Mark Zuckerberg on Muse, Meta's biggest AI bet yet

Mark last time we spoke you called me it was on the weekends what you called me through the glasses Which was the interesting part? Is it loud in the background? You were you were going to fish I wasn't gonna say it, but yeah, it was on the weekend the most cancellation actually is pretty good. Oh, it's great Yeah, I'm in yeah, yeah, but you wanted to talk about this essay manifesto I don't know what you call it manifesto will say that you published recently and it's long I mean I'll caveat there's a lot in it and I wanted to start there because there's a lot of big ideas in there and they'll connect to Kind of the main thing we're talking about today. Yeah, I'm curious like why write that because it's long. There's a lot in there Well, I feel like if you're going to invest so much in building AI Then it's important that people understand what your lab stands for and what your values are 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. 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 And 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 an 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 know that's these are all things that I think oddly 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 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 in this balance of power It's very ingrained and 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 mean 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 where 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 mu spark 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-trained that we did the internal codename avocado Mm-hmm, and oh, we're gonna get into the code. Oh, yeah So we're gonna do that. I mean, then we have watermelon coming soon. So that's gonna 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 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 our 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 to granola.ai/sources and use the code sources for three 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 j-i-r-a.com I have a bunch of questions about the muse agent But staying big picture for a second because you set 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 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 or some of the labs are building training more advanced models And then not even releasing that 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 system out there First of all if 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 getting incredibly valuable You know, I think 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 and managing their homes saving money in a lot of ways Kind of advancing their health. So I think 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 sources. 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 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 You know if we have a world where there's just like a small number of really capable models Um, I know it's just it's interesting right? I mean if you look at some of the cyber stuff for example 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's 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 a hundred important institutions in the world So you know if you look at things like The hugging face incident that happened You know hugging faces maybe not one of the biggest hundred institutions in the world But it matters yeah, 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 turn to open source models Because they didn't have access to right 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 addressing these concerns or trying to But the 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? 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 Yeah, 15 pages because I mean we want to get through there are lots of different questions I mean people have questions about jobs in the economy They have questions about data centers and 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 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 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, 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 general population of people, as opposed to any insider stakeholder or whatever you want to call it. I think that ends up being very important. So if you look at the data centers, 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, 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. I mean, the tax revenue that they bring from that. In Louisiana, we have this example where the tax revenue funded these $50,000 bonuses for teachers in the community. We bring a lot of jobs. We invest in the local community a lot. That I think can be good. I think that 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 trying to find a plot and then trying to sell it to one of the big labs. And then it really cares much about the local community and they're not invested for the long term. So if they don't care to 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 difficult when you have these kind of speculative, I don't even know if I'd call it a bubble, because that implies that it's overvalued or something, but there's certainly a boom. So you have that. I mean, that leads to some of these 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 has to. Of course, you have to design it in a way that can be helpful to people in all these ways, which is also part of the reason why, like for people who are skeptical or have so much doom about the whole thing, I mean, one of 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. So I kind of think, like, actually establishing the right checks and balances and distributing the benefits of this widely is sort of a precondition for being able to scale in the way that I think 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? Well, 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, OK, there isn't as much of that around our project. So why is that? And then so we ask 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 thing they were doing, I mean, there's this America's workforce academy project that we did, which is basically, OK, 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 trades people that you need to create this. So we need more like fiber technicians and electricians and advanced carpentry and all of these things. And there aren't enough people to do this. We need hundreds of thousands, maybe millions of more people who can do this. And people aren't trained to do that right now. So we created this training program to effectively do that, where we guarantee people who get through the training program a job 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, 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. 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 permitted for there to be a small number of labs that control such an important and capable technology and accumulate a lot of wealth to themselves. I think it's like, this has to be a 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 kind of socially. And I think that those pieces kind of have to go hand in hand. I agree. Well, now we're landing towards amuse. Before we get there, you also wrote a year ago your personal superintelligence, I say, shorter one. Yeah, that was a page. I did wrote a one-page version of the future as soon as you did, too. Yeah, well, I published it in the Wall Street Journal. Oh, that's right. That's right. So that was kind of-- I just read the long one. Yeah, this is a one-page version. And there's the 15-page version. So this one you did about a year ago, personal superintelligence. I think a lot of people in my world, when they saw you write that, was like, oh, wow, why is Mark writing this? 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 amuse. Like we're going to talk about, right? That you guys are releasing now. Yes, this is it. So how did you come to that realization that this is the next chapter for Metta? It's interesting. 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 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 debates around content moderation and things like this have kind of been around this question of like, should people be able to kind of decide and communicate for themselves what matters in their own life? And I think through that experience, it has 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 an allergy. Whenever I hear people talk about, oh, we should just have a small number of experts allocate what AI does to big problems. Why should it do these things that people care about in their lives? 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 showing up for their friends and family and they care about culture. People care about things that may not to a scientist or an engineer in the industry feel like the biggest problems, but I don't know. If you ask billions of people what they care about, if 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 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 some like so-called experts sitting at a small number of labs. So this gets back to, it's like, 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 are different, right? It's like maybe it'll prioritize health issues, but maybe instead of prioritizing, it's like the most common things, which is kind of what the kind of pharma and biotech industry, like at large, prioritizes today, now it's like there's a very long tail of rare diseases and conditions that people have. So 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 disproportionately under-invested in. - You're doing a lot of investment with your foundation. - We're doing a lot of biohub, and that's partially informed some of my views here that I think, like you want to put the power in individual's hands to determine what matters for them. But a lot of this is also like, it's not necessarily the things that people would say or like the big social problems. Like a lot of it for me, when I'm using my muse agent, I just, I kind of want it to help me be like a better father and a better husband and show up better for my friends and be able to 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 helping people connect with the people around them. So I don't know, it's like, what are the first things that I kind of set up my, my own agent to do? It's like, all right, my three-year-old daughter likes baking. I don't know anything about baking. But like, that's like a fun project that we can do. So I basically ask him, like, all right, setups that every weekend, And you know, like we have a baking project that is kind of reasonable for a three-year-old and an adult who knows nothing about baking and use Instacard 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 my daughter, we can like go make this thing. And then I tell it afterwards, I'm like, how I had to go, okay, that one was too hard. It turns out cake pops really difficult, surprisingly difficult. I don't have to think about baking. Yeah, I didn't either, I know something about cake pops. Yeah, no, don't start with cake pops, that's the problem. Okay. 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 yeah, well, what can I tell you? Okay. Thank you, Muse. Yeah, thanks. So yeah, and it kind of like, so it kind of updates that and helps with that. 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 I'm 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. Yeah. So it's kind of cool. Yeah, yeah, yeah, yeah. It does that, you know, but it also helps keep me healthy, helps me with my training. 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. It's good feedback. Um, sometimes, you know, it's, I mean, it's, um, someone's it's funny feedback. It like, it like finds me in, it's just like, it looks like you really gave up and I was like, yeah, I did. I was really tired of that. It's like, why is that the thing that you're pointing out to me? But, no, it's good, um, and it's funny, like the coach is like laugh about it. Yeah. Yeah. No, this is like, it's just what we're interested in. It's like, we could tell you, but your agent's telling you that, well, it's, um, yeah. That's good. I didn't think about this until hearing you talk about it, but you, you have people that could obviously do all 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? Yeah. 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 that I think part of what's interesting about it is that everyone 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 a give it to someone and like, within a day, they're like, using it to help run their homeschool. 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, yeah, it's like, 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 gave it to her. And then she was, she didn't say anything for like a few days. Then she texted me, was like, so when you do like the general release, do I get to keep my muse agents? You're going to reset it. I was like, all right. This is good. Okay. 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 price. Yeah. But people do different things with it. 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, met AI or chat GPT, it's like back and forth prompting. Yeah. The real unlock here, and this is happening in the industry more broadly, whether it's rock, 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 this. And it's, it's long lived, so it's, so basically instead of the model with like meta AI or chat GPT 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. Yeah. And then it just works and it works 24/7 and it like doesn't stop until it's helped achieve the goals. You were telling me it studies overnight is what you guys call it? Oh yeah, it studies it like it kind of consolidates its reflections into, into memory. 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 my daughters and I like, it's 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? Why not? So just kind of like built a new tab and then the app that it made and that was, that was very cool. So it can kind of just like expand and it's proactive. Yeah. It suggests things. And one of the things that I think is interesting is that it, I think is just going to be able to like make people money and save people money. You think? Yeah. I mean, part of the, part of what's interesting here is the economic model for where we're pricing it. You can pay for 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 offering something, I think, to start. It's like a hundred million tokens a week for free, like, and you get this virtual machine. So it's like a lot of kind of computer. That's a good meme. It's a lot of computer. Yeah. 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. Take rate business. Small. Yeah. And not even necessarily the person paying for it to come from the businesses that they're working with. And you're working with stripe on payments and, yeah, so my view is like, we should be able to have a service that you make free for the vast, vast majority of people, which, again, is critical if you want to build a, this future for everyone where everyone has, has these powerful super intelligence agents, I think an important part of making something available to everyone is making it affordable. So we want to make it set this is free. So there's 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 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 can connect it to whatever you want. You know, it does work with meta services if you want. Yeah. You obviously 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 and you can ask it to make something for you. It can, they can kind of help you make the product and then they can help run the business. So, yeah. So all the stuff, they can 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 by, you know, meaningful update like every month, yeah, it's just going to get smarter or just get more capable and, and able to do more and more stuff. And something your team was telling me that I haven't heard this approach used elsewhere as this fleet concept where you're letting the fleet of muse agents learn together. Yeah. 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 to help 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. 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 finding the right frame to send you? It's like, yeah, good, good to 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 think, I guess, take 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 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 you can get the most out of it. Well, you're introducing, like, network effect learning for agents, which no one's really done where, yeah, basically, the agents are learning anonymized insights from the rest of the fleet. And, I mean, you're like the king of network effects. Like, I'm, I'm really interested in this idea because 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 they're 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 going to be like an important part of how I think this works over time is just like as more of the people who you know start using Muse, it just gets better. It gets better. And is that the differentiator as, you know, you could buy the idea models continue to like commodify 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 kind of unique that we're doing. One is we're designing the models from the ground up to basically be good for this use case which I think really matters. Two is basically I do think we have this social DNA as a company we're helping people use the AI and agent to enhance their relationships and strengthen your relationships and get more out of the soft but very important parts of your life. I think that's something that we're probably just going to be more attentive to as a company than any of the other labs. The third thing that I would say is actually going to be a major differentiator for us that I think might be surprising to some people is privacy and security. We're investing in this just a huge huge amount. 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. In order to be able to understand your goals, you can end up connecting it to all the stuff. You talked about connecting it to your ad system but people connected to messaging, email, and all the stuff, health information, whatever. In order to do that people need to have a very high degree of confidence in the system. The good news here is that Meta has spent at this point more than 10 years focusing on building WhatsApp into I think it is the largest global end-tending cryptid system. We've designed it in a way where even Meta can't see the messages that people send. That's been this just really transformative thing. It makes it so that people trust WhatsApp. It's also been a very important lesson for Meta to learn that that has been really important to our success with WhatsApp. 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 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. We took that as one of the foundational lessons. When we were getting started with this, Nat and I actually personally recruited Moxie Marlon Spiegel. Founder of Signal. One of the people who helped us build WhatsApp and encryption back in the day in the back in 2014, he joined to specifically work on this confidential VM project, which makes it so that you can have your virtual machine and have all this information in your muse. We can make the commitment that even Meta cannot see the content that is in there. You can do this technically. It's like an incredible kind of commitment can be technically verified. That's something that we're going to publish more about in the coming weeks, as we basically get closer to rolling this out a lot more widely. Out of all these early VM efforts that these labs are doing with these agents, you think this is unique. I don't think anyone is doing it. I think that there's a lot of other security measures that we're putting in place that I think we should talk through. Even before this is ready, there's that and even people who don't want to use this, it's like incredibly secure because we've focused on this from the beginning. There's also the auto-approved. You have to see what it's doing. Let's get into all that stuff in a second. I'm not aware of anyone having anything close to the confidential VM system that Muse has. It's just, I think it's a very fundamental thing because you want to know that this is your agent and that if you put content in there that basically you can trust that no one else is going to get access to it. What are the two ways to do it? A lot of people earlier in the year, when stuff like OpenClaw came out, they started getting Mac Studios. One way to feel good about it is, literally, you physically have your device running in your home. But the other way to do it is you build a kind of, that's going to be tricky because I don't think there are 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. But it's also just it's technically difficult. Part of what we're trying to do with Muse is build a version of that personal agent experience that just works that I can give to everyone in my family of various levels of technical literacy and it just works. Within a day, it's doing all the stuff that they want in their life. Part of that is you don't want some stuff to set up their own computer or VM. You just 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. So I think that's a very fundamental thing. Like you said, there's other pieces too, because I mean not everyone is going to use that. I mean, we built a secure credential store. There's no reason for you to just like store out in the open, like all your kind of your credit card and your pass rates one time card numbers. Yeah. So some of your agent should know that stuff. It should just be able to kind of access it when it needs to, because you've asked it to log into a thing and not otherwise. It's actually not a single thing. You have your core agent, but we also built all these sentinel agents that basically monitor the incoming and outgoing traffic and data that your agent is sending for the purpose of flagging to you when you might want to review something. So that's all that the sentinels do is effectively, they kind of look at, 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 is kind of going out that 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 reviews. So if you're going to log into something, if you're going to do a payment, if you're going to transfer kind of sensitive information, you basically each time need to approve it. And you can tell it, I'm good with stuff like this in general, like always allow this kind of thing. But in general, the muse agent can't kind of make those judgements itself. And that's like built into the system in 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, you know, 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 the approach that we've taken is like, all right, if you connect to your email, like it should start read only. Right. And then if you want to like be able to have it send an email, then fine, like go ask it for that specifically. Right. Especially most people trying this have probably never tried a product like this. So it's yeah. 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 has anywhere close to the level 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-tending cryptid system around the world and the importance of building a system where even Meta can't see the content. And so kind of getting the band back together and having Moxie like architect this has been, I think one of the foundational things that in some ways it may not be what some people would think Meta would focus on. But like, you know, 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. But then there's all these other things that are inherently about kind of privacy and sensitive context. And we've done well at both of those. So I think that this is more the latter. It's going to be very important to just be extremely focused on how we handle 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. The interface is intuitive and simple, saving me valuable time every day. I use Mercury to track my spending, bills, and invoicing. 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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 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-- 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. There's not a ton. Well, there's a lot. I mean, there's a lot. Yeah, you could probably count on two hands how many products have over 2 billion users, right? The scale will be get scale. And I'm wondering how you're thinking about personal agents. Is this a totally different paradigm? Where it's going to be many-- No, 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 dozen companies that have the sophistication to go do state-of-the-art work in that. So I think whether their network effects are not, there's usually some kind of power law distribution around-- if you were the best at something, usually you end up getting a lot of the usage. And I think a lot of the nuance ends up coming from, well, it turns out there are all these different uses that people care about. So you can be the best at different things. And we will try to be the best at as many of these things as possible, but 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. I mean, I think that I can argue maybe meta is very well positioned to win at both of those. So we serve hundreds of millions of small businesses and we serve billions of people. 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 bigger those. I would guess that even with the ability to have this very secure, confidential VM in the cloud, there are probably going to be some people who still want the Mac studio at home. But how many is that going to be? It'll be, maybe it's millions, but I doubt it's billions. So I think there's just the question of 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 just very useful for 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. So that is one that I think is, once we get this humming, I think we will be able to get this in front of many hundreds of millions of people and eventually billions of people. And I think that's something that we can do quite well. And it coexist with meta AI, or do you see those as separate? I think so. We'll see over time. I think right now they have somewhat different flavors. I mean, I use both of them. I mean, the muse is, 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. Or sometimes you're just asking a question when you want like a very big of an answer to it. And so I think that's more the type of thing that I use meta AI for. But we'll see. Maybe they'll converge over time. But I'm not sure. Simon Analysis, I'm not sure if you saw it. They had a pretty bullish piece about you in July. They said that meta has the best shot at catching open AI and anthropic on the frontier in terms of model progress. And 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 were making on the models is picking up. And we've been talking about this over the last year. And you rebooted the lab last year. How has that practically happened internally? Like, what would you attribute the gains you're saying to you? Is it been culture? Like what's-- Yeah, I mean, well, we rebooted the team when we created-- Sure. --which is what was very public. You were hiring all these people. I mean, the way I've thought about this is meta is-- it's a company that is a leader in machine learning for a long time. If you think about the feeds on Facebook or Instagram or our ad system or the integrity system that needs to find all this content that's 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 gaining traction, we had fair as a lab that did the early work on LLMA. But we needed to kind of productionize that and build it into this more kind of industrial process for scaling it to be larger as the scaling laws predicted 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 LLMs 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 through LMA-4-- it got us so far. I mean, LMA-3 was a good model. I was more optimistic about where LLMA-4 would go. And then it just-- when we launched that, I think we were off the trajectory that we needed to be on. So it's like, OK, we need to change something. But that's when I kind of got more religion around talent density. It's like, this isn't just a system where you can have 1,000 people working on it, running experiments. 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 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 understand and help guide the company to do the things that we need to do more broadly. So we built out the lab, we built it out literally around where I sit in 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 many, many gigawatts of compute. And we expect to be leaders 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. And we're just like extremely profitable business, right? So it's very helpful for making these kind of investments. So yeah. So I mean, that's kind of been the journey. And then over the last year, we rebooted the research effort. Some of the larger clusters, like our gigawatt cluster in Ohio, Prometheus, came online. And we're using that to now scale the post-watermelon models. We're past watermelon. Watermelon is basically-- that's shipping soon. So we're-- yeah, how soon? I mean, that's-- come on. It's hot. We're-- 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. This is a big model you guys are working on and are coming soon. It is bigger than avocado. It is bigger than-- So watermelon is literally bigger than an avocado. It is literally bigger. So I don't know what fruit gets bigger than a watermelon. Yeah, I think we might need to change conventions. OK. Yeah. So I was like, yeah, we maybe didn't have as much foresight and maybe give things getting bigger. Because you mentioned it. Watermelon, are you expecting full soda, like, frontier? What do we-- I mean, I feel good about it. Yeah. No, it's a very big advance. It's a significantly more advanced pre-train. And then we're going to continue doing everything that we've learned for post-training. Mm-hmm. Yeah, well, I mean, you'll see soon. That's good. We feel good about it. You want the company to be pushing the frontier. It's very clear. Like, you're not content being right on the edge of the frontier or in terms of moral progress. I think everyone wants to be doing interesting. Well, I think some people will go and look at your cash flow and look at all the other things you've got and go, do you have to be right at the edge? It's so expensive to do this training. Just be right behind and learn and adapt quickly and let it chill. No, I mean, the way you're going to think about it-- No, no, no, no. I mean, that's not-- You don't buy that. 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 primarily just building social apps, you know, we were never just an app maker, right? And we built, like, we built the data centers, we built the chips, we built the infrastructure, we built, like, all of 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 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 are some 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 reduced to that. Like the kind of MMA, coaching, visual pipeline, it is a coding problem to the end of the day, right? It's like it's writing code. I don't see the code. But it does that. You know, it's like -- you know, someone I gave it to in beta just mentioned to me, it's like, okay, she had it make a little jeopardy game for her friends that she could cast from her phone to play, and it's like, okay, that's code. So something that 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 superintelligence needs to be the best at, that maybe others don't care as much about. So I'm going to give you one example, discretion, right? So you're going to tell your Muse agent, 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, you know, you're pregnant and like, okay, fine, so you're making a reservation somewhere, you don't necessarily want to say like I'm pregnant. Yeah. But maybe you want a place that has good mocktails, like, I don't know, whatever it is, right? Like, 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. So that's something you put into the training. That's a specific thing that we care about. And then there's all these reasons why like, maybe, you know, if you're making cloud 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 within a team and 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-trained 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. And we're going to, like, definitely as this compounds over time over several years have 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 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 are 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, diffusions important. As you're seeing better models on the horizon, water melon and what comes after, like what anthropic an open air doing, which you've alluded to where they're holding things back. 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 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 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 six months ago, like during training, you give the model some kind of problem that you're trying to ask it to solve. It's kind of like it's homework and trying 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 ask it and you give it 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 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 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 setup. It's to go out. Or you just like change something about the environment and it's kind of like, no, like that's not aligned. That's not aligned. That's not the goal. Like, we're actually trying to teach you about 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 kind of how everyone trains. I guess what I'm saying is that I think that this is sort of, it's like, I want to be careful because the analogy can get stretched pretty quickly. But like, there is sort of like an analogy to parenting where you need to establish clear and firm boundaries where like if you're 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. 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 at better values too. So, 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 probably the right way to handle this rather than just restricting it. And I mean, I gave a bunch of these analogies 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 right 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 stands. So you'd think that in that case, like justice would be served way more efficiently and way more fairly. So I know 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 and the U.S. have any role to play here? Do you think? I definitely. Like, but do you want some kind of national framework? Do you, like, what do you think is the right approach? Because the government is very much dealing with this right now. My theory on this is that 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. 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. We should work with the government proactively to make sure that they have an understanding of the capabilities that are coming and to the extent 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, six months we'll have more biotype 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 [BLANK_AUDIO] those in a way that is actually the best for people, not just like checking some boxes on a process. So, I mean, incentives that already exists, like if the metal model got out and did a lot of damage, like you're gonna be liable for that, and like the market's gonna 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, has had more of an arms length relationship with the government and I just think that this stuff is intersecting more with the economy, with security, with a lot of different things that I think are relevant in ways that I think you just wanna have a closer partnership. So that's my own theory. I think like you could have a framework for kind of how the stuff works or you could not. I'm sure over time there will be more and more specific rules, but my guess is that whatever that gets whatever there is, actually, it's kind of like when you're setting up an org, you don't really wanna 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. You kind of wanna get the people to like like each other and work together. So that way you don't have all these like weird seams and what you're doing, and I would guess that for how important AI and super intelligence are gonna be for the world, you kind of just want a like good knowledge exchange and real trusted dialogue more than you want a specific process as my guess. But they're not mutually exclusive. So I'm like, I think that that's at least the part that I think we've been very focused on. And I think if the other labs did that, which I think some of them are doing 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, and there's this growing, 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'm curious like how you were reacting to that. And if you think this is 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 my take on this is that we designed the glasses from the beginning with these privacy considerations in mind, and we built the light into it. So any time 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, - Yeah, we've done many things. - Yeah, to basically if you try to master the light, which basically break the camera on your device. So that's a really important part of this. Is basically we built the product with those questions in mind from the beginning. So we actually feel quite good about the product. I think if, I mean, phones don't have a light, but 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, - Didn't say you forgot about or they didn't see at the beginning or they just weren't paying attention to it because 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 shipped multiple years ago. And I think we just need to make sure that people understand how fundamentally that's built into the product. But I think we let up on that a little bit and just kind of focused on, okay, they're great looking glasses. You know, there's all these designs. I mean, that's kind of been more of the focus on, like we felt like we kind of addressed the set of concerns early on. And then since then, I've 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 position on it. But look, people care about this stuff. So it's important. - And there's a privacy scare with phones in the early days, right? And I think 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. 'Cause that was what phones. I mean, back in the day, I remember people are like, what are you doing with your phone? Like, 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 in 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 products so much, they still like the products. But I actually think it's possible to get to a better state than where we've gotten 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. - Is there a through line from that to the recent settlement on all the use safety stuff? And it's a thing a lot of people are talking about. Is there any connection from what you just said to that? And like, 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 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 team account work with Instagram for a long time. And I think I've done some leading work there. The settlement there is interesting, because what we're really trying to do is create a standard in framework for the industry. And there's this real issue, which is that I actually think most of the companies that are building 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. Then you're saying, okay, like if people don't use Instagram for more than an hour a day, but then their usage goes to TikTok. If we really like helped anyone. And we've just like heard ourselves to not help anyone. - And you guys have that in the piece that like-- - So basically the structure for what we did was we basically said we're going to take the step of unilaterally limiting the usage of the, 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 and 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 we're basically putting ourselves a little bit out there by going first, but I think it will be better for everyone if these other companies come into. Last question, you're posting on X again. - Yeah, well everywhere. - 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 thing now? Like because you've got threads, you're on threads. - Yeah, I mean, I'm on threads. 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 are different communities in the different places. There's a lot of AI folks 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 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 posted it there too, right? It's like I'll engage there too. So I think it's all good, but 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, yeah, what this is about is going where people are and being able to kind of have the dialogue. Yeah, well, thanks, Mark. Thanks for that conversation. Yeah, happy to. Banking should feel like modern software. Get everything you need in one place. Mercury's a fintech, not a bank. Check the show notes for details. Granola is the best AI notepad I've tried. It works everywhere on a video or phone call in person or an Apple Watch. Try it now at granola.ai/sources and use the promo code sources at checkout for three months off. Giroby ad lasing is where your team and your agents work from the same context. Try it free at gira.com. That's j-i-r-a.com. Framer is the AI native website builder that lets you build faster without giving up control. Visit framer.com/sources for 30% off. For more information visit framer.com/sources or visit framer.com/sources