Okay, Alex, thanks for your time today. It's a huge honor to have you on the show. Thank you very much.
Of course. Excited to be on the show.
Yeah. So, Muse started in the US and Canada over a month ago. So, I was wondering how you think about the reaction so far, and now you've brought it to Japan. So, why did you pick Japan after North America?
Yeah. Well, first, the reaction has been absolutely incredible. I think it's been beyond many of our original expectations and what we thought was possible. Frankly, I think there's many things that have been very exciting about it. I think the first was just seeing how widely it's been adopted and how easy it has been for people from all walks of life to be able to use Muse and, you know, use it to actually save time and save money. That has been just one of the most rewarding things for the whole team. Also, it's great to see, you know, one of our big focuses in releasing Muse has been our focus on safety and privacy and security, and, you know, we invested many, many months in the development process to get those things right before we ultimately launched in the United States, and it's been great to see that resonate with so many of the users and so many of the people who have been adopting Muse. And then it's been, you know, in the US, it's been very exciting because it's become a bit of a cultural phenomenon, and, you know, we've seen our mascot Jolly become a meme online, and so we've seen just really exciting adoption, and it's something that's clearly struck a chord with people, and people have connected with it more than I think we ever would have expected. And we're very excited to be launching in Japan. You know, one of our focuses is to continue making Muse more and more accessible globally, and we wanted to really prioritize, you know, countries and places where we felt that Muse would really resonate with the populations of those countries, and that have had, you know, strong, friendly regulatory frameworks for us to be able to launch our products in. So Japan hit many of those boxes, and, you know, not that this ended up making the biggest difference, but I am also just such a big fan of Japan. I have visited nearly every year for the past, you know, many, many years, and it's just an incredible country.
But Japan is a non-English country, unlike the US and Canada. So isn't it hard to choose a country whose people are not speaking English, like because the default language is English? So why...
Yeah, well, we've actually, because we've known for a long time that our mission as a company is to bring personal super intelligence to billions of people all around the world. We've known for many, many years now at Meta that our goal is to deliver AI that can be used by billions of people regardless of what language they speak. So we've made a lot of investments over many years into the multilingual capabilities of our models and have actually published a lot of this research on our ability to make these models very useful and multilingual. And so this has been a long-term investment of ours, which enables us to release the product very quickly in countries like Japan.
Mhm. Right. So I was curious about the journey of the Meta Super Intelligence Lab, which you're leading. So from day one, from all the way up to now, the launch of Muse agents or like a bunch of Muse models, tell us about the journey.
It's been, I mean, it's been so exciting, but it's also been a real journey. You know, I came into the company last summer, the summer of 2025, and it was after the company had released the Llama 4 model, which, you know, we publicly talked about as not being on the trajectory that the company needed. And so, you know, I came in and immediately one of the most important things was really setting up the foundation and developing the best culture of scientific discovery to be able to propel the company to develop frontier models and develop the most advanced AI capabilities, while also developing a strong culture that enabled product experimentation and enabled us to really explore, you know, what is the realm of the possible with new AI products and really be at the front end of exploring that to be able to deliver those to our users. So, you know, I came in, we had this very large-scale effort to bring in the very best talent into a very talent-dense research environment. This included many of the most talented folks from within Meta as well as a very extensive recruiting process to bring in the best talent from outside of Meta. We established a great research lab with an incredibly strong focus on a culture of, you know, scientific discovery, strong scientific foundations and technical decision-making. That team has remained quite small even to this day. And through that team we've been able to, over time, build, you know, nearly from the ground up, the foundations of this incredible model series, which started with Muse Spark, and obviously most recently we released Muse Wonder, which has been a really big moment. In parallel with that, we've also been iterating on the product side, and, you know, since the start of the year we've been extremely excited about personal agents. You know, many of us, Nat Friedman, myself, Mark, we all really saw and were excited about the opportunity and potential with personal agents, with strong agentic models. But it took a lot of time for us to get that product right. You know, we iterated on the model for many, many months. We iterated on the product, the privacy and security architecture to be able to support releasing this model, and delayed the launch for many months to be able to support that. And then ultimately, you know, this culminated in this incredible moment of launching Muse, which has really resonated.
Right. So when you started, what were your thoughts on the competition with AI labs like OpenAI or Anthropic, and how did you see the gap and what you needed to fill in the gap? And so how did you overhaul Meta's AI efforts like Llama?
Yeah. Well, first of all, I have a lot of respect for those companies. I think they're incredibly innovative companies that have delivered both incredible models, both of them. But, you know, one of the things that we saw, in addition to obviously needing to develop the research foundations to be able to innovate at the frontier and develop the most powerful models, we saw a real gap in the market, which was focusing on the consumer and focusing on how do you bring powerful agentic AI experiences into a product and form factor that can really resonate with all consumers. And that was a real gap that we saw forming because so much of the AI industry, including many of the leading labs, were so focused on coding agents and so focused on the B2B market and enterprise that it really left open this lane for building the best personal agents for everyone, for every single person to use. And I think that really was the opportunity that we seized with Muse. And, you know, we've obviously been very excited and fortunate to see all the reaction, and I think it's really been validated that, you know, this powerful AI can make a really big difference for people. You know, lots of our users are saving real time, real money. You know, it's making a very tangible difference in their lives, whether it's enabling them to spend more time with their loved ones or do amazing things for their loved ones. So we've just been really excited about all of that.
You've been a founder since 19 years old. When you were at MIT, you founded Scale AI, and the huge deal came up and you jumped into a huge global company like Meta. What was it like personally for you?
Yeah. Well, I think first and foremost, the thing that was exciting at Meta was the potential and the vision, right? And that's, you know, Mark wrote this memo right around when I started around personal super intelligence, and that's what we had talked about before I joined Meta as the really exciting opportunity, which is how do we build AI that can be delivered to billions of people in the world and really make everyone's life better. And I think that was an incredibly exciting mission to work on, that was an incredibly exciting thing to work towards. But also, I was joining Meta, which is a very large company, and, you know, there's a lot of complexity in being able to join a large company, especially as kind of the new guy with a really, really big mandate and, you know, a big thing to accomplish. And so, you know, it's very different from starting a — in some ways it's very different, in some ways it's also the same as starting my own company. You know, it was in very many ways a blank slate, and there was a lot that had to be defined and written, and, you know, stuff that Mark and I both had to do that was very entrepreneurial and that was very exciting for me. And then I also had to learn how to do that in the context of a large organization and a larger company and work more cross-functionally with many of my partners, which has been a great experience but also a new experience for me.
And the Muse Spark and Muse Wonder are the foundation behind the Muse agents. So what was the process like to build those models to make personal agents like Muse work in real life? Or tell us specifically about what use cases, what kind of tasks and scenarios you wanted the models to be good at.
Yeah. So a huge percentage of our overall effort just went into building great foundations for these models. You know, building the right foundations on pre-training, being very careful about scaling these models, and then building the right foundations on reinforcement learning and post-training to be able to continue scaling the intelligence of these models. So a lot of our effort went into the science, scaling, and data to be able to support all these efforts. But then we also took a lot of focus to really understand and think deeply about what were the things that were going to be very important for a personal agent product. Some of these things are, you know, multimodal capabilities. That is one area that is incredibly important for personal agent products. Other areas that we thought a lot about were, you know, memory or the ability of the model to focus. And so, you know, these were real highlights in the Muse Wonder family, which is active memory and active focus. So, you know, we really thought deeply about all of the — what are the things that would make a personal agent like Muse really amazing. And we wrote all of those down into a big list. And this list includes, you know, hundreds of different areas that we're investing in to make sure the models are really good at. And then we developed evaluations and methods to measure for every model that came out of the lab how well did it perform. And then we continued iterating and iterating to continue improving the quality of these models. So it's been a real labor of love for the entire team to be able to develop these advanced models.
Mhm. You even built the internal benchmark, like those super intelligence index. So what did you focus on those fronts?
Yeah. So one of the things that we had found is, in particular, many of the things that mattered for developing a great personal agent were not well captured or measured by the existing benchmarks. So many of the existing benchmarks that most companies report on are focused on coding or math or science — like, a lot, you know, capabilities that are really valuable and important, but not necessarily the things that matter if you're trying to build the very best personal agent. So we had to develop a lot of that ourselves, and we developed many individual axes of behavior that all coalesced into the personal super intelligence index. You know, to give a few examples, like I mentioned, memory is very important. You know, focus is very important. The ability to chain together multiple tool calls — so to bring together many tools into one continuous process — is very important. Personality is very important, and how it communicates with you. You know, that makes a really, really big difference in terms of the user experience. Also, discretion was very important. We really had to train the model to be very thoughtful about, you know, when it shared certain pieces of information and how it thought about that, and was it being thoughtful about when to reach out to the user versus when it didn't need to. You know, these are things that are very, very important. You know, fault tolerance was also critical. You know, if something happened, whether, you know, some external or internal failure happened, how is the model able to respond to that. You know, I could keep going on and on. I'll maybe mention one other area which has been really important, which is browser use. So the ability for Muse to be able to use a browser, and, you know, many of our users, you know, you can see when it's using a browser and you can take over if you need to, but that was so important for really unlocking the abilities of the model. And so the list goes on and on, and the personal super intelligence index captures so many of these all into, you know, just one number, but, you know, this really required a lot of thoughtful effort.
