In conversation with Sam Altman

I first met our next guest, Sam Altman, almost 20 years ago when he was working on a local mobile app called Looped. 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 experiment, you did Uber?

I've never heard that before.

Yeah, I think so. It's possible. You've got it starting already. You should write a book, Jacob.

Maybe. We'll let your winners ride.

Rain Man, David Sacks.

And instead, we open sources to the.

Fans, and they've just gone crazy with it. That tiny experimental fund that Sam and I were part of, a Scouts, is Sequoia's highest multiple returning fund. A couple of low digit millions turned into over 200 million, I'm told. And then he did, yeah, that's what I was told by Rule of Law, 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 going to go work at Microsoft. A bunch of heart emojis went viral on X slash Twitter, and people started speculating that the team had reached artificial general intelligence. The world was going to end, and suddenly, a couple of 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 Son to create an iPhone killer with Johnny 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.

Thank you. Thank you, guys.

Sach, you want to leave us off here?

Okay, sure. I mean, 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 are you in the release of GPT-5?

We take our time on releases of major new models, and I don't think it will be great when we do it. And I think we'll be thoughtful about how we do it. We may release it in a different way than we've released previous models. Also, I don't even know if we'll call it GPT-5. What I will say is a lot of people have noticed how much better GPT-4 has gotten since we've released it, and particularly over the last few months.

I.

Think that's a better hint of what the world looks like, where it's not the one, two, three, four, five, six, seven, but you use an AI system and the whole system just gets better and better fairly continuously. I think that's both a better technological direction. I think that's easier for society to adapt to. But I assume that's where we'll head.

Does that mean that there's not going to be long training cycles and it's continuously retraining or training submodels, Sam? And maybe you could just speak to us about what might change architecturally going forward with respect to large models?

Well, I mean, one thing that you could imagine is just that you keep training a model. That would seem like a reasonable thing to me.

Do you think- when you talked about.

Releasing it differently this time, are you thinking maybe releasing it to the paid users first, or a slower rollout to get the red teams tight since now there's so much at stake, you have so many customers actually paying, and you've got everybody watching everything you do.

Is it- yeah, it's 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. And this idea that we build AI tools and make them super widely available, free or 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 showing it down upon us. 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. It's something we really want to do.

It's just very expensive.

I, I think it's very expensive.

Yeah. Chima, your thoughts?

I think maybe 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. 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 MIT license, like Open Devin. 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 closed source and where do you see things going in the next couple of years?

So on the first part of your question, speed and cost, those are, hugely important to us. And I don't want to give a timeline on when we can bring them down a lot because research is hard, but I am confident we'll be able to. We want to cut the latency super dramatically. We want to cut the cost really, really dramatically. 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 don't know 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 a pretty high level of intelligence.

And.

It'S important to us, it's clearly important to users, and it'll unlock a lot of stuff. On the sort of open source, closed source thing, I think there's great roles for both, I think. We've open sourced some stuff, we'll open source more stuff in the future. But really our mission is to build towards AGI and to figure out how to broadly distribute its benefits. We have a strategy for that. It seems to be resonating with a lot of people. It obviously isn't for everyone and there's a big ecosystem and there will also be open source models and people who build that way. 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 a good version of that yet, but that seems like a really important thing to go do at some point.

Will you do that? Will you release it?

I don't know if we will or.

Someone will, but someone- Will it be Llama 3?

Llama 3 running on a phone?

Well, I guess maybe there's like a 7 billion.

Yeah, yeah.

I don't know if that will fit on a phone or not, but- that.

Should be fittable on a phone, but I'm not sure if that one is like, I haven't played with it. I don't know if it's good enough to kind of do the thing I'm thinking about here.

So when Lama 3 got released, I think the big takeaway for a lot of people was, oh wow, they've caught up to GPT-4. I don't think it's equal in all dimensions, but it's pretty close or pretty in the ballpark. I guess the question is, you know, you guys released four a while ago. You're working on five or, you know, more upgrades to four. I mean, I think to Jamil's point about Devin, 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?

What we're trying to do is not.

Make.

The sort of smartest set of weights that we can, but we're trying to make is 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. But there's a lot of other work around the whole system that's not just that 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 deliver it at a great price.

When you founded the organization, 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 had some fear about that, I think. Is that accurate? Because the cynical side is like, well, this is a capitalistic move. And then the, I think, you know, I'm curious what the decision was here in terms of going from open, the world needs to see this, it's really important to close, only we can see it. Well, how did you come to that conclusion? What were the decisions?

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 October of 2022, Not that many people thought it was going to be that important or that it was really happening. And a huge part of what we try to do is put the technology in the hands of people. 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 run ads on, we don't try to make money on, we just put out there because we want people to have these tools. I think has done a lot to provide a lot of value and teach people how to fish, but also to get the world really thoughtful about what's happening here. Now, we still don't have all the answers and 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. When we started OpenAI, we had really no idea about how things were going to go. that we'd make a language model that we'd ever make a product. 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 was 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 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.

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. 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?

It doesn't, so I definitely don't think it'll be an arms race for data because 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. Look, the one thing that I have learned most throughout all of this is that it's hard to make confident statements a couple of years in the future about where this is all going to go. And so I don't want to try now. I will say that I expect lots of very capable models in the world.

And.

It feels to me like we just stumbled on a new fact of nature or science or whatever you want to call it, which is like, we can create, you can like, I mean, I don't believe this literally, but it's like a spiritual point. Intelligence is just this emergent property of matter and that's like a rule of physics or something. So people are going to 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, Like any other industry, I would expect there to be multiple approaches and different people like different ones. You know, some people like iPhones, some people like an Android phone. I think there will be some effect like that.

Let's go back to that first section of just 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?

Well, we'll make huge algorithmic gains for sure, and I don't want to discount that. I'm very interested in chips and energy, but if we can make a same quality model twice as efficient, that's like we had twice as much compute. And I think there's a gigantic amount of work to be done there. And I hope we'll start really seeing those results. Other than that, the whole supply chain is very complicated. There's logic fab capacity, there's how much HBM the world can make, There's how quickly you can 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.

I think when there's this much value to people, the world will do its thing. We'll try to help it happen faster. And there's probably, I don't know how to give it a number, but there's some percentage chance where there is, as you were saying, a huge substrate breakthrough. and we have like a massively more efficient way to do computing, but I don't bank on that or spend too much time thinking about it.

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're thinking about that. But then does the device itself change? I mean, does it need to be as expensive as an iPhone?

I'm super interested in this. I love great new form factors of computing, and it feels like with every major technological advance, a new thing becomes possible. Phones are unbelievably good. So I think the threshold is very high here. I personally think an iPhone is the greatest piece of technology humanity has ever made. It's really a wonderful product.

What comes after it?

I don't know. I was going to establish what I was saying. It's so good that to get beyond it, I think the bar is quite high.

Well, you've been working with Johnny Ive on something, right?

We've been discussing ideas, but I don't — if I knew — Is it.

That that it has to be more complicated or actually just much, much cheaper and simpler?

Well, almost everyone's willing to pay for a phone anyway. So if you could 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, given that we're all willing to pay for phones, or most of us are, I don't think cheaper is the answer.

Different is the answer then?

Would there be like a specialized chip that would run on the phone that was really good at powering a phone size AI model?

Probably, but the phone manufacturers are going to do that for sure. That doesn't necessitate a new device. I think you'd have to find some really different interaction paradigm that the technology enables. And if I knew what it was, I would be excited to be working on it right now.

But it's- well, 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. It's got latency issues, but it's really-.

We'Ll get that better. And I think voice is a hint to whatever the next thing is. Like, if you 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, we already have that, by the way. Like, why is it not responsive? And, you know, it feels like a CB, you know, like over, over. It's really annoying to use, you know, in that way. But it's also brilliant when it gives you the right answer.

We are working on that. It's so clunky right now. It's slow. It's like kind of doesn't feel very smooth or authentic or organic. Like we'll get all that to be much better.

What about computer vision? I mean, they have glasses or maybe you could wear a pendant. I mean, you take the combination of visual or video data, combine it with voice, and now AI knows everything that's happening around you.

Super powerful to be able to like, the multimodality of saying like, Hey, ChatGPT, what am I looking at? Or like, what kind of plant is this? I can't quite tell. That's another, I think, hint. But whether people want to wear glasses or hold up something when they want that, there's a bunch of just like the sort of societal, interpersonal issues here are all very complicated about wearing a computer on your face.

We saw that with Google Glass. People got punched in the face in the mission.

Started a lot of- I forgot about that. I forgot about that. So I think it's like.

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 want to see built?

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

So 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 what I'm reading into what you're saying.

I think there'd be agent-like behavior, but there's like a difference between a senior employee and an agent. Yeah. and I want it, if I think of my, I think of one of the things that I like about a senior employee is they'll push back on me, they will sometimes not do something I ask, or they're sometimes will say, 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?

Hmm.

I definitely want that kind of vibe, which not just like this thing that I do a task and it blindly does.

It can be a reason.

Yeah.

Yeah.

And push back.

For the reason it 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.

The thing in that world where if you have this like Jarvis like thing that can 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 8 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 agentially interfacing to the world.

I'm actually very interested in designing a world that is equally usable by humans and by AIs. So 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 DoorDash could just expose some API to my future AI assistant and they could go put the order in or whatever. Or I could be holding my phone and I could say, okay, AI assistant, you put in this order on DoorDash, please. And I could watch the app open and see the thing clicking around and I could say, hey, no, not this. There's something about designing a world that is usable equally well by humans and AIs that I think is an interesting concept.

And same reason I'm more excited about.

Humanoid robots than robots of very other shapes. The world is very much designed for humans, and I think we should absolutely keep it that way. And a shared interface is nice.

So you see voice chat, that modality 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.

It's hard for me to imagine that we just go to a world totally where you say, hey, ChatGPT, order me sushi, and it says, okay, do you want it from this restaurant, what kind, what time, whatever.

I.

Think visual user interfaces are super good for a lot of things. And it's hard for me to imagine a world where you never look at a screen. and just use voice mode only, but I can't imagine that for a lot of things. I mean, Apple tried with Siri.

Supposedly you can order an Uber automatically with Siri. I don't think anybody's ever done it because it's... Why would you take the.

Risk of not putting in your phone number? Well, the quality, to your point, the quality is not good, but when the quality is good enough, you'll actually prefer it just because it's just lighter weight. You don't have to take your phone out. You don't have to search for your app and press it. Oh, it automatically logged you out.

Oh, hold on, log back in. Oh, TFA.

It's a whole pain in the ass.

You know, it's like setting a timer with Siri I do every time because it works really well and it's great and I don't need more information. But ordering an Uber, like I want to see the prices for a few different options. I want to see how far away it is. I want to see like maybe even where they are on the map because 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 all through the audio channel.

I like your idea of watching it happen. That's kind of cool.

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.

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?

I met with a new company this morning, or barely even a company, it's like two people that are going to work on a summer project, trying to actually finally make the AI tutor. And I've always been interested in this space. A lot of people have done great stuff on our platform, but if someone can deliver the way that you actually They used a phrase I love, which is this is going to be like a Montessori level reinvention for how people learn things. But if you can find this new way to let people explore and learn in new ways on their own, I'm personally super excited about that. A lot of the coding related stuff you mentioned Devin earlier, I think that's a super cool vision of the future. The thing that I am, healthcare, 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 Alpha-4-3, yeah.

That's like... But Sam, that will be a triumph.

Those are not, like these models are trained and built differently than the language models. I mean, to some, obviously there's a lot that's similar, but there's a lot, there's kind of a ground up architecture to a lot of these models that are being applied to these specific problem sets, these specific applications, like chemistry interaction modeling, for example.

You'll need some of that for sure, but 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.

So I guess, yeah, that's the important question I wanted to kind of 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. But one of the things that arises In biology, it's networks of systems that have cross interactions that the aggregation of the system, the aggregation of the network produces an output rather than one thing calling another, that thing calling another. Do we see 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?

I don't know. I don't know how much reasoning.

Is.

Going to 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 don't know if that's like.

But.

Let'S walk through 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 the model.

There's so many ways where that could go. Maybe it trains a literal model for it, or maybe it just knows the one big model, it can go pick what other training data it needs and ask a question and then update on that.

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.

There's a version of this I think you can already see. When you were talking about biology and these complicated networks of systems, the reason I was smiling, I got super sick recently and I'm mostly better now, but it was just like body got beat up one system at a time. You can really tell okay, it's this cascading thing. And that reminded me of you talking about biology as just these, you have no idea how much these systems interact with each other until things start going wrong. And that was interesting to see. But I was using ChatGPT to try to figure out what was happening, whatever, and would say, well, I'm unsure of this one thing. And then I just like posted a paper on it without even reading the paper, like in the context. And it says, oh, that was the 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 item, I don't know this thing. And you can put more information. You don't retrain the model. You're just adding it to the context here.

And now you're getting them. So these models that are predicting protein structure, like let's say, right? This is the whole basis. And now now other molecules at AlphaFold 3. Can they, 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 use an example for us. Can you tell us about Sora, your video model that generates amazing moving images, moving video, and what's different about the architecture there, whatever you're willing to share? on how that is different.

Yeah.

So on the general thing first, you clearly will need specialized simulators, connectors, pieces of data, whatever. But my intuition, and again, I don't have this 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 doable?

It's like a faster unlock. Faster unlock than- I think so.

But yeah, Sora does not start with a language model. That's a model that is customized to do video. And so we're clearly not at that world yet. Right.

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 AGI, whatever system theoretically could render that by figuring out how to do it.

Yeah. I mean, one example of this is like, okay, you know, as far as I know, all the best tax models in the world are still a lot of regressive models, and the best image and video models are diffusion models. That's like sort of strange in some sense.

Yeah.

So there's a big debate about training data. You guys have been, I think, the most thoughtful of any company. You've got licensing deals now, FT, et cetera. And 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 in 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's your personal position on the rights of artists who create beautiful music, lyrics, books, and you taking that and then making a derivative product out of it and then monetizing it. And what's fair here? And how do we get to a world where artists can make content in the world and then decide what they want other people to do with it? Yeah, and I'm just curious your personal belief, 'cause 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.

So I think it's very different for different kinds of, I mean, look, on fair use, I think we have a very reasonable position under the current law, but I think AI is so different. Good for things like art, we'll need to think about them in different ways. But I would 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 another set of people who might have a different opinion. Well, what if you like... Actually, let me not get into that just in the interest of not making this answer too long. So I think there's one category people are like, okay, there's generalized human knowledge. You can kind of go, if you learn that, that's like open domain or something if you kind of go learn about the Pythagorean theorem. That's one end of the spectrum. And then I think the other extreme end of the spectrum is art. And maybe even more specifically, I would say it's a system generating art in the style or the likeness of another artist. would be kind of the furthest end of that. And then there's many, many cases on the spectrum in between. 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 as training data becomes less valuable and what the system does accessing information in context in real time or taking something like that, what happens at inference time will become more debated and what the new economic model is there. So if you say create me a song 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 that may have read about Taylor Swift, it may know about her themes, Taylor Swift means something. And then the question is, should that model, even if it were never trained on any Taylor Swift song whatsoever, be allowed to do that? And if so, how should Taylor get paid?

Right.

So I think there's an opt-in, opt-out in that case, first of all, and then there's an economic model. Staying on the music example, There is something interesting to look at from the historical perspective here, which is sampling and how the economics around that work. This is not quite the same thing, but it's like an interesting place to start looking.

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, learns about, and their brain is processing and building all those same sort of predictive models or those same sort of discoveries or understandings. What's the difference here? And why are you making the case that perhaps artists should be uniquely paid? This is not a sampling situation. The AI is not outputting and it's not storing in the model the actual original song.

Yeah, I was — It's a learning structure, right? I wasn't trying to make that point, because I agree, in the same way that humans are inspired by other humans. I was saying if you say, Generate me a song in the style of Taylor Swift.

I see.

Right.

Okay.

Where the prompt leverages some artist. I think, personally, that's a different case.

Would you be comfortable asking, or would you be comfortable letting the model train itself, a music model being trained on the whole corpus of music that humans have created? without royalties being paid to the artists that music is being fed in. And then you're not allowed to ask artist-specific prompts. You could just say, hey, play me a really cool pop song that's fairly modern about heartbreak with a female voice.

We have currently made the decision not to do music, and partly because exactly these questions of where you draw the lines and what, like, even I was meeting with several musicians that I really admire recently, and I was just trying to 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 song structure, and.

What.

Makes a good catchy beat and everything else and only trained on that. Let's say we could still make a great music model, which maybe we could. I was kind of 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. And yet there's still something I don't like about it. Now, that's not a reason not to do it 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 into one really thin iPad.

What was your take on it? People got really emotional about it. Yeah. Stronger reaction than you would think.

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 for an AI that just does better science, great, bring that on. But an AI that is going to do this deeply beautiful human creative expression, I think we should figure out it's going to happen. It's going to 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 preserves the spirit of what we all care about here.

And 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 you call it internally. We try. 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 there's an interesting question about your spectrum.

Right? Yeah.

You know, we put out this thing yesterday called the spec. where we're trying to say, here's how our model is supposed to behave. And it's very hard. It's a long document. It's very hard to specify exactly in each case where the limits should be. And I view this as a discussion that's going to need a lot more input. 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 Jedi at this point is like part of the culture. These are all hard decisions.

Yeah. 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 Disney has an argument that, hey, you know, if you're gonna make Sora versions of Ashoka or whatever Obi-Wan Kenobi, That's Disney's opportunity, and that's a great partnership for you, you know, to pursue.

So we're, I think this section I would label as AI and the law. So let me ask maybe a higher level question. What does it mean when people say regulate AI?

Totally.

Sam, what does that even mean?

And comment on California's new proposed regulations as well, if you, if you're up for it.

I'm concerned. I mean, there's so many proposed regulations, 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 themselves. 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. 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, we're not talking decades and decades from now, where the frontier AI systems are capable of causing significant global harm. And for those kinds of systems, in the same way we have global oversight of nuclear weapons or synthetic bio or things that can really have a very negative impact way beyond the realm of one country. I would like to see some sort of international agency that is looking at the most powerful systems and ensuring reasonable safety testing. These things are not going to escape and recursively self-improve or whatever.

The criticism of this is that you have the resources to cozy up, to lobby, to be involved, and you've been very involved with politicians. And then startups, which are also passionate about and invest in, are not going to have the ability to resource 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.

You know, if the line where we're only going to 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 I don't think that puts any.

Regulatory burden on startups. So if you have the nuclear raw material to make a nuclear bomb, there's a small subset of people who have that, therefore you use the analogy of a nuclear inspectors kind of situation. I think that's interesting.

Sachs, you have a question? Well, Tamás, go ahead, you had a follow up.

Can I say one more thing about that?

Of course.

I'd be super nervous about regulatory overreach here. I think we can get this wrong by doing way too much, or even a little too much. I think we can get this wrong by doing not enough. But I do think part of.

And.

Now, I mean, we have seen regulatory overstepping or capture just get super bad in other areas. And, you know, also maybe nothing will happen, but I think it is part of our duty and our mission to talk about what we believe is likely to happen and what it takes to get that right.

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 trade secrets. 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 federal, 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, the government agencies for fear. And, and because folks have a hard time understanding this and are scared about the implications of it, they want to control it. And because they want, and the only way to control it is to say, give me a right to audit before you can release it.

Yeah, as a, as a clueless. And they're clueless.

I mean, the way that the stuff is written, you read it, you're going to pull your hair out because, as you know, better than anyone, in 12 months, none of this stuff's going to make sense anyway.

Totally. Right. Look, the reason I have pushed for an agency-based approach for kind of the big picture stuff and not a write it in laws, in 12 months, it will all be written wrong. And I don't think, even if these people were true world experts, I don't think they could get it right. looking out in 12 or 24 months. And I don't, these policies, which is like, we're going to look at, you know, we're going to audit all of your source code and like, look at all of your weights one by one. Like, I think there's a lot of crazy proposals out there.

By the way, especially if the models are always being retrained all the time, if they become more dynamic.

Again, this is why I think it's, yeah, but, but like, when, before an airplane gets certified, there's like a set of safety tests. We put the airplane through it. and totally. It's different than reading all of your code.

That's reviewing the output of the model, not reviewing the insides of the model.

And so what I was going to say is that is the kind of thing that I think as safety testing makes sense.

How are we going to get that to happen, Sam? And I'm not just speaking for OpenAI, I speak for the industry, for humanity, because I am concerned that we draw ourselves into almost like a dark ages type of era by restricting the growth of these incredible technologies that can prosper, that humanity can prosper from so significantly. How do we change the sentiment and get that to happen? Because this is all moving so quickly at the government levels and folks seem to be getting it wrong. And I'm just concerned.

Just to build on that, Sam, the architectural decision, for example, that Llama took is pretty interesting in that it's like we're going to 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 at the.

Current strength of models, definitely some things are going to go wrong. And I don't want to make light of those or not take those seriously, but I'm not like, I don't have any catastrophic risk worries with a GPT-4 level model. And I think there's many safe ways to choose to deploy this. Maybe we'd find more common ground if we said that, and I think, you know, the specific example of models that are capable, that are technically capable, not even if they're not going to be used this way, of recursive self-improvement or.

Of.

Autonomously designing and deploying a bioweapon or something like that.

Or a new model.

That was the recursive self-improvement point. We should have safety testing on the outputs at an international level for models that have a reasonable chance of posing a threat there. I don't think GPT-4, of course, does not for posing any sort of, well, I don't want to say any sort because we don't, yeah, I don't think the 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. 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 going to be Right.

Right.

It'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, Our results on that come out very soon.

I just, it was a five year study that wrapped up, or started five years ago. Well, there was like a beta study first and there was like a long one that ran. But, well, Mark, what can you explain?

Yeah, why'd you start?

Why'd you start it? Maybe just explain UBI and why you started it.

So we started thinking about this in 2016, kind of about the same time 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 sort of in some deeper sense than that, like what the social contract looks.

Like.

Meant that we should have many studies to study many ideas about new ways to arrange that. 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. But I'm interested in better ways to do that than what we have tried for the existing social safety net 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 solve some problems and it might give people a better horizon with which to help themselves. And I'm interested in that. I think that now that we see some of the ways, so 2016 was a very long time ago. Now that we see some of the ways that AI is developing, I wonder if there's better things to do than the traditional conceptualization of UBI. I wonder, I wonder if the future looks something more like universal basic compute than universal basic income. And everybody gets 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 what you get is not dollars.

But this productivity slice.

Yeah, you own part of the productivity.

Right.

I would like to shift to the gossip part of this.

Gossip. What gossip?

Sam, let's go back to November. The flying f*** happened.

You know, I, if you have specific questions, I'm happy to, maybe I'll answer.

You said you were gonna talk about it at some point, so here's the point. What happened? I am here. You were fired, you came back, it was palace intrigue. Did somebody stab you in the back? Did you find AGI? What's going on?

Tell us.

This is a safe space, Sam.

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 because I was very upset. 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 going to be hard. It was even harder than I thought. But I kind of was like, all right, fine. I agreed to come back. The board took a while to figure things out, and then we were kind of like trying to keep the team together and keep doing things for our customers and sort of started making other plans. Then the board decided to hire a different interim CEO, and then everybody, there are many people.

Oh my gosh, what was that guy's name? He was there for like a Scaramucci, right?

Like, and it's great.

And I have nothing but good things to say about Scarabouche.

I was in your first Scarabouche.

And then- Where were you when you found the news that you'd been fired?

I was in Vegas. I was in a hotel room in.

Vegas for F1 weekend. I think that's how I found out. They texted me and they're like, well.

Did you see the fire pickup truck?

I said, I think that's happened to you before, J. Kelly.

I'm trying to think if I ever got fired.

I don't think I've gotten fired.

Yeah, it's a weird thing. It's a text from who?

Actually, no, I got a text the night before, and then I got on a phone call with the board, and then that was that. And then I kind of like, I mean, then everything went crazy. I was like, it was like, I mean, I have, my phone was like unusable. It was just a nonstop vibrating thing of like text messages, calls, basically, it's.

Like, you got fired by tweet. That happened a few times during the Trump administration, a few, They did call.

Me first before tweeting, which was nice of them. And then, like, you know, I kind of did, like, a few hours of just this, like, absolute fugue state in the hotel room, trying to, like, I was just confused beyond belief trying to figure out what to do.

So weird.

And then, like, flew home. It may be, like, we got on a plane, like, I don't know, 3 p.m. or something like that. still just like, you know, crazy nonstop phone blowing up, 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 yeah, you have options.

And then the next morning, had this call with a couple of board members about coming back and that led to a few more days of craziness. And then, and then it kind of, I think it got resolved. Well, it was like a lot of insanity in between.

But what percent of it was because of these nonprofit board members?

Um, well, we only have a nonprofit board. So it was all the nonprofit board members. Uh, there, the board had gotten down to six people. Um, they, And then they removed Greg from the board and then fired me. So, but it was like, you know.

But I mean, like, was there a culture clash between the people on the board who had only nonprofit experience versus the people who had startup experience?

And maybe you can share a little bit about if you're willing to the motivation behind the action, anything you can.

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. I have never once doubted their integrity or commitment to the sort of shared mission of safe and beneficial AGI. Do I think they 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 I think that like — the intent. The intent of the magnitude of AGI and getting that right.

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. Does that create 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 it- I think.

A lot of the, well.

I mean.

First I'll say, I'll answer the first question in the second one. I think it does create a great deal of fear. I think a lot of the world is understandably very afraid of AGI or very afraid of even current AI and very excited about it and even more afraid and even more excited about where it's going.

And.

We wrestle with that, but 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 going to change and change is, you know, pretty uncomfortable for people. So there's a lot of pieces that we got to get right.

Can I ask a different question? 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?

Yeah, I wish I had taken, I wish I had taken equity, so I never had to answer this question. If I could go back in time.

Why don't they give you a grand now? Why don't the board just give you a big option grant like you deserve?

Yeah, give you five points.

What was the decision back then? Like why was that so important?

The decision back then, the re, the original reason was just like the structure of our nonprofit. It was 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, but like, But in.

A weird way, now that you're running a company, yeah, it creates these weird questions of like, well, what's your real motivation versus?

That's, it is so deeply, one thing I have noticed, it is so deeply unimaginable to people to say, I don't really need more money. Like, and I think, I think people think it's a little bit of an ulterior motive. I think, well, yeah, yeah, yeah.

No, it so it assumes you're doing.

Something that is not, what is he doing on the side to make money?

Something's going on. If I were just trying to say.

Like, I'm going to try to make a trillion dollars with OpenAI, I think everybody would have an easy and it wouldn't save me. Well, it would save a lot of conspiracy theories.

Totally. This is the back channel. You are a great deal maker. 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. You're fantastic at it. And you got this Johnny Ive thing going. You're inhumane. You're investing in companies. You got the orb. You're raising $7 trillion to build fabs, all this stuff.

News.

All of that put together, JK loves fake news. I'm kind of being a little facetious here. Obviously, it's not, you're not raising seven trillion dollars, but maybe that's the market cap of something. Putting all that aside, the T was, you're doing all these deals, they don't trust you because what's your motivation? You're end running and what opportunities belong inside of OpenAI? What opportunity should be Sam's? And this group of nonprofit people didn't trust you. Is that what happened?

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.

They would.

Okay, that's not the public's perception.

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 because we haven't announced the stuff because it's not done. I don't think most people in the world like are thinking about this, but I agree it spins up a lot of conspiracies, conspiracy theories in like tech commentators.

Yeah, in a vacuum.

And if I could go back, yeah, I would just say like, let me take equity and make that super clear. And then everyone would be like, all right, like I'd still be doing it cuz 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.

What's the chip project that the $7 trillion, where'd the $7 trillion number come from?

It makes no sense. I don't know where that came from actually. I genuinely don't. 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. The exact way for us to play there is we're still trying to figure that out.

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?

Oh, I have to go in a minute. 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. At the time, before it was obvious to do this, you have DeepMind or whatever has all these different teams doing all these different things and they're spreading their bets out. You had OpenAI say, We're going to basically put the whole company and work together to make GPT-4. And that was 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 big, hard, complicated things, we do have a lot of coordination on what we work on.

All right, Sam, I know you got to go. You've been great on the hour. come back anytime.

Great talking to you guys.

Yeah, thanks for coming. Thanks for being so open about it.

We've been talking about it for like a year plus. I'm really happy it finally happened.

Yeah, it's awesome. I really enjoyed it. I would love to come back on.

After our next major launch and I'll.

Be able to talk more directly about something. Definitely. You got the Zoom link. Same Zoom link every week.

Just same time, same Zoom link.

Drop it.

Just put it on your calendar. Come back to the game. Come back to the game.

We'll have you back in a while.

I, you know, I would love to play poker. It has been forever. That would be a lot of fun.

Come on, send me an invite. I've been playing with Chamath, where Chamath.

When you and I were heads up.

And you, you had- I don't remember, remind me. 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 am in the tank trying to figure out if I want to lose, because back we were playing small stakes. They 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. And I'm like, I'm trying to figure out if I make the call here, I make the call. And it was like, you had a really good hand, but I just happened to have a set. I think you had top pair, top kicker or something. But you made a great move because the board was so textured, almost like a bottom set.

Sam has a great style of playing.

Which I would call ram and jam.

Totally. You got to ask Sam. I don't really know if you, I don't know if you could say that about anybody. I don't. I'm not gonna.

You haven't taught me about playing the last 18 months. It's a lot different.

I've tried to get into the game.

Much more so much fun now. Find out.

I find that really hard to believe.

Have you played Bomb Pots?

I don't know what that is.

Okay, you'll love it. We'll see.

Yeah, he's nuts. It's been a while. Sam, thank you.

Two awards. And congrats on everything, honestly.

Thanks for coming on.

And thanks, Sam.

I'd love to have you back when the next after the big launch. Sounds good. Please do. Cool. But, gentlemen, some breaking news here. All those projects, he said, are part of opening AI. 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?

I think that these guys are going to be one of the four major companies 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 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. And then it's all this other stuff that you'll have to pay for.

Interface.

Whoever builds all this other stuff is going to be in a position to build a really good business.

Freiberg, he talked a lot about reasoning. It seemed like that he kept going to reasoning and away from the language model. Did you note that and anything else that you noted in our arrow with him?

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 a, I think there's a lot of people that have different schools of thought on this and how much.

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 also 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 Friedberg, 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 going to 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 all these little systems that go into making that decision in your brain. And 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.

Sachs, you went right there with the corporate structure. the board and he, he, he gave us a lot more information here. 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 open AI. People just don't realize it in that moment. And then, you know, your questions to him about equity, your thoughts on, I'm.

Not sure I was like the main guy who asked that question, Jake out.

But, well, no, you did talk about the, the nonprofit, the difference between the.

Nonprofit versus the, the question about the, there clearly was some sort of culture clash. on the board between the people who originated from the nonprofit world and the people who came from the startup world.

And the tech side.

We don't really know more than that, but there clearly was some sort of culture clash. I thought 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 beyond just having a ChatGPT app on your phone or maybe even having a Siri on your phone. Clearly something bigger there. He doesn't know exactly what it is, but it's going to require more inputs. It's that, you know, personal assistant that's seeing everything around you and helping you.

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 context. I thought that was like a very interesting to me.

Yeah, he's talking about an executive assistant or an assistant that has executive function as opposed to being like just an alter ego. for you or what he called a sycophant. That's kind of interesting. I thought that was interesting, yeah.

Yeah.

And clearly he thinks there's a big opportunity in biology and scientific discovery.

After the break, I think we should talk about AlphaFold 3.

It was just announced yesterday. Yeah, let's do that. And we can talk about 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 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 going to be able to comment on that. So it'd be a no comment.

And we're not going to- yeah, like our time was limited and there's a lot of questions that we could ask him that would have just been a waste of time.

Well, they would have been no comment.

So I just want to make sure people understand. Yeah, of course he's going to no comment on any lawsuit.

And he's already been asked about that 500 times.

Should we take a quick break before the next- before we come back? Yeah, I'll 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. All right, welcome back, everybody. Second half of the show. Great guests, Sam Altman. Thanks for coming on the pod. We've got a bunch of news on the docket, so let's get started. Freiberg, you told me I could give some names of the guests that we booked for the All In Summit.

I did not.

You did. You've said each week, every week that I get to say seven names.

I did not. I appreciate your interest. in the All In Summits lineup, but we do not yet have enough critical mass to feel like we should go out there.

Well, I am a loose cannon, so I will announce my two guests. And I created the summit and you took it from me. So I've done a great job. I will announce my guests. I don't care what your opinion is. I have booked two guests for the summit and it's going to 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 a 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 Freeberg'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.

Speaking of billionaires who are coming, isn't coming too?

Yes, coming.

Yes, he's booked. So we have three billionaires.

Three billionaires, yes.

Okay. Hasn't fully confirmed, so don't. Okay, well we're gonna say it anyway.

As penciled in. Don't say it. Don't back out. We'll say penciled.

Yeah, don't back out.

This is gonna be catnip for all these protest organizers. Like, if you have one place poke the bear.

Well, by the way, speaking of updates, what'd you guys think of the bottle for the All in Tequila? Oh, beautiful. Honestly, honestly, I will just say, I think you are doing a marvelous job. That, I was shocked at the design, shock meaning it is so unique and high quality. I think it's amazing. It would make me drink tequila.

You're going to, you're going to want to.

Gotcha. It is stunning. Just congratulations. And yeah, it was just, when we went through the deck at the monthly meeting, It was like, oh, that's nice. Oh, that's nice. We're going to do the concept bottles. And then that bottle came up and everybody went like crazy. It was like somebody hitting like a, Steph Curry hitting a half court shot. It was like, oh my God. 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- Looks like we can't. Oh, is it going to be amazing?

Yeah. It's going to be amazing.

I'm excited. I'm excited for it. You know, it's like- 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 all in summit.

I mean, why not? I mean, it's great when we get.

Barricaded in by all these protesters, we can drink the tequila.

Did you guys see Peter Teal?

Did you see Peter Teal got barricaded by these ding dongs at Cambridge?

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 I think you're exaggerating what happened.

Well, I don't know exactly what happened.

Because all we see is these videos. 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 because they were blocking the driveway, his car couldn't leave. But he wasn't physically, like, locked in the building or something.

Yeah, that's. That's what the headlines say. But that could be fake news, fake social. Yeah.

This was not on my bingo card. This Protestering support by Saks was not on the bingo card, I gotta say.

I think the Constitution, the Constitution in the United States in the First Amendment provides for the right of assembly, which includes protests and sit-ins, 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 threat to your safety, then that somehow should be treated as valid, like that's basically violent. Well, that's not what the Constitution says. And these people understood well just A few months ago that that was basically snowflakeery, that, you know, just because somebody, you know what I'm saying? Like we have the rise of the woke right now where they're buying into this. Yeah, the woke right. They're buying into this idea of safetyism, which is being exposed to ideas you don't like, to protest you don't like, is a threat to your safety.

No, it's not.

So now we have snowflakes on both sides.

Everybody has snowflakeery on both sides now. It's ridiculous. The only thing I will say that I've seen and is this surrounding individuals who you don't want there and locking them in a circle and then moving them out of like a protestor's camp. Yeah, obviously that's not cool.

Yeah, obviously you can't do that. But look, I think that most of the protests on most of the campuses have not crossed the line. They've just occupied the lawns of these campuses. And look, I've seen some troublemakers try to barge through the encampments and claim that because they can't go through there, that somehow they're being prevented from going to class. Look, you just walk around the lawn and you get to class. Okay? And, you know, some of these videos are showing that these are effectively right-wing provocateurs who are engaging in left-wing tactics. And I don't support it either way.

By the way, some of these camps are some of the funniest things you've ever seen. It's like there are like one tent that's dedicated to, like, a reading room, and you go in there and there's, like, these, like, blind center. oh, my God. It's unbelievably hilarious.

Look, there's no question that because the protests are originating on the left, that there's some goofy views, like, you know, you're dealing with, like, a left-wing idea complex.

Right.

But, and, you know, it's easy to make fun of them doing different things. But the fact of the matter is that most of the protests in most of these campuses are, even though they can be annoying because they're occupying part of the lawn, they're not violent.

Yeah.

And the way they're being cracked down on, they're sending the police in at 5:00 a.m. to crack down on these encampments with batons and riot gear. And I find that part to be completely excessive.

Well, it's also dangerous because things can escalate when you have mobs of people and large groups of people. So I just want to make sure people understand that. Large group of people, you have a diffusion of responsibility that occurs when there's large groups of people who are passionate about things and people could get hurt. People have gotten killed at these things, so just you know, keep it calm, everybody. I agree with you, Like what's the harm of these folks protesting on a law and it's a big deal.

When they break into buildings, of course.

Yeah, thatn it crosses the line obvious. yeah. But I mean, let them sit out there and then they'll run out their.

Food cards, their,, palyze food card and they'll run out of waffles. Did you guy see the clip? I think it was on the University.

Of Washington campus where one kid challenged.

This antifa guy to a push up contest announced up I mean, it is some of the funniest stuff. Some of the, some content is coming.

Out that's just, my favorite was the woman who came out and said that the Columbia students needed humanitarian aid.

Oh my God. The overdubs on her were hilarious.

I was like, humanitarian aid. I was like, we need our door dash right now. We need, we double dash some boba and we can't get it through the police. We need our boba. Low sugar boba. with the popping boba bubbles wasn't getting in. But, you know, people have the right to protest and peaceable, by the way. There's a word I've never heard. Very good, sex. Peaceable, inclined to avoid argument or violent conflict.

Very nice.

Well, it's in the Constitution. It's in the First Amendment. Is it really? I've never, yeah, I haven't heard the word peaceable before. I mean, you and I are simpotical on this. Like, I don't, we used to have the ACLU, like, backing up the KKK going down Main Street and really fighting for, yeah, they were really fighting for, and I have to say the Overtown window is opened back up and I think it's great. All right, we got some things on the docket here. I don't know if you guys saw the Apple new iPad ad. It's getting a bunch of criticism. They use like some giant hydraulic press to crush a bunch of creative tools. DJ turntable, trumpet. Piano, people really care about Apple's ads and what they represent. We talked about that Mother Earth little vignette they created here. What do you think, Freeberg? You see the ad? What was your reaction to it?

Made me sad. It did not make me want to buy an iPad.

So, huh?

Did not seem like it made you sad.

It actually elicited an emotion, meaning like commercials, it's very rare that commercials can actually do that. Most people just zone out.

Yeah, they took all this beautiful stuff and heard it. It didn't feel good. I don't know, it just didn't seem.

Like a good ad.

I don't know why they did that.

I don't get it. I don't know. I think maybe what they're trying to do is the selling point of this new iPad is that it's the thinnest one. I mean, there's no innovation left, so they're just making the devices thinner. So I think the idea was that they're going to take this hydraulic press to represent how ridiculously thin the new iPad is. Now, I don't know if the point there was to smush all of that good stuff into the iPad. I don't know if that's what they were trying to convey. But yeah, I think that by destroying all those creative tools that Apple is supposed to represent, it definitely seemed very off-brand for them. And I think people were reacting to the fact that it was so different than what they would have done in the past. And of course, everyone was saying, well, Steve would never have done this. I do think it did land wrong. I mean, I didn't care that much, but I was kind of asking the question, like, why are they destroying all these creator tools that they're renowned for creating or for turning into the digital version?

Yeah, it just didn't land. I mean, Chamath, how are you doing emotionally after seeing that? Are you okay, buddy? Yeah.

I think this is, you guys see that in the Berkshire annual meeting last weekend, Tim Cook was in the audience and Buffett was very laudatory. This is an incredible company, but he's so clever with words. He's like, you know, this is an incredible business that we will hold forever, most likely. And it turns out, that he sold $20 billion worth of Apple shares.

Caveat. We're gonna hold it forever.

Which, by the way, if you guys remember, we put that little chart up, which shows when he doesn't mention it in the annual letter, it's basically like, it's foreshadowing the fact that he is just pounding the sell. And he sold $20 billion.

Well, also holding it forever could mean one share.

Yeah, exactly.

We kind of need to know, like how much are we talking about?

I mean, it's an incredible business that has so much money with nothing to do. They're probably just going to buy back the stock, just a total waste.

They were floating this rumor of buying Rivian, you know, after they shut down Titan project, the internal project to make a car. It seems like a car is the only thing people can think of that would move the needle in terms of earnings.

I think the problem is, J. Cal, like you kind of become afraid of your own shadow, meaning the folks that are really good at M&A, like you look at Benioff, the thing with Benioff's M&A strategy is that he's been doing it for 20 years. And so he's cut his teeth on small acquisitions and the market learns to give him trust so that when he proposes like the $27 billion Slack acquisition, he's allowed to do that. Another guy, you know, Nikesh Arora at P&G, these last five years, people were very skeptical that he could actually roll up security because it was a super fragmented market. he's gotten permission. Then there are companies like Danaher that buy hundreds of companies. So all of these folks are examples of you start small and you earn the right to do more. Apple hasn't bought anything more than 50 or a hundred million dollars. And so the idea that all of a sudden they come out of the blue and buy a 10, 20 billion dollar company, I think is just totally doesn't stand logic. It's just not possible for them because they'll be so afraid of their own shadow. That's the big problem. It's themselves.

Well, if you're running out of in-house innovation and you can't do M&A, then your options are kind of limited. I mean, I do think that the fact that the big news out of Apple is the iPad's getting thinner does represent kind of the end of the road in terms of innovation. It's kind of like when they added the third camera to the iPhone. Yeah, it reminds me of those, remember like when the Gillette- yeah, they did the three came out and then they.

Did the- It was the best onion thing was like, we're doing five.

But then Gillette actually came out with the Mach 5. So, like, the parody became the reality. What are they gonna do? Add two more cameras to the iPhone? You have five cameras on it.

No, it makes no sense. And then I don't know anybody who wants to, remember the Apple Vision was.

Like gonna, plus why are they body shaming the fat iPads?

That's a fair point.

That's a fair point. Actually, you know what? It's actually, this didn't come out yet, but it turns out the iPad is on Ozempic. It's I actually dropped a lot.

That would been the one you're at.

Yeah, exactly.

Oh oh, I.

We can just work.

Shop that right here. there was another funny one which was making the iPhone smaller and smaller and smaller, and the iPd smaller and smaller and small, to the point it was like, you know, like a thumb size.

Iphone, likeke the Ben Stiller phone in landnder or somewhere in. That was a great scene.

Is there a category that you can think of that you would love an Apple product for? There's a product in your life that you would love to have Apple's version of it.

They killed it. I think a lot of people would be very open-minded to an Apple car. They just would. It's a connected internet device, increasingly so. And they managed to flub it. They had a chance to buy Tesla. They managed to flub it.

Yeah.

Right.

There are just too many examples here where these guys have so much money and not enough ideas. That's a shame.

It's a bummer, yeah. The one I always wanted to see them do, Zach, was- TV? The one I always wanted to see them do was the TV, and they were supposedly working on it, like the actual TV, not the little Apple TV box in the back. And like, that would have been extraordinary to actually have a gorgeous, you know.

Big television- what about a gaming console? They could have done that. You know, there's just all these things that they could have done. It's not a lack of imagination cuz these aren't exactly incredibly world beating ideas. They're sitting right in front of your face. It's just the will to do it. Yeah. Yeah.

One TV would've been good.

If you think back on Apple's product lineup over the years, where they've really created value is on how unique the products are. They almost create new categories. Sure, there may have been a tablet computer prior to the iPad, but the iPad really defined the tablet computer era. Sure, there was a smartphone or two before the iPhone came along, but it really defined the smartphone. And sure, there was a computer before the Apple II, and then it came along and it defined the personal computer. In all these cases, I think Apple strives to define the category. So it's very hard to define a television, if you think about it, or a gaming console in a way that you take a step up and you say, this is the new thing. This is the new platform. So I don't know, that's the lens I would look at if I'm Apple in terms of like, can I redefine a car? Can I make, you know, we're all trying to fit them into an existing product bucket. But I think what they've always been so good at is identifying consumer needs and then creating an entirely new way of addressing that need in a real step change function from the, like the, the, the iPod. It was so different from any MP3 player ever.

I think the reason why the car could have been completely reimagined by Apple is that they have a level of credibility and trust crust that I think probably no other company has and absolutely no other tech company has. And we talked about this, but I think this was the third Steve Jobs story that, that I left out. But in 2000 and, I don't know, was it one? I launched a 99 cent download store.

Right.

I think I've told you this story in Winamp and Steve Jobs just ran total circles around us. But the reason he was able to is he had all the credibility to go to the labels and get deals done for licensing music that nobody could get done before. I think that's an example of what Apple's able to do, which is to use their political capital to change the rules. So if the thing that we would all want is safer roads and autonomous vehicles, there are regions in every town and city that could be completely converted to level five autonomous zones. If I had to pick one company that had the credibility to go and change those rules, it's them. Because they could demonstrate that there was a methodical, safe approach to doing something. And so the point is that even in these categories that could be totally reimagined, it's not for a lack of imagination. Again, it just goes back to a complete lack of will. And I understand because if they had, if you, if you had $200 billion of capital on your balance sheet, I think it's probably pretty easy to get fat and lazy.

Yeah, it is. And they want to have everything built there. People don't remember, but they actually built one of the first digital cameras. You must have owned this, right, Fred Berg? You're learning Apple. I know this. Yeah, totally. It was beautiful.

What did they call it?

Was it the iCamera or something? Quick take. Quick take, yeah. The thing I would like to see Apple build, and I'm surprised they didn't, was A smart home system, the way Apple has Nest, a drop cam, a door lock, you know, AV system, go after Questron or whatever, and just have your whole home automated, thermostat Nest, all of that would be brilliant by Apple. And right now I'm an Apple family that has our, all of our home automation through Google. So it's just, it kind of sucks. I would, I would like that all together.

Actually, that would be pretty amazing. Like if they did a Questron or Savon. because then when you just go to your Apple TV, all your cameras just work.

You don't need to. Yes, that's the. That. I mean, and everybody has a home, and everybody automates their home. So.

Well, everyone has Apple TV at this point, so you just make Apple TV the brain for the home system.

Right.

That would be your hub.

And you can connect your phone to it, and then. Yes, that would be very nice.

Yeah.

Like, can you imagine, like, the ring cameras, all that stuff being integrated? I don't know why they didn't go after that. That seems like the easy layout. Hey, you know, everybody's been talking Friedberg about this alpha fold, this folding proteins. And there's some new version out from Google. And also Google reportedly, and we talked about this before, is also advancing talks to acquire HubSpot. So that rumor for the $30 billion market cap HubSpot is out there as well. Friedberg, you're as our resident science Sultan, our resident Sultan of Science, and as a Google alumni, pick either story and let's go for it.

Yeah, I mean, I'm not sure there's much more to add on the HubSpot acquisition rumors. They are still just rumors, and I think we covered the topic a couple of weeks ago. But I will say that AlphaFold 3 that was just announced today and demonstrated by Google is a real, I would say, breathtaking moment for biology, for bioengineering, for human health, for medicine. and maybe I'll just take 30 seconds to kind of explain it. You remember when they introduced AlphaFold and AlphaFold 2, we talked about DNA codes for proteins. So every three letters of DNA codes for an amino acid. So a string of DNA codes for a string of amino acids, and that's called a gene that produces a protein. And that protein is basically a long, like think about beads. There's 20 different types of beads, 20 different amino acids. that can be strung together. And what happens is that necklace, that bead necklace basically collapses on itself and all those little beads stick together with each other in some complicated way that we can't deterministically model. And that creates a three-dimensional structure, which is called a protein, that molecule. And that molecule does something interesting. It can break apart other molecules. It can bind molecules. It can move molecules around. So it's basically the machinery of chemistry. of biochemistry. And so proteins are what is encoded in our DNA, and then the proteins do all the work of making living organisms. So Google's AlphaFold project took three-dimensional images of proteins and the DNA sequence that codes for those proteins, and then they built a predictive model that predicted the three-dimensional structure of a protein from the DNA that codes for it. And that was a huge breakthrough years ago. What they just announced with AlphaFold 3 today is that they're now including all small molecules. So all the other little molecules that go into chemistry and biology that drive the function of everything we see around us. And the way that all those molecules actually bind and fit together is part of the predictive model. Why is that important? Well, let's see that you're designing a new drug and it's a protein-based drug, which biologic drugs, which most drugs are today. You could find a biologic drug that binds to a cancer cell. And then you'll spend 10 years going to clinical trials and billions of dollars later, you find out that that protein accidentally binds to other stuff and hurts other stuff in the body. And that's an off target effect or a side effect. And that drug is pulled from the clinical trials and it never goes to market. Most drugs go through that process. They are actually tested in animals and then in humans. And we find all these side effects that arise from those drugs because we don't know how those drugs are going to bind or interact with other things in our biochemistry. And we only discovered after we put it in. But now we can actually model that with software. We can take that drug, we can create a three-dimensional representation of it using the software, and we can model how that drug might interact with all the other cells, all the other proteins, all the other small molecules in the body to find all the off-target effects that may arise and decide whether or not that presents a good drug candidate. That is one example of how this capability can be used. And there are many, many others, including creating new proteins that could be used to bind molecules or stick molecules together, or new proteins that could be designed to rip molecules apart. We can now predict the function of three-dimensional molecules using this capability, which opens up all of the software-based design of chemistry, of biology, of drugs, And it really is an incredible breakthrough moment. The interesting thing that happened though is Google Alphabet has a subsidiary called Isomorphic Labs. It is a drug development subsidiary of Alphabet. And they've basically kept all the IP for AlphaFold 3 in isomorphic. So Google is going to monetize the heck out of this capability. And what they made available was not open source code, but a web based viewer that scientists for quote, non-commercial purposes can use to do some fundamental research in a web-based viewer and make some experiments and try stuff out and how interactions might occur. But no one can use it for commercial use. Only Google's isomorphics labs can. So number one, it's an incredible demonstration of what AI outside of LLMs, which we just talked about with Sam today, and obviously we talked about other models, but LLMs being kind of this consumer text predictive model capability, But outside of that, there's this capability in things like chemistry with these new AI models that can be trained and built to predict things like three dimensional chemical interactions that is going to open up an entirely new era for, you know, human progress. And I think that's what's so exciting. I think the other side of this is Google is hugely advantaged and they just showed the world a little bit about some of these jewels that they have in the treasure chest. And they're like, look at what we got. We're going to make all these drugs and they've got partnerships with all these pharma companies. at isomorphic labs that they've talked about. And it's gonna usher in a new era of drug development design for human health. So all in all, I'd say it's a pretty like astounding day. A lot of people are going crazy over the capability that they just demonstrated. And then it begs all this really interesting question around like, you know, what's Google gonna do with it and how much value is gonna be created here? So anyway, I thought it was a.

Great story and I just rambled on for a couple minutes, but I don't know, it's pretty cool. Super interesting.

Is this AI capable of making a.

Science one or that David Sacks pays attention to?

Well, it will, it will predict the cure, I think, for the common cold and for herpes. So he should pay attention.

Absolutely. Folding cells is the app that casual game Sacks just download is playing. How many, how many chess moves did you make during that segment?

Sorry. Let me just say one more thing. Do you guys remember we talked about Yamanaka factors and how challenging it is to basically we can reverse aging if we can get the right proteins into cells to tune the expression of certain genes to make those cells youthful. Right now, it's a shotgun approach to trying millions of compounds and combinations of compounds to do them. There's a lot of companies actually trying to do this right now to come up with a fountain of youth type product. We can now simulate that. So with this system, one of the things that this AlphaFold 3 can do is predict what molecules will bind and promote certain sequences of DNA. which is exactly what we try and do with the Yamanaka factor based expression systems and find ones that won't trigger off target expression. So meaning we can now go through the search space and software of creating a combination of molecules that theoretically could unlock this fountain of youth to de-age all the cells in the body and introduce an extraordinary kind of health benefit. And that's just, again, one example of the many things that are possible with this sort of platform. I, I, and I'm really, I gotta be honest, I'm really just throwing , skimming the surface here of what this can do. The, the capabilities and the impact are gonna be like, I don't know, I know I say this sort of stuff a lot, but it's gonna be pretty profound.

There's a, on the blog post, they have this incredible video that they show of the coronavirus that creates a common cold. I think it, the 7 pnm protein. And not only did they literally like predict it accurately, they also predicted how it interacts. with an antibody, with a sugar, it's nuts. So you could see a world where like, I don't know, you just get a vaccine for the cold and it's kind of like you never have colds again.

Amazing.

I mean simple stuff, but so powerful.

And you can filter out stuff that has off-target effects. So so much of drug discovery and all the side effect stuff can start to be solved for in silico. And you could think about running extraordinarily large, use a model like this, run extraordinarily large simulations in a search space of chemistry to find stuff that does things in the body that can unlock, you know, all these benefits, can do all sorts of amazing things to destroy cancer, to destroy viruses, to repair cells, to de-age cells.

And this is a hundred billion dollar business, they say.

Oh my God, I mean, this alone, I feel like this is where I, I've said this before, I think Google's got this like portfolio of like quiet, yeah. Yeah, yeah, yeah.

What if they hit?

And the fact, and I think the fact that they didn't open source everything in this, Says a lot about their intentions.

Yeah.

Open source when you're behind, closed source when you're ahead. Yamanaka, actually, interestingly, Yamanaka is the Japanese whiskey that Sack serves on his plane as well. It's delicious. I love that Hokkaido Yamanaka.

If you didn't find your way to Silicon Valley, you could be like a Vegas lounge comedy guy.

Absolutely. Yeah, for sure. Yeah.

I was actually, yeah, somebody said I should do, like, 1950s, Those 1950s talk shows where the guys would do like the stage show.

The shtick.

Yeah, they show. Somebody told me I should do like Spalding Gray, Eric Bogosian style stuff. I don't know if you guys remember the monologue is from the '80s in New York. I was like, oh, that's interesting. Maybe. All right, everybody, thanks for tuning in to the world's number one podcast. Can you believe we did it, Shamath? The number one podcast in the world and the All In Summit, the Ted Killer, if you are going to Ted, Congratulations for Jen you're lecting. If you want to talk about real issues, come to the All In Summit. And if you are protesting at the All In Summit, let us know what mock meat you would like to have. Freeberg is setting up mock meat stations for all of our protesters. And what milk you would like. Yeah, all vegan. If you want, if you're oat milk.

Soy nut milk, just please, when you come to protest, we have five different.

Kinds of xantham gum you can choose from, right? We have all of the nut milks.

You could want, and then they'll be.

Mindful of the soy. Can we have some soy lactose? Yes, on the the South Lawn will have the goat yoga going on. So just please, don't, the goat yoga will be going on for all of you.

It's very thoughtful for you to make sure that our protesters are gonna be well, well fed, well taken care of.

Yes, we're actually, Freeberg is working on the protester gift bags. The protester gift bags. They're, they're made of Yakunama folding proteins. So you're good. Folding proteins. I think I saw them opening for the smashing Pumpkins in 2003.

Enough.

Enough. I'll be here for three more nights.

Love you, boys. Bye-bye.

Love you, besties.

Is this the all-in pot or open mic night? What's going on?

It's basically.

I'm just bored.

We'll let your winners ride.

Rain Man, David Saxon.

And it said we open sources to the fans and they've just gone crazy with it.

Love you at night.

Queen of Kinwa. Besties are back.

We should all just get a room.

And just have one big, huge orgy.

Because they're all just.

It's like this, like, sexual.

Tension, but they just need to release them.

Out. All right, that's episode 178. And now the plugs, the all-in Summit is taking place in Los Angeles on September 8th. through the 10th, you can apply for a ticket at summit.allinpodcast.co. Scholarships will be coming soon. If you want to see the four of us interview Sam Altman, you can actually see the video of this podcast on YouTube, youtube.com/allin, or just search All In podcast and hit the alert bell and you'll get updates when we post. We're doing a Q&A episode live when the YouTube channel hits 500,000. and we're going to do a party in Vegas my understanding when we hit a million subscribers so look for that as well you can follow us on x.com the all in pod Tick Tock is all underscore in underscore talk Instagram the all in pod and on LinkedIn just search for the all in podcast you can follow chamath@x.com chamath and you can sign up for a substack@chamath.substack.com I do freeberg can be followed at x.com freeberg and ohalo is hiring click on the careers page at hollow genetics.com and you can follow sax@sax.com slash David sax sax recently spoke at the American Moment conference and people are going crazy for it it's pinned to his tweet on his X profile I'm Jason Calacanis I am x.com Jason and if you want to see pictures of my Bulldogs and the food I'm eating go to instagram.com Jason in the first name Club you can listen to my other podcast this week in startups just search for it on YouTube or your favorite podcast player we are hiring a researcher apply to be a researcher doing primary research and working with me and producer Nick, working in data and science and being able to do great research, finance, et cetera. AllinPodcast.co/research. It's a full-time job working with us, the besties.

We'll see you all next time on the All in Podcast.