· David Senra, Alexandr Wang
[00:00:00] S0: Disappeared. [00:00:00] S1: Yeah. It didn't persist, and I think a lot of people kind of had this experience, which was it was just like you would have these magic moments, but for the most part didn't like work that well, and there were all these problems, and then you know it just kind of like died down. And so part of I think the building the product, a lot of the focus of the product was just like, how do you make this into an actual reliable consumer experience that you feel comfortable putting in front of, over time, billions of people, and you can feel confident that like most of those people have an amazing experience? [00:00:00] S0: Why were you able to do that and the developers of OpenClaw were not? [00:00:00] S1: Well, first of all, I think we respect Peter Steinberger and everyone who's contributed to OpenClaw immensely. Like I think Peter is definitely a visionary. I think whatever happened in his beautiful brain that created OpenClaw is magical, and I think maybe the unglamorous part of what made Muse so magical was just really grinding out and sanding out all of the details to make the combined model plus product experience really amazing. And we did many, many iterations on the model itself. We aimed the overall model roadmap towards building towards personal superintelligence, broadly speaking, but certainly personal agents as like- [00:00:00] S0: Say more about that and how that differentiates between your other competitors. [00:00:00] S1: Yeah. Well, this is kind of- [00:00:00] S0: That's actually really interesting.
· David Senra, Alexandr Wang
[00:00:00] S0: That's actually really interesting. [00:00:00] S1: Yeah. It's actually useful to go back to the moment because if you go back to the start of the year, February, Opus and Claude were way ahead in coding, way ahead of anyone else, and they were exploding. It was the beginning of the sort of like you know Anthropic takeover in the whole industry. And it was a very scary moment, I think, generally speaking, which is like, "Oh, wow, they're so ahead on coding." And then obviously they had Mythos, and they had like this clear lead. I think the conventional wisdom in the industry was that coding agents were the only form factor that mattered, or maybe the only technology that mattered, or only like capability that mattered for advanced AI. You know, there's this incredible force in the industry to just like go and crowd that use case and compete with Anthropic on coding agents. Because of our experiences and our conviction that I think formed around this idea of personal agents and the sort of taking a step back, I think the reason why we did personal superintelligence in the first place was that it was also what we believed Meta was uniquely positioned to do. And we can talk more about that, but like I think because of that- [00:00:00] S0: Well, let's talk about that now. [00:00:00] S1: Okay. [00:00:00] S0: We'll just do the weave.
· Alexandr Wang
[00:00:00] S1: I think one of the first things I told Mark, even before the whole Scale and Meta deal happened and everything, one of the first things I told him was, "I think Meta is actually a really special company if we have AGI or ASI or whatever you want to call it, because part of the promise of AI is that for all of us, we have a mix of things that we want to do and things that we have to do. And the promise of AGI is that we're going to spend almost none of the time doing things that we have to do because the AIs will just start doing a lot of that for us, and we can spend all of our time on things that we want to do." And for me, Meta's products represent the things that we all want to do. Like my use of Instagram or WhatsApp or Facebook, like these are products that represent what I follow my interests, I follow my hobbies, I follow people that I like, my friends and the people I care about. Like the product span of Meta encapsulates the sort of like what we want to spend our times doing sort of realm. I think on a long arc, Meta's an incredible winner through all this. That's like one of the first things that I believed about Meta and superintelligence, and then ultimately a lot of those ideas made its way into this concept of personal superintelligence. You know, you can kind of look at it a few ways. One is, what's the heritage of the company? The heritage of Meta is connecting people with their friends and family, helping them discover their interests. Like these are things that are very human. And then there's also the sort of like you can take the business lines, which is Meta has distribution to three and a half billion people using the apps every day, and the natural area where Meta was going to be successful is consumer AI.
· David Senra, Alexandr Wang
[00:00:00] S0: It's not work, it's not coding, it's personal. [00:00:00] S1: Yeah, exactly. So- Personal agents and personal AI and consumer AI, broadly speaking, were always the zones where we always felt that Meta was going to be able to do something very special. So that brings us to early 2026. [00:00:00] S0: Now you have the prototype, you have the buy-in, everybody knows, Mark's on board, and now you're doing the seven months of the difficult work of refining this product.
· David Senra, Alexandr Wang
[00:00:00] S0: Now you have the prototype, you have the buy-in, everybody knows, Mark's on board, and now you're doing the seven months of the difficult work of refining this product. [00:00:00] S1: Yes. There was kind of this specter, which was like, oh, personal agents, they came and went. And we always felt that it was because the experience wasn't perfect, it wasn't polished yet, all that kind of stuff. But we knew that that was something that we had to think about as we were developing this product. We focused our model development roadmap, the top focus, or one of the top focuses, was towards building on personal agents. And we had these long spreadsheets of hundreds or even thousands of different... not thousands, but more than a hundred specific behaviors and capabilities that we knew the model needed to have to be able to build the ideal personal agent. It was very, very meticulous. And then over time, we built evals for all of those. We figured out how to train the model to be really, really good at every single one of those things. And we have these reviews where we look at that spreadsheet, and we figure out the things that we're bad at and make sure that we're improving on them. We did this very meticulous process to slowly just keep improving and sanding out all the edges of the model so that it could power the product. [00:00:00] S0: Is that really the culture of Meta, too? This constant sanding of the products?
· David Senra, Alexandr Wang
[00:00:00] S0: Is that really the culture of Meta, too? This constant sanding of the products? [00:00:00] S1: Yeah, a lot of the culture of Meta is like, once you know what you're measuring, there's very talented people, and you can optimize for those things. You can sand out all the details. You can make those things amazing. I think in the sort of pre-AI consumer product era, a lot of this was around, how do you optimize your conversion? How do you optimize the referral rates? How do you optimize every part of the onboarding flow? How do you make all that stuff just perfect? And then I think the AI era, I think a lot of that, especially for Muse, has been in the form of sanding out every detail of the model to be perfect for the product.