· 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.
· David Senra
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· David Senra
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