Go back to Llama 1, Llama 2, Llama 3. We really were, you know, kind of at the forefront and advancing things. And you of course know this, we the Facebook AI fundamental AI research group goes back a decade more.
I've been I mean, that's where I actually first got tuned into what was going on with AI. It's when M, the AI messaging bot, popped up in my feed. And then I met Yann and started to meet the FAIR people and was like, 'Oh, this technology is progressing really fast.' So, Meta was on it very early.
And so, the real gap, which I think has been pretty public, was what we didn't really realize at the time was when we were pulling Llama 4 together, sorry, when we were pulling Llama 3 together, we had really pulled in all the research, all the every we pulled out every single stop we had, and unwittingly kind of killed the pipeline. So, researchers, you know, the way that it works is you build a base and you've got people pioneering an incremental version of the base, and you've got people out there pathfinding entirely new strategies. And kind of unbeknownst to us at the time and kind of speaks to the fact that we weren't focused enough on it, Llama 3, which was a great model and was well received, to get to that model, they had kind of pulled the forward all the future bets into that to deliver that model. What that meant when it came time for Llama 4, we didn't have any of the pathfinding the other labs still had going. So, that makes you now now you're behind on reasoning. Now you're behind on mixture of experts. Now you're behind on a bunch of these critical technologies that have been used to continue the pace of progress. This is a pretty public, you know, disappointment, I think, a year ago for us and led to Mark shifting from, 'Okay, AI isn't one of our bets.' Which is what how we thought of it up to that point. AI was just one of the many bets we had. AI is a bet that's foundational to the entire company. And so, we're going to change how we're thinking about this. And this is such a cliché, but I don't have a better word for it. He goes founder mode. Like, he really did flip into a mode that is like unique and reserved for Mark. That where he just became so focused on getting us all the compute we needed, getting us all the talent that we needed, the researchers that we've signed that you said, you know, and they really landed about a year ago. I think Alexander Wang just hit his one year anniversary. And I have loved working with him. I've learned so much from him already. And we are seeing the fruit of that. So, if you look at Muse Spark,
Which is our latest model.
Which is not our frontier model, but it's the latest model that we've released. So, it's a very very well-received model. And depending on the benchmark, it does really well on things that we care the most about that we think are unique to our products. And so, yeah, you're absolutely right on where we are in terms of what the public, you know, perception of it is model-wise. We've built the team I really believe in. We've got all the compute and the data that we need. So, I'm very confident that we're going to be where we need to be. I'll add a second piece to this, which I think is strategically very important though. Which is that, you know, models are available. Like you can go rent a model. You can go use Anthropic's, you can use OpenAI's, you can use Google's, they're great models. You can go get them. You can use them. And that's pretty great. The real value we're going to create in the world is the product. And the products that we the vision that we have for personal super intelligence, I think is a vision that we're uniquely suited to deliver. It's not just that we have data, that's cool. We actually have a better chance of understanding you and what you're trying to do and who you are in the world and what matters to you than I think almost anybody else does. So, having the model is one piece and you want to have that strategic so you don't have a dependency on somebody else. But you mostly want to be able to control your destiny with that. The model itself isn't the value. And I think we're going to get to a world very soon where consumers they don't care. They don't want to specify the model they're using. They don't want to they don't care if it's 4.7 or 4.8. Like you don't care what Oracle if I'm if I'm using Oracle or SQL databases. Like you just want the functionality. You want the thing to work well. That's the standard to which I think we're all going to be held. So, today the discussion is about models, which suggests to me at least that we're a little under indexed on the user side of it and how humans are going to benefit. So, I think that's the story that we needed to tell in addition to showing the work that we've done technically, we need to actually demonstrate the value to consumers.
So, I just want to talk about this the science scientifically for a moment. The thing that I brought up in the beginning was this idea that you could kind of brute force your way to a competitive model. I think the answer that I'm hearing from you is not anymore because there are new techniques like mixture of experts and reasoning that you actually can you need some level of refinement of that base pre-train in order to be able to build the models that were the top tier models that we're seeing today and that's what Meta is working through right now.
Yeah, it's not just that. It's by the way this is the whole industry. The era of the monolithic model kind of died around Llama 3 launch. Like the idea of like here's one model and just like let's just test how smart this model is and that would be how good it's going to be at lots of things. We're now in a world where when you're using these harnesses, whether it's open code, Claude code, Codex, using these harnesses, they're shopping underneath to lots of different models depending on the task. So, they might be going to a multimodal model. You know, if you're using Gemini, it'll farm tasks out to Nano Banana if it's trying to do image generation. So, we've really moved past this world where this is just one model that rules everything. What you really want to have is a very expensive to run intelligent model that you can distill down in all these interesting ways and places and use it at for its exquisite intelligence only when necessary because it's very expensive to run those models and otherwise have models that are cheaper and faster and have lower latency in all of these other places where it turns out you don't need to have a genius level intellect. Because if you think about human tasks, I really believe in scaling laws. So, you're going to see this continued growth up into the right of as compute scales up that the raw intelligence the model scales up. But like human tasks don't have infinite intelligence demands. Like there's a lot of human tasks that like you can do with conventional levels of intelligence. And so I do think there's going to be a stratification then where it's not just okay, cool. What's the one model that rules them all? It's cool. What is the collection of models that are brought together in such a way that they solve these problems with the right balance of performance and price and value.
