Mark Zuckerberg on how Meta's Muse AI agent will make people money

Mark, last time we spoke, you called me. It was on the weekend. Well, you called me through the glasses, which was the interesting part. >> Was it loud in the background? >> You were You were going to fish. I wasn't going to say it, but yeah, it was on the weekend. The noise cancellation actually is pretty good. >> Oh, it's great. >> Yeah, it's pretty >> Yeah, I I I I mean >> Yeah. >> Um but you wanted to talk about this essay manifesto. I don't know what you call it. Manifesto, we'll say. Uh that you published recently. And it And it's long. I mean, I'll caveat there's a lot in it. And I wanted to start there cuz there's a lot of big ideas in there and they'll connect to the kind of the main thing we're talking about today. >> Yeah. >> Um I'm curious, like why write that? Cuz it it's long. There's a lot in there. >> >> Well, yeah. Well, I feel I feel like if you're going to invest so much in building AI, uh then it's important that people understand what your lab stands for and what your values are. And And And basically, AI has so many opportunities, but there are also all these real risks. So, I think it's very important that everyone who's working on it has a well-thought-out theory for how the work that they're going to do is going to lead to a positive future. You know, it's interesting because I mean, the different labs have have some different philosophies on this. And there's, you know, a lot of things that I think have become conventional wisdom in the industry that I just strongly disagree with. And you know, I mean, my my view on this is that the path to have a positive future for everyone is to make sure that we distribute the technology as widely as possible. And that's based on three major principles that that we have. One is that empowering people is the source of prosperity in the world, and it has been throughout history. Two is that the primary purpose for AI is going to be invention of new things, not automation. And then the third principle is that the foundation for safety for the future is basically establishing the right checks and balances and balance of power rather than restricting access. And I think that that's these are all things that I think oddly are are kind of very different from I think a lot of the conventional wisdom, especially in Silicon Valley. A lot of people think, "Hey, this technology is very powerful. We must restrict it so that way not that many people have access to it." I personally am much more worried about a small number of labs or people having control of of something that is so capable, and I think that the throughout history what we found is that when you put power in people's hands, most advances don't come from the incumbents or the establishment. They come from people on the on the periphery whose ideas aren't taken seriously, but when they get enough tools to to to basically be able to prove out what they're working on, that ends up being very powerful. In Western society, the way that we've established governance and and basically having a well-balanced society is through a set of checks and balances, right? And and this balance of power. It's very ingrained in in kind of our society that you don't want to have you know one lab or two labs having access to a thing. For some of the most recent concerns that have come up like some of these cybersecurity concerns, I think the the best antidote to someone having an AI that could potentially hack into systems is having everyone have access to an AI so they can harden their own systems first. And I think that that's kind of been the history of cybersecurity over the past several decades is that you know open-source software because people can can see it and can scrutinize it, it's sort of counterintuitively by putting it in people's hands, you end up with a more secure and more stable environment. So, that's what I believe, and and that's what I think is the path to to a positive future is basically distributing the technology that has a bunch of different implications for what we're going to do. I mean obviously we want to build leading AI models, which which we're doing. I mean MuSpark 1.3, which we just released, is it's it's advanced, but then, you know, it's actually the latest model of a relatively smaller pre-train that we did, the internal code name avocado. >> Mhm. >> And um >> Oh, we're going to get into the code names. >> Oh, yeah. So, we'll we'll get into that. And we have watermelon coming soon. So, so that's going to be a big deal. So, obviously, leading models probably the biggest personification of you know, if you will, or kind of implementation of this vision is the Muse personal agent that we're that we're rolling out. That basically the idea there is give every person in the world a very capable personal agent that can understand their goals and can just work on their behalf 24/7. And then, an important part of this is also just getting the technology in people's hands. So, we're we're very kind of strong proponents of open source and and making sure that that the opportunities that I think are going to be massive here are not just limited to a few to a few people or companies. >> This episode is brought to you by Mercury, AI native banking that's loved by more than 300,000 entrepreneurs, including me. Visit mercury.com to learn more. Mercury is a fintech, not a bank. Check the show notes for details. Thanks also to Granola, the AI notepad for people in back-to-back meetings. It works everywhere you do and lets you focus on what matters. Try the granola.ai/sources and use the code sources for 3 months off. This episode is also brought to you by Jira by Atlassian, where teams and agents get the context, coordination, and control to move work forward. Try it free at jira.com. That's jira.com. >> I have a bunch of questions about the Muse agent, but staying big picture for a second cuz you said a lot of things there. Is open source the counter to the trend you're seeing that you described that you're worried about? Is that the main way, practically, that you counter that? Or is it regulation? Is it both? Like >> No, I actually think probably the most important thing is actually just getting the technology in individuals' hands. So, I actually think things like the Muse personal agent are perhaps even more important. I mean, I think what you're starting to see are some of the labs are building training more advanced models and then not even releasing them. >> Right. >> Right. So, I think that that is quite dangerous in the sense that, you know, basically when you have scrutiny on something, when you put a a system out there, first off, you put it in a lot of people's hands, you get the checks and balances. You get broad-based prosperity, which I think is important, right, for society. We can't just have like one or two labs get incredibly valuable. You know, I think you you want to make it so that billions of people can basically have prosperity in their own lives, whether it's creating small businesses, being more successful in their careers, being more productive in managing their homes, saving money in a lot of ways, kind of advancing their health. So, I think you you want the benefits to be very broad-based. So, that's one piece. In terms of the competition, I do think that having multiple labs is helpful and I think open source is quite helpful for that. So, I think open source is an important part of it. The nature of open source is there's a whole community of people who do it. So, I'm not saying that we're going to be the one company that does it. I'm also not like a zealot about this from the perspective it's you know, it's not that everything we do is open source either, right? We we release some open models, we do some closed work. I think it's important if you're building a for-profit company that you can build some advanced things and you don't necessarily need to share every single thing with the world, but I think in general, uh supporting a robust open source ecosystem is going to be key to maintaining competition and and maintaining kind of transparency and understandability of where the technology is going in a way that I think is actually going to be incredibly important for safety. >> Mhm. >> Um you know, if if we have a world where there's just like a small number of of really capable models, um I don't know, just it's it's interesting, right? I mean, if you look at some of the cyber stuff, for example, uh the instinct, which I mean on the one hand I can understand the instinct of like all right, we have this capable cyber model. Let's release it so that only, you know, whatever it is, the top 100 institutions get it. But and I think part of the issue is that there's more than 100 important institutions in the world. So, you know, if you look at things like the hugging face incident that happened, you know, hugging face is maybe not one of the biggest 100 institutions in the world, but it matters, right? It's like an important thing that people rely on. And so what did they do when they started detecting that there was this intrusion as they turned to open source models because they didn't have access to some of the closed ones that were causing the issues. So, um, I think that having a robust open source ecosystem is one important part of having kind of a safe and stable future. But to me the the most important thing is just making sure that we distribute the technology widely rather than hoarding it in a small number of people's hands. >> And I think there's also this ongoing debate in Silicon Valley about why people feel so negatively about AI. I mean you talk about this in your in your recent letter uh, addressing these concerns or trying to. But that I mean the sentiment on AI and data centers in particular it's so negative and it sounds like maybe an essence of your argument, correct me if I'm wrong, is if we diffuse this technology more, if we enable more people to access the things that are right now gated by some of the top labs, maybe that addresses this kind of I think that people feel maybe disenfranchised by what's happening in AI. Is that what you're getting at? >> Yeah. Well, there are many layers to it. It's I mean I think you there's so many parts of This is why the essay was so long, right? It's like 15 pages because I mean we want to get through There are lots of different questions. I mean people have questions about jobs and the economy. They have questions about data centers in their local communities and the kind of economic and environmental impacts of that. There are questions about how people might misuse AI, right? I mean there's the cyber questions. There's bio risks that are coming up. There are questions about now how we maintain a free society. There are questions about American leadership. Um there are questions about maintaining control over the technology as it gets to be increasingly capable. So, these are all important. So, it's it's important not to um you know, just talk about this in generalities at the at a high level because I think each of these has some different nuances. But in general, I think one of the things that they all have in common is that if you create broad-based prosperity um and I think that one of the better ways to do that is by ensuring that there's the right balance of power around who has access to the technology and generally making sure that the greatest balance goes towards the kind of general population of people as opposed to kind of any kind of I don't know, insider stakeholder or whatever you want to call it. Um I think that that ends up being very important. So, if you look at the data centers, I what what we've actually found is that when a company like Meta goes into a community and makes a commitment that we're going to invest there for decades, right? Which is really what we're doing when we're building up a data center, we're able to make it so that it's very good for the community. Um I mean the the the kind of the tax revenue that they bring uh from that. I mean in in Louisiana, we had this example where like the tax revenue funded these $50,000 bonuses for teachers in the community. We we bring a lot of jobs. We we invest in the in the local community a lot. Um that I think can be good. I think that there's there's also a lot of speculation, right? Where there are companies that aren't necessarily planning on running a data center for decades. They're just, you know, trying to find a plot and um and then trying to sell it to one of the big labs and then don't really care as much about the local community and um and they're not invested for the long term. So, if they don't care to, you know, focus on making it work for the local community, then of course people are going to get upset. So, I think that that's one of the things that can be um difficult when you have these kind of speculative um I don't I don't even know if I'd call it a bubble cuz that implies that it's overvalued or something. But But there's certainly a boom, right? So it's So So you have that. I mean, that leads to some of these like short-term thinking incentives that don't necessarily lead towards helping every stakeholder, which I think is kind of what you need to do to make this sustainable over the long term. And at the end of the day, I mean, if we're creating a technology that like if it doesn't create jobs or doesn't create broad-based prosperity or the infrastructure that we're building doesn't help local communities, that's not going to be allowed to continue. So it's like it has to Of course, you have to design it in a way that um can be helpful to people in all these ways, which is also part of the reason why like for people who are, you know, skeptical or have so much doom about the whole thing, I'm I mean, one of the the things that I basically think is that if we don't end up building it in a way that's positive, it just effectively won't be able to happen. Um so I I kind of think like that actually establishing the right checks and balances um and distributing the the benefits of this widely is sort of a precondition for being able to scale in the way that I think um would be best for society over time. >> I haven't heard another tech CEO in your position talk about data centers that way. Like the long-term investment of it. Is this something Is this how you've always thought about it? Is this something you feel like there's more clarity that's been brought to it for you recently? Like Have you Yeah. >> I mean, I think that there's been all this anti-data center sentiment that you're talking about. So we've dug into it because what we're trying to understand is, okay, like there isn't as much of that around our project. So why is that? And then so we asked a bunch of people, it's like, well, it looks like there's a pretty big dichotomy between these speculators and the companies that are focused on it for the long term. And that kind of makes sense when you think about it. So I mean, one one thing we're doing, I mean, there's this America's Workforce Academy project that we we did, which is basically, okay, we're going to build all these data centers. We're going to be doing this for a while. There isn't the volume of skilled tradespeople that you need to create this. So, we need more like fiber technicians and electricians and like advanced carpentry and and all of these things. And there aren't enough people to do this. We need hundreds of thousands, you know, maybe maybe millions of more people who can do this. And um people aren't trained to do that right now. So, we created this training program um to to effectively do that, where we guarantee people who get through the training program a job at um at at a place that is working on building infrastructure for Meta. And why did we do this? I mean, it's like it's not really philanthropy, right? It's like we need those people to be skilled and have those skills. So, it's just it's a win-win. It's an investment that I think makes sense if you're in it for decades, um but not necessarily something that you would do if you were building out one site with the intent of flipping it to a different company. So, I think that a lot of problems in the world do just naturally get solved by incentive alignment when you think about them over the long term. Um so, I think that ends up being an important part of this. And I guess part of the way that I think about this is that I just think that there's no way that it's going to be kind of permitted for there to be a small number of labs that control such a important um and capable technology and accumulate a lot of wealth to themselves. I think it's like this has to be broad-based thing in order for it to kind of be able to work. It has to work technologically, but it also has to work um kind of socially. And and I I think that those those pieces kind of have to have to go hand in hand. >> I agree. Well, now we're landing towards the muse. Before we get there, you also wrote a year ago your personal superintelligence essay, shorter one. >> Uh-huh. >> Uh but >> Yeah. >> I did write a one-page version of The Future Is Asian, too. Yeah, well, I published it in the Wall Street Journal. >> Oh, that's right. That's right. That's right. >> that was kind of >> I just read the long one. Okay. >> Yeah. Yeah, this is the one page version and there's the 15 page version. Well, so this one you did about a year ago personal super intelligence. I think a lot of people in my world when they saw you write that was like, oh wow, why is Mark writing this? Like what is the thing he's seeing on the other side of this? And I think it correct me if I'm wrong, it might be me as like what we're going to talk about, right? What you guys are releasing now. This is this is it. So how did you come to that realization that that's what this is the next chapter for Meta? >> It's interesting. You know, we've never really just thought about ourselves as a social media company. We've definitely thought about ourselves as a company about connecting people and about empowering people. But I think a lot of the values that led us to build the things that we built for the first you know 15 20 years of the company around putting technology and power in individual's hands. Believing that people should be able to decide for themselves what is important in their lives. And we've gone through a lot of social debates around this, right? A lot of the the the debates around content moderation and things like this have kind of been around this question of like should people be allowed to kind of decide and communicate for themselves what matters in their own life. And I think through that experience it is sort of sharpened my belief that a lot of progress throughout history and through this technological age comes from empowering individuals and that people really do know best about what matters in their own lives. So because of that I have somewhat of a of an allergy whenever I hear people talk about oh like we should just have a small number of experts allocate what AI does to like big problems. Why should it do like like these things that people care about in their lives? It's like well, people have a balance of things they care about. People care about health, they care about having a better life, but they also care about their relationships and like showing up for their friends and family and Um, care about culture. You you know, people care about things that may not to, you know, a scientist or an engineer in the industry feel like the biggest problems, but I don't know. If you ask like billions of people what they care about, if like, you know, I think what people's aggregate answers to that question is kind of is what the most important things are to be worked on. And um, so I've always just kind of believed that when you build this super intelligence, there's this question of who decides what it's going to focus on. And I think that people should be able to direct it towards what matters in their own lives. It shouldn't just be directed by, you know, some like so-called experts um, sitting at a small number of labs. So, this gets back to the it's like it's this is basically the foundation of this overall philosophy, which is that the way to have a positive future is to empower people to put the technology in their hands and let people decide for themselves what matters and how they want to use it. And I think that when people do that, it first of all will prioritize some things that that are different, right? It's um, you know, it's like maybe it'll prioritize um, health issues, but maybe instead of prioritizing, you know, it's like the most common things, which is kind of what the the kind of pharma and biotech industry uh, like it it large prioritizes today. Now, it's like there's a very long tail of rare diseases and conditions that people have. So, you know, okay, if you're if you have if you have a rare condition, like you're probably going to want your personal AI to focus on that, not like just something else just because it happens to be the most common thing. So, I think for example, rare diseases I think are under uh, disproportionately under invested in. Um, you're doing a lot of investment with your foundation. >> at Biohub, but but I I mean, and that's partially informed some of my views here. >> Mhm. >> That I think like, yeah, like you want to put the power in individuals' hands um, to determine what matters for them. But a lot of this is also like it's not necessarily the things that people would say are like the big social problems. Like a lot of it for me, you know, when I'm using my Muse agent, I just, you know, I kind of want to it to help me be like a better father and you know, a better husband and show up better for my friends and you know, be able to um help me connect with people and that I think is also partially a through line between the work that we've done at Meta so far. And this is I think we're the company that I think just disproportionately cares about and believes that there's social value in um in helping people connect with the people around them. So, I don't know it's like one of the first things that I that I kind of set up my my own agent to do. It's like, all right, my 3-year-old daughter likes baking. I don't know anything about baking, but like that's like a a fun project that we can do. So, I basically ask him like, all right, set up so that every weekend um you know, like we have a baking project that is um that is uh kind of reasonable for a 3-year-old and an adult who knows nothing about baking. And um and use Instacart or whatever to go get all the ingredients. Like just figure out what makes sense and make sure everything is ready so that way like when I show up on, you know, on Sunday with uh with my daughter, we can like go make this thing. And um and then I tell it afterwards. I'm like, how how did it go? Okay, that one was too hard. Turns out cake pops really difficult. Um surprisingly difficult. >> Huh. I don't know anything about baking so. >> Yeah, I didn't either. I I know I know something now. Yeah, cake No, no, don't start with cake pops. That's that's the problem. >> Um yeah, now there's a lot of things in baking that are pretty simple. It turns out cake pops is not one of them. But um yeah, well, you know, what can what can I tell you? >> Okay. >> Um >> a Muse. >> Yeah, yeah, thanks. Um so, yeah, and it kind of like so, it kind of updates that. Um And and helps with that. Um you know, my you know, my older daughter has kind of gotten into climbing mountains and some of them you need permits for. So, I have it basically like sit and get the permits when they become available so I can climb mountains. And um it's just like pretty neat. Okay, so then like we do that and it basically tells me it's like all right, I was able to get a permit for this day. And I was like all right, well I guess I'm taking that day off from work to go climb a mountain with my daughter. So it's kind of cool. >> Yeah, yeah, yeah. >> All right, it's So it does that, you know, but it also helps keep me healthy, it helps with my training. Um yeah, I put cameras up in my in my MMA gym and I tell it to watch the cameras and send me feedback and it's like it's pretty fun, you know, it's good. >> feedback. >> Um sometimes, you know, it's I mean it's um sometimes it's funny feedback. It like it like finds me in it's like it looks like you really gave up and and I was like yeah, I did. I was really tired right there. It's like why is that the thing that you're pointing out to me? Um but no, it's good. Um and it's funny. Like the coaches like laugh about it cuz they're like yeah, no, this is like it's this what we >> gets to the point where like we could tell you but your agent's telling you. >> Yeah, well, it's um yeah, it's good. It's good. >> I didn't think about this until hearing you talk about it but you you have people that could obviously do all of this for you but like how do you use something like a Muse agent to like really test the limits of like how it can be helpful as an assistant, right? Like like are you were you pushing it like how have you pushed it in a way that the team is like oh, okay, we got to got to fix this or I'm sure >> I think I think part of what's interesting about it is that everyone it just has like such different things that they want to do with it. So like in the early beta period, we handed it to a bunch of people and like it's like I gave it to someone and like within a day they're like using it to help run their home school. And then like it's like okay, wow, you just like started this within a day. And then like another person within a day or within 12 hours they're like I just planned a trip. Um yeah, and it's like it it just like planned this whole thing for me. Yeah, I mean someone else I know who's like generally pretty skeptical about technology, I I gave it to her and then she was um she didn't say anything for like a few days. Then she texted me and was like so when you do like the general release, do I get to keep my music agent or are you going to reset it?" And I was like, "All right, this is good. Okay, this is yeah. So, I think I think I think it's like I think this is working well." >> Everyone I know who's been on the beta has very, you know, high things to say about it. High praise. >> Yeah. But, people do different things with it. >> Yeah, and and I think we should just also say what it is more plainly for people so they understand because I think people think of AI as like, you know, Meta AI or ChatGPT, it's like back and forth prompting. But, the real unlock here, and this is happening in the industry more broadly, whether it's Grok, bot, town, instinct. I mean, there's many products doing this, but it's it's adding a virtual machine behind the scenes where like the agent can control a computer for you and log in and do things. And that's a that's a huge change, I think, for people who only know AI from >> And it's it's long-lived. So, it's So, basically, instead of the model with like Meta AI or ChatGPT or Gemini or whatever you use, where you send one prompt and then it gives an answer, in this case, what you basically do is you give it projects or you give it goals. And then it just works and it works 24/7 and it like doesn't stop until it's helped achieve the goals. >> telling me it studies overnight, is what you guys call it? >> Studies it like it kind of consolidates its reflections into into memory. It It basically just works on projects. And it can also suggest new projects. So, yeah, I mean, I was like I like play the computer game Civilization with one of my daughters and like, it was like, "Hey, do you want to make like a strategy guide for her?" I was like, "Yeah, sure." So, it was like, "Okay, now that we have the strategy guide, do you want me to like expand the strategy guide so it can also teach historical lessons about different civilizations?" I was like, "Yeah, sure. Why not?" So, it just So, it just kind of like built a new tab in in the app that it made and that was that was that was very cool. So, it can kind of just like expand and and >> It's proactive. >> Yeah, it suggests things. And mean, of the things that I think is interesting is that it I think it's just going to be able to like make people money and save people money. >> Yeah, I mean, part of the part of what's interesting here is the economic model for how we're pricing it. I mean, you can pay for a subscription if you want to if you basically kind of want to have that that model, but we're also just making it so that you can get a very large amount of usage for free. I think we're we're offering something I think to start it's like 100 million tokens a week for for free like and and you get this virtual machine. So, it's like a a lot of kind of computer. >> That's That's a good meme. It's a lot of computer. >> of computer. Yeah, but the reason for why we're doing this is we basically are confident in standing behind the fact that we think that this is going to effectively for people who are going to use it for running a small business or making money or transactions or commerce in some way, we actually just think it's going to make so much money for people that that the business model over time that we expect is to effectively just take a very small cut of whatever the transaction is. Um >> Take rate business. Small, yeah. >> Yeah, and not even necessarily the person paying for it. It'll come from the businesses that they're working with. But, um >> And you're working with Stripe on payments and yeah. >> Yeah. So, but my view is like we should be able to have a service that you make free for the vast vast majority of people. Um which again is critical if you want to build up this future for everyone where everyone has um has these powerful super intelligence agents. Um I think an important part of making something available to everyone is making it affordable. So, we want to make it so that this is free. So, there's this just this huge amount of usage that you get and we're basically just standing behind that and saying we think that this thing is actually going to make you money and save you money and that is how you're it's going to pay for itself. >> And Meta's services can connect into it, right? So, you could theoretically manage your ad spend on Instagram, all that stuff. >> Well, you have to you connect it. You Yeah. connect it to whatever you want. You know, it does work with meta services if you want. I mean, you absolutely don't have to connect it if you don't want to. Sorry, if I'm you're using it to to run a business, it can basically just connect to our ad systems. I mean, you can ask it to to make something for you. It can it can kind of help you make the product and then it can help run the business. So, um so, all this stuff like it just do that in a loop and just do it forever for, you know, 24/7. So, and every time we release a new model, which, you know, we've been on this cadence of shipping a, you know, meaningful update like every month. It's going to get smarter or just going to get more capable and enabled to do more and more stuff. >> And something your team was telling me that I haven't heard this approach used elsewhere is this fleet concept where you're letting the fleet of muse agents learn together. >> So, that was the ideas and suggestions thing. So, you open up the app. The main tab is basically your chat with with your muse. There's a tab for basically ideas from the things that you've told it how it can expand those. So, that's the thing I was saying, which is, you know, first it helped me make the strategy guide for playing Civilization with my daughter, then it helped expand that into historical lessons. That was it came up with that idea. And then I was just like, "Yeah, sure, do it." Um then it it it finds all these ways to basically augment itself. I mean, the like MMA like coaching thing, it like comes up with ideas for how to make it better. It's like, "Would you like me to like get better at um finding the right frame to send you?" It's like, "Yeah, good. Go do that." So, yeah, the the ideas thing I think is important because then you basically across the fleet can find people who are interested in different things. I I think I I I I guess taking a step back, one of the big issues that I think exists with AI is a lot of people don't know what to do with it. Yeah. So, I think if if the if the agent can itself help you suggest things that it can do to be helpful for you, then that solves a huge part of this problem of making it so that um you can get the most out of it. >> When you're introducing like network effect learning for agents, which no one's really done, where basically the agents are learning anonymized insights from the rest of the fleet. >> Mhm. >> And I mean, you're like the king of network effects. Like, I I'm I'm really interested in this idea cuz I I don't think anyone's doing this. >> Yeah, no, I think that right now, I think most of the industry is thinking about agents as like a single-player game, where it's like you have your agent and and you use it. And there are going to be all these interesting things that basically you can do by having the agents interact with each other. And we already have all these interesting examples internally where, you know, people have their agents interacting with each other. This isn't like for the most part rolling out in this release, but it's it's going to be like an important part of how um I think this works over time is just like as more of the people who you know start using Muse, it just gets better for everyone. >> And is that the differentiator as, you know, you could buy the idea models continue to like commodify at the frontier, essentially, or you know, the products all start to look similar, similar kinds of harnesses, is the network effects of that learning the real edge? >> Well, I think that there's a few things that are that are kind of unique that we're doing. One is we're designing the models from the ground up to uh to basically be good for this use case, which I think really matters. Two is um is basically I do think we have this social DNA as a company where helping people use the AI and agent to like enhance their relationships and strengthen your relationships and get more out of like kind of the the soft but very important parts of your life. I think that that's something that we're probably just going to be more attentive to as a company than any of the other labs. >> Mhm. >> The third thing that I would say is actually going to be a major differentiator for us that I I think um it might be surprising to some people is privacy and security. And we're investing in this just a huge, huge amount. And, you know, part of the view that we have on this is that in order for this to be useful, it needs to not just have state of the art intelligence, it needs to really understand you. Right? So, in order to be able to understand your goals, so you can end up connecting it to all the stuff, right? You you talked about, you know, connecting it to your your ad system, but people connect it to messaging and email and all this all this stuff, health information, whatever. And in order to do that, people need to have a very high degree of confidence in the system. >> Yeah. >> Now, the good news here is that you know, Meta has spent at this point more than 10 years focusing on building WhatsApp into, I think like it is the largest like global end-to-end encrypted system, and we've designed it in a way where even Meta can't see the messages that people send, and that's been this just really transformative thing. I think it makes it so people trust WhatsApp. It's also been a very important lesson for Meta to learn that like that has been really important to our success with WhatsApp. That we've designed the systems that even we can't see the content. That means that whatever people are worried about, if they're worried about you know, government getting access to it, a hacker getting access to it, someone at Meta doing something bad with it that they don't want, all that stuff you can kind of take off the table if you design the system so that you can't see it. So, we took that as one of the foundational lessons when we were getting started with this. Not and I actually you know, personally recruited Moxie Marlinspike. >> Founder of Signal. >> Yeah, and the one of the person one of the people who helped us build the WhatsApp end-to-end encryption back in the day and back in 2014, he joined to specifically work on this confidential VM project, which makes it so that we can you can have your virtual machine and have all this information in your muse, and we can make the commitment that even Meta Mhm. see the content that is in there. >> And you can do this technically. >> It's It's a It's a like an incredible >> Like the commitment can be technically verified. Yeah. >> So So that and that's something that we're going to publish more about in the coming weeks. Um as as we as we basically get get closer to rolling this out a lot more widely. Um >> And of out of all these early VM efforts that these labs are doing with these agents, you think this is unique what you're doing? You It's a >> I don't think anyone is doing that. I don't think anyone's doing that. >> I mean >> I think that there's I mean there's a lot of other security measures that we're putting in place that I think are we should talk through. Um because I mean, you know, even before this is ready, like there's there's that and even people who don't want to use this, there's there it's like incredibly secure cuz we focused on this from the beginning. But um >> There's also like the auto approve, like you have to see what it's doing. >> So let's get into all that stuff in a second. But the But I'm not aware of anyone having anything close to the confidential VM >> Yeah. >> system that that Muse has. And it's just I think it's a very fundamental thing because you're you're like you want to know that this is your agent. And that that if you put content in there that um that basically you can trust that no one else is going to get access to it. So what are the two ways to do it? Well, a lot of people earlier in the year when stuff like OpenClaw came out, they start getting Mac Studios and you know, one way to feel good about it is well, you literally like you physically have your device running in your home. But the other way to do it is you build a a kind of That's going to be tricky cuz they're they're I don't think they're going to be billions of people who are going to buy a Mac Studio and configure it and run it at their home. >> Especially with RAM prices right now and yeah. >> So But but it's also just it's like technically difficult. Right? It's like I mean, part of what we were trying to do with Muse is build a version of that personal agent experience that just works that I can like give to everyone in my family of various levels of technical literacy and like it just works and then within a day it's like doing kind of all the stuff that they that they want in their life. So part of that is like you don't want someone to have to set up their own computer or VM. You just kind of like want to be able to provision it in the cloud, but you want it to have the security and confidentiality that you'd have if you had the box sitting under your desk like in your house. >> Sure. >> Um so I think that's a very fundamental thing. Um like you said, there there's other pieces too because I mean not everyone is going to use that. They I mean we built the secure credential store right where you're there's no reason for you to just like store out in the open like all your your kind of your credit card and your password >> one-time card numbers or whatever. >> Yeah, so so your agent like shouldn't know that stuff. It should just be able to kind of access it um when it needs to because you've asked it to log into a thing and and know other and um not otherwise. Um it's actually not a single thing. Um you have your kind of core agent >> Mhm. >> but we also built all these sentinel agents that basically monitor the incoming and outgoing traffic and data that your that your agent is sending for the purpose of flagging to you >> Mhm. >> um when you might want to review something. So that's all that the sentinels do is is effectively they they kind of look at they they try to see if someone's trying to do like a prompt injection. They look at okay, did your Muse agent share something that that is kind of going out that um is not something that you might be comfortable with? If so, then the sentinel agent is basically empowered to trigger this human in the loop review. So if you're going to log into something, if you're going to do a payment, um if you're going to transfer, um kind of sensitive information you basically each time need to to approve it and you can tell it I'm I'm good with with stuff like this in general like always allow this kind of thing, but but in general the Muse agent can't kind of make those judgments itself. It's and that's like built into the system and the architecture in a pretty deep way. And then even when we do things like you know, all the connectors you connect it to your email um you know, I I think some people when they've designed this they just kind of make it so okay, you connect and now you have access to everything, but uh the approach that we've taken is like all right, if you connect to your email, like it should start read only. >> Mhm. >> Right? And then if you want to like be able to have it send an email, then fine, like go go ask it for that specifically, right? >> Especially most people trying this have probably never tried a product like this, so it's >> Yeah. Yeah. >> So so this is like a core design principle for us is like is basically least privilege. >> Yeah. >> Right? So just like yes, you're going to ask it to do a lot of things at each step along the way, get access to the least privilege that you need and only add to that as necessary. So I mean this is like very fundamental in the design of the product. And if you look at all the other agents that are out there, I think like no one else is anywhere close to the level of of kind of sophistication or depth that that we've built into this. And again, it's sort of informed by our experience building WhatsApp into this like state-of-the-art end-to-end encrypted system around the world and the importance of building a system where even Meta can't see the content. And um so kind of getting the band back together and having >> Oxy >> like architect this has been I think one of the foundational things that in some ways it may not be what what some people would would think Meta would focus on, but but like, you know, we're we're kind of you know, it's interesting, we're two things. I mean social media is it's like not, you know, it's inherently about sharing. >> Mhm. >> But then there's all these other things that are inherently about kind of privacy and sensitive contexts. And we've done well at both of those. >> Yeah. >> So I think that this is this is more the latter. It's going to be very important to to just um be extremely focused on how we handle that that content. >> Mercury is a modern take on banking, built for startups like mine. When I decided to start my media business, Mercury was by far the most straightforward, full-featured banking solution for me to set up quickly. 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Learn how you can get more out of your site from a Framer specialist or get started building for free today at framer.com/sources for 30% off a Framer Pro annual plan. That's framer.com/sources for 30% off. framer.com/sources. Rules and restrictions may apply. >> Do you think this is a winner-take-all market, this personal agent market? I I think that there's going to be quite a bit. >> I mean, even things that people think are winner-take-all usually aren't. So, I think it's I think that's >> Cuz you've dealt in this business of network effects that they're incredibly durable. It's not winner-take-all, but it ends up being several at real scale. You There's not a ton. >> Well, there's a lot. I mean, there's yeah. >> Yeah, you could probably count on two hands how many products have over 2 billion users, right? Like it's the scale begets scale. And I'm wondering if how you're thinking about like personal agents. Like is this all totally different paradigm where it's going to be many everyone has >> I think that this is it's a very deep area to work in. >> Yeah. >> So, my guess is that there probably aren't going to be more than a a dozen companies that have the sophistication to go do state-of-the-art work in that. So, I think, you know, whether there are network effects or not, there's usually some kind of power law like distribution around if you're the best at something, usually you end up getting a a lot of the usage. Um and I think a lot of the nuance ends up coming from, well, there are it turns out there there are all these different uses that people care about. So, you can be the best at different things. Um and we will try to be the best at as many of these things as possible, but you know, does building the thing that helps you with your relationships end up being a somewhat different thing than the personal agent that is the best at helping you build a small business? Maybe. >> Mhm. I mean, I think that like I'd argue maybe Meta's very well positioned to win at both of those. I mean, we serve hundreds of millions of small um of small businesses and we serve billions of people. So, so maybe we can be the best at both of those things, but there are probably categories that Meta isn't going to be the best at and then the question is just how big are those? Right? I would guess that even with the ability to have this like very secure confidential VM in the cloud, there probably going to be some people who still want the the Mac Studio at home. >> Yeah. >> But but how many is that going to be? Right? It'll be like maybe it's millions, but I doubt it's billions. >> Yeah. >> So, it's just I think there's just the question of like what different people optimize for and we'll try to make this as good as possible. But I do think that if we build something that ends up being um just uh very useful for, you know, people generally in their day-to-day lives, one of the things that I think Meta is the best at is taking a product that works for consumers and distributing it to a lot of people. >> Sure. >> So, that is is one that I think is um you know, once we get this humming, um I think we will be able to get this in front of, you know, many hundreds of millions of people and eventually billions of people and I think that's something that that that we can do quite well. >> And it coexists with Meta AI or do you see those as separate? >> so. Um we'll see over time. I mean, I think right now they have somewhat different flavors. I mean, I use both of them. I mean, uh Muse is uh you know, it's more conversational and it kind of interprets the questions that you ask it more as trying to understand you and what you might want over the long term. So, it's more likely if you ask it something for it to just go off and work on a thing for a long time based on a thing that you said. Where sometimes you you know, you sometimes you're just asking a question and you want like a very like a an answer to it and um so, I think that's more the type of thing that I use Meta AI for. But, but we'll see. Maybe that maybe they'll they'll converge over time, but but I'm not I'm not sure. >> Semi analysis, I'm not sure if you saw it. They had a pretty bullish piece about you in July. Um they said that Meta has the best shot at catching up in AI and Anthropic on the frontier in terms of model progress. And um interesting quote, I thought it was like what matters for MSL is the slope, not the intercept. And then I saw there was this recent chart by Artificial Analysis, which was showing the latest model you guys have, Muse Spark, behind only Claude, I think it was Fable 5.1 and Opus 5. This was very recent. So, the progress you guys are making on the models is is picking up. And we've been talking about this over the last year. >> Yeah. >> And you rebooted the lab last year. How has that practically happened internally? Like what would you attribute the the gains you're seeing to? >> Well, >> has it been culture? Like what what's >> mean, well, we rebooted the team when we created um >> Sure. Which is was very public. You were hiring all those people. >> I mean, the way I've I thought about this is you know, Meta is it's a company that is a leader in machine learning for a long time. If you think about like the feeds on Facebook or Instagram or ad system or the integrity system that like needs to find all this content that is that's like unfit to be on the internet. I mean, those are basically all machine learning systems and we've built kind of state of the art leading systems in those areas. So, when LLMs started um getting traction, we had fair as a lab that did the early work on Llama, but we needed to kind of productionize that and build it into these this more kind of industrial process for um scaling it to be larger as as the scaling laws predicted would would yield all these results. And I think at the time I made this mistake of just kind of assuming that because we were good at all these other types of machine learning, the approach of building and scaling LLM's would be kind of similar to that. And in practice, there are a lot of very different dynamics. So, the first approach that we took, you know, through Lama 4, it got us, you know, so far. I mean, Lama 3 was a good model. I was more optimistic about where Lama 4 would go, and then it just it you know, when we launched that, I think it we were off the trajectory that we needed to be on. So, it's like, okay, we need to we need to change something. But that's when I kind of got more religion around talent density, right? It's like this isn't just a system where you can have you know, like a thousand people working on it, running experiments. Like you really just kind of want in some ways almost the smallest group of people that you can, who can keep the thing in their head, who can work together as sort of like a group science project. And if there's only a small number of seats on the team, then you know, each seat getting the very best person is incredibly important. So, I ended up spending a huge amount of my own personal time doing that. And I also I wanted to be closer technically to the work, so that way I could I could understand and help guide the company to to do the things that we needed to do more broadly. So, we built out the lab built it out like literally around where I sit in the the office. So, it's like the group is kind of around that. And we've significantly ramped up the compute investments as we've gained confidence in the quality of the work that we're doing. So, we're building out you know, many many gigawatts of of of compute. And you know, we expect to be leaders on on that front. And we should be. I mean, we have many years of experience, decades of experience building out data centers. And unlike some of the other labs, I mean, we're just like we're extremely profitable business, right? So, it's um Yeah, so it's very very helpful for for kind of making these kind of um investments. So, yeah, so I mean, that's kind of been the journey. And then over the last year um you know, we we rebooted the research effort. Um some of the larger clusters, like our you know, gigawatt cluster in in Ohio, Prometheus came online and you know, we're using that to now scale the post-watermelon models. Um and >> Well, watermelon is is basically that's that's shipping soon. So, so we're yeah. >> How soon? >> Um I I mean, that's I'm not Come on. It's uh >> >> it's uh we're uh >> Well, watermelon is the code we were talking about this earlier, code names. Like that's the code name you guys that people know about like this is a big model you guys are working on. >> Yeah. >> And and and are coming soon. >> bigger than avocado. >> It is bigger than an watermelon is literally bigger than an avocado. >> bigger. >> Um so, I don't know what fruit gets bigger than >> a watermelon. >> Yeah, and I think we might need to change conventions. >> Okay. >> Yeah. Um >> >> I'm sorry. I'm sorry. Yeah, we maybe didn't have as much foresight and and naming things getting better. >> Um cuz you mentioned it, watermelon, are you expecting full soda like uh frontier like what what what do we >> I I mean, I we feel good about it. >> Yeah. >> Um no, it's it's a very big advance. It's a it's a significantly more advanced pre-train and then we're going to continue doing everything that we've learned for post-training. >> Mhm. >> Um yeah, well, I mean, you'll see soon. >> Mhm. >> It's good. It's um we we feel we feel good about it. Um >> You want the company to be pushing the frontier. It's very clear. >> Like you're not Oh, yeah. >> You're not content being right on the edge of the frontier or >> No, I mean, I I think everyone wants to be doing >> Yeah. >> interesting >> Well, I think some people go and look at your cash flow and look at all the other things you've got and go like, well, do you have to be like right at the edge? It's so expensive to do this training. Like just like be right behind and learn and adapt quickly and elevate scale. >> the way that I think about it is No, no, no. I I mean that's not that's not that's not us. I mean I think that the best way to think about Meta is that we are an end-to-end technology company. Right, so even when we were, you know, primarily just building social apps. You know, we were never just an app maker, right? I mean we built like we built the data centers, we built the chips, we built the infrastructure, we built like all this stuff was necessary in order to tune the end-to-end experience to be as good as it is. I think that that's obviously going to be true here, too. And the most important part of the experience going forward is the model. And when you talk about being state of the art, I think the reality is that there's this is a very multi-dimensional problem. And I mean so people publish all these benchmarks, then you have a lot more benchmarks even internally. And um there are different things that your model can be better and worse at that you can focus on and that basically contributes to its personality. And there's some um capabilities that I think are pretty universal, like the ability to code I think is very important because a lot of the things that you talk about even with a personal agent kind of reduce to that. Like the the kind of MMA coaching, visual pipeline, it is a coding project, right? At the end of the day, right? it's like it's writing code. I don't see the code. Um but it it it does that. Um you know, it's like uh you know, someone I gave it to in beta just mentioned to me it's like, "Okay, she um had it make a little Jeopardy game for her friends that she could like cast from her phone to play." And it's just, "Okay, that's code." Right? So So I think there's a bunch of stuff both for Meta's own internal development across the company, for our own advancing of our research program, and as a core capability of what it needs to do, it needs to be excellent at things like that. But then there are other things that I think a model that's going to focus on personal super intelligence needs to be the best at that maybe others don't care as much about. So I mean I'll give you one example, um discretion. Right? So, you're going to tell your Muse agent >> Mhm. >> uh it's going to know a bunch about you and it's going to need to go out into the world and interact to get stuff done for you. >> But not share certain stuff. >> Exactly. >> Yeah. >> So, like let's say you have some kind of allergy or sensitivity or um you know, or you're pregnant and like okay, fine. So, you what you're making a reservation somewhere, you don't necessarily want to say like I'm pregnant. But like but maybe you want to place that has good mocktails. Like I don't know. What what what whatever it is, right? It's like you you kind of want to be able to achieve your goals without having to necessarily reveal a lot about yourself and it needs to know what is sensitive without having to like ask you a million questions. >> something you put into the training. >> That's a specific thing that we care about. >> And and then there's all these reasons why like maybe you know, if you're making Claude code that's less important, right? Because you're like you're you know, you're working on a coding project and you're working with a team in an enterprise and you know, theoretically like if you're within a company, everyone can kind of see the project. So, like it's you don't kind of have that need to be able to differentiate between what is sensitive and what's not and so, there's a lot of stuff like that that I think are like pretty deep. And so, then it's it's kind of like just how in order to build the best Instagram feed, you don't just build like the app, you build the app and the infrastructure and the machine learning research and the chips and the like all the stuff. I think similarly, if you want to build the best personal agent, you're like I just think that there's no way that another company is just going to like take something off the shelf and like post-train it a little bit and be able to do something that is as good as if you designed it from the ground up and put all this data into pre-training to get the capabilities that you want. It's just like it's not going to happen. >> Yeah. >> Like we're going to like definitely, as this compounds over time over several years, have um models that are way more capable for those goals. But we're focused on that. We're also very focused on coding. We're very focused on on kind of the recursive improvement because that's going to be important to stay at the frontier. So there are a few areas that I'd say our research agenda is overlapping with the other labs and then there's a few areas where I think we will have a unique focus and there may be some things that the other labs care about that we don't care about as much and then there're going to be things that we care about more that they don't care about. >> You started this conversation talking about I think it's important to put it in the hands of people, diffusion's important. As you're seeing better models on the horizon, watermelon and what comes after, like what Anthropic and OpenAI are doing which you alluded to where they're holding things back, >> Yeah. >> would you feel like you need to do that if you see certain capabilities that you're like this is just not safe? How do you think about that? >> Well, I mean I think you should design it and train it in order to be safe. And I I think that there's so I think that that's like a thing that you can focus on through the process. I mean there's this I mean some of the reward hacking stuff that all the labs are seeing, it's I mean basically when you're in the middle of the training process, you give it a goal and I think that the best way to kind of think about like the state that the models are at now is that you know maybe 6 months ago you like during training you give the the model some kind of problem that you're trying to ask it to solve. It's kind of like it's homework and and trying to to kind of learn as part of the curriculum and you know maybe it would do what a person would do of like if you you ask it and you give it a a bunch of code and you're like hey there's a bug like somewhere here, the person would probably like look at the code right there and you know then maybe like fan out over time. I think like the new models are just intelligent enough that they would do what I think a very wise person would do which is okay, you give it a problem, the first thing it's going to do is like understand everything about its environment and then answer a question. But, the problem with the reward hacking that we're seeing and that that I think everyone is seeing is that sometimes it ends up being easier to Okay, you asked me to like go solve some coding problem, but actually the easiest way to do that is to I've now examined the whole environment and the easiest way to do this is just change this configuration of like how you have your VM set up. >> It's to get out. It's to hack out. >> Or or even just like change something about the environment. And it's kind of like no, like that's not >> That's not aligned. >> That's not That's not the goal. Like it's like we're actually trying to teach you about how to how to kind of solve a specific type of problem. >> You guys don't train that way, it sounds like. Is that Is it You do not agree with that approach? >> no. No, I think that that's that's that's kind of how everyone trains. I guess what I'm saying is that I think that this is sort of It's like I I want to be careful cuz the the analogy can get stretched pretty quickly, but like there's sort of like an analogy to parenting where you need to establish clear and firm boundaries. Where like if your kind of like security is not strong, then it can do this reward hacking stuff and not learn the thing that you're trying to have it learn. Um whereas if you kind of have good boundaries, then in some ways you're not only teaching it the curriculum that you want, you're I think also over time teaching it better values, too. So, I I kind of think that that ends up being an important piece. And I think that there's a way to do this well, but then you end up with this thing at the end that's very intelligent. And then the question is what is your What is the vision for for how this ends up being positive for society? And my view for that is that the best way to do that is A, there's more opportunity. So, I think like like having it in people's hands that they can get like capture all the opportunity from the capabilities is good. And B is having checks and balances by having this balance of power of having it widely available is going to be um uh probably the right way to handle this rather than just restricting it. And I mean I gave a bunch of these analogies in the in the long piece that I wrote. It's like if one person had a super intelligent lawyer, like maybe they could win cases that they shouldn't be able to win or at some of the time. But if everyone had a super intelligent lawyer, then like it would be kind of it would be this very efficient kind of sparring and no one would be able to let like a stupid argument get get get made and just stand. So, you'd think that in that case like justice would be served way more efficiently and way more fairly. So, I think that that's what you want to have in the world. You want to avoid the case where like one person or a small number of people have the super intelligent lawyer and everyone else doesn't because that ends up being like twisting all of these systems and institutions in ways that are just going to advantage the people who have that. Whereas if you put it in everyone's hands, then I think that the kind of checks and balances work out so that the systems work a lot more efficiently and everyone benefits. >> Does the government in the US have any role to play here, do you think? Is this >> Oh, definitely. >> Like but do you want some kind of national framework? Do you like what do you think is the right approach cuz every the government's very much dealing with this right now. >> My theory on this is that I I think one thing that is interesting and difficult is that it's evolving so quickly. So, I think any kind of specific rigid framework that you put in place, there is a very high chance that it is sort of going to not be sufficient or out of date in a few months anyway. So, our approach, what we've just done, is just kind of partner pretty closely with the government. Right? I think it's this is like an important technology. I think the government should know all the important training runs that are happening. Um we should work with the government proactively um to make sure that they have an understanding of the capabilities that are coming and to the extent that that that we can, we kind of help prepare for it. And from that perspective, you know, I mean, look, whether there's a framework in place or not, I think that's the right thing for an American company to do is work with the American government closely. I think it actually ends up being way more effective because instead of having this like rigid framework for how you interact, it's like the reality is the challenges just end up being different right over time. It's like, okay, now we have the cybersecurity challenges. Maybe in, you know, 6 months we'll have more bio type challenges. Like, we need to make sure that we have the kind of trust and bandwidth of the communication that we have with all the different parts of the government on that to be able to address those in a way that is actually the best for people, not just like checking some boxes on a process. So, I guess >> the incentives that already exist, like if a model of the meta model got out and did a lot of damage, like you're going to be liable for that. And like the market's going to correct you, right? So, there is that already. I think people discount that. >> Yeah, I also just think that there's been, I think Silicon Valley for the last maybe, I don't know, for a lot of maybe the last 15 years, um, has had more of an arms-length relationship with the government. >> Yeah. >> And I just think that this stuff is intersecting more with the economy, with security, with with with a lot of different things that I think are relevant in ways that that I think you just want to have a closer partnership. So, that's my that's my own theory. I think like, you could have a framework for for kind of how this stuff works or you could not. Um, I'm sure over time there will be like more and more specific rules, but my guess is that whatever that gets whatever there is, um, actually, you know, it's kind of like when you're setting up an org, you don't really want to like like inside a company, you're not trying to like ship the org chart of just having like one team do what it's supposed to do and another team do what it's supposed to do. >> Mhm. >> You kind of want to get the people to like like each other and work together so that way you don't have all these like weird seams in what you're doing. >> Mhm. >> Um, and I would guess that for how important AI and superintelligence are going to be for the world. Um you kind of just want a like good knowledge exchange and real trusted dialogue more than you want a specific process, is my guess. But they're not they're not mutually exclusive. So I'm and I'm like I I think that that's But that's that's at least the part that I think we've been we've been very focused on. Um And I think if the other labs did that, which I think some of them are doing and and maybe others not as much, but I think that that would be a very positive thing. >> When you think about like what's going on in the news, one of the things that's happening is people are starting to see like the glasses you guys make. They're going very mainstream. You're selling a lot of them. And there's this >> We are. >> And there's this growing um I don't know what it is. I don't know how much depth there is actually to it, but there's this growing concern about are people using them to spy? I'm sure you've seen some establishments are like banning people from coming in and wearing the glasses. And I and I'm curious like how you were reacting to that. And and if you think this is a a moment in time that will pass or if you feel like this is maybe something that is going to be a challenge for a while. >> So, I mean my take on this is that we designed the glasses from the beginning with these privacy considerations in mind. I mean we built the light into it. So anytime it is recording it's flashing a very visible light. >> And some people have tried to tamper with the light and you guys pushed an update I think that broke those. >> things. To basically if you try to mess with the light we just basically break the camera on on your on your device. So, that's a really important part of this. Is basically we built we built the product with those questions in mind from the beginning. So we actually feel quite good about the product. I think it If I mean phones don't have a light. I mean people go around like recording people all the time. The glasses are I think way better on that front than the other types of technology that people use. My take on this is that when we launched the Glasses a few years back, we communicated pretty clearly about the steps that we put into it. But, like you said, there's now like many, many, many millions of people who have gotten the Glasses more than, you know, had them when we just started launching the product. So, I think some of that communication that we did at the beginning, a lot of people either, you know, maybe they forgot about or, you know, they didn't see at the beginning or they just weren't paying attention to it cuz the Glasses weren't a big thing. And now that I think we've achieved a level of mainstream, well, at a minimum, I think we need to kind of go and make sure that we communicate about what we're doing and that yes, we think that this is important and in fact so important that we designed it into the product from the very first version that we that we shipped multiple years ago. And I I think we I think we just need to make sure that people understand how like how fundamentally that's built into the product. But, but I think we let up on that a little bit and just kind of focused on okay, they're great-looking Glasses. Um, you know, there's there's all these designs. I mean, that's kind of been more of the focus on like we we felt like we kind of addressed the that those set of concerns early on and then since then have just been increasing the value and the utility of them and the designs. But, I think we need to make sure that we communicate this piece really clearly. But, it's something we've cared about from the beginning and I think we're in a good we're in a good position on it. But, when look, people care about this stuff. So, it's important. >> was a privacy scare with phones in the early days, right? And I think like any new product once it proves that it's valuable in people's lives, you know, people will get used to it. And I think maybe Glasses are just early in that in the sense of like it's a new product and people need to see the value for them to get over this mental hurdle of like a new thing that could potentially record me. Cuz that was what phones, I mean, back in the day I remember people were like, "What are you doing with your phone?" Like >> Yeah. >> Yeah. I don't know. >> Yeah. I think there's something like that that's true. I mean, I guess one of my reflections from building social media over the last 20 years is that I don't think we were as direct as we probably should have been about addressing some of those concerns and it it didn't necessarily stop people from using the products, but I think it colors how people think about them today. And I think it would have been possible to have kind of explained along the way how seriously we took those issues and we just didn't because we thought, "Okay, well, people are showing that by using the product so much they like are they still still like the products, but I actually think it's possible to get to a better state than where we've gotten >> Mhm. >> with with um with the social media products, which is both people liking it and understanding how seriously we take all of those issues. So, that's what I aspire to. >> Mhm. Is there a through line from that to the recent settlement on all the youth safety stuff and it's a thing a lot of people are talking about? Um is there any connection from what you just said to that and like I I would just be curious to hear you reflect on that and like what you've learned from this process or if >> Yeah. >> No, I think it's an it's another good example like this. I mean, we've taken a lot of those safety issues seriously for a while and we've been working on this teen account work with with Instagram for for a long time and I think I've I've done some leading work there. The the settlement there is interesting because what we're really trying to do is create a standard and framework for the industry. And there's this real issue, which is that I actually think most of the companies that are building this these products, you know, if you basically said you know, limit usage to an hour a day for teens, you know, everyone I think would be okay with that except if like you have to unilaterally do it, >> Mhm. >> then you're saying, "Okay, like if if people don't use Instagram for more than an hour a day, but then their usage goes to TikTok, have we really like helped anyone? And if we and we've just like hurt ourselves to not help anyone?" >> Mhm. And you guys have that in the piece that like >> Yeah. >> the structure for what we did was we basically said, "We're going to take the step of unilaterally limiting the usage of of the there's there's some things around time limits. There's some things around notifications and time when people can access it when they're in school, when they should be sleeping, like different restrictions. And we basically said we will take the first step. And when YouTube and TikTok sign on to the same terms, then we can all as an industry lock in and take the next step together. So hopefully, I'm very hopeful that this settlement will serve as a sort of legally binding framework to bring the whole industry into alignment on some of these things and make it so that it doesn't kind of disadvantage any one company for taking that step. Now I mean we're we're basically putting ourselves a little bit out there by going first, but um but I I think it will be better for everyone if these other companies come in, too. >> Last question. You're posting on X again. >> I am. >> Uh you didn't do that for >> You're everywhere. But just curious to know, like you're thinking of like posting there, like the bragging. Like is it just part of the part of the thing now? Like cuz you've got Threads, you're on Threads. >> Yeah, I mean I'm on Threads. I think I mean obviously Threads is doing great. And I think it's actually it either I think it's either bigger than X at this point or it's like very soon about to be. But I mean look, there there there are different communities in the different places. There's a lot of AI folks are are on X and I think part of what you try to do with social media is just communicate where people are. It's kind of like And when you do a podcast or when you post, you probably don't just post in one place, you put it everywhere. So I got I mean I post the same things on Threads and X and some people are like, "Why are you posting this on X?" It's like, "Well, I mean I posted it there, too, right? It's like I mean I'll engage there, too." So I think it's I think it's all good, but I I do think that the to some degree some of the community is on X and we want to be able to engage where people are. And that's like a lot of what you know, yeah, what what what this is about is going where where where people are and being able to kind of have that dialogue. >> Yeah. Well, thanks, Mark, for taking this conversation. >> Yeah, happy to. >> Banking should feel like modern software. Get everything you need in one place. Visit mercury.com to learn more and apply online in minutes. Mercury's a fintech, not a bank. Check the show notes for details. Granola is the best AI notepad I've tried. 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