Dwarkesh Patel: belief

Transcript context

…We might be able to, because we might be able to run other models and be able to tell. That's one of the advantages of open source. You have a good community of folks who can poke holes in your stuff and point out, "Okay, where is your model not good, and where is it good?" The reality at this point is that all these models are optimized for slightly different mixes of things. Everyone is trying to go towards the same end in that all the leading labs are trying to create general intelligence, superintelligence, whatever you call it. AI that can lead toward a world of abundance where everyone has these superhuman tools to create whatever they want. That leads to dramatically empowering people and creating all these economic benefits. However you define it, that's what a lot of the labs are going for. But there's no doubt that different folks have optimized toward different things. I think the Anthropic folks have really focused on coding and agents around that. The OpenAI folks, I think, have gone a little more toward reasoning recently. There’s a space which, if I had to guess, I think will end up being the most used one: quick, very natural to interact with, natively multimodal, fitting throughout your day in the ways you want to interact with it. I think you got a chance to play around with the new Meta AI app that we're releasing. One of the fun things we put in there is the demo for the full-duplex voice. It's early. There’s a reason why we haven't made that the default voice model in the app yet. But there's something about how naturally conversational it is that's really fun and compelling. Being able to mix that in with the right personalization is going to lead toward a product experience where… If you fast-forward a few years, I think we're just going to be talking to AI throughout the day about different things we're wondering about. You'll have your phone. You'll talk to it while browsing your feed apps. It'll give you context about different stuff. It'll answer your questions. It'll help you as you're interacting with people in messaging apps. Eventually, I think we'll walk through our daily lives and have glasses or other kinds of AI devices and just seamlessly interact with it all day long. That’s the north star. Whatever the benchmarks are that lead toward people feeling like the quality is where they want to interact with it, that's what will ultimately matter the most to us. I got a chance to play around with both Orion and also the Meta AI app, and the voice mode was super smooth. It was quite impressive. On the point of what the different labs are optimizing for — to steelman their view — I think a lot of them believe that once you fully automate software engineering and AI research, then you can kick off an intelligence explosion. You would have millions of copies of these software engineers replicating the research that happened between Llama 1 and Llama 4 — that scale of improvement again — but in a matter of weeks or months rather than years. So it really matters to just close the loop on the software engineer, and then you can be the first to ASI. What do you make of that? I personally think that's pretty compelling. That's why we have a big coding effort too. We're working on a number of coding agents inside Meta. Because we're not really an enterprise software company, we're primarily building it for ourselves. Again, we go for a specific goal. We're not trying to build a general developer tool. We're trying to build a coding agent and an AI research agent that advances Llama research specifically. And it's fully plugged into our toolchain and all that. That's important and is going to end up being an important part of how this stuff gets done. I would guess that sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going toward these efforts is written by AI. And I don't mean autocomplete. Today you have good autocomplete. You start writing something and it can complete a section of code. I'm talking more like: you give it a goal, it can run tests, it can improve things, it can find issues, it writes higher quality code than the average very good person on the team already. I think that's going to be a really important part of this for sure. But I don't know if that's the whole game. That's going to be a big industry, and it's going to be an important part of how AI gets developed. But I think there are still… One way to think about it is that this is a massive space. I don't think there's just going to be one company with one optimization function that serves everyone as best as possible. There are going to be a bunch of different labs doing leading work in different domains. Some will be more enterprise-focused or coding-focused. Some will be more productivity-focused. Some will be more social or entertainment-focused. Within the assistant space, there will be some that are more informational and productivity-focused, and some that are more companion-focused. It’s going to be a lot of stuff that’s just fun and entertaining and shows up in your feed. There's just a huge amount of space. Part of what's fun about going toward this AGI future is that there are a bunch of common threads for what needs to get invented, but also a lot of things that still need to be created. I think you're going to start seeing more specialization between different groups, if I had to guess.…

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