馬克·扎克伯格：AI 在 Meta、Facebook、Instagram 與 WhatsApp 的未來

Lex Fridman Podcast #383 – Mark Zuckerberg: Future of AI at Meta, Facebook, Instagram, and WhatsApp Machine transcription by Qwen3-ASR 0.6B (MLX, local, Apache-2.0). May contain recognition errors. The following is a conversation with Mark Zuckerberg, his second time on this podcast. He's the CEO of Meta that owns Facebook, Instagram, and WhatsApp, all services used by billions of people to connect with each other. We talk about his vision for the future of Meta and the future of AI in our human world. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got Numai for the world's hardest data science tournament, Shopify for e commerce, and BetterHelp for mental health. Choose wisely, my friends. Also, if you want to work with our amazing team, we're always hiring. Go to lexfreeman dot com slash hiring. And now onto the full ad reads. As always, no ads in the middle. I find those annoying. 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This is the Lex Friedman podcast, and now dear friends, here is Mark Zuckerberg. So you competed in your first jujitsu tournament, and me as a fellow jujitsu practitioner and competitor. I think that's really inspiring given all the things you have going on. So I got to ask, what was that experience like? Oh, it was fun. I know, yeah, I mean, well, look, I am a pretty competitive person. Yeah, doing sports that basically require your full attention. I think is really important to my like mental health and and the way I just stay focused at doing everything I am doing. It's like I decided to get into martial arts and it's it's awesome. I got like a ton of my friends into it. We all train together. We have like a mini academy in my garage. And I guess one of my friends was like, "Hey, we should go do a tournament." I was like, "Okay, yeah, let's do it. I am not going to shy away from a challenge like that." So yeah, it was but it was it was awesome. It was it was just a lot of fun. You weren't scared. There was no fear. I don't know. I was I was pretty sure that I that I do okay. I like the confidence. Well, so for people don't know jujitsu is a martial art where you are trying to break your opponent's limbs or choke them to sleep and do so with grace and elegance and efficiency and all that kind of stuff. It's a it's a kind of art form I think they can do for your whole life and it's a basically a game a sport of human chess. You can think of there is a lot of strategy. There is a lot of sort of interesting human dynamics of using leverage and all that kind of stuff and. It's kind of incredible what you could do. You can you could do things like a small opponent could defeat a much larger opponent and you get to understand like the way the mechanics of the human body works because of that. But you certainly can't be distracted. No, it's it's one hundred percent focus sport to to compete. I you know I needed to get around the fact that I didn't want it to be like this. This big thing, so I basically just I rolled up with a hat and sunglasses and I was wearing a COVID mask and I registered under my first and middle name so Mark Elliot and and it wasn't until I actually like pulled all that stuff off right before I got on the mat that I think people knew it was me. So it was it was pretty low key. But you are still a public figure. Yeah, I mean I didn't want to lose. Right, the thing you are partially afraid of is not just the losing but being almost like embarrassed. It's so raw the sport in that like it's just you and another human being. There is a primal aspect there. Oh yeah, it's great. For a lot of people can be terrifying, especially the first time you are doing the competing and it wasn't for you. I see the look of excitement in your face. Yeah, it wasn't no fear. I just think part of learning is failing. Okay, right. So I mean the main thing like people who who train jujitsu, it's like you need to not have pride because I mean all the stuff you were talking about before about. You know getting choked or getting you know joint lock. It's you only get into a bad situation if you are not willing to tap once you you've already lost right and. But obviously, when you are getting started with something, you are not going to be an expert at it immediately. So you you just need to to be willing to go with that. But I think that this is like I don't know. I mean maybe I've just been embarrassed enough times in my life. Yeah, I do think that there is a thing where like as people grow up, maybe they don't want to be embarrassed or anything. They've built their adult identity and they they kind of have have a sense of of who they. They are and and what they want to project and I don't know I think maybe to some degree. You know your ability to keep doing interesting things is your willingness to be embarrassed again and go back to step one and start as a beginner and get your ass kicked and you know look stupid doing things and you know I think so many of the things that we're doing whether it's. Whether it's this, I mean this is just like a kind of a physical part of my life, but but running the company, it's like we we just take on new adventures and. You know all the big things that we're doing. I think of is like ten plus year missions that we're on where you know often early on, you know people doubt that we're going to be able to do it and the initial work seems kind of silly and our whole ethos says we don't want to wait until something is perfect to put it out there. We want to get it out quickly and get feedback on it and so I don't know. I mean there is probably just something about how I approach things in there but I just kind of think that the moment that you decide that you are going to be too embarrassed to try something new then you are not going to learn anything anymore. But like I mentioned, that fear that anxiety could be there could creep up every once in a while. Do do you feel that in especially stressful moments sort of outside of the jujitsu mat, just in work. Stressful moments, big decision days, big decision moments. How do you deal with that fear? How do you deal with that anxiety? The thing that stresses me out the most is always is always the people challenges. You know I I kind of think that you know strategy questions. You know I tend to have enough conviction around the values of what we're trying to do and what I think matters and what I want our company to stand for that. Those don't really keep me up at night that much. I mean I I kind of you know it's not that I I get everything right of course I don't right I mean we make a lot of mistakes but. But I at least have a pretty strong sense of where I want us to go on that the the thing and in running a company for you know almost twenty years now one of the things that's been pretty clear is when you have. A team that's cohesive, you can get almost anything done, and you know you can you can run through super hard challenges. You can make hard decisions and push really hard to to do the best work even you know in in kind of optimize something super well. But when when there is that tension, I mean that's that's when when things get really tough and you know when I talk to other friends who run other companies and things like that. I think one of the things that I actually spend a disproportionate amount of time on in running this company is just fostering a. Pretty tight core group of of people who are running the company with me and that to me is is kind of the thing that. Both makes it fun right having having you know friends and people you've worked with for a while and new people and new perspectives but like a pretty tight group who can who you can go work on some of these crazy things with. But to me that's also the most stressful thing is when when there are when there is tension you know that's that that weighs on me. I think the you know just it's it's. It's maybe not surprising. I mean we're like a very people focused company and it's the the people is the the part of it that that you know weighs on me the most to make sure that we get right. But yeah that that that I'd say across everything that we do is probably. The big thing. So when there is tension in in that inner circle of of close folks, so when you trust those folks to help you make difficult decisions. About Facebook, WhatsApp, Instagram, the future of the company in the metaverse with AI, how do you build that close knit group of folks to make those difficult decisions? Is there people that you have to have critical voices, very different perspectives on focusing on the past versus the future, all that kind of stuff. Yeah, I mean I think for one thing it's just spending a lot of time with whatever the group is that you want to be that core group. Grappling with all of the biggest challenges and that requires a fair amount of openness and you know so I mean a lot of how I I run the company is you know it's like every Monday morning we get our it's about the top thirty people together and we. And this is a group that just worked together for a long period of time and I mean people people rotate in I mean new people join people leave the company people go do other roles in the company so it's it's not the the same group over time but then we spend you know a lot of times a couple of hours a lot of the time it's you know can be somewhat unstructured we like I'll come with maybe a few topics that I. That are top of mind for me, but I'll ask other people to bring things and people you know raise questions whether it's okay there is an issue happening in some country with with some policy issue there is like a new technology that's developing here we're having an issue with this partner. You know there is a design trade off and WhatsApp between two things that that end up. Being values that we care about deeply and we need to kind of decide where we want to be on that and I just think over time when you know by working through a lot of issues with people and doing it openly, people develop an intuition for each other and a bond and camaraderie. And to me, developing that is is like a lot of the fun part of running a company or doing anything right. I think it's like having having people who are kind of along on the journey that you are that you feel like you are doing it with nothing is ever just one person doing it. Other people that disagree often within that group. It's a fairly combative group. Okay, so combat is part of it. So this is making decisions on design, engineering, policy, everything, everything, everything. Yeah, I have to ask just back to jujitsu for a little bit. What's your favorite submission? Now that you've been doing it, what's how do you like to submit your opponent, Mark Zuckerberg? I mean. Well, first of all, I do you prefer no gi or gi jujitsu. So gi is this outfit you wear that is maybe mimics clothing so you can choke. It's like a kimono. It's like the traditional martial arts or pajamas. Um, pajamas. That you could choke people with. Yes, well, it's got the lapels. Yes, yeah. Um, so I like jujitsu. I also really like MMA, and so I think no gi more closely approximates MMA. And I think my style is is maybe a little closer to an MMA style. So like a lot of jujitsu players are fine being on their back right. Obviously having a good guard is is is a critical part of of of jujitsu but. But in MMA, you don't want to be on your back, right? Because even if you have control, you are just taking punches while you are on your back, so that's no good. Do you like being on top? My my style is I am probably more pressure and um and yeah and and I'd probably rather be the top player. But um but I am also smaller, right? I am not I am not like a a heavyweight guy, right? So from that perspective, I think like. You know it's especially because you know if I am doing a competition, I'll compete with people who are my size, but a lot of my friends are bigger than me. So um so back takes probably pretty important right because that's where you have the most leverage advantage right where where um you know people you know their arms your arms are very weak behind you right so. So being able to get to the back and and and take that pretty important, but I don't know I feel like the right strategy is to not be too committed to any single submission. That said, I don't like hurting people, so um so I always think that chokes are are a somewhat more humane way to go than than joint locks. Yeah, and it's more about control. It's less dynamic, so you are basically like a Habib Nurmagomedov type of fighter. So let's go, yeah, back take to a rear naked choke. I think is like the clean the clean way to go. Straightforward answer right there. What advice would you give to to people looking to start learning jujitsu? Given how busy you are, given where you are in life, you are able to do this, you are able to train, you are able to compete and get to learn something from this interesting art. I just think you have to be willing to to just get beaten up a lot. Yeah, I mean it's but but I mean over time I think that there is there is a flow to all these things and there is you know one of the one of I don't know my my experiences that I think kind of transcends. You know running a company and the different different activities that I like doing are I really believe that like if you are going to accomplish whatever anything, a lot of it is just being willing to push through right and having the grit and determination to to to push through difficult situations. And I think for a lot of people that um that ends up being sort of a difference maker between the people who who who kind of get the most done and and not. I mean there is all these questions about like. Um, you know how how many days people want to work and things like that. I think almost all the people who like start successful companies or things like that are just are working extremely hard. But I think one of the things that you learn both by doing this over time or you know very acutely with things like jujitsu or surfing is um. You can't push through everything, and I think that that's you you learn this stuff very acutely running sports compared to running a company because running a company. The cycle times are so long, right? It's like you start a project and then you know it's like months later or you know if you are building hardware it could be years later before you are actually getting feedback and able to you know make the next set of decisions for the next version of the thing that you are doing. Whereas one of the things that I just think is mentally. So nice about these very high turnaround conditioning sports things like that is you get feedback very quickly, right? It's like okay, like I don't counter something correctly, you get punched in the face, right? So not jujitsu, you don't you don't get punched in jujitsu, but in MMA. There are all these analogies between all these things that I think actually hold that are that are like. Important life lessons, right? It's like okay, you are surfing a wave. It's like you know sometimes you are like you can't go in the other direction on it, right? It's like there are limits to kind of what you know. It's like foil you can you can pump the foil and push pretty hard in a bunch of directions, but like yeah. You you know it's at some level like the momentum against you is is strong enough. You are that's not going to work and and I do think that um that's sort of a. A humbling but also an important lesson for I think people who are running things or building things. It's like yeah you you um. You know, a lot of the game is just being able to kind of push and and and and work through complicated things, but you also need to kind of have enough of an understanding of like which things you just can't push through and where where um, um the finesse is more important. Yeah. What are your jujitsu life lessons? Well, I think you did it. You made it sound so simple, and were so eloquent that it's easy to miss. But basically, being okay and accepting the wisdom and the joy in the getting your ass kicked in the full range of what that means. I think that's a big gift of the being humbled. Somehow being humbled, especially physically, opens your mind to the the full process of learning what it means to learn, which is being willing to suck at something. And I think jujitsu just very repetitively efficiently humbles you over and over and over and over to where you can carry that lessons to places where you you don't get humbled as much whether it's research or running a company or building stuff the the cycle is longer in jujitsu you can just get humbled in a period of an hour over and over and over and over especially when you're beginner you have a little person just you know somebody much smaller than you just kick your ass repeatedly definitively where there's no argument oh yeah and then you literally tap because if you don't tap you're going to die so this is an agreement you could have killed me just now but we're friends so we're going to agree that you're not going to and that kind of humbling process it just does something to your psyche to your ego that puts it in its proper context to realize that you know everything in this life is like a journey from sucking through a hard process of improving rigorously day after day after day after day and you kind of success requires hard work yeah jujitsu more than a lot of sports I would say because I've done a lot of them really teach you do that and you made it sound so simple like I'm you know it's it's okay it's part of the process you just get humbled get you've just I've just failed and been embarrassed so many times in my life that like you know I'm I'm it's a core competence at this it's a core competence well yes and there's a deep truth to that being able to and you said it in the very beginning which is that's the thing that stops us especially as you get older especially as you develop expertise in certain areas the not being willing to be a beginner in a new area yeah because that's where the growth happens is being willing to be a beginner being willing to be embarrassed saying something stupid doing something stupid a lot of us to get good at one thing you want to show that off and it sucks being a beginner but it's it's where growth happens yeah well speaking of which let me ask you about AI it seems like this year for the entirety of the human civilization is an interesting year for the development of artificial intelligence a lot of interesting stuff is happening so Meta is a big part of that Meta has developed llama which is a 65 billion parameter model there's a lot of interesting questions they can ask here one of which has to do with open source but first can you tell the story of developing of this model and making the complicated decision of how to release it yeah sure I think you're right first of all that in the last year there have been a bunch of advances on scaling up these large transformer models so there's the language equivalent of it with large language models the sort of the image generation equivalent with these large diffusion models there's a lot of fundamental research that's gone into this and Meta has taken the approach of being quite open an academic in in in our development of of AI part of this is we want to have the best people in the world researching this and and a lot of the best people want to know that they're going to be able to share their work so that's part of the deal that we that we have is that you know we can get you know if if you're one of the top AI researchers in the world you can come here you can get access to kind of industry scale infrastructure and and and part of our ethos is that we we want to share what's what's invented broadly we do that with a lot of the the different AI tools that we create and llama is the language model that that our research team made and you know we we did a limited a limited open source release for it right which was intended for researchers to be able to use it but you know responsibility and getting safety right on these is is very important so we didn't think that for the first one there were there were a bunch of questions around whether we should be releasing this commercially so we kind of punted on that for for v1 of of llama and and just released it for research now obviously by releasing it for research you know it's out there but but companies know that that they're they're not supposed to kind of put it into commercial releases and you know we're we're working on the follow up models for this and thinking through how how what what the how exactly this should work for for follow on now that we've had time to to work on a lot more of the the safety and and the pieces around that but but overall I mean this is I just kind of think that that it would be good if there were a lot of different folks who had the ability to build state of the art technology here you know it's in not just a small number of of big companies where to train one of these AI models the state of the art models is no just takes you know hundreds of millions of dollars of infrastructure right so there are not that many organizations in the world that can do that at the biggest scale today and now it gets it gets more efficient every day so so I I do think that that will be available to more folks over time but but I just think like there's there's all this innovation out there that people can create and and and I just think that we'll we'll also learn a lot by by seeing what the whole community of students and and hackers and startups and different folks build with this and that's kind of that's kind of been how we've approached this and it's also how we've done a lot of our infrastructure and we took our whole data center design and our server design and we we built this open compute project where we just made that public and part of the theory was like all right if we make it that more people can use this server design then then that'll enable more innovation it'll also make the server design more efficient and that'll that'll make our business more efficient too that's worked and we've we've just done this with a lot of our our infrastructure so for people don't know you did the limited release I think in February of of this year of llama and it got quote unquote leaked meaning like it escaped the the the limited release aspect but it was you know that something you probably anticipated given that it's just released to research we shared it with researchers right so it's it's just trying to make sure that there's like a slow release yeah but from there I just would love to get your comment on what happened next which is like there's a very vibrant open source community that just builds stuff on top of it there's a llama CPP basically stuff that makes it more efficient to run on smaller computers there's combining with reinforcement learning with human feedback with some of the different interesting fine tuning mechanisms there's then also like fine tuning in a GPT three generations there's a lot of GPT for all alpaca colossal AI all these kinds of models just kind of spring up like run on top of what yeah what do you think about that no I think it's been really neat to see I mean there's been folks who are getting it to run on local devices right so if you're an individual who just you want to experiment you know with this at home you probably have a large budget to get access to like a large amount of cloud compute so getting it to run on your local laptop you know is is is pretty good right and pretty relevant and then there are things like yeah llama CPP re-implemented it more efficiently so you know now even when we run our own versions of it we can do it on way less compute and it just way more efficient save a lot of money for everyone who who uses this so that that is is is good I do think it's worth calling out that because this was a relatively early release llama isn't quite as on the frontier as for example the biggest open AI models or the biggest Google models right I mean you mentioned that the largest llama model that we released had 65 billion parameters and no one knows you know I guess outside of open AI exactly what the specs are for for for GPT four but but I I think the you know my understanding is it's like 10 times bigger and I think Google's palm model is is also I think has about 10 times as many parameters now the llama models are very efficient so they they perform well for for something that's around 65 billion parameters so for me that was also part of this because there's this whole debate around you know is it good for everyone in the world to have access to to the most frontier AI models and I I think as the AI models start approaching something that's like a super human intelligence like that that's a bigger question that we'll have to grapple with but right now I mean these are still you know very basic tools they're you know they're they're powerful in the sense that you know a lot of open source software like databases or web servers can enable a lot of pretty important things but I don't think anyone looks at the the you know the current generation of llama and thinks that's you know anywhere near a super intelligence so I think that a bunch of those questions around like is it is it good to to kind of get out there I think at this stage surely you you want more researchers working on it for all the reasons that that open source software has a lot of advantages and we talked about efficiency before but another one is just open source software tends to be more secure because you have more people looking at it openly and scrutinizing it and finding holes in it and that makes it more safe so I think at this point it's more I think it's generally agreed upon that open source software is generally more secure and safer than things that are kind of developed in a silo where people try to get through security through obscurity so I think that for the scale of of what we're seeing now with AI I think we're more likely to get to you know good alignment and good understanding of of of kind of what needs to do to make this work well by having it be open source and that's something that I think is is quite good to have out there and and happening publicly at this point Meta released a lot of models as open source so the massively multilingual speech model the image binding model that's I mean I'll ask you questions about those but the point is you've open sourced quite a lot you've been spearheading the open source movement where's that's really positive inspiring to see from one angle from the research angle of course there's folks who are really terrified about the existential threat of artificial intelligence and those folks will say that you know you have to be careful about the open sourcing step but where do you see the future of open source here as part of Meta the tension here is do you want to release the magic sauce that's one tension and the other one is do you want to put a powerful tool in the hands of bad actors even though it probably has a huge amount of positive impact also yeah I mean again I think for the stage that we're at in the development of AI I don't think anyone looks at the current state of things and thinks that this is super intelligence and you know the models that we're talking about the llama models here are you know generally in order of magnitude smaller than what open AI or Google are doing so I think that at least for the stage that we're at now the equity is balanced strongly in my view towards doing this more openly I think if you got something that was closer to super intelligence then I think you'd have to discuss that more and and think through that a lot more and we haven't made a decision yet as to what we would do if we were in that position but I don't think I think there's a good chance that we're pretty far off from that position so so I'm I'm not I'm certainly not saying that the position that we're taking on this now applies to every single thing that we would ever do and you know certainly inside the company and we probably do more open source work than you know most of the other big tech companies but we also don't open source everything right a lot of our the core kind of app code for WhatsApp or Instagram or something I mean we're we're not open sourcing that it's not like a a general enough piece of software that would be useful for a lot of people to do different things you know whereas the software that we do whether it's like an open source server design or or basically you know things like memcache right like a good you know it was it was probably our earliest project that that I worked on it was probably one of the last things that I that I coded and and led directly for the company but but basically this like caching tool for for quick data retrieval these are things that are just broadly useful across like anything that you want to build and and I think that some of the language models now have that feel as well as some of the other things that we're building like the translation tool that that you just referenced so text to speech and speech to text you've expanded it from around 100 languages to more than 1100 languages and you can identify more than the model can identify more than 4000 spoken languages which is 40 times more than any known previous technology to me that's really really really exciting in terms of connecting the world breaking down barriers that language creates yeah I think being able to translate between all of these different pieces in real time this has been a kind of common sci-fi idea that we'd all have you know whether it's I don't know an earbud or glasses or something that can help translate in real time between all these different languages and that's one that I think technology is basically delivering now so I think yeah I think that's pretty pretty exciting you mentioned the next version of llama what can you say about the next version of llama what what can you say about like what what were you working on in terms of release in terms of the vision for that well a lot of what we're doing is taking the first version which was primarily you know this research version and trying to now build a version that has all of the latest state of the art safety precautions built in and and we're we're using some more data to train it from across our services but but a lot of the the work that we're doing internally is really just focused on making sure that this is you know as aligned and responsible as as possible and you know we're building a lot of our own you know we're talking about kind of the open source infrastructure but you know the the main thing that we focus on building here you know a lot of product experiences tell people connect and express themselves so you know we're going to I've I've talked about a bunch of this stuff but you'll have you know an assistant that you can talk to in WhatsApp you know I think I think in the future every creator will will have kind of an AI agent that can kind of act on their behalf that their fans can talk to I I want to get to the point where every small business basically has an AI agent that people can talk to for you know to do commerce and customer support and things like that so there are going to be all these different things and llama or the language model underlying this is is basically going to be the engine that powers that the reason to open source it is that as as we did with with the the first version is that it I basically it unlocks a lot of innovation in the ecosystem we'll we'll make our products better as well and also gives us a lot of valuable feedback on security and safety which is important for making this good but yeah I mean the the work that we're doing to advance the infrastructure it's it's basically at this point taking it beyond a research project into something which is ready to be kind of core infrastructure not only for our own products but you know hopefully for for a lot of other things out there too do you think the llama or the language model underlying that version two will be open sourced do you do you have internal debate around that the pros and cons and so on this is I mean we were talking about the debates that we have internally and I think I think the question is how to do it right I mean it's I think we you know we we did the research license for v1 and and I think the the the big thing that we're that we're thinking about is is basically like what's the what's the right the right way so there was a leak that happened I don't know if you can comment on it for v1 you know we released it as a research project for researchers to be able to use but in doing so we put it out there so you know we were very clear that anyone who uses the the code and the weights doesn't have a commercial license to put into products and we've we've generally seen people respect that right it's like you don't have any reputable companies that are basically trying to put this into into their commercial products but but yeah but by sharing it with you know so many researchers it's it's you know it did leave the building but what have you learned from that process that you might be able to apply to v2 about how to release it safely effectively if if you release it yeah well I mean I think a lot of the feedback like I said is just around you know different things around you know how do you fine tune models to make them more aligned and safer and you see all the different data recipes that you mentioned a lot of different projects that are based on this I mean there's one Berkeley there's you know it's just like all over and and people have tried a lot of different things and we've tried a bunch of stuff internally so kind of where we're we're making progress here but also we're able to learn from some of the best ideas in the community and I think it you know we want to just continue continue pushing that forward but but I don't have any news to announce on on if that's if that's what you're you're asking I mean this is a thing that we're uh. We're still we're still kind of, you know actively working through the the the right way to move forward here. The details of the secret sauce are still being developed. I see. Can you comment on what do you think of the thing that worked for GPT, which is the reinforcement learning with human feedback? So, doing this alignment process, do you find it interesting? And as part of that, let me ask because I talked to Yann Lacun before talking to you today. He asked me to ask or suggested that I ask. Do you think LLM fine tuning will need to be crowdsourced Wikipedia style? So crowdsourcing. So this kind of idea of how to integrate the human in the fine tuning of these foundation models. Yeah, I think that's a really interesting idea that I've talked to Yann about a bunch. And you were talking about how do you basically train these models to be as as safe and aligned and responsible as possible and you know different groups out there who are doing development. Test different data recipes in fine tuning, but this idea that you you just mentioned is. That at the end of the day, instead of having kind of