise $7 trillion for an AI chip project. This, after it was reported that Sam was looking to raise a billion from Masayoshi-san to create an iPhone killer with Jony Ive, the co-creator of the iPhone. All of this while ChatGPT has become better and better, and a household name. It's having a massive impact on how we work and how work is getting done, and it's reportedly the fastest product to hit 100 million users in history in just two months. And check out OpenAI's insane revenue ramp-up. They reportedly hit 2 billion in ARR last year. Welcome to the All-In Podcast, Sam Altman.

Sam Altman 2:25 Thank you. Thank you, guys.

Jason Calacanis 2:26 Sacks, you wanna lead us off here?

David Sacks 2:28 Okay, sure. I mean, I, I think the whole industry is waiting with bated breath for the release of GPT-5. I guess it's been reported that it's launching sometime this summer, but that's a pretty big window. Can you narrow that down? I guess, where, where are you in the release of GPT-5?

Sam Altman 2:44 Uh, we, we take our time on releases of major new models, and I don't think we, uh... I think it will be great, uh, when we do it, and I think we'll be thoughtful about how we do it. Uh, like, we may release it in a different way than we've released previous models. Um, also, I don't even know if we'll call it GPT-5. Um, what I, what I will say is, you know, a lot of people have noticed how much better GPT-4 has gotten, um, since we've released it, and particularly over the last few months. I think, I think that's, like, a better hint of what the world looks like, where it's not the, like, one, two, three, four, five, six, seven, but you, you just, you use an AI system, and the whole system just gets better and better fairly continuously. Um, I think that's, like, both a better technological direction. I think that's, like, easier for society to adapt to. Um, but, but I assume that's where we'll head.

David Friedberg 3:41 Does that mean that there's not gonna be long training cycles, and it's continuously retraining or training sub-models, Sam? And maybe you could just speak to us about what might change architecturally going forward with respect to large models.

Sam Altman 3:56 Well, I mean, one, one, one thing that you could imagine is this... just that you keep training-

David Friedberg 4:01 Right.

Sam Altman 4:01 ... a model. Uh, that, that would seem like a reasonable thing to me.

David Friedberg 4:06 Do you think that-

Jason Calacanis 4:06 And when you talk about-

David Friedberg 4:07 Yeah.

Jason Calacanis 4:07 ... releasing it differently this time, are you thinking maybe releasing it to the paid users first or, you know, a slower rollout to get the red teams tight since now there's so much at stake? You have so many customers-

Sam Altman 4:20 Yeah.

Jason Calacanis 4:20 ... actually paying, and you've got everybody watching everything you do. You know, is it, is it a-

Sam Altman 4:26 Well, G-

David Friedberg 4:26 You have to be more thoughtful now-

Sam Altman 4:27 GPT-4 is-

David Friedberg 4:27 ... yeah?

Sam Altman 4:28 ... still only available to the paid users, but one of the things that we really want to do is figure out how to make more advanced technology available to free users too. I think that's a super important part of our mission. Uh, and, and this idea that we build AI tools and make them super widely available, free or, you know, not that expensive, whatever it is, so that people can use them to go kind of invent the future rather than the magic AGI in the sky inventing the future and showering it down upon us. Uh, that seems like a much better path. It seems like a more inspiring path. I also think it's where things are actually heading. So, it makes me sad that we have not figured out how to make GPT-4 level technology available to free users. That's something we really wanna do.

Jason Calacanis 5:10 It's just very expensive, I, I take it?

Sam Altman 5:11 It's very expensive.

David Friedberg 5:13 Yeah. Chamath, your thoughts?

David Sacks 5:15 I think maybe the, the two big vectors, Sam, that people always talk about is that underlying cost and sort of the latency that's kind of rate limited a killer app.

Sam Altman 5:26 Yeah.

David Sacks 5:26 And then I think the second is sort of the long-term ability for people to build in an open source world versus a closed source world. And I think the crazy thing about this space is that the open source community is rabid. So, one example that I think is incredible is, you know, we had these guys do a pretty crazy demo for Devin. Remember? Like, even, like, five or six weeks ago, that looked incredible. And then some kid just published it under an open, uh, MIT license, like OpenDevin, and it's incredibly good and almost as good as that other thing that was closed source. So, maybe we can just start with that, which is...Tell me about the business decision to keep these models close source, and where do you see things going in the next couple of years?

Sam Altman 6:15 So on, on the first part of your question, um, speed and cost, those are hugely important to us. And I don't wanna, like, give a timeline on when we can bring them down a lot 'cause research is hard, but I am confident we'll be able to. Um, we wanna, like, cut the latency super dramatically. We wanna cut the cost really, really dramatically. Um, and I believe that will happen. We're still so early in the development of the science and understanding how this works. Plus, we have all the engineering tailwinds. So I- I don't know, like, when we get to intelligence too cheap to meter and so fast that it feels instantaneous to us and everything else, but I do believe we can get there for, you know, a pretty high level of, of intelligence. And, um, I- it's important to us, it's clearly important to users, and it'll u- unlock a lot of stuff. On the sort of open source/closed source thing, I think there's great roles for both. I, I think, um, you know, we've open source some stuff, we'll open source more stuff in the future, but really, like, our mission is to build towards AGI and to figure out how to broadly distribute its benefits. We have a strategy for that, seems to be resonating with a lot of people. It obviously isn't for everyone and there's, like, a big ecosystem and there'll also be open source models and people who build that way. Um, one area that I'm particularly interested personally in open source for is I want an open source model that is as good as it can be that runs on my phone, and that I think is gonna... You know, the world doesn't quite have the technology for, for a good version of that yet, but that seems like a really important thing to go do at some point.

Jason Calacanis 7:55 Will you do? Will you do that when you release it?

Sam Altman 7:57 I don't know if we will or someone will, but someone will.

David Friedberg 7:59 What about LLaMA 3?

Sam Altman 8:01 LLaMA 3 running on a phone?

David Friedberg 8:03 Well, I guess maybe there's like a 7 billion version.

Jason Calacanis 8:06 Yeah, yeah, yeah.

Sam Altman 8:07 Yeah, yeah. Uh-

David Friedberg 8:08 I don't know if that's t- if that will fit on a phone or not, but-

Sam Altman 8:10 That sh- should be fittable on a phone, but I don't, I don't... I'm not, I'm not sure if that one is, like... I haven't played with it. I don't know if it's, like-

David Friedberg 8:17 Yeah.

Sam Altman 8:17 ... good enough to kind of do the thing I'm thinking about here.

David Friedberg 8:20 So when, when LLaMA 3 got released, I think the big takeaway for a lot of people was, "Oh, wow, they've, like, caught up to GPT-4." I don't think it's equal in all dimensions, but it's, like, pretty, pretty close. They're pretty in the ballpark. I guess the question is, you know, you guys released 4 a while ago, you're working on 5 or, you know, more upgrades to 4. I mean, I think to Jamal's point about Devon, how do you stay ahead of open source? I mean, it's just... That's just like a very hard thing to do in general, right? I mean, how do you think about that?

Sam Altman 8:51 What we're trying to do is not make the sort of smartest set of weights that we can, what we're trying to make is, like, this useful intelligence layer for people to use, and a model is part of that. I think we will stay pretty far ahead of... I hope we'll stay pretty far ahead of the rest of the world on that. Um, but there's a lot of other work around the whole system that's not just that, you know, the m- the model weights, and we'll have to build up enduring value the old-fashioned way like any other business does. We'll have to figure out a great product and reasons to stick with it and, uh, you know, deliver it at a great price.

Jason Calacanis 9:32 When you founded the organization, you... The stated goal or part of what you discussed was, "Hey, this is too important for any one company to own it, so therefore it needs to be open." Then there was the switch, "Hey, it's too dangerous for anybody to be able to see it, and we need to lock this down," because you, you had some fear about that, I think. I- i- is that accurate? 'Cause the cynical side is like, "Well, this is a capitalistic move." And then the, I think, you know, I'm, I'm curious what the decision was here in terms of going from open, "We, the world needs to see this. It's really important," to closed, "Only we can see it."

Sam Altman 10:09 Well-

Jason Calacanis 10:09 How did you come to that, that conclusion?

Sam Altman 10:11 I mean-

Jason Calacanis 10:11 What were the discussions like? Yeah.

Sam Altman 10:12 ... part of the reason that we released ChatGPT was we want the world to see this, and we've been trying to tell people that AI is really important. And if you go back to, like, uh, October of 2022, not that many people thought AI was gonna be that important or that it was really happening.

Jason Calacanis 10:25 No.

Sam Altman 10:25 Um, and a huge part of what we, we try to do is put the technology in the hands of people. Uh, now again, there's different ways to do that, and I think there really is an important role to just say like, "Here's the weights, have at it," but the fact that we have so many people using a free version of ChatGPT that we don't, you know, we don't run ads on, we don't try to, like, make money on, we just put out there 'cause we want people to have these tools, um, I think has done a lot to provide a lot of value and, uh, you know, teach people how to fish, but also to get the world, um, really thoughtful about what's happening here. Now, we still don't have all the answers and, uh, we're fumbling our way through this like everybody else, and I assume we'll change strategy many more times as we learn new things. You know, when we started OpenAI, we had really no idea about how things were gonna go, um, that we'd make a language model, that we'd ever make a product. We, we started off just... I remember very clearly that first day we were like, "Well, now we're all here. That was... You know, it's difficult to get this set up, but what happens now? Maybe we should write some papers, maybe we should stand around a whiteboard." And we've just been trying to, like, put one foot in front of the other and figure out what's next and what's next and what's next, and I think we'll keep doing that.

David Sacks 11:38 Can I just replay something and just make sure I heard it right? I think what you were saying on the open source/closed source thing is, if I heard it right, all these models, independent of the business decision you make, are going to become asymptotically accurate towards some amount of accuracy. Like, not all, but, like, let's just say there's four or five that are well capitalized enough, you guys, Meta, Google, Microsoft, whomever, right? So let's just say four or five, maybe one startup, a- and on the open web, and then quickly...... the accuracy or the value of these models will probably shift to these proprietary sources of training data that you could get that others can't, or others can get that you can't. Is that how you see this thing evolving, where the open web gets everybody to a certain threshold, and then it's just an arms race for data beyond that?

Sam Altman 12:32 It doesn't f- uh, so I, I definitely don't think it'll be an arms race for data 'cause when the models get smart enough at some point, it shouldn't be about more data. At least not for training. It may matter, data, to make it useful. Um, look. The, the one thing that I have learned most throughout all of this is that, uh, it's hard to make confident statements a couple of years in the future about where this is all gonna go. And so I don't wanna try now. I, I will say that I, I expect lots of very capable models in the world. And, you know, like, it feels to me like we just, like, stumbled on a new fact of nature or science or whatever you wanna call it, which is like, we can create ... You can, like ... Uh, I mean, I, I don't believe this literally, but it's like a spiritual point. You know, intelligence is just this emergent property of matter, and that's like a, that's like a rule of physics or something. Um, so people are gonna figure that out, but there will be all these different ways to design the systems. People will make different choices, figure out new ideas. And I'm sure, like, you know, like any other industry, I would expect there to be multiple approaches, and different people like different ones. You know? Some people like iPhone, some people like an Android phone. I, I think there will be some effect like that.

David Sacks 13:48 Let's go back to that first section of just the, the cost and the speed. All of you guys are sort of a little bit rate limited on literally NVIDIA's throughput, right? And I think that you and most everybody else have sort of effectively announced how much capacity you can get, just because it's as much as they can spin out. What needs to happen at the substrate so that you can actually compute cheaper, compute faster, get access to more energy? How are you helping to frame out the industry solving those problems?

Sam Altman 14:21 Well, we'll, we'll make huge algorithmic gains, for sure, and I don't wanna discount that. I'll, you know ... I, very interested in chips and energy, but if we can make our... if we can make a same quality model twice as efficient, that's like we had twice as much compute.

David Sacks 14:34 Right.

Sam Altman 14:35 And I think there's a gigantic amount of work to be done there. Uh, and I hope we'll start really seeing those results. Um, other than that, the whole supply chain is, like, very complicated, you know? There's, there's logic fab capacity. There's how much HBM the world can make. There's how quickly you can, like, get permits and pour the concrete, make the data centers, and then have people in there wiring them all up. There's finding the energy, which is a huge bottleneck. But, uh, I think when there's this much value to people, uh, the world will do its thing. We'll try to help it happen faster. Um, and there's probably, like ... I don't know how to give it a number, but there's, like, some percentage chance where there is, as you were saying, like, a huge substrate breakthrough, and we have, like, a massively more efficient way to do computing. But I don't, I don't, like, bank on that or spend too much time thinking about it.

David Sacks 15:28 What about the device side, and sort of ... You mentioned sort of the models that can fit on a phone, so obviously, whether that's an LLM or some SLM or something. I'm sure you were thinking about that. But then does the device itself change? I mean, is it, does it need to be as expensive as an iPhone?

Sam Altman 15:44 Uh ... I'm super interested in this. Uh, I, I love, like, great new form factors of computing, and it feels like with every major technological advance, a new thing becomes possible. Uh, phones are unbelievably good, so I think the threshold is, like, very high here. Like, what-

David Sacks 16:05 Mm-hmm.

Sam Altman 16:05 Like, I think, I think, like, I personally think an iPhone is, like, the greatest piece of technology humanity has ever made. It's really a wonderful product. And-

Jason Calacanis 16:15 What comes after it? Like, what-

Sam Altman 16:16 I don't know. I mean, t- I was gonna s- that was what I was saying. It's so good that to get beyond it, I think the bar is, like, quite high.

Jason Calacanis 16:24 Well, you've been, you've been working with Jony Ive on, on something, right? Like, uh ...

Sam Altman 16:26 We've been discussing ideas, but, uh, I don't-

Jason Calacanis 16:29 Where-

Sam Altman 16:29 Like, if I knew ...

David Sacks 16:31 Is it that that it has to be more complicated, or actually just much, much cheaper and simpler?

Sam Altman 16:36 Well, every- most- almost everyone's willing to pay for a phone anyway. So if you could, like, make a way cheaper device, I think the barrier to carry a second thing or use a second thing is pretty high. So I don't think c- given that we're all willing to pay for phones, or most of us are, I don't think cheaper is the answer.

Jason Calacanis 16:53 Hmm. Different is the answer, then?

David Friedberg 16:56 Would there be, like, a specialized chip that would run on the phone that was really good at powering a, you know, a phone-sized AI model?

Sam Altman 17:03 Probably, but the phone manufacturers are gonna do that, for sure. That doesn't, that doesn't necessitate a new device. I think you'd have to, like, find some really different interaction paradigm that the technology enables. Uh, and if I knew what it was, I would be excited to be working on it right now. But it's-

Jason Calacanis 17:20 Well, you have, you have voice working right now in the app. In fact, I set my action button on my phone to go directly to ChatGPT's voice app, and I use it with my kids, and they love it, talking to it.

Sam Altman 17:30 I think-

Jason Calacanis 17:30 It's got latency issues, but it's really great.

Sam Altman 17:32 Well, we'll get, we'll, we'll get that, we'll get that better.

Jason Calacanis 17:34 Yeah.

Sam Altman 17:34 And I think voice is a hint to whatever the next thing is. Like, if you-

Jason Calacanis 17:39 Hmm.

Sam Altman 17:39 ... can get voice interaction to be really good, it feels ... I think that feels like a different way to use a computer. But again, like, where do-

Jason Calacanis 17:47 What's the blocker with that, by the way? Like, why, why is it not responsive and ... You know, it's, it feels like a CB. You know, like over, over-

Sam Altman 17:55 (laughs) Yeah. Yeah, yeah. Totally.

Jason Calacanis 17:55 It's really annoying to use, you know, uh, in that way. But it, it's also brilliant when it gives you the right answer.

Sam Altman 18:01 We are working on that. Uh, it's, it's so clunky right now. It's slow. It's, like, kinda doesn't feel very smooth or authentic or organic. Like, we'll get all that to be much better.

Jason Calacanis 18:13 Hm.

David Friedberg 18:14 What about-

Jason Calacanis 18:14 I want-

David Friedberg 18:14 ... computer vision? I mean, they have glasses, or maybe you could wear a pendant.

Sam Altman 18:19 Yeah.

David Friedberg 18:19 I mean, you take the combination of-... visual or video data, combine it with voice, and now-

Sam Altman 18:26 Super-

David Friedberg 18:26 ... AI knows everything that's happening around you.

Sam Altman 18:28 Super powerful to be able to, like, the multi-modality of saying, like, "Hey, ChatGPT, what am I looking at?" Uh, or, like, "What kind of plant is this? I can't quite tell." Um, that's obvi- that, that's like a good, that's another, I think, like, hint, but whether people wanna wear glasses or, like, hold up something when they want that, like, I, I, there's a bunch of just, like, the, like, the, the sort of, like, societal, interpersonal issues here are all- Oh, yes. ... very complicated about wearing-

David Sacks 18:57 Yeah.

Sam Altman 18:57 ... a computer on your face. Um-

David Sacks 18:59 We s- we saw that with Google Glass. People got punched in the face in the mission. Started a lot of fight, yeah.

Sam Altman 19:03 I forgot about that. I forgot about that. So, so I, I, I think it's like...

David Friedberg 19:08 What are the apps that could be unlocked if AI was sort of ubiquitous on people's phones? Do you have a sense of that or what would you wanna see built?

Sam Altman 19:19 Uh, I, I think what I want is just this always on, like, super low friction thing where I can, either by voice or by text or ideally, like, some other... it just kinda knows what I want, have this, like, constant thing helping me throughout my day that's got, like, as much context on, as possible. It's like the world's greatest assistant, um, and it's just this, like, thing working to make me better and better. Uh, there's, there, there's, like, a... I know when you hear people, like, talk about the AI future they're imag- they imagine, there's sort of two different approaches. And they don't sound that different, but I think they're, like, very different for how we'll design the system in practice. There's the, "I want a extension of myself. Um, I want, like, a ghost or an alter ego or this thing that really, like, is me, is acting on my behalf, is, um, responding to emails, not even telling me about it. I- i- is sort of, like, it, it becomes more me and is me." And then there's this other thing, which is like, "I want a great senior employee. It may get to know me very well. I may, ah, delegate it. You know, you can, like, have access to my email, and I'll tell you the constraints, but, but I think of it as this, like, separate entity." And I personally like the separate entity approach better and think that's where we're gonna head. Um, and so in that sense, the thing is not you, but it's, it's like a always available, always great, super capable assistant, executive, whatever.

David Sacks 20:57 It's an agent in a way. Like, it's out there working on your behalf and understands what you want and anticipates what you want, is, is what I'm reading into what you're saying.

Sam Altman 21:05 I think there'd be agent-like behavior, but there's, like, a difference between a senior employee and an agent.

David Sacks 21:13 Yeah.

Sam Altman 21:13 And, uh, like, I wanted... You know, if I think of, like, my, I think, like, of... Like, one of the things that I like about a, a senior employee is they'll, they'll push back on me. They will sometimes not do something I ask, or they sometimes will say, like, "I can do that thing if you want, but if I do it, here's what I think would happen, and then this and then that, and are you really sure?"

David Sacks 21:38 Hmm.

Sam Altman 21:39 And I definitely want that kind of vibe, which not, not just like this thing that I-

David Sacks 21:43 Reason.

Sam Altman 21:43 ... give a task and it blindly does-

David Sacks 21:45 Yeah.

Sam Altman 21:45 ... for sure.

David Sacks 21:45 It can reason.

Sam Altman 21:45 For sure.

David Sacks 21:46 Yeah. Yeah, and push back and so-

Sam Altman 21:47 It can reason and has, like, the kind of relationship with me that I would expect out of a really competent person that I worked with, which is different from, like, a sycophant.

David Sacks 21:56 Yeah. The thing in that world where if you have this, like, Jarvis-like thing that can r- reason, what do you think it does to products that you use today where the interface is very valuable? So for example, if you look at an Instacart or if you look at an Uber or if you look at a DoorDash, these are not services that are meant to be pipes that are just providing a set of APIs to a smart set of agents that ubiquitously work on behalf of eight billion people. What do you think has to change in how we think about how apps need to work, of how this entire infrastructure of experiences need to work in a world where you're agentically interfacing to the world, you know?

Sam Altman 22:36 I'm actually very interested in designing a world that is equally usable by humans and by AIs. So I, I, I, uh, I, I like the interpretability of that. I like the smoothness of the handoffs. I like the ability that we can provide feedback or whatever. So, you know, DoorDash could just expose some API to my future AI assistant, and they could go put the order in or whatever. Or I could say, like, I could be holding my phone, and I could say, "Okay, AI assistant, like, you put in this order on DoorDash, please." And I could, like, watch the app open and see the thing clicking around, and I could say, "Hey, no, not this," or, like... Um, there, there's something about designing a world that is usable equally well by humans and AIs that I think is a interesting concept.

David Sacks 23:27 And can manage the handoffs.

Sam Altman 23:28 For some reason, I'm, like, more excited about humanoid robots than sort of robots of, like, very other shapes. The world is very much designed for humans, and I think we should absolutely keep it that way.

David Sacks 23:36 Mm-hmm.

Sam Altman 23:37 And a shared interface is nice.

David Sacks 23:39 So you see voice chat, that modality kinda gets rid of apps. You just ask it for sushi. It knows sushi you liked before. It knows what you don't like and does its best shot at doing it.

Sam Altman 23:49 I, it's hard for me to imagine that we just go to a world totally where you say, like, "Hey, ChatGPT, order me sushi," and it says, "Okay, do you want it from this restaurant? What kind, what time?" Whatever. I think user, I think visual user interfaces are super good for a lot of things. Um, and it's hard for me to imagine, like, a world where you never look at a screen and just use voice mode only. But I, I can imagine that for a lot of things.

David Sacks 24:20 Yeah, I mean, Apple tried with Siri. Like, you could, you, supposedly you can order at Uber automatically with Siri. I don't think anybody's ever done it because it's-

Jason Calacanis 24:27 ... why would you take the risk of not

David Sacks 24:29 Well, the quality-

Jason Calacanis 24:30 ... bringing your phone? Yeah.

David Sacks 24:30 To your point, the quality is not good, but when the quality is good enough, you'll a- you'll actually prefer it just because it's just lighter weight. You don't have to take-

Jason Calacanis 24:37 Mm-hmm.

David Sacks 24:37 ... your phone out, you don't have to search for your app and press it and, oh, it automatically logged you out. Oh, hold on, log back in. Oh, TFA. It's a whole pain in the ass.

Sam Altman 24:45 You know, it's like setting a timer with Siri, I do every time because it works really well and it's great.

David Sacks 24:51 It works really well, exactly.

Sam Altman 24:51 And I don't need more information. But ordering an Uber, like, I wanna see the prices for a few different options, I wanna see how far away it is, I wanna see, like, maybe even where they are on the map 'cause I might walk somewhere. I get a lot more information by- I think in less time by looking at that Order the Uber screen than I would if I had to do that-

David Sacks 25:10 Hmm.

Sam Altman 25:10 ... all through the audio channel. So-

Jason Calacanis 25:12 I like your idea of watching it happen. That's kinda cool.

Sam Altman 25:15 I think there will just be, like, yeah, different- there are different interfaces we use for different tasks, and I think that'll keep going.

David Sacks 25:22 Of all the developers that are building apps and experiences on OpenAI, are there a few that stand out for you where you're like, "Okay, this is directionally going in a super interesting area," even if it's, like, a toy app, but are there things that you guys point to and say, "This is really important"?

Sam Altman 25:40 Um, I met with a new company this morning, or b- barely even a company, it's, like, two people that are gonna work on a summer project, trying to actually finally make the AI tutor. Like-

Jason Calacanis 25:51 Hmm.

Sam Altman 25:51 A- a- and I've always been interested in the space. I- a lot of people have done great stuff on our platform, but if- if someone can deliver, like, the way that you actually, like, uh, they used a phrase I love, which is, "This is gonna be like a Montessori level reinvention for how people, how people learn things."

Jason Calacanis 26:10 Wow, yeah.

Sam Altman 26:10 Um, but if you can, like, find this new way to, like, let people explore and learn in new ways on their own, I'm personally super excited about that. Um, a lot of the coding related stuff you mentioned, Devin, earlier, I- I think that's, like, a super cool vision of the future. The thing that I am... Heal- healthcare, I- I believe should be pretty transformed by this, but the thing I'm personally most excited about is the sort of doing faster and better scientific discovery. GPT-4 clearly not there in a big way, although maybe it accelerates things a little bit by making scientists more productive, but-

David Sacks 26:46 But AlphaFold 3, yeah.

Sam Altman 26:49 That's, like-

David Friedberg 26:50 But Sam-

Sam Altman 26:51 That will be a triumph.

David Friedberg 26:52 Those are not... Like, these- these models are trained and built differently than the language models. I mean, to some, th- obviously there's a- a lot that's similar, but there's a lot, um... There's kind of a ground up architecture to a lot of these models that are being applied to these specific problem sets, to these prec- specific applications, like chemistry interaction modeling, for example. Does-

Sam Altman 27:19 You'll- you'll need some of that, for sure, but the- the thing that I think we're missing across the board for many of these things we've been talking about, is models that can do reasoning. And once you have reasoning, you can connect it to chemistry simulators or whatever else.

David Friedberg 27:32 So I- I guess, yeah, that's the important question I- I wanted to kinda talk about today, was this idea of networks of models. People talk a lot about agents as if there's kind of this linear set of call functions that happen.

Sam Altman 27:47 Yeah.

David Friedberg 27:47 But one of the things that arises in biology is networks of systems that have cross interactions, that the aggregation of the system pro- the aggregation of the network produces an output rather than one thing calling another, that thing calling another. Do we see, like, an emergence in this architecture of either specialized models or network models that work together to address bigger problem sets, use reasoning? There's computational models that do things like chemistry or arithmetic, and there's other models that do... Rather than one model to rule them all that's purely generalized.

Sam Altman 28:25 I don't know. Um... I don't know how much reasoning is gonna turn out to be a super generalizable thing. I suspect it will, but that's more just, like, an intuition and a hope and it would be nice if it worked out that way. I- I don't know if that's, like...

David Friedberg 28:46 But let's walk through the- the protein modeling example. There's a bunch of training data, images of proteins, and then sequence data, and they build a model, predictive model, and they have a set of processes and steps for doing that. Do you envision that there's this artificial general intelligence or this great reasoning model that then figures out how to build that submodel, that figures out how to solve that problem by acquiring the necessary data and then resolving to a solution?

Sam Altman 29:13 There's so many ways where that could go. Like, maybe it is, it trains a literal model for it, or maybe it just, like, knows the one big model, what, uh, it can, like, go pick what other training data it needs and ask a question and then update on that.

David Friedberg 29:26 Right.

Sam Altman 29:26 Um...

David Friedberg 29:28 I guess the real question is, are all these startups gonna die? Because so many startups are working in that modality, which is go get special data and then train a new model on that special data from the ground up, and then it only does that one sort of thing and it works really well at that one thing and it works better than anything else at that one thing.

Sam Altman 29:43 You know, there- there's, like, a version of this I think you can, like, already see. When you were- when you were talking about, like, biology and these complicated networks of systems, the reason I was smiling, I- I got super sick recently and I'm mostly better now, but it was just, like, body, like, got beat up, like, one system at a time. F- like, you can really tell, like, okay, it's this cascading thing, and, uh, and that reminded me of you, like, talking about the, like, biology is just these, like, you have no idea how much these systems interact with each other until things start going wrong, and that was sort of, like, interesting to see. But I was using, um, I was, like, using ChatGPT, uh, to try to, like, figure out, like, what was happening, whatever, and- and- and would say, "Well, I'm, you know, unsure of this one thing," and then I just, like, posted a paper...... on it without even reading the paper, um, like in the context and it says, "Oh, that was a thing I was unsure of. Like now I think this instead." So there's like a- that was like a small version of what you're talking about, where you can like- can say this- I don't- I don't know this thing, and you can put more information. You don't retrain the model. You're just adding to the context here, and now you're getting the-

Jason Calacanis 30:52 So these models that are predicting protein structure, like let's say, right? This is the whole basis, and now- now other molecules at AlphaFold3, can they- could... yeah, I mean is it basically a world where the best generalized model goes in and gets that training data and then figures out on its own? And maybe you could- maybe you could use an example for us. Can you tell us about Sora, your video model that generates amazing moving images, moving video? And- and what's different about the architecture there, whatever you're willing to share on how m- how that is different?

Sam Altman 31:28 Yeah. So my- on the general thing first, my... you clearly will need specialized simulators, connectors, pieces of data, whatever. But my intuition... a- and again, I don't have this like backed up with science. My intuition would be if we can figure out the core of generalized reasoning, connecting that to new problem domains in the same way that humans are generalized reasoners, would, I think, be- be doable.

Jason Calacanis 32:02 It's like a fast unlock-

Sam Altman 32:03 Um...

Jason Calacanis 32:03 ... faster unlock than...

Sam Altman 32:04 I think- I- I think so.

Jason Calacanis 32:06 Mm-hmm.

Sam Altman 32:07 Um... but yeah, you- you- Sora like does not start with a language model. Um, it's- it- that- that's a model that is like customized to do video uh... and- and so like we're clearly not at that world yet.

Jason Calacanis 32:24 Right. So you guys... so just as an example, for you guys to build a good video model, you built it from scratch using, I'm assuming, some different architecture and different data. But in the future, the generalized reasoning system, the j- the AGI...

Sam Altman 32:40 Yeah.

Jason Calacanis 32:40 ... whatever system theoretically could render that by figuring out how to do it.

Sam Altman 32:46 Yeah, I mean, one example of this is like, okay, you know, as far as I know, all the best text models in the world are still auto-regressive models, and the best image and video models are diffusion models, and that's like sort of strange in some sense.

Jason Calacanis 32:59 Right. That's it. Yeah. Yeah. Yeah. So

Jason Calacanis 33:01 there's a big debate about, uh, training data. You guys have been, I think, the most thoughtful of any company. You've got licensing deals now, FT, et cetera. And, uh, we gotta just be gentle here because you're involved in a New York Times lawsuit you weren't able to settle, I guess, an arrangement with them for training data. How do you think about fairness and fair use? We've had big debates here on the pod. Obviously, your actions are- you know, speak volumes that you're trying to be fair by doing licensing deals. So what- what's your personal position on the rights of artists who create beautiful music, lyrics, books, a- and you taking that and then making a derivative product out of it, and- and then monetizing it, and- and what's fair here, and- and how do we get to a world where, you know, artists can make content in the world and then decide what they want other people to do with it?

Sam Altman 33:59 Yeah.

Jason Calacanis 33:59 Yeah. And- and- and I'm just curious of your personal belief, because I know you to be a thoughtful person on this, and I know a lot of other people in our industry are not very thoughtful about how they think about content creators.

Sam Altman 34:11 So I think it's very different for different kinds of in-... I mean, look, on unfair use, I think we have a- a very reasonable position under the current law, but I think AI is so different. But for things like art, we'll need to think about them in different ways. But let's say if you go read a bunch of math on the internet and learn how to do math, that, I think seems unobjectionable to most people. And then there's, like, you know, another set of people who might have a different opinion. Well, what if you, like... eh, actually, let me not get into that, just in the interest of not making this answer too long. So I- I think there's, like, one category of people who are like, okay, there's, like, generalized human knowledge. You can kind of, like, go... if you learn that, like, that's- that- that's like open domain or something if you kind of go learn about the Pythagorean theorem. Um, that's one end of the spectrum, and then I think the other extreme end of the spectrum is, um... is art. A- and maybe even, like, more than- more specifically I would say it's, like, doing... it's a system generating art in the style or the likeness of another artist, um, would be kind of the furthest end of that, and then there's many, many cases on the spectrum in between. Uh... I think the conversation has been historically very caught up on training data, but it will increasingly become more about what happens at inference time.

Jason Calacanis 35:41 Oh.

Sam Altman 35:41 As training data becomes less valuable and the- what the system does, accessing, you know, information in- in context, in real time, or, uh, you know, taking li- like something like that, what happens at inference time will become more debated and- and how the- what the new economic model is there. So if you say, like, uh... if you say, like, create me a song in this- in the style of Taylor Swift, even if the model were never trained on any Taylor Swift songs at all, you can still have a problem, which is it may have read about Taylor Swift. It may know about her themes. Taylor Swift means something, and then- and then the question is, like, should that model, even if it were never trained on any Taylor Swift song whatsoever, be allowed to do that? And if so, um...... how should Taylor get paid?

Jason Calacanis 36:37 Right.

Sam Altman 36:38 So I think there's an opt-in/opt-out in that case, first of all, and then there's an economic model. Um, staying on the music example, there is something interesting to look at from the historical perspective here, which is, uh, sampling and how the economics around that work.

Jason Calacanis 36:53 Yes.

Sam Altman 36:53 This is not quite the same thing, but it's like an interesting place to start looking.

David Friedberg 36:56 Sam, let me just challenge that. What's the difference in the example you're giving of the model learning about things like song structure, tempo, melody, harmony relationships, all the... discovering all the underlying structure that makes music successful and then building new music using training data, and what a human does that listens to lots of music, t-... learns about, and f-... and their brain is processing and building all those same sort of predictive models or those same sort of, uh-

Sam Altman 37:26 Yeah.

David Friedberg 37:26 ... discoveries or understandings? What's the difference here and w- why, why are you making the case that perhaps artists should be uniquely paid? This is not a sampling situation. You're not... the AI is not outputting, and it's not storing in the model the actual original song.

Sam Altman 37:42 Yeah. I wasn't-

David Friedberg 37:42 It's learning structure, right? So...

Sam Altman 37:44 I wasn't trying to make-

David Friedberg 37:45 Yeah.

Sam Altman 37:45 ... that, that point 'cause I agree.

David Friedberg 37:47 Okay.

Sam Altman 37:47 Like, in the same way that humans are inspired by other humans.

David Friedberg 37:50 Right.

Sam Altman 37:50 I was saying if you, if you say, "Generate me a song in the style of Taylor Swift."

David Friedberg 37:54 I see, right. Okay. Well, the prom-

Sam Altman 37:55 Yeah, I think that's like a-

David Friedberg 37:55 Well, the, well, the prompt leverages some artist, but-

Sam Altman 37:58 I, I think personally that's a different case.

David Friedberg 38:01 Would you be comfortable asking or would you be comfortable letting the model train itsel-... a music model being trained on the whole corpus of music that humans have created without royalties being paid to the artists that, um, that music is being fed in, and then you're not allowed to ask, you know, artist-specific prompts. You could just say, "Hey, pay me a... play me a, a really cool pop song that's fairly modern about heartbreak, uh, you know, with a female voice."

Sam Altman 38:26 Yeah.

David Friedberg 38:26 You know.

Sam Altman 38:27 We, we have currently made the decision not to do music, and partly because exactly these questions of where you-

David Friedberg 38:33 Mm-hmm.

Sam Altman 38:33 ... draw the lines and-

David Friedberg 38:34 Mm-hmm.

Sam Altman 38:34 ... you know, like, even... I was meeting with several musicians I really admire recently and I was just trying to, like, talk about some of these edge cases, but even the world in which... if we went and let's say we paid 10,000 musicians to create a bunch of music just to make a great training set where the music model could learn everything about strong s- song structure, um, and what makes a good catchy beat and everything else, um, and only trained on that, let's say we could still make a great music model, which may- maybe we could. Um, you know, I was kind of like posing that as a thought experiment to musicians, and they're like, "Well, I can't object to that on any principle basis at that point, um, and yet there's still something I don't like about it." Now, that's not a reason not to do it, um, necessarily, but it is... Did you see that ad that Apple put out, maybe it was yesterday or something, of, like, squishing all of human creativity down-

Jason Calacanis 39:34 Yeah, yeah.

Sam Altman 39:34 ... into one really thin iPad?

Jason Calacanis 39:35 What was your take on it?

Sam Altman 39:38 Uh...

Jason Calacanis 39:39 People got really emotional about it, yeah. It's-

Sam Altman 39:41 Yeah.

Jason Calacanis 39:41 ... stronger reaction-

Sam Altman 39:42 Yeah, yeah, yeah.

Jason Calacanis 39:42 ... than you would think. Yeah.

Sam Altman 39:43 There's something about... I'm obviously hugely positive on AI, but there is something that I think is beautiful about human creativity and human artistic expression, and, you know, for an AI that just does better science, like great, bring that on. But an AI that is gonna do this, like, deeply beautiful human creative expression, I think we should, like, figure out... it's gonna happen. It's gonna be a tool that will lead us to greater creative heights, but I think we should figure out how to do it in a way that, like, preserves the spirit of what we all care about here.

Jason Calacanis 40:18 And I, I think your actions speak loudly. We were trying to do Star Wars characters in DALL-E, and if you ask for Darth Vader, it says, "Hey, we can't do that." So you've, I guess, red teamed or whatever y- you call it internally-

Sam Altman 40:34 We tried.

Jason Calacanis 40:34 ... you're... yeah, you're not allowing people to use other people's IP, so you've taken that decision. Now, if you asked it to make a Jedi bulldog or a Sith Lord bulldog, which I did, it made my bulldogs as Sith bulldogs. So-

Sam Altman 40:47 There's an interesting question about like where-

Jason Calacanis 40:48 ... it's really about your spectrum, right? Yeah.

Sam Altman 40:50 Yeah. You know, we put out this thing yesterday called the spec, um, where we're trying to say here are... here's, here's how our model's supposed to behave, and it's very hard. It, it's a long document. It's very hard to, like, specify exactly in each case where the limits should be, and I view this as, like, a discussion that's gonna need a lot more input. Um, but, but these sorts of questions about, okay, maybe it shouldn't generate Darth Vader, but the idea of a Sith Lord or a Sith style thing or a Jedi at this point is like part of the culture, or like, like... these are, these are all hard decisions.

Jason Calacanis 41:26 Yeah. And, and I think you're right. The music industry is going to consider this opportunity to make Taylor Swift songs their opportunity. It's part of the four-part fair use test is, you know, these... who gets to capitalize on new innovations for existing art? And, and Disney has an argument that, "Hey, y- you know, if, if you're gonna make Sora versions of Asho- Ashoka or whatever, Obi-Wan Kenobi, that's Disney's opportunity, and that's a great partnership for you, you know, to pursue."

David Sacks 41:55 So we're... I think this section I would label as AI and the Law. So let me-

Jason Calacanis 41:59 Yeah.

David Sacks 41:59 ... ask maybe a higher level question.

David Sacks 42:02 What does it mean when people say, "Regulate AI"?

Jason Calacanis 42:06 Totally.

David Sacks 42:07 Sam, what does it... what does that even mean?

David Friedberg 42:09 And comment on California's new (laughs) proposed regulations-

Jason Calacanis 42:12 Yeah.

David Friedberg 42:12 ... as, as well if you, if you're up for it.

Sam Altman 42:15 Uh, I'm concerned. I mean, there's so many proposed regulations-

David Friedberg 42:18 Yeah.

Sam Altman 42:18 ... but most of the ones I've seen on the California state, things I'm concerned about. I also have a general fear of the states all doing this them- themselves. Um, when people say, "Regulate AI," I don't think they mean one thing. I think there's like... some people are like, "Ban the whole thing." Some people are like, "Don't allow it to be open source, require it to be open source." Um, the thing that I am personally most interested in is I think there will come...... look, I may be wrong about this. I will acknowledge that this is a forward-looking statement, and those are always dangerous to make. But I think there will come a time in the not super distant future, like, you know, we're not talking, like, decades and decades from now, where AI sys- the frontier AI systems are capable of causing significant global harm. And for those kinds of systems, in the same way we have, like, global oversight of nuclear weapons or synthetic bio, or things that can really, like, have a very negative impact way beyond the realm of one country, uh, I would like to see some sort of international agency that is looking at the most powerful systems and ensuring, like, reasonable safety testing. You know, these things are not going to escape and recursively self-improve or whatever.

Jason Calacanis 43:31 The criticism of this is that you're- you have the resources to cozy up, to lobby, to be involved, and you've been very involved with politicians, and then startups, which you're also passionate about and invest in, um, are not gonna have the ability to resource, uh, and deal with this, and that this regulatory capture, as per our friend, you know, Bill Gurley did a great talk last year about it. So, maybe you could address that head-on. Do- do you feel like-

Sam Altman 43:57 You know, if the- if the line were, "We're only gonna look at models that are trained on computers that cost more than $10 billion, or more than $100 billion, or whatever dollars," I'd be fine with that. There'd be some line that'd be fine. And, uh, I don't think-

Jason Calacanis 44:10 Oh.

Sam Altman 44:10 ... that puts any regulatory burden on startups.

Jason Calacanis 44:12 So if you have, like, the- the nuclear raw material to make a nuclear bomb, like there's a small subs- set of people who have that, therefore you use the analogy of, like, a- a nuclear inspectors-

Sam Altman 44:22 Yeah.

Jason Calacanis 44:22 ... kind of situation.

Sam Altman 44:23 Yeah.

Jason Calacanis 44:23 I think that- that's interesting. Sax, you have a question?

David Friedberg 44:26 Well, Chamath, go ahead. You had a follow-up.

Sam Altman 44:29 Can I say one more thing about that?

Jason Calacanis 44:31 Yeah.

David Friedberg 44:31 Of course.

Sam Altman 44:31 I- I'd be super nervous about regulatory overreach here. I think we can get this wrong by doing way too much, I th- or even a little too much. I think we can get this wrong by doing not enough. But- but I do think part of... And I... And that, I mean, you know, we have seen regulatory overstepping or capture just get super bad in other areas. Um, and, you know, the- also, maybe nothing will happen, but- but I think it is part of our duty and our mission to, like, talk about what we believe is likely to happen, and what it takes to get that right.

David Friedberg 45:08 The challenge, Sam, is that we have statute that is meant to protect people, protect society at large. What we're creating, however, is statute that gives the government rights to go in and audit code, to audit business, um, trade secrets. Uh, we've never seen that to this degree before. Basically, the California legislation that's proposed, and some of the federal legislation that's been proposed basically requires the fed- the government to audit a model, to audit software, to audit and review the parameters and the weightings of the model, and then you need their check mark in order to deploy it for commercial or public use. And for me, it just feels like we're trying to rein in the- the- the government agencies for fear, and- and because folks have a hard time understanding this and are scared about the implications of it, they wanna control it.

Sam Altman 46:08 Yeah.

David Friedberg 46:08 And because they want... And- and the only way to control it is to say, "Give me a right to audit before you can release it."

46:11 Yeah, and they're clueless.

46:12 As opposed to say...

46:13 These people are clueless.

46:13 And they're clueless. I mean, the way that the- the stuff is written, you read it, you're, like, gonna pull your hair out 'cause as you know better than anyone, in 12 months, none of this stuff's gonna make sense anyway.

Sam Altman 46:21 Totally.

David Friedberg 46:22 Right?

Sam Altman 46:22 Look, the reason I have pushed for an agency-based approach for the- for- for kind of, like, the big picture stuff and not a, like, write it in laws. I don't... In 12 months, it will all be written wrong, and I don't think... Even if these people were, like, true world experts, I don't think they could get it right looking out 12 or 24 months. Um, and I don't... These policies, which is like, "We're gonna look at, you know, we're gonna audit all of your source code and, like, look at all of your weights one by one," like, uh, yeah, I think there's a lot of crazy proposals out there. Um-

David Friedberg 46:55 By the way, especially if the models are always being retrained all the time, if they become more dynamic.

Sam Altman 46:59 Y- yeah, yeah, yeah.

David Friedberg 46:59 Right? Yeah.

Sam Altman 46:59 Again, this is why I think it's... Yeah.

David Friedberg 47:00 Yeah.

Sam Altman 47:00 But- but, like, when... Before an airplane gets certified, there's, like, a set of safety tests. We put the airplane through it, um, and-

David Friedberg 47:09 Totally.

Sam Altman 47:10 ... it's different than reading all of your code.

David Friedberg 47:11 That's- that- that's reviewing the output of the model-

Sam Altman 47:13 Yeah.

David Friedberg 47:13 ... not reviewing the in- insides of the model.

Sam Altman 47:16 And- and- and so what I was gonna say is, I-

David Friedberg 47:18 Yeah.

Sam Altman 47:18 ... that is the kind of thing that I think as safety testing makes sense.

David Friedberg 47:24 How are we going to get that to happen, Sam? A- and I'm not just speaking for OpenAI. I speak for the industry, for- for humanity, because I am concerned that we draw ourselves into almost like a Dark Ages type of-

Sam Altman 47:35 Yeah.

David Friedberg 47:35 ... era by restricting the growth of these incredible technologies that can prosper human- that- that humanity can prosper from so significantly. How do we change the- the sentiment and get that to happen, 'cause this is all moving so quickly at the government levels, and folks seem to be getting it wrong, and I'm- I'm personally concerned.

David Sacks 47:51 Well, just to- just to build on that, Sam.

Sam Altman 47:52 Yeah.

David Sacks 47:52 The architectural decision, for example, that LLaMA took is pretty interesting in that it's like, we're gonna let LLaMA grow and be as unfettered as possible, and we have this other kind of thing that we call LLaMA Guard that's meant to be these protective guardrails. Is that how you see the problem being solved correctly, or do you see that-

Sam Altman 48:11 Look, at the current- at the current strength of models, definitely some things are gonna go wrong. And I don't wanna, like, make light of those or not take those seriously, but I'm not, like... I don't have any, like, catastrophic risk worries with a GPT-4 level model. Um, and I think there's many safe ways to choose to deploy this. Uh... May- maybe we'd find more common ground if we said that, uh... And I think, like, you know, the specific example of models that are capable, that are technically capable, not... even if they're not gonna be used this way, of recursive self-improvement.... um, or of, you know, autonomously designing and deploying a bio weapon or something like that.

Chamath Palihapitiya 49:01 Or a new model.

Sam Altman 49:03 That was the recursive self-improvement point.

Chamath Palihapitiya 49:05 Yeah, right.

Sam Altman 49:05 Um, you know, we should have safety testing on the outputs at an international level for models that, you know, have a reasonable chance of, of posing a threat there. Uh, I don't think, like, GPT-4, of course, does not pro- pose an- any sort of... Well, I don't wanna say any sort 'cause we don't... Yeah, I don't think that GPT-4 poses a material threat on those kinds of things, and I think there's many safe ways to release a model like this. Um, but, you know, when, like, significant loss of human life is a serious possibility, like airplanes or any number of other examples where I think we're happy to have some sort of testing framework. Like, I don't think about an airplane when I get on it. I just assume it's gonna be safe.

Chamath Palihapitiya 49:54 Right. Right.

Jason Calacanis 49:55 There's a lot of hand-wringing right now, Sam, about jobs, and you had a lot of... I think you did, like, some sort of a test when you were at YC about UBI, and you've been public-

Sam Altman 50:04 Our results on that come out very soon. Uh, just- it was a five-year study that wrapped up, um, or started five years ago. Well, there was, like, a beta study first, and then there was, like, a long one that ran. But, uh-

Jason Calacanis 50:15 Well, Mark, what, what did you learn about that?

Chamath Palihapitiya 50:16 Can you explain- yeah, why'd you start, why'd you start it? Maybe just explain UBI and why you started it.

Sam Altman 50:21 Um, so we started thinking about this in 2016, uh, kind of about the s- same time we started taking AI really seriously. And the theory was that the magnitude of the change that may come to society and jobs and the economy and, and sort of in some deeper sense than that, like, what the social contract looks like, um... Meant that we should have many studies to study many ideas about new, new ways to arrange that. Um, I also think that, you know, I'm not, like, a super fan of how the government has handled most policies designed to help poor people, and I kind of believe that if you could just give people money, they would make good decisions, and the market would do its thing. And, you know, I'm very much in favor of lifting up the floor and reducing, eliminating poverty, um, but I'm interested in better ways to do that than what we have tried for the existing social safety net and, and kind of the way things have been handled. And I think giving people money is not gonna go solve all problems. It's certainly not gonna make people happy, but it might, it might solve some problems, and it might give people a better horizon with which to help themselves, and I'm interested in that. I, I think that now that we see some of the ways... So 2016 was very long time ago. Uh, you know, now that we see some of the ways that AI is developing, I wonder if there's better things to do than the traditional, um, conceptualization of UBI. Uh, like, I wonder, I wonder if the future looks something like more like universal basic compute than universal basic income, and everybody gets, like, a slice of GPT-7's compute. And they can use it. They can resell it. They can donate it to somebody to use for cancer research. But, but what you get is not dollars, but this, like-

Chamath Palihapitiya 52:21 You get a productivity.

Sam Altman 52:21 ... slice. Yeah, you own, like,

Sam Altman 52:23 part of the productivity.

Chamath Palihapitiya 52:24 Right.

Jason Calacanis 52:24 I would like to shift to the gossip part of this.

Sam Altman 52:27 (laughs)

Chamath Palihapitiya 52:27 Okay.

Jason Calacanis 52:27 Gossip? What gossip?

Sam Altman 52:29 (laughs)

Chamath Palihapitiya 52:29 We still haven't gotten to the gossip.

Jason Calacanis 52:29 Sam, let's go back, let's go back to November. What the flying (censored) happened?

Chamath Palihapitiya 52:34 (laughs)

Sam Altman 52:36 Um, you know, I, I... If you have specific questions, I'm happy to-

Chamath Palihapitiya 52:41 (laughs)

Sam Altman 52:41 Maybe I said, maybe I won't, but like-

Jason Calacanis 52:42 You said you were gonna talk about it at some point, so here's the point. What the hell happened?

Sam Altman 52:47 I, I mean, I-

Jason Calacanis 52:47 You were fired, you came back. There was palace intrigue. Did somebody stab you in the back?

Sam Altman 52:52 Yeah.

Jason Calacanis 52:52 Did you find AGI? What's going on? Tell us.

Sam Altman 52:55 Uh-

Jason Calacanis 52:55 This is a safe space, Sam.

Sam Altman 52:57 (laughs) Um, I was fired. I was... I talked about coming back. I kind of was a little bit unsure at the moment about what I wanted to do 'cause I was very upset. Um, and I realized that I really loved OpenAI and the people and that I would come back, and I kind of... I knew it was gonna be hard. It was even harder than I thought, but I, I kind of was like, "All right, fine." Um, I agreed to come back. Um, the board, like, took a while to figure things out, and then, uh, you know, we were kind of, like, trying to keep the team together and keep doing things for our customers and, uh, you know, sort of started making other plans. Then the board decided to hire a different interim CEO, um, and then everybody... There were many people

David Sacks 53:48 Oh my gosh. What was, what was that guy's name? He was there for, like, Scaramucci, right? Like-

Sam Altman 53:52 Uh-

David Sacks 53:53 (laughs)

Sam Altman 53:53 ... 10 days.

Jason Calacanis 53:54 Em- Em- Emmett's great, and I, I have nothing but good things to say about Emmett-

David Sacks 53:58 Scaramucci. (laughs) I wasn't here for Scaramucci.

Sam Altman 53:59 ... in that whole process. Um, and then-

Jason Calacanis 54:01 Where were you when they, um... when you found the news that you'd been fired? Like, well, take me to that moment.

Sam Altman 54:06 I was in Vegas. I was at a hotel room in Vegas for F1 weekend.

Chamath Palihapitiya 54:09 I think that's happened to you before, J Cal.

Jason Calacanis 54:10 So you get a text and they're like, "You've been fired. Pick up your pack."

Chamath Palihapitiya 54:13 What'd you say to them? I said I think that's happened to you before, J Cal.

Sam Altman 54:16 (laughs)

Jason Calacanis 54:16 I, I'm trying to think if I ever got fired. I don't think I've gotten fired.

Sam Altman 54:19 Um, yeah, I got a text.

Jason Calacanis 54:20 It's just a weird... No, it's just a weird thing, like, it's a text from who?

Sam Altman 54:22 Actually, no, I got a text the night before, and then I got on a phone call with the board, uh, and then that was that. And then I kind of, like... I mean, then everything went crazy. I was like a... It was like... I mean, I have... My phone was, like, unusable. It was just a non-stop vibrating thing of, like, text messages, calls-

Chamath Palihapitiya 54:41 (laughs) Basically, you got fired by tweet. That happened a few times, uh, during the Trump administration. A few, uh, cabinet appointments got, got tweeted out.

Sam Altman 54:48 They did call me first be- before, before tweeting, which was nice of them.

Chamath Palihapitiya 54:51 Oh, okay.

Sam Altman 54:51 Um, and then, like, you know, I kind of did, like, a few hours of just this, like, absolute fugue state, um, in the hotel room-... trying to like, I was just confused beyond belief trying to figure out what to do. And, uh-

David Sacks 55:05 So weird.

Sam Altman 55:06 And then like, flew home at maybe like, got off the plane at like, I don't know, 3:00 PM or something like that. Um, still just like, you know, crazy non-stop phone blowing up. Uh, met up with some people in person. By that evening, I was like, "Okay, you know, I'll just like go do AGI research," and was feeling pretty happy about the future and-

David Sacks 55:31 Yeah, you have options.

Sam Altman 55:32 And then, and then the next morning, uh, had this call with a couple of board members about coming back and that led to a few more days of craziness. And then, uh, and then it kind of, I think it got resolved. Well, there was like a lot of insanity in between but 4 minutes-

David Friedberg 55:52 What, what percent of, what percent of it was because of these nonprofit board members?

Sam Altman 55:57 Um, well, we only have a nonprofit board, so it was all the nonprofit board members. Uh, their, the board had gotten down to six people. Um, they, and then they removed Greg from the board, and then fired me. Um, so, but it was like, you know-

David Friedberg 56:15 But I mean like, was there a culture clash between the people on the board who had only nonprofit-

Sam Altman 56:19 Hmm.

David Friedberg 56:19 ... experience versus the people who had startup experience?

David Sacks 56:22 And maybe you can share a little bit about, if you're willing to, the motivation behind the action. Anything you can...

Sam Altman 56:29 I think there's always been culture clashes at... Look, obviously not all of those board members are my favorite people in the world. But I have serious respect for the gravity with which they treat AGI and the importance of getting AI safety right. And even if I stringently disagree with their decision-making and actions, which I do, um, I have never once doubted their integrity or commitment to, um, the sort of shared mission of safe and beneficial AGI. You know, do I think they like, made good decisions in the process of that or kind of know how to balance all the things OpenAI has to get right? No. But, but I think that like-

David Sacks 57:26 The intent.

Sam Altman 57:27 The intent of the magnitude of-

David Sacks 57:31 Yeah.

Sam Altman 57:31 ... AGI and getting that right, I-

David Friedberg 57:36 Actually, let, let me ask you about that. So the mission of OpenAI is explicitly to create AGI, which I think is really interesting. A lot of people would say that if we create AGI, that would be like an unintended consequence of something gone horribly wrong, and they're very afraid of that outcome. But OpenAI makes that the actual mission.

Sam Altman 57:57 Yeah.

David Friedberg 57:58 Does that create like more fear about what you're doing? I mean, I understand it can create motivation too, but how do you reconcile that? I guess, why is that

Sam Altman 58:07 I think a lot of- ... the mission? ... I think a lot of the... Well, I mean, first I'll say that, I'll answer the first question and then the second one. I think it does create a great deal of fear. Uh, I think a lot of the world is understandably very afraid of AGI, or very afraid of even current AI, and, and very excited about it, and even more afraid and even more excited about where it's going. Um, and we, we wrestle with that, but like, I think it is unavoidable that this is going to happen. I also think it's going to be tremendously beneficial. But we do have to navigate how to get there in a reasonable way. And like, a lot of stuff is gonna change and change is y- you know, pretty, pretty uncomfortable for people. So, there's a lot of pieces that we gotta get right. And-

David Sacks 58:55 Can I ask a-

Sam Altman 58:56 Sure.

David Sacks 58:56 ... can I ask you a different question? You, you have created, I mean, it's the hottest company, and you are literally at the center of the center of the center. But then it's so unique in the sense that all of this value, you eschewed economically. Can you just like walk us through like why?

Sam Altman 59:18 Yeah, I wish I had taken, I wish I had taken equity so I never had to answer this question.

David Friedberg 59:21 (laughs)

Sam Altman 59:22 If I could go back in time-

David Friedberg 59:23 Why don't they gi- why don't they give you a grant now? Why doesn't-

David Sacks 59:25 Yeah, why don't they-

David Friedberg 59:26 ... the board just give you a big option grant like you deserve?

David Sacks 59:28 Yeah, give you five points. What was the decision back then? Like, why was that so important to you?

Sam Altman 59:32 Uh, the decision back then, the re- the original reason was just like the structure of our nonprofit. It was, uh, like there was something about, yeah, okay, this is like nice from a motivations perspective, but mostly it was that our board needed to be a majority of disinterested directors. And I was like, "That's fine. I don't need equity right now." I kind of-

David Sacks 59:53 Hmm.

Sam Altman 59:55 ... but like, I, I wish-

David Sacks 59:56 But in this weird way now that you're running a company, yeah, it, it creates these weird questions of like, well, what's your real motivation versus-

Sam Altman 1:00:02 Tot-, tota-

David Sacks 1:00:03 Yeah.

Sam Altman 1:00:03 ... that's, that, it, uh, it is so deeply unim- I, I, one thing I have noticed, it i- just- is it's so deeply unimaginable to people to say-

David Sacks 1:00:12 Yeah.

Sam Altman 1:00:12 ... "I don't really need more money." Like, and I get how tone deaf-

David Sacks 1:00:15 Well no, people think, I think, I think people think it's a little bit of an ulterior motive, I think is why.

Sam Altman 1:00:19 Yeah, well, yeah, yeah, yeah, no, so it assumes, you assume that, it's like-

David Sacks 1:00:22 Right, it's like what else is he doing on the side to make money, something sketchy is going on.

Sam Altman 1:00:25 If I were, if I were just trying to say like, "I'm gonna try to make a trillion dollars with OpenAI," I think everybody would have an easier time and it wouldn't save me.

David Sacks 1:00:31 Totally.

David Friedberg 1:00:31 Well, I-

Sam Altman 1:00:31 It would save a lot of conspiracy theorists.

David Sacks 1:00:33 Totally.

David Friedberg 1:00:33 Sam-

Sam Altman 1:00:34 Totally.

David Friedberg 1:00:35 ... this is The Back Channel. You are a great deal maker. I, I've watched your whole career. I mean, you're just great at it. You got all these connections, you're really good at raising money. Uh, you're fantastic at it. And you got this Jony Ive thing going, you're in Humane, you're investing in companies, you got the Orb, you're raising $7 trillion to build, uh-

Sam Altman 1:00:56 (laughs)

David Friedberg 1:00:56 ... uh, fabs, all this stuff.

Sam Altman 1:00:58 News to me.

David Friedberg 1:00:58 All of that put together-

David Sacks 1:01:00 JKL loves-

David Friedberg 1:01:01 Wait, that one's fake news?

David Sacks 1:01:02 JKL loves fake news. He loves fake news.

David Friedberg 1:01:02 But I'm, I'm kind of being a little facetious here.

Sam Altman 1:01:04 Yeah.

David Friedberg 1:01:04 You know, obviously it's not, you're not raising $7 trillion, but maybe that's the market cap or something. Putting all that aside-

Jason Calacanis 1:01:09 ... the tea was you were doing all these deals, they don't trust you because what's your motivation? You- you- you're end running and, and what opportunities belong inside of OpenAI, what opportunities should be Sam's, and this group of nonprofit people didn't trust you. Is that what happened?

Sam Altman 1:01:26 Yeah. So the things like, you know, device companies or if we were doing some chip fab company, it's like those are not Sam project. Those would be, like, OpenAI would get that equity. Um-

Jason Calacanis 1:01:36 They would? Okay, that's not-

Sam Altman 1:01:37 Oh, yeah.

Jason Calacanis 1:01:37 ... the public's perception.

Sam Altman 1:01:39 Well, that's not, like, kind of the people like you who have to, like, commentate on this stuff all day's perception, which is fair-

Jason Calacanis 1:01:44 (laughs)

Sam Altman 1:01:44 ... because we haven't announced the stuff because it's not-

Jason Calacanis 1:01:46 Yeah.

Sam Altman 1:01:46 ... done. I don't think most people in the world, like, are thinking about this, but I, I, I agree it spins up a lot of conspiracies, conspiracy theories in, like, tech commentators.

Jason Calacanis 1:01:57 Yeah, in a vacuum.

Sam Altman 1:01:58 And, and if I could go back, yeah, I would just say, like, "Let me take equity and make that super clear."

Jason Calacanis 1:02:03 (laughs)

Sam Altman 1:02:03 And then everyone would be like, "All right." Like, I'd still be doing it because I really care about AGI and think this is like the most interesting work in the world, but it would at least hype check to everybody.

Jason Calacanis 1:02:12 What's the chip project that's a $7 trillion... Ah-

Sam Altman 1:02:15 Um-

Jason Calacanis 1:02:15 Where'd the 7 trillion number come from? Makes no sense.

Sam Altman 1:02:17 I don't know where that came from actually.

Jason Calacanis 1:02:19 Okay.

Sam Altman 1:02:19 I genuinely don't. Uh, I think, I think the world needs a lot more AI infrastructure, a lot more than it's currently planning to build and with a different cost structure. Um, the exact way for us to play there is, we're still trying to figure that out.

Jason Calacanis 1:02:36 Got it.

David Sacks 1:02:36 What's your preferred model of organizing OpenAI? Is it sort of like the move fast, break things, highly distributed small teams? Or is it more of this organized effort where you need to plan because you want to prevent some of these edge cases?

Sam Altman 1:02:52 Um... Oh, I have to go in a minute. Uh, it's not because... It's not to prevent the edge cases that we need to be more organized, but it is that these systems are so complicated and concentrating bets are so important. Like, one, you know, at the time, before it was, like, obvious to do this, you have, like, DeepMind or whatever has all these different teams doing all these different things and they're spreading their bets out and-

David Sacks 1:03:18 Right.

Sam Altman 1:03:19 ... you had OpenAI say, "We're going to, like, basically put the whole company and work together to make GPT-4." And that was, like, unimaginable for how to run an AI research lab. But it is, I think, what works. At a minimum, it's what works for us. So not because we're trying to prevent edge cases, but because we want to concentrate resources and do these, like, big, hard, complicated things. We do have a lot of coordination on what we work on.

Jason Calacanis 1:03:42 All right, Sam, I know you gotta go. You've been great on the hour. Come back any time, uh-

Sam Altman 1:03:46 Great talking to you guys.

Jason Calacanis 1:03:47 ... and yeah.

David Sacks 1:03:47 Yeah, it was fun. Thanks for coming on, Sam. I, I-

Jason Calacanis 1:03:49 Yeah, it was wonderful. Thanks for being so open about everything. Really appreciate it.

David Sacks 1:03:50 We've been talking about it for, like, a year plus and have Sam come in the chat.

Sam Altman 1:03:52 I'm really happy it finally happened.

David Sacks 1:03:54 Yeah, it's awesome. I really appreciate it. Thank you.

Sam Altman 1:03:55 Um, I would love to come back on after our next, like, major launch and I'll be able to-

David Sacks 1:03:58 You're, you're-

Sam Altman 1:03:58 ... talk more directly about some of these things.

David Sacks 1:04:00 Definitely. Let's, let's, uh-

Jason Calacanis 1:04:00 Yeah, you got the Zoom link. Same Zoom link every week.

David Sacks 1:04:02 Yeah.

Jason Calacanis 1:04:02 Just same time, same Zoom link.

David Sacks 1:04:03 (laughs) .

Jason Calacanis 1:04:04 Drop in anytime.

David Sacks 1:04:05 Let's just drop in. Please drop in.

Jason Calacanis 1:04:05 Yep, just put it on your, uh, calendar.

David Sacks 1:04:07 Come back to the game. Come back to the game.

Jason Calacanis 1:04:09 Oh, yeah. Yeah, come back to the game.

David Sacks 1:04:09 We haven't seen you at poker in a while.

Sam Altman 1:04:11 I, I, you know, I would love to play poker. It has been forever. That would be a lot of fun.

David Sacks 1:04:15 Yeah. I'm gonna-

Sam Altman 1:04:15 Send me an invite, I'll come.

Jason Calacanis 1:04:16 That famous hand where Chamath... When you and I were heads up and you, you had-

Sam Altman 1:04:19 I don't... Remind me?

Jason Calacanis 1:04:21 You and I were heads up and you went all in. I had a set, but there was a straight and a flush on the board. And I'm in the tank trying to figure out if I want to lose... This is back when we were playing small stakes. It might have been, like, 5K pot or something. And then Chamath can't stay out of the pot and he starts taunting the two of us, "You should call, you shouldn't call. He's bluffing, he..." And I'm like, "Chamath, I'm going... I'm trying to figure out if I make the call here." I make the call and, uh, it was like, uh, you had a really good hand and... But I just happened to have a set. I think you had, like, top pair, top kicker or something. But you made a great move because the board was so textured, it almost like all bottom set.

David Sacks 1:04:57 Sam has a great style of playing, which I would call Ram and Jam. Totally.

Sam Altman 1:05:01 (laughs)

David Sacks 1:05:01 You got to just get out of the way.

Sam Altman 1:05:02 Yeah, Chamath, I don't really know if you... I don't, I don't know if you can say about anybody else in that.

David Sacks 1:05:05 (laughs)

Sam Altman 1:05:06 I don't, I don't, I'm not gonna...

David Sacks 1:05:07 You haven't seen Chamath play in the last 18 months.

Jason Calacanis 1:05:09 Yeah.

David Sacks 1:05:09 It's a lot different.

Jason Calacanis 1:05:10 I've tightened around the game.

1:05:11 He's come back to the game.

1:05:11 I'm much more snug.

1:05:12 So much fun now.

Sam Altman 1:05:13 Uh, I find, I find that really hard to believe.

David Sacks 1:05:16 Have you played Bomb Pots before?

Jason Calacanis 1:05:17 (laughs)

1:05:17 Have you played Bomb Pots here in this game?

Sam Altman 1:05:18 I, I don't know what that is.

David Sacks 1:05:19 Okay, you'll love it.

Jason Calacanis 1:05:20 All right.

David Sacks 1:05:20 We'll see you next time.

Sam Altman 1:05:20 All right.

David Sacks 1:05:21 It's nuts.

Jason Calacanis 1:05:22 You, it's PRO-

David Sacks 1:05:22 Thank you. Sam, thank you.

Jason Calacanis 1:05:23 But you do two boards.

David Sacks 1:05:24 And congrats on everything, honestly.

Sam Altman 1:05:26 It's nuts.

Jason Calacanis 1:05:27 Thanks for coming on and-

David Sacks 1:05:28 Thanks, guys.

Jason Calacanis 1:05:28 ... we'd love to have you back when the next after big launch.

Sam Altman 1:05:30 Sounds good.

David Sacks 1:05:30 Yeah, please do.

Sam Altman 1:05:31 Cool. Bye.

Jason Calacanis 1:05:33 Gentlemen, some breaking news here. All those projects, he said, are part of OpenAI. That's something people didn't know before this and a lot of confusion there. Chamath, what was your major takeaway from our hour with Sam?

David Sacks 1:05:47 I think that these guys are going to be one of the four major companies-

Jason Calacanis 1:05:52 Okay.

David Sacks 1:05:52 ... that matter in this whole space. I think that that's clear. I think what's still unclear is where is the economics going to be. He said something very discreet but I thought was important, which is, I think he basically... My interpretation is these models will roughly all be the same, but there's going to be a lot of scaffolding around these models that actually allow you to build these apps. So in many ways, that is like the open source movement. So even if the model itself is never open source, it doesn't much matter because you have to pay for the infrastructure, right? There's a lot of open source software that runs on Amazon. You still pay AWS something. So I think the right way to think about this now is the models will basically be all really good.

Jason Calacanis 1:06:37 Mm-hmm.

David Sacks 1:06:37 And then it's all this other stuff that you'll have to pay for.

Jason Calacanis 1:06:40 The interface, yeah.

David Sacks 1:06:41 Whoever builds all this other stuff is going to be in a position to build a really good business.

Jason Calacanis 1:06:48 Freeberg, he talked a lot about reasoning. It seemed like that... He kept going to reasoning and away from the language model. Did you no- did you note that? And anything else that you noted in our hour with him?

David Sacks 1:06:57 Yeah, I mean, that's a longer conversation because there is a lot of talk about language models eventually evolving to be so generalizable that they can resolve pretty much, like, all intelligent function. And so the language model is the foundational model that, that yields AGI.... but that's all... I think there's a lot of people that have different schools of thought on this and how much-

Jason Calacanis 1:07:19 Oh, my, my other takeaway, I think, is that the... I think what he also seemed to indicate is, there's, like, so many, like, we're all so enraptured by LLMs but there's so many things other than LLMs that are being baked and rolled by him and by other groups. And I think we have to pay some amount of attention to all those, because that's probably where... And I think, Freyberg, you tried to go there in your question, that's where reasoning will really come from, is this mixture of experts approach. And so you're gonna have to think multi-dimensionally to reason, right? We do that, right? Do I cross the street or not in this point in time? You reason based on all these multi inputs and so there's, there's all these little systems that go into making that decision in your brain. And if you, if you use that as a simple example, there's all this stuff that has to go into making some experience being able to reason intelligently. Sacks, you went right there with the corporate structure, the board. And, uh, he, he, he gave us all a lot more information here. Wha- what are your thoughts on the, "Hey," you know, "the chip stuff and the other stuff I'm working on," that's all part of OpenAI, people just don't realize it. And in that moment and then, you know, your questions to him about equity. Your, your thoughts on, um, that.

David Friedberg 1:08:34 Um, I'm not sure I was, like, the main guy who asked that question, J-Cal, but, um... (laughs)

Jason Calacanis 1:08:39 Well, no, you do talk about the, the non-profit, the, the difference between-

David Friedberg 1:08:42 Well, he added a follow-up question about the-

Jason Calacanis 1:08:42 ... the non-profit verse that. Yeah, yeah. That, that's what I'm talking about.

David Friedberg 1:08:44 There clearly was some sort of culture clash on the board-

Jason Calacanis 1:08:47 Yes.

David Friedberg 1:08:47 ... between the n- the people who originated from the non-profit world and the people that came from the start-up world.

Jason Calacanis 1:08:51 And the tech side, yeah.

David Friedberg 1:08:52 We don't really know more than that, but there clearly was some sort of culture clash. I thought one of the... a couple of the other areas that he drew attention to that were kind of interesting is, he clearly thinks there's a big opportunity on mobile that goes-

Jason Calacanis 1:09:04 Yes.

David Friedberg 1:09:04 ... beyond just, like, having, you know, a ChatGPT app on your phone or maybe even having, like, a Siri on your phone. There's clearly something bigger there, he doesn't know exactly what it is, but it's gonna require more inputs. It's that, you know, personal assistant that's seeing everything around you and helping you.

Jason Calacanis 1:09:21 That was a really... I think that's a great insight, David, because he was talking about, "Hey, I'm looking for a senior team member who can push back on me and understands all contexts." I thought that was, like, a very interesting to think about-

David Friedberg 1:09:32 Yeah, he's talking about an executive assistant or an assistant that's... it has executive function as opposed to being, like, just an alter ego-

Jason Calacanis 1:09:41 Yes.

David Friedberg 1:09:41 ... for you or what he called a sycophant. That's kind of interesting.

Jason Calacanis 1:09:44 I thought that was interesting, yeah.

David Friedberg 1:09:45 Yeah. And clearly, he thinks there's a big opportunity in biology and scientific discovery.

David Sacks 1:09:50 After the break, I think we should talk about AlphaFold 3, it was just announced today, so-

Jason Calacanis 1:09:53 Yeah, let's do that and we can talk about the, the Apple ad in depth. I just want to also make sure people understand, when people come on the pod, we don't show them questions, they don't edit the transcript, nothing is, uh, out of bounds. If you were wondering why I didn't ask or we didn't ask about the Elon lawsuit, he's just not gonna be able to comment on that, so it'll be a no comment, so, you know.

David Sacks 1:10:11 And we're not-

Jason Calacanis 1:10:12 And that's why I didn't go to the-

David Friedberg 1:10:12 Yeah, like our time is limited and there's a lot of questions that we could have asked him that would have just been a waste of time. And frankly he's already been asked-

Jason Calacanis 1:10:17 But they would have got... would have been no comment, so I just want to make sure people understand that.

David Friedberg 1:10:20 Yeah. Of course he's gonna know how to comment on any lawsuit and he's already been asked about that 500 times.

Jason Calacanis 1:10:24 All right. Yeah.

David Sacks 1:10:25 Should we take a quick break before the next... before we come back?

Jason Calacanis 1:10:27 Yeah, take a bio break and then we'll come back with some news for you and some more banter with your favorite besties on the number one podcast in the world, the All-In podcast.

Jason Calacanis 1:10:38 All right. Welcome back everybody. Second half of the show, great guest Sam Altman. Thanks for coming on the pod. We've got a bunch of news on the docket, so let's get started. Freyberg, you told me I could give some names of, uh, the guests that we booked for the All-In Summit.

David Sacks 1:10:53 I did not.

Jason Calacanis 1:10:54 You did. You've said each week, every week, that I get to say some names.

David Friedberg 1:10:59 (laughs)

David Sacks 1:10:59 I did not say that. I did not. I appreciate your interest in the All-In Summit's lineup, but we do not yet have, uh, enough critical mass, uh-

Jason Calacanis 1:11:08 Okay.

David Sacks 1:11:08 ... to feel like we should go out there.

Jason Calacanis 1:11:09 Well, uh, I am a loose cannon, so I will announce-

David Friedberg 1:11:13 (laughs)

Jason Calacanis 1:11:13 ... my two guests and, uh, I created the summit and you took it from me, so... and done a great job. I will announce my guests, I don't care what your opinion is.

David Sacks 1:11:21 (laughs)

Jason Calacanis 1:11:22 I have booked two guests for the summit and it's gonna be sold out. Look at these two guests I booked. For the third time coming back to the summit, our guy Elon Musk will be there. Hopefully in person. If not, you know, from 40,000 feet on Starlink connection, wherever he is in the world. And for the first time, our friend Mark Cuban will be coming. And so two great guests for you to look forward to. But Freyberg's got like a thousand guests coming. He'll tell you when it's, like, 48 hours before the conference. But yeah, two great guests coming.

David Friedberg 1:11:51 Wait, speaking of billionaires who are coming, isn't (beep) coming too?

Jason Calacanis 1:11:54 Yes, (beep) coming. Yes, he's booked.

David Friedberg 1:11:56 So we have three billionaires coming.

Jason Calacanis 1:11:58 Three billionaires, yes.

David Friedberg 1:11:59 (beep)

David Sacks 1:12:00 Hasn't fully confirmed, so don't-

Jason Calacanis 1:12:01 Okay, well, we're gonna say it anyway.

David Friedberg 1:12:02 (beep)

Jason Calacanis 1:12:03 ... has penciled in and that's it.

David Sacks 1:12:04 Don't say it and he'll back out.

Jason Calacanis 1:12:05 We'll say penciled. Yeah, don't back out.

David Sacks 1:12:07 This is going to be catnip for all these protest organizers, like, if you have to pick one place-

Jason Calacanis 1:12:11 Oh, God. Do not poke the bear.

David Friedberg 1:12:13 Well, by the way, speaking of updates-

Jason Calacanis 1:12:15 Oh.

David Friedberg 1:12:15 ... what did you guys think of the bottle for the All-In Tequila?

Jason Calacanis 1:12:19 Ooh.

David Sacks 1:12:19 Oh, beautiful.

Jason Calacanis 1:12:20 Ooh.

David Sacks 1:12:20 Honestly, honestly, I will just say, I think you are doing a marvelous job. That, I was shocked at the design. Shocked meaning, it is so unique and high quality, I think it's amazing.

Jason Calacanis 1:12:35 It would make me drink tequila.

David Friedberg 1:12:38 You're going to, you're gonna want to.

Jason Calacanis 1:12:39 Going to. It, it is, uh, stunning, just congratulations. And, um, yeah, it was just... when we went through the deck at the, uh, at the monthly meeting, it was like, "Oh, that's nice. Oh, that's nice." We were going through the concept bottles. And then that bottle came up and everybody went like crazy. It was like somebody hitting like a ha- a Steph Curry hitting a half-court shot. It was like, "Oh my God."

David Friedberg 1:13:00 Yeah.

Jason Calacanis 1:13:00 It was just so clear that you've made an iconic bottle, that if we can produce it, oh Lord, it is going to be a game.

David Friedberg 1:13:09 Looks like we can. the... we can make it.

Jason Calacanis 1:13:12 Oh, it's gonna be good?

David Sacks 1:13:12 Can we use it?

David Friedberg 1:13:12 Yeah.

David Sacks 1:13:12 It's gonna be amazing.

Jason Calacanis 1:13:13 I'm excited. I'm excited for it. You know, it's like-

David Friedberg 1:13:15 I mean, the bottle design is so complicated that we had to do a feasibility analysis on whether it was actually manufacturable but it is. So... or at least the early reports are good. So we're gonna...Hopefully we'll have some made for the- in time for the Allen Summit.

Jason Calacanis 1:13:28 I'm in.

Guest 1:13:29 Amazing.

Jason Calacanis 1:13:29 Why not? Sounds great.

David Sacks 1:13:30 I mean, it's great when we get barricaded in by all these protestors, we can drink the tequila.

David Friedberg 1:13:34 (laughs)

Guest 1:13:35 (laughs)

David Sacks 1:13:35 Did you guys see-

Jason Calacanis 1:13:36 Yeah, what happened to your boy Peter Thiel?

David Sacks 1:13:36 ... did you see Peter Thiel? Peter Thiel got barricaded by these ding-dongs at Cambridge.

Guest 1:13:40 (laughs)

Jason Calacanis 1:13:40 My God. Listen, people have the right to protest. I think it's great people are protesting, but surrounding people and threatening them is a little bit over the top and dangerous.

David Friedberg 1:13:48 I think you're exaggerating what happened.

Jason Calacanis 1:13:51 Well, I don't know exactly what happened because all we see is these videos.

David Friedberg 1:13:54 Look, they're not threatening anybody, and I don't even think they tried to barricade him in. They were just outside the building and-

Jason Calacanis 1:14:00 Oh.

David Friedberg 1:14:00 ... because they were blocking the driveway, his car couldn't leave.

Jason Calacanis 1:14:04 Got it.

David Friedberg 1:14:04 But he wasn't physically, like, locked in the building or something.

Jason Calacanis 1:14:09 You know, that- that- that's- that's what the headlines say, but that could be fake news, fake social.

Guest 1:14:13 Yeah.

David Sacks 1:14:13 This was not on my bingo card, this pro-protesting support by Sachs was not on the bingo card, I gotta say.

David Friedberg 1:14:21 (laughs)

David Sacks 1:14:21 I didn't see it coming.

David Friedberg 1:14:21 The Constitution, the Constitution of the United States in the First Amendment provides for the right of assembly which includes-

David Sacks 1:14:28 Yes.

David Friedberg 1:14:28 ... protests and sit-ins, as long as they're-

David Sacks 1:14:30 It's beautiful. Yeah.

David Friedberg 1:14:30 ... as long as they're peaceable. Now, obviously, if they go too far and they vandalize or break into buildings or use violence, then that's not peaceable. However, expressing sentiments with which you disagree does not make it violent. And there's all these people out there now making the argument that if you hear something from a protester that you don't like and you subjectively experience that as a, as a threat to your safety, then that somehow should be, you know, treated as valid. Like, that's basically violent. Well, that's- that's not what the Constitution says. And these people understood well just a few months ago that that was basically snowflakery. That, you know, just because somebody-

David Sacks 1:15:15 (laughs)

David Friedberg 1:15:15 You know what I'm saying? Like, we have now the-

David Sacks 1:15:16 Snowflakery, peaceable. We're getting all these great words.

David Friedberg 1:15:17 We have the- we have the rise of the woke right now, where they're buying into the-

Jason Calacanis 1:15:21 The woke right?

David Friedberg 1:15:21 Yeah, the woke right. They're buying into this idea of safetyism, which is being exposed to ideas