Meta CTO Andrew Bosworth: Our Path To Frontier AI, Renting Models, Consumer AI's Struggles

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1:32Big Technology Podcast Host:That's coming up right after this. Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond. We have a great show for you today. We're joined today by Meta Chief Technology Officer Andrew Bosworth, who's going to talk to us all about the company's AI efforts, its new AI glasses, the company's culture, and some big thoughts at the end. Bos, great to see you. Welcome back to the show. Thanks for having me. We were just talking before we started rolling about what a crazy moment it is in the tech world. We haven't seen progress like this as far as I can remember.

2:06Big Technology Podcast Host:The core part of it is the AI model. The AI model underpins everything. Without a working AI model or a leading AI model, it's tough to build. The theory for a long time was that to build a great AI model, you needed a ton of compute and great researchers to work on the algorithm. Meta has a ton of compute and a team of the best researchers to work on the algorithm, but the leading AI model hasn't materialized yet. So can you talk a little bit about what you've learned there and whether that core assumption about what it takes to make great AI models is wrong? Well, the only other ingredient I would add is great data.

2:48And you have that. And we do have that, I think, as well. So yeah, there's two stories here. The first one is, I think, go back to Llama 1, Llama 2, Llama 3. We really were kind of at the forefront and advancing things. And you, of course, know this. The Facebook Fundamental AI Research Group goes back a decade more.

3:05Big Technology Podcast Host:I mean, that's where I actually first got queued into what was going on with AI is when M, the AI messaging bot, popped up in my feed. and then I met Jan and started to meet the fair people yeah and was like oh this technology is progressing really fast yeah so meta was on it very early and and so the real gap which I think has been pretty public was what we didn't really raise 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 pull 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.

3:52And kind of unbeknownst to us at the time and kind of speaks to the fact that we weren't focused enough on it, LAMA 3, which was a great model and was well received, to get to that model, they had kind of pulled forward all the future bets into that model. Well, that meant when it came time for Lama 4, we didn't have any of the pathfinding the other labs still had going. So that makes you, 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.

4:26And led to Mark shifting from, okay, AI isn't one of our bets. Which is how we thought of it up to that point. AI was just one of the many bets we had. said, 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 cliche, but I don't have a better word for it. He goes founder mode. He really did flip into a mode that is unique and reserved for Mark, 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. And they really landed about a year ago.

5:02I 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 your latest model, which is not our frontier model, but it's the latest model we've released. So it's a 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 as well. We've built the team I really believe in.

5:34We'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 Anthropics. You can use OpenAI. You can use Google. 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 superintelligence, I think is a vision that we're uniquely suited to deliver.

6:05It'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 as one piece and you want to have that strategically 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 care if it's 4.7 or 4.8.

6:36Like you don't care what Oracle, if I'm using Oracle or SQL databases, like you just want the functionality. You are 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 need to tell. In addition to showing the work that we've done technically, we need to actually demonstrate the value to consumers.

7:01Big Technology Podcast Host:So I just want to talk about this scientifically for a moment. The thing that I brought up in the beginning was this idea that you 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 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 um the era of the monolithic model kind of died around llama three launch like the idea like there's one model and just like let's just test how smart this model is and that was how good it's going to be at lots of things we're now in a world where um when you're using these harnesses whether it's um you know open code cloud code codex using these harnesses they're shopping underneath to lots of different models depending on the task.

7:59So they might be going to a multimodal model. 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 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.

8:36Because 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 of the model scales up. But human tasks don't have infinite intelligence demands. There's a lot of human tasks that you can do with conventional levels of intelligence um 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 yeah you said a couple

9:12Big Technology Podcast Host:interesting things first of all it's the product that matters i would agree with you and that it's important to have your model your own model for self-reliance so let's talk about that um i'm sure you saw what Apple did where they made a deal with Google to distill Gemini or do some fork of Gemini. And it looks like from the early reports, Siri is working pretty well with that technology. So have you considered doing a similar deal with Google and then building your own in parallel for that self-reliance, but at least being able in the near term to advance your products as fast as you can? Well, there's two parts.

9:45So we use lots of different models today. And I think, again, you want to provide consumers the best model that's going to work for them. And so there's obviously there's a there's a price and a performance that makes that matters here and there's a latency that matters here um but like having your own model gives you the ability to not just control your destiny you also have much stronger negotiating terms when you're trying to figure out the types of deals that you want to make to make sure that you're getting the consumers the best available answer they can spend that much money it was like a billion dollars to to google and it's it's too early to tell i don't know what the experience is going to be yet i don't have access to it.

10:17So we'll find out. Um, I also, I, for, for us, at least we're talking about personal super intelligence, the ability we want to be able to have to bring a tremendous specific capability to bear, not just a general intelligence, but a specific capability to bear for the products that we build that really matters to us a lot. Um, we're not seeing this as, um, like a value add for an existing system. We're seeing this as an entirely new way that people are going to interact with their computers. It does go back to a lot of the work we've done in reality labs for a long time. We've always tried to model ourselves after pioneers like Xerox PARC or Stanford Research Institute or Bell Labs, where we're trying to think about what is the way that we get information from our brains into the machine?

11:05And that's, hence our work on neural interfaces, hence our work on all these things. And what's the way to get the information from the machine back into our brains? hence our work on augmented reality and virtual reality um ai is potentially the best tool we've ever seen to get information from our brains into the machine especially if it's able to observe a lot of things around us um those are unique capabilities that i think we're trying to bring to bear that don't have any it's not just the model it's like what's the model's ability to work with all these novel inputs and create a closed loop system out of it um so i think that we are working on having incredible models.

11:39And I'm very confident in the team that we've assembled to do that. My point is just that it's not enough. And whether it's enough for Apple to just go rent that model, I don't know if they have a broader vision for how it integrates with people's lives. Okay, so you wouldn't rent the model? No, we do rent models. Like I said, we use, you know, we're - From where? There's no reason for us, we, you know, we, when we're doing development internally, we do have a lot of development happening on our own models. There's also some areas of development that we do on models that we use from Google or from Anthropic or from OpenAI, the ability to be model agnostic and have that be economically sensible actually kind of hinges on you having a competitive model that you can go back to if you need to.

12:21And it creates a real backstop on like how much rent somebody can try to charge you on top of that. But it's also worth noting, whether it's, I'm talking about a developer inside of the company or I'm talking about a consumer, I don't want them to worry about the model over time. Today they have to. Today it's like, it's all very tight, tied together. But over time, they just have a goal they're trying to accomplish. And that's the major focus that they should have. So there's this like strategic construct of having a model and having it be an absolute leading state of the art model. And that's super important.

12:54But it's not like when you have that, suddenly you win. There's a bunch of pieces that you have to connect that to in product and in distribution and in the consumer experience. And I think it is the collection of all four of those things that we see as our superpower relative to the competitors, most of whom, whether it's Apple or Anthropoc OpenAI or Google, only have one of those things.

13:15Big Technology Podcast Host:Yeah, I'm going to get into product, deeper into product in a moment. But first, last time we spoke, you told me you wouldn't merge with AI. But the way you're talking about this is you use technology to get your thoughts from your mind to a computer and then from a computer back to your mind. Sounds a lot like that. Have you changed your mind? No, I don't see this as merging with AI. I still want to have a very clear separation between things. I'm going to ask you again next time we talk. I know, keep it going. It's a continuous, it's really a continuation of a trend, an acceleration of a trend where the bit rate between us and machines and machines back to us goes up over time.

13:52And like there's funny versions of this that we've already been doing. Autocorrect. Autocorrect is like a little AI that sits between you and the computer that like helps improve, reduce the loss and effectively improve the bit rate between you and the machine. And there's all these like little tools that we use all the time to accelerate the loop. QR codes, one of my favorite ones, QR codes. It's like a way of like being like, cool, I want to like enter a URL, but I definitely don't want to type a URL because the error rate is going to be too high and it won't take me to the right website and I'll have to look.

14:23So we use QR codes. I think if AI, if you have an AI that's really able to, in very human terms, in human language terms, understand things, that is a potentially profound improvement of our ability to take advantage of the compute we already have, even if it's just on the input side. Now you combine that with the AI's ability to synthesize information more effectively to give back to us, you've really tremendously improved the bit rate. This is what Doug Engelbart, when he left NASA to start Stanford Research Institute, his idea was that human problems were getting harder at a steeper rate than human capability was improving and he wanted to create this human computer symbiosis and he said that the only way he could do it is if teams of people could merge with computers in some way to make it do and that's why he led you know the first ever video call the first ever joint document editing the mouse like all these things came from wanting to increase the bit rate i think ai is exactly that kind of thing

15:21Big Technology Podcast Host:okay and so the way that it manifests could be in this personal assistant right that knows your context, goes out and gets things done for you. It could happen via a chat interface on a phone or a computer or through glasses like the type that Meta is making. And so from a product standpoint, and I think you've already previewed a little bit of this, but would like to talk to you a little bit about it a little bit more, don't all products end up converging? Don't all AI products end up converging on this personal assistant use case? If you think about what OpenAI is, we just had Greg Brockman on the show.

15:55Big Technology Podcast Host:And what OpenAI is trying to do is trying to create this super app that will get things done for you and understand you and really help you out as you talk to it. It will go out and do things in the world for you. Same thing with Anthropic, similar with Meta. And Apple has, again, a similar vision, although we'll wait to see what it looks like when it's in the wild. So how do you differentiate and do you agree that everything sort of converges on the central assistant use case? Yeah, well, I think everyone's doing exciting work and run the very forefront of it so it's hard to say i would you know the work today the business that anthropic is doing and that open ai appears to be increasingly pursuing concentrating things under under greg is an enterprise business where they're building these harnesses um that and that's where the money is and i understand that's they need money so it's an important place to start where it's like it's actually very much attached to the enterprise um that's where all the revenue is um as a practical matter um and i get that that's like that's that's you know big companies although there's a lot of money in one place so you have a small number of sales that you have to make and you can get like larger amounts of capital and this is a capital intensive of game that they're playing um i think their major focus is definitely on these like work use cases and i think those are super valuable obviously we take advantage of them as well in terms of our professional work that's not our major focus like our major focus is 100 on how this is going to help consumers in their lives um and i think the real question i don't know that the ai's become indistinguishable from one another at all i think there's a real question of actually you you framed it yourself you think you these are kind of like a personal assistant and they have access to information about you that you certainly wouldn't want broadly distributed it's available to that personal assistant it's a trusted assistant well if you've ever had a personal assistant and hired a new one there's like a ramp up period um that is that that involves that so like if you have a personal assistant that's actually quite embedded in your life and is doing well i think that creates a real connection that you have that requires a lot of value from some other competitor to go replace why do you think consumer ai has been so slow to take off i mean there have been some attempts there's been like the character ais the replicas um but you saw with open ai you're right they definitely pivoted from from a money standpoint they do have some consumer applications that they want like nutrition health right these are a consumer thing that might tap into some of our you know some of our broader industries but um this idea you would imagine that like consumer ai would be very appealing to people um from an entertainment standpoint a a companionship standpoint and helping you i guess get done things in your life in a way that you wouldn't you know call on when you're doing it from a business standpoint but it's been slow yeah well i think you know i don't know why we thought this one was going to be immune, but the hype cycle is an evergreen concept that our industry continues to fall for.

18:48And it's not that people often misunderstand the hype cycle. They think how there's this, the hype cycle, for those who don't know, you know, there's a, there's a peak of hype, then there's the Valley of discontent, and then there's the ultimate eventual product market fit. And the point of the hype cycle isn't that the technology is it's fake. It's just that people willing to go through a bunch of hoops to make it work are a relatively small percentage of the population. And the work of bringing it to everybody is actually hard work. And it's hard work that is not just a matter of, great, you've done this hard technology problem.

19:19It's also, you've made the user interface workable. You've made it easy to use. People understand the value. Because people are living in their lives, they're having great success, living their lives without this tool. You're asking them to change their habits. You're asking them to change how they deal with computers kind of in a pretty dramatic way. They mostly don't like it. It's not going to, it's not the, you have to lead with value. What are we doing? What are the specific things that we're going to do for you that are going to make your life better? Maybe my favorite example of this is the agentic work.

19:50You know, so like many other people in our industry, I was very early on in December with Pi and then MyClaw, you know, using, building, playing with these agentic frameworks. And I find them very powerful, but they're not very user-friendly. they're very hard to build to maintain they have drift over time um and so when i think about hey um i built one for my my wife and i uh and i put it like on a whatsapp chat and she could use it she never uses it i use it all the time she doesn't use it um it's just it's hard to like integrate into a workflow she just asks me to do things i'm the agent and then i like you know go from there and you don't yeah and then i go to the agent so it's like that's a that's the pass through it's actually not it's pretty reasonable it's working well for her i don't blame her if I succeed, I'm actually worried if I make an agent that successfully gets me out of that loop.

20:36So I'm not that eager for that. So my point is like, we have not made these things easy to use yet. I think we've done a great job of like handling search use cases and research use cases. I think people understand those. I think people understand generative AI for content. Like I want to make this funny image. I think there's a few use cases that people understand our capabilities now and they want to go use those, but we have not done the work to make it something that people want to integrate into their daily life yet. It's not easy enough to use. It doesn't create enough value. It's too fussy.

21:06And so that is the problem to tackle. It's the product problem to tackle. You need great models to do it, but great models are not enough.

21:13Big Technology Podcast Host:Right. Where do you stand on AI companions? Because when it comes to what will be a assistant that people rely on, there is this belief that you build the functionality and then people will come to it. The other side of it is you build a avatar, an AI avatar that people feel like they're friends with. And that is the way that you differentiate. We know personality matters a lot. So I will say that one thing we've learned, and I certainly, you know, I think Anthropic has learned over the various generations of Claude, we certainly, we care a lot as humans about the way natural language appeals to us or doesn't appeal to us.

21:53And so personality matters for these models. Having said that, I think what you're going to find is a very big distribution among the population. I think some people absolutely would like this AI to be embodied and have, you know, a personality and have a face. In fact, there's been some people who, when they, in the agentic world, they want to go create 20 different agents that each have a different personality for different parts of their lives, a trainer and a nutritionist and a doctor's assistant and all these different types of things. I'm not one of those people. I actually like, nope, I just want my AI to be like extremely reliable and trustworthy.

22:30And like, I'm fine with it being an amorphous entity. It doesn't have to have a human structure for me to care about it. And I certainly don't want to deal with 20 of them. I just want to deal with one of them and have it do all the things I need. So I think that what we're, it's very early. It's too early to say for sure. I think you're going to see a big range of how people want to engage this technology and what makes them comfortable with it. And as a consequence, I would expect the market to deliver that.

22:53Big Technology Podcast Host:You know, there is a future where these AI companions become, this is a blunt way to put it, but the new social media, right? Social media is a place where you go to see what's going on with your friends and you engage with it. It's like, it's very, it can be, you know, all-encompassing and in its best case, fulfilling. And time spent is a pretty important metric, although how you feel after you spend that time is also important. Time well spent. Time well spent. And maybe that gets replaced by people spending time with... I mean, ultimately, it's like how do you engage with something on your computer?

23:31Big Technology Podcast Host:Maybe that gets replaced with people spending time with some AI entity that cares a lot about them. yeah i mean i try not to judge the way people choose judging no i agree yeah with technology my my instinct is that for the overwhelming majority of people um the major benefit of ai is going to be increased time for human contact with people they care about people they love um and you know i talked about this a lot in the context of of augmented reality for example you know even just the the camera glasses that we have you know when i'm with the kids i'm able to both record something and share it with my wife, which is meaningful to us and also be fully present.

24:10And I don't have a phone between me and them. And that's an important piece for me. I've talked about if you were able to be more effective with your work, that's more time that you're not spending commuting. That's more time that you're not spending away from your families, from the ones that you love. My personal sense is that the overwhelming majority of people, the value of authentic human connection only goes up over time. It doesn't go down over time. And I think we're seeing that a little bit in how people's reactions to AI early on have been. I think people are worried that it's a replace of technology.

24:42I don't find it that way myself. I think I'm an avid user of it. And actually mostly I'm spending more time not having to be at my computer thanks to it, not the opposite. So I think that's how the, I think that's my prediction on how the overwhelming majority of people interact with it and how it will affect their relationship to media and to their loved ones, which I think it's a premium on authentic connection in authentic human moments. But I'm sure the entire distribution will exist. Yeah, and of course, the AI glasses are kind of core to that vision.

25:12Big Technology Podcast Host:Yeah, that's right. So we'll talk about that right after this. Hi, everyone. Alex Kantrowitz here. I want to tell you about a documentary I've made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous agents, I sat down with MIT professor Ramesh Raskar, former White House CIO Teresa Payton, Michelin's Group Chief Data and AI Officer, Ambika Rajagopal, and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this evolving landscape. We conclude with Rory Blundell, CEO of Gravity, to discuss the path forward. With Gravity leading the way, join us on this journey.