Mark Zuckerberg — Llama 3, $10B models, Caesar Augustus, & 1 GW datacenters

25:20No, it's nearly as good. Okay, that's not, I don't know, I'm overestimated. But does that mean like the Lama 4 or Ragnitude? Does that mean like the Lama 4 or 70B will be as good as the Lama 3, 4 or 5B? Like, what is the future of this? This is one of the great questions, right? that I think no one knows is basically, you know, it's one of the trickiest things in the world to plan around is when you have an exponential curve, how long does it keep going for? And I think it's likely enough that it will keep going, that it is worth investing the tens or, you know, 100 billion plus in building the infrastructure to assume that if that kind of keep going, you're going to get some really amazing things that are just going to make amazing products.

26:03But I don't think anyone in the industry can really tell you that it will continue scaling at that rate for sure. In general, in history, you hit bottlenecks at certain points. Now, there's so much energy on this that maybe those bottlenecks get knocked over pretty quickly. But I don't know. I think that's an interesting question. What does the world look like where there aren't these bottlenecks? as you know, suppose like progress just continues at this pace, which seems like plausible. Like zooming out. Well, they're pretty much like, they're going to be different bottlenecks. Right. So if not training, then like, oh yeah, go ahead.

26:40Well, I think at some point, over the last few years, I think there was this issue of GPU production. Yeah. Right. So even companies that had the models, sorry, that had the money to pay for the GPUs, couldn't necessarily get as many as they wanted because there were all these supply constraints. Now I think that's sort of getting less. So now I think you're seeing a bunch of companies think about, wow, we should just like really invest a lot of money in building out these things. And I think that that will go for some period of time. I think there is a capital question of like, okay, at what point does it stop being worth it to put the capital in, but I actually think before we hit that, you're going to run into energy constraints, right?

27:27Because I just, I mean, I don't think anyone's built a gigawatt single training cluster yet, right? And then you run into these things that just end up being slower in the world, like getting energy permitted is like a very heavily regulated government function. Right? So you're going from on the one hand software, which is somewhat regulated. I'd argue that it is more regulated than I think a lot of people in the tech community feel. Although it's obviously different. If you're starting a small company, maybe you feel that less. If you're a big company, you know, we just interact with people.

28:03But different governments and regulators are, you know, we have kind of lots of rules that we need to kind of follow and make sure we do a good job with around the world. But I think that there's no doubt that like energy. And if you're talking about building large new power plants or large buildouts and then building transmission lines that cross other private or public land, that is just a heavily regulated thing. So you're talking about many years of lead time. So if we wanted to stand up to some like massive facility to power that, I think that that is that's a very long -term project. And so I don't know.

28:45I think that that's, I think people do it. But I don't think that this is like something that can be quite as magical. It's just like, OK, you get a level of AI and you get a bunch of capital and you put it in. And then like all of a sudden the models are just going to, like I think you do hit different bottlenecks along the way. Yeah. Is there something a project? Maybe I really did. Maybe not. That even a company like Meta doesn't have the resources for it. If you're R &D budget or a CAPEX budget was 10X what it is now, then you could pursue it. Like it's in the back of your mind, but meta today, maybe you could like, even you can't even issue a stocker bond for it.

29:18It's like just 10X bigger than your budget. Well, I think energy is one piece, right? I think we would probably build out bigger clusters than we currently can if we could get the energy to do it. So I think that's fundamentally money bottlenecked in the limit. like you had a trillion dollars. It's time. Well, if you look at it in terms of, but it depends on how far the the exponential curves go. Right. Like I think a number of companies are working on, you know, right now I think I did like a lot of data centers around the order of 50 megawatts or 100 megawatts or like a big one might be 150 megawatts.

29:57Okay. So you take a whole data center and you fill it up with just all the stuff that you need to do for training and you build the biggest cluster you can. I think you're, you, that's kind of, I think a bunch of companies are running at stuff like that. But then when you start getting into building a data center, that's like 300 megawatts, or 500 megawatts, or a gigawatt, I mean, just known as built a single gigawatt data center yet. So I think it will happen. I mean, this is only a matter of time, but it's not going to be like next year. I think that some of these things will take, I don't know, some number of years to to build out.

30:35And then the question is, okay, well, if you, I mean, just to, I guess, put this in perspective, I think a gigawatt, it's like around the size of like a meaningful nuclear power plant only going towards training a model. Didn't Amazon do this? There's like, they have a 950 gigawatt. Yeah, I'm not exactly sure what you did. What they did, you don't have to ask them. But it doesn't have to be in the same place, right? If distributed training works, it can be distributed. That I think is a big question. Right. It is basically how that's going to work. And I do think in the future, it seems quite possible that more of what we call training for these big models is actually more along the lines of inference generating synthetic data to then go feed into the model.

31:23So I don't know what that ratio is going to be, but I consider the generation of synthetic data to be more inference than training today. but obviously if you're doing it in order to train a model, it's part of the broader training process. So, I don't know, that's an open question, is to kind of where, what the balance of that and how that plays out. If that's the case, would that potentially also be the case with Lama 3, and maybe like Lama 4 onwards, where you put this out and if somebody has a ton of compute, then using the models that you've put out, you can just keep making these things arbitrarily smarter.

31:57Like some Kuwait or UAE or some random country has a ton of compute and they can just actually use Lama for it to just make something much martyr. I do think that there are going to be dynamics like that, but I also think that there is a fundamental limitation on the network architecture, or the model architecture. So I think like a 70 billion model that kind of we trained with the Lama 3 architecture can get better, right? It can keep going like I was saying it's you know, we felt like if we kept on feeding it more data or rotated the high value tokens through again, then you know it would continue getting better.

32:43But and we've seen a bunch of other people around the world, you know different companies basically take the a Lama 270 billion base, like take that model architecture and then build a new model. It's still the case that when you make a generational improvement to the Lama 370 billion or the Lama 305, there's nothing open source, anything like that today. Right, like it's not, I think that that's like, it's a big step function and what people are gonna be able to build on top of that I don't think can go infinitely from there. I think it can be some optimization in that until you get to the next step function.

33:21Yeah. Okay, so let's zoom out a little bit from specific models, and even the many years lead times you would need to get energy, purples, and so on. Like, big picture. These next couple of decades. What's happening with AI? Does it feel like another technology, like metaverse or social, or does it feel like a fundamentally different thing in the course of human history?

33:45I think it's going to be pretty fundamental. I think it's going to be more like the creation of computing in the first place, right? So you'll get all these new apps in the same way that when you got the web or you got mobile phones, you got like people basically rethought all these experiences and a lot of things that weren't possible before now became possible. Something that will happen, but I think it's a much lower level innovation. It's going to be more like going from people didn't have computers to people have computers. Is my sense. But it's also it's, I don't know. It's very hard to reason about exactly how this goes.

34:33I tend to think that, you know, in like the cosmic scale, obviously, it'll happen quickly over a couple of decades or something. But I do think that there is some set of people who are afraid of like, you know, it really just kind of spins and goes from being like Someone intelligent to extremely intelligent overnight And I just think that there's all these physical constraints that make that so that that's unlikely to happen I just don't I don't really see that that playing out So I think you'll have I think we'll have time to kind of acclimate a bit But it will really change the way that we work and give people all these creative tools to do different things that they I think it's going to be, it's going to really enable people to do the things that they want a lot more as it is my view.

35:20Okay, so maybe not overnight, but is it your view that on a cosmic scale, if you think like, humans of all, and then AI happened and then they went out through the galaxy, or maybe it takes many decades, maybe it takes a century, but like, is that like the grand scheme of what's happening right now in history? Sorry, in what sense? I mean, in the sense that there were other technologies like computers and even like fire, but like the AI happening is as significant as like humans evolving in the first place. I think that's tricky. I think people like to... I mean, the history of humanity I think has been people basically, you know, thinking that certain aspects of humanity are like...

36:05really unique in different ways. And then coming to grips with the fact that that's not true, but humanity is actually still super special. Right, so it's like we thought that the Earth was the center of the universe. And it's like it's not, but like, like humans are still pretty awesome, right? And pretty unique. I think that another bias that people tend to have is thinking that intelligence is somehow kind of fundamentally connected to life. And it's not actually clear that it is. Right? I think like people think that, I don't know that we have a clear enough definition of consciousness or life to kind of fully interrogate this, but I think there's all the science It's fiction about, okay, you create intelligence, and now it starts taking on all these human behaviors and things like that.

37:06But I actually think that the current incarnation of all this stuff, at least, kind of feels like it's going in a direction where intelligence can be pretty separated from consciousness and agency and things like that. I think just makes it a super valuable tool. So I don't know. I mean, obviously it's very difficult to predict what direction the stuff goes in over time, which is why I don't think anyone should be dogmatic about how they plan to develop it or what they plan to do. I think you want to kind of look at each release. We're obviously very pro -open source, but I haven't committed that we're going to release every single thing that we do.

37:41But it's basically, I'm just generally very inclined to thinking that open sourcing it is going to be good for the community and also good for us, because we'll benefit from the innovations. But if it's some point, like there's some qualitative change in what the thing is capable of and we feel like it's just not responsible to open source it then we won't. But it's all very difficult to predict. What is a kind of qualitative change? Like a specific thing. You're training Lamaphah, Lamaphore, and you've seen this and like, you know what, I'm not sure about open sourcing it. But I think that it's a little hard to answer that in the abstract because there are negative behaviors that any product can exhibit that as long as you can mitigate it, it's like, it's okay, right?

38:34So there's bad things about social media that we work to mitigate, right? There's bad things about Lama II that we spend a lot of time trying to make sure that it's not like helping people commit violent acts or things like that, right? I mean, that doesn't mean that it's like a, a kind of autonomous or intelligent agent. It just means that it's learned a lot about the world and a can answer a set of questions that we think it would be unhelpful for a dancer. So, I don't know. I think the question isn't really what behaviors would it show? It's what things would we not be able to mitigate after it shows that.

39:12And I don't know. Now, I think that there's so many ways in which something can be good or bad that it's hard to actually enumerate them all up front. If you even look at what we've had to deal with in social media and the different types of harms, we've basically gotten to. It's like there's like 18 or 19 categories of harmful things that people do and we've basically built AI systems to try to go identify what those things are, the people are doing and try to make sure that that doesn't happen on our network as much as possible. So yeah, I think you can over time, I think you'll be able to break down this into more of a taxonomy too.

39:49And I think this is a thing that we spend time researching too, because we want to make sure that we understand that. So one of the things I asked Mark is what industrial scale use of LLUMS would look like? You see this in previous technological revolutions where at first they're thinking in a very small scale way about what's enabled. And I think that's what chatbots might be for other LUMS. And I think the large scale use case might look something like what v7 go is. And by the way, it's made by v7 labs who are sponsoring this episode. So it's like a spreadsheet. You put in raw information, like documents, images, whatever, and they become rows, and the columns are populated by an LLM of your choice.

40:27And in fact, I used it to prepare for Mark. So I fed in a bunch of blog posts and papers from Meta's AI research. And as you can see, if you're on YouTube, it summarizes and extracts exactly the information I want as columns. And obviously mine is a small use case. We can imagine, for example, a company like FedEx has to process half a million documents a day. Obviously a chatbot can do that. A spreadsheet can, because this is just like a fire hose of intelligence in there, right? Anyways, you can learn more about them at v7labs .com slashgo or the link in the description. Back to Mark. Yeah, like it seems to me it would be a good idea.

41:05I would be disappointed in a future where AI systems aren't broadly deployed and everybody doesn't have access to them. At the same time, I want to better understand the mitigations. Because if the mitigation is the fine tuning, well, the whole thing about open weights is that you can then remove the fine tuning, which is often superficial on top of these capabilities. If it's talking on Slack with a biology researcher, I think models are very far from this. They're right now they're like Google search. But I can show them my P3DIS and they can explain, like here's why you're a smallpox sample, then grow.

41:38Here's what to change. How do you mitigate that? Because somebody can just fine tune that in there, right? Yeah, I mean, that's true. I think a lot of people will basically use the off -the -shelf model and some people who have basically bad faith are going to try to strip out all the bad stuff. So I do think that that's an issue. The flip side of this is that, And this is one of the reasons why I'm kind of philosophically so pro open source is I do think that a concentration of AI in the future has the potential to be as dangerous as kind of it being widespread. So I think a lot of people are, you think about the questions of, okay, well, if we can do this stuff, is it bad for it to be outwiled?

42:26Like just in kind of widely available. I think another version of this is like, okay, well, it's probably also pretty bad for one institution to have an AI that is way more powerful than everyone else's AI. So if you look at, I guess one security analogy that I think of is, it doesn't take AI to basically, okay, there's security holes in so many different things. And if you could travel back in time a year or two years, right, it's like, that's not AI. It's like, you just, let's say you just have like one year or two years more knowledge of the security holes. It's pretty much hacking to like any system, right?

43:10So it's not that far fetched to believe that a very intelligent AI would probably be able to identify some holes and basically be like a human who could potentially go back in time a year or two and compromise all these systems. Okay, so how have we dealt with that as a society? Well, one big part is open -source software that makes it so that when improvements are made to the software, it doesn't just kind of get stuck in one company's products, but it can kind of be broadly deployed to a lot of different systems, whether it's banks or hospitals or government stuff, and just everyone can kind of, like as the software gets hardened, which happens because more people can see it, and more people can bang on it and there are standards on how the stuff works.

43:53The world can kind of get upgraded together pretty quickly. And I kind of think that a world where AI is very widely deployed in a way where it's gotten hardened progressively over time and is one where all the different systems will be in check in a way that seems like it is fundamentally more healthy to me than one where this is more concentrated. So there are risks on all sides, but I think that's one risk that I think people I don't hear them talking about quite as much. I think there's sort of the risk of like, okay, well, what if the AI system does something bad? I am more like, you know, I stay up at night more worrying, well, what if like some actor that whatever, it's like from wherever you sit, there's going to be some actor who you don't trust.

44:41If they're the ones who have like the super strong AI, whether it's some like other government that we that does sort of like an opponent of our country or some company that you don't trust or whatever it is. Like I think that that's potentially a much bigger risk as in they could like overthrow our government because they have a weapon that like nobody else has. Cause a lot of mayhem right? It's I think it's like I think the intuition is that this stuff ends up being pretty kind of important and valuable for both kind of economic and kind of security and other things. And I don't know. I just think, yeah, if someone who you don't trust or is an adversary of you gets something that is more powerful than I think that that could be an issue.

45:31And I think the probably the best way to mitigate that is to have good open -source AI that basically becomes the standard. And in a lot of ways, kind of can become the leader. and in that way it just ensures that it's a much more kind of even and balanced playing field. Yeah, that seems plausible to me. And if that works out, that would be the future I prefer. I guess I want to understand mechanistically how if somebody was going to cause me him with AI systems, how the fact that there are other open source systems in the world prevents that. This specific example of somebody coming with a bio weapon, is it just that we'll do a bunch of R &D in the rest of the world to figure out The vaccine's really fast.

46:13What's happening? You take the computer, the security one that I was talking about. I think someone with a weaker AI trying to hack into a system that is protected by a stronger AI will succeed less. So I think that that's, I mean, in terms of how do you know everything in the world is like that? Like what if bio -uppens aren't like that? No, I mean, I don't know that everything in the world is like that. I think that that's, I guess, one of the bio -uppens are one of the areas where I think the people who are most worried about this stuff are focused and I think that makes a lot of sense to think about that.

46:54I think that there are certain mitigations. You can try to not train certain knowledge into the model. There's different things, but yeah, I mean, it's some level. If you get a sufficiently bad actor and you don't have other AI that can sort of balance them and understand what's going on and what the threats are, then that could be a risk. So I think that that's one of the things that we need to watch out for. Is there something you could see in the deployment of these systems where you observe like you're training Lama 4 and it's like light you because you thought you were noticing or something.

47:32You're like, whoa, what's going on here? Not that this is probably not likely with the Lama 4 tie system, but is there something you can imagine like that where you'd like We're really concerned about deceptiveness. If billions of copies of things are on the wild.

47:48Yeah, I mean, I think that that's not necessarily, I mean, right now we see a lot of hallucinations. Right, so I think it's more that. I think it's an interesting question how you would tell the difference between a hallucination and deception. But yeah, look, I think there's a lot of risks and things to think about. The flip side of all this is that But there are also a lot of, I try to, in running our company, at least, balance what I think of as these longer term theoretical risks with what I actually think are quite real risks that exist today. So like, when you talk about deception, the form of that that I worry about most is people using this to generate misinformation and then like pump that through whether it's our networks or others.

48:37So the way that we've basically combated a lot of this type of harmful content is by building AI systems that are smarter than the adversarial ones. And like, this is part of this kind of informs part of my theory on this, right? Is if you look at like the different types of harm that people do or try to do through social networks, there are ones that are not very adversarial. So for example, hate speech, I would say is not super adversarial in the sense that like, people aren't getting better at being racist, right? They're just like, you just like, okay, that's one where I think the AIs are generally just getting way more sophisticated faster than people are at those issues.

49:26So we have issues both ways. It's like, people do bad things that whether they're trying to incite violence or something. But we also have a lot of false positives, right? So where we basically sense our stuff that we shouldn't and I think understandably make a lot of people annoyed. So I think having the AI that just gets increasingly precise on that, that's going to be good over time. But let me give you another example, which is like nation states trying to interfere in elections. That's an example where they are absolutely, they have cutting edge technology and absolutely get better each year.

49:58So we block some technique. They learn what we did. They come at us with a different technique, right? It's not like a person trying to, you know, say, say mean thing. So right, it's like, it's, it's, they're, they're basically they have a goal. They're sophisticated. They have a lot of technology. In those cases, I still think the ability to kind of have R .A .I. systems grow and in sophistication at a faster rate than theirs have. It's an arms race, but I think we're at least currently winning that arms race So I don't know I think that that's but this is like a lot of the stuff that I that I spend time thinking about is like okay Yes, it is possible that Whether it's llama for or llama five or llama six.

50:40Yeah, we need to think about like what behaviors were we're observing and it's not just us I think part of the reason why you make this open source is that there are a lot of other people who study this too so Yeah, we want to see what other people are observing what we're observing what we can mitigate and then we'll make our assessment on whether we can make it open source. But I think for the foreseeable future, I'm optimistic we will be able to. And in the near term, I don't want to take our eye off the ball of what are actual bad things that people are trying to use the models for today, even if they're not existential.

51:12But they're like, they're like pretty bad kind of day -to -day harms that we're familiar with in running our services. is that's actually a lot of what we have to spend our time on as well. Yeah, actually, I found this synthetic data thing really curious. I'm actually interested in why you don't think like current models, it makes sense why there might be an asymptote with just doing synthetic data again and again. It gets smarter and uses the techniques you talk about in the paper or the blog post that's coming out on the date this will be released where it goes to the thought chain that is the most correct.

51:48Correct. Why this wouldn't lead to a loop that of course it wouldn't be overnight But over many months or years of training potentially with a smarter model it gets smarter makes better output get smarter and so forth Well, I think it could within the parameter of whatever the model architecture is it's just that like it's some level I don't know I think that today is 8 billion parameter models, I just don't think you're going to be able to get to be as good as the state of the art multi -hundred billion parameter models that are incorporating new research into the architecture itself. But those will be open sources as well, right?

52:31Well, yeah, but I think that that's, I mean, subject to all the questions that we just talked about. Yes, I mean, we would we would hope that that'll be the case, but I think that at each point. I don't know. It's like when you're building software, there's like a ton of stuff that you can do with software, but then at some level you're constrained by the chips that it's running on, right? So there are always going to be different physical constraints. And it's like how bigger the models is going to be constrained by how much energy you can get. And and use for inference. So I guess I'm simultaneously very optimistic that the stuff will continue to improve quickly.

53:15And also a little more measured than I think some people are about kind of it's, I just don't think the runaway case is like a particularly likely one. I think it makes sense to keep your options open. Like there's so much we don't know. There's a case in which like it's really important to keep the balance of power. So when nobody becomes like a tie -out or indicator, there's a case in which like you don't want to open source the architecture because like China's catch, it can use it to catch up to America's AI's and like there is an intelligence explosion and they like win that. Yeah, a lot of things can be possible just like keeping your options open considering all of them seems reasonable.

53:57Yeah. Let's talk about some other things. Okay, metaverse, what time period in human history would you be most interested in going into? A 100 ,000 BCE to now. You just want to see what it was like for the past. Yeah, it has to be the past.

54:17I don't know. I mean, I have the periods of time that I'm interested in, I'm really interested in American history and classical history and I'm really interested in the history of science too. So I actually think seeing and trying to understand more about how some of the big advances came about. I mean, all we have are like somewhat limited writings about some of that stuff. I'm not sure the metaverse is going to let you do that because I mean, it's, you know, we can't, it's going to be hard to kind of go back in time for things that we don't have records of.

54:53But I'm actually not sure that going back in time is going to be that important thing for them. I mean, I think it's gonna be cool for like history classes and stuff, but that's probably not the use case that I'm most excited about for the metaverse overall. I mean, it's, I mean, the main thing is just the ability to feel present with people no matter where you are. I mean, that's gonna be killer. I mean, there's, I mean, in the AI conversation that we're having, I mean, it's so much of it is about physical constraints that kind of underlie all of this, right? And you want to move, I mean, one lesson of technology is you want to move things from the physical constraint realm into software as much as possible because software is so much easier to build and evolve.

55:37And like, you can democratize it more because like not everyone is going to have a data center, but like a lot of people can kind of write code and take open source code and modify it. The metaverse version of this is, I think, enabling realistic digital presence. is going to be just an absolutely huge difference for making it so that people don't feel like they have to physically be together for as many things. Now, I think that there are gonna be things that are better about being physically together. So it's not, I mean, these things aren't binary, it's not gonna be like, okay, now you don't need to do that anymore, but overall, I mean, I think that this, it's just gonna be really powerful for socializing, for feeling connected with people, for working, for parts of industry, for medicine, for like so many things.

56:36I wanna go back to something you said at the beginning of the conversation where you didn't sell the company for a billion dollars and like the metaverse, you knew we were gonna do this even though the market was hammering you for it. And then I'm actually curious like, what is the source of that edge? And you said like, oh, values, I have this intuition but like everybody says that, right? Like, what if you had to say something that's specific to you? What is, how would you express what that is? Like, why are you so convinced about the metaverse?

57:06Well, I think that those are different questions. So, what are the things that kind of power me? I think we've talked about it a bunch of the things. So, I mean, I just really like building things. I specifically like building things around how people communicate and sort of understanding how people express themselves and how people work. Right? When I was in college, I was studying computer science and psychology. I think a lot of other people in the industry studied computer science. Right? So it's always been sort of the intersection of those two things for me, but I think it's also sort of this like really deep drive.

57:48I don't know how to explain it, but I just feel like in the constitutionally, like I'm doing something wrong if I'm not building something new. Right? And so I think that there's like, even when we're putting together the business case for, you know, investing like $100 billion in AI or some huge amount in the metaverse, it's like, yeah, I mean, we have plans. that I think make it pretty clear that if our stuff works, it'll be a good investment. But like, you can't know for certain from the outset. And there's all these arguments that people have, whether it's like, you know, with advisors or different folks, it's like, well, how could you, like, it's how are you confident enough to do this?

58:36And it's like, well, the day I stop trying to build new things, I'm just done. I'm going to go build new things somewhere else, right? It's like, it's like, it is, I'm fine. fundamentally incapable of running something or in my own life and not trying to build new things that I think are interesting. It's like, that's not even a question for me, right? It's like whether we're gonna go take a swing at building the next thing. It's like, I'm just incapable of not doing that.

59:13And I don't know. And I'm kinda like this and all the different aspects of my life. It's like we built this family built this ranch in Kauai and worked like design all these buildings. I'm like, we started raising cattle and I'm like, all right, well, I want to make the best cattle in the world. So it's like, how do we architect this so that way we configure this out and build and call the stuff up that we need to try to do that? So I don't know. That's me. What was the other part of the question? Look, Meta is just a really amazing tech company, right? They have all these great software engineers and even they work with Stripe to handle payments.

59:56And I think that's just a really notable fact that Stripe's ability to engineer these checkout experiences is so good that big companies like Ford, Zoom, Meta, even OpenAI, they work with Stripe to handle payments. Because just think about how many different possibilities you have to handle. If you're in a different country, you'll pay a different way. And if you're buying a certain kind of item, that might affect how you decide to pay. And Stripe is able to test these fine -grained optimizations across tens of billions of transactions a day to figure out what will convert people. And obviously, conversion means more revenue for you.

1:00:27And look, I'm not a big company like Meta or anything, but I've been using Stripe since long before they were advertisers. Stripe Atlas was just the easiest way for me to set up an LLC, and they have these payments and invoicing features that make it super convenient for me to get money from advertisers. And obviously without that, it would have been much harder for me to earn money from the podcast. And so it's been great for me. Go to Stripe .com to learn more. Thanks to them for sponsoring the episode. Now back to Mark. I'm not sure, but I'm actually curious about something else, which is, um, so in 19 year old Mark reads a bunch of like antiquity and classics high school college.

1:01:04What important lesson did you learn from it? Not just interesting things you found, but like there aren't that many tokens who consumed by the time you're 19. A bunch of them were about the classics. Clearly that was important in some way. That's a good question. I mean, one of the things that I thought was really fascinating is so when a gustus was first, so he became emperor and he was trying to establish peace. And there was no real conception of peace at the time. Like the people's understanding of peace was, it is the temporary time between when you're and amuseable inevitably attack you again.

1:01:55So you get like a short rest. And the end of this view, which is like, look, we wanna change the economy from instead of being so mercenary and in kind of militaristic to actually this positive something. It's like a very novel idea at the time.

1:02:18I don't know. I think that there's like something that's just really fundamental about that. It's like in terms of the the bounds on like what people can conceive at the time of like what are rational ways to work and I'm going back to like I'm this applies to both the metaverse and the AI stuff But like a lot of investors and just different people just can't wrap their head around why we would open source this and it's like are you like Like I don't understand. It's like open source that much just be like the temporary time between which you're making things proprietary right? And it's um but I actually think it's like this very profound thing in tech that has actually it creates a lot of winners, right?

1:03:03And it's and um so I don't know I don't want to strain the analogy too much but but I do think that there's um there's a lot of times I think ways where you can, that are just like models for building things that people can't even, like they just like often can't wrap their head around how that would be a valuable thing for people to go do or like a reasonable state of the world that it's, I mean, it's, I think that there's more reasonable things than people think. That's super fascinating. Could I give you my answer, but I was thinking you might have gotten from it. This is probably totally off, but just how young some of these people are who have very important roles in the Empire.

1:03:49Like Cesar Augusta is like, by the time he's 19, he's actually incredibly one of the most prominent people in Roman politics. And he's like leading battles and forming the second trimembrit. I wonder if you're like, the 19 year old is like, I can actually do this because like Cesar Augusta did this. I think that's an interesting example both from a lot of history and American history. Yeah, I mean, it's, I mean, one of my favorite quotes is it's this Picasso quote that all children are artists and the challenges, how do you remain an artist when you grow up? And it's like, basically, I think, because when you're younger, I think it's just easier to have kind of wild ideas and you're not, you know, you have no, there are all these analogies to the innovators, dilemma that exists in your life as well as your company or whatever you've built.

1:04:36So you're kind of early around your trajectory. It's easier to pivot and take in new ideas without it disrupting other commitments that you've made to different things. And so I don't know, I think that's an interesting part of running a company is like how do you how do you kind of stay dynamic? Going back to the investors in open source, the $10 billion model. Suppose it's totally say if you've done these evaluations and unlike in this case the evaluators can also fine -tune the model which hopefully will be the case in future models. Would you open source set the 10 billion dollar model? Well, I mean as long as it's helping us then yeah.

1:05:13But would it like to 10 billion dollars of R &D and then now it's like open source anyway? Well, I think here's I think a question which we'll have to evaluate this as time goes on too but we have a long history of open sourcing software. We don't tend to open source our product. So it's not like we don't take the code for Instagram and make it open source, but we take a lot of the low -level infrastructure and we make that open source. The biggest one in our history was OpenCompute Project, where we took the designs for all of our servers and network switches and data centers and made it open source and ended up being super helpful.

1:05:55because now I mean a lot of people can design servers, but now like the industry standardized on our design, which meant that the supply chains basically all got built out around our design, the volumes went up, so it got cheaper for everyone and saved us billions of dollars. So awesome, right? Okay, so there's multiple ways where open source I think could be helpful for us. One is if people figure out how to run the models more cheaply, well, we're gonna be spending tens or like a hundred billion dollars or more over time on all this stuff. So if we can do that 10 % more effectively, we're saving billions or tens of billions of dollars.

1:06:29Okay, that's probably worth a lot by itself. Especially if there's other competitive models out there. It's not like our thing is like giving away some kind of crazy advantage. So is your view that the trading will be commodified?

1:06:45I think there's a bunch of ways that this could play out. That's one. And the other is that so commodity kind of implies that it's gonna get very cheap because there's lots of options. The other direction that this could go in is qualitative improvements. So as you mentioned fine tuning, right? It's like right now it's pretty limited. What you can do with fine tuning major other models out there. And there are some options, but generally not for the biggest models. So I think being able to do that and be able to kind of do different app specific things, or use case specific things, or build them into specific tool chains, I think we'll not only enable kind of more efficient development, it could enable qualitatively different things.

1:07:34Here's one analogy on this. is, so one thing that I think generally sucks about the mobile ecosystem is that like you have these two gatekeeper companies, Apple and Google that can tell you what you're allowed to build. And there are lots of times in our history. So there's the economic version of that, which is like, all right, we build something there just like I'm going to take a bunch of your money. But then there's the, there's the, um, the qualitative version, which is actually what kind of upsets me more, which is there's a bunch of times when we've launched or wanted into launch features, and then Apple's just like, nope, you're not launching that.

1:08:09It's like that sucks, right? And so the question is, what is, like, are we kind of set up for a world like that with AI where like, you're going to get a handful of companies that run these closed models that are going to be in control of the APIs, and therefore are going to be able to tell you what you can build? Well, for one, I can say, for us, it is worth it to go build a model ourselves to make sure that we're not in that position. I don't want any of those other companies telling us what we can build. But from an open source perspective, I think a lot of developers don't want those companies telling them what they can build either.

1:08:50So the question is, what is the ecosystem that gets built out around that? What are interesting new things? How much does that improve our products? I think that there's a lot of cases where if this ends up being like our databases or or caching systems or architecture, we'll get valuable contributions from the community that will make our stuff better. And then our app specific work that we do will still be so differentiated that it won't really matter. Right, it's like, we'll be able to do what we do, we'll benefit in all the systems, ours, and the communities will be better because it's open source.

1:09:22There is one world where maybe it's not that. I mean, maybe the model just ends up being more of the product itself. In that case, then I think it's a trickier economic calculation about whether you open source that because then you are kind of Commodetizing yourself a lot, but I don't from what I can see so far. It doesn't seem like we're in that zone Do you expect to earn significant revenue from licensing your model to the Clip providers? So they have to pay you a fee to actually serve the model?

1:09:52We want to have an arrangement like that, but I don't know how significant it'll be and we have this This is basically our license for Alama. In a lot of ways, it's a very permissive open source license except that we have a limit for the largest companies using it. This is why we put that limit in. We're not trying to prevent them from using it. We just want them to come talk to us because if they're going to basically take what we built and resell it and make money off of it, then it's like, okay, well, if you're Microsoft Azure or Amazon, then yeah, if you're going to resell the model, then we're going to We should have some revenue share on that, so just come talk to us before you go do that.

1:10:31And that's how that's played out. So for Lama 2, it's, I mean, we basically just have deals with all these major cloud companies, and Lama 2 is available as a hosted service on all those clouds. I assume that as we release bigger and bigger models, that'll become a bigger thing. It's not the main thing that we're doing, but I just think if those companies are going to be selling our models, it makes sense that we should share the upside of that somehow. Yeah. With the rest of the other open source dangers, I think I'm genuinely legitimate of points about the balance of power stuff. And potentially the harms you can get rid of because we have better alignment techniques or something.

1:11:08I wish there were some sort of framework that Meta had. Other labs have this where they say, if we see this concrete thing, then that's a no -go on the open source or even potentially on deployment. I'm just writing it down so the company is ready for it. People have expectations around it and so forth. Yeah, no, I think that that's a fair point on the existential risk side. Right now we focus more on the types of risks that we see today, which are more of these content risks. So, you know, we have lines on, we don't want the model to be basically doing things that are helping people commit violence or fraud or, you know, just harming people in different ways.

1:11:46So in practice for today's models, and I would guess the next generation, then maybe even the generation after that, I think while it is somewhat more, maybe intellectually interesting to talk about the existential risks, I actually think the real harms that need more energy being mitigated are things that are going to like have someone take a model and do something to hurt a person with today is parameters and the types of more mundane harms that we see today. People committing fraud against each other, things like that. I just don't want a short change that. I think we have a responsibility to make sure we do a good job on that.

1:12:33It matters a bit company, you can handle both.

1:12:38As far as the opening source goes, I'm actually curious if you think the impact of the open source from PyTorch React, OpenCompute, these things has been bigger for the world than even the social media aspects of meta. Because I've talked to people who use these services who think it's plausible. Because a big part of the internet runs on these things. It's an interesting question. I mean, I think almost half the world uses are. Yeah, that's a tricky question. So I think it's hard to beat that. But no, I think open sources, it's really powerful as a new way of building things. and yeah, I mean, it's possible.

1:13:18I mean, it's, you know, it may be one of these things where I don't know, like L Labs, right, where they, you know, it's like they were working on the transistor because they wanted to enable long distance calling. And they did, and it ended up being really profitable for them, that they were able to enable long distance calling. And if you ask them five to 10 years out from that, What was the most useful thing that they invented? It's like, okay, well we enabled long distance calling and now all these people are long distance calling But if you ask a hundred years later, maybe it's a different question.

1:13:52So I think that that's true of a lot of the things that we're building right reality labs Some of the AI stuff some of the open source stuff. I think it's like the specific products evolve and to some degree come and go But I think like the advances for humanity persist and that's like a cool part of what we all get to do. By when will the Lamma models be trained on your own custom silicon?

1:14:22Soon, not Lamma 4. The approach that we took is first we basically built custom silicon that could handle inference for our ranking and recommendation type stuff. So, real news feed ads. And that was consuming a lot of GPUs. But when we were able to move that to our own silicon, we now were able to use the more expensive and video GPUs only for training. So at some point, we will hopefully have silicon ourselves that we can be using for probably first training, some of the simpler things, not eventually training these like really large models. But in the meantime, I'd say the program is going quite well and we're just rolling it out methodically and have a long -term roadmap for it.

1:15:19Final question, this is totally out of the left field, but if you were a mate CEO of Google Plus, could you have made it work? Google Plus? Us? Oof. Well, I don't know. I don't know. That's a very difficult, very difficult, counterfactual. Okay, then the real final question will be when Gemini was launched, was there any chance that somebody in the office ordered Carthagot Dylinda S'd? Yeah. No, I think we're tamer now. Google, awesome. I don't know. Yeah, I don't know, it's a good question. I don't know. The problem is there was no CEO of Google+. It was just like a division within a company.

1:16:00I think it's like, and you asked before about what are the kind of scaricist commodities, but you asked about it in terms of dollars. And I actually think for most companies, it's of this scale, at least it's focus, right? It's like when you're a startup, maybe you're more constrained on capital. You know, you just are working on one idea and you might not have all the resources. I think you cross some threshold at some point where the nature of what you're doing, you're building multiple things and you're creating more value across them, but you become more constrained on what can you direct and to go well.

1:16:35And like, there's always the cases where something just random awesome happens in the organization. I don't even know about it and those are, that's great. But like, but I think in general, the organization's capacity is largely limited by what like the CEO and the management team are able to kind of oversee and kind of manage. I think that that's just been a big focus for us. It's like, all right, keep the, as I guess Ben Horowitz says, keep the main thing the main thing. All right. And try to kind of stay focused on your key priorities. Yeah. All right. Awesome, that was excellent, Mark. Thanks so much.

1:17:17That was a lot of fun. Yeah, really fun. Thanks, Mark. Yep, absolutely. Hey everybody, I hope you enjoyed that episode with Mark. As you can see, I'm now doing ads. So if you're interested in advertising on the podcast, go to the link in the description. Otherwise, as you know, the most helpful thing you can do is just share the podcast with people who you think might enjoy it. You know, your friends, group chats, Twitter, I guess threads. Yeah, I hope you enjoyed, and I'll see you on the next one. wn.