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

Episode Date: July 8, 2026

Andrew "Boz" Bosworth is the chief technology officer of Meta. Bosworth joins Big Technology to discuss why Meta fell behind in the frontier AI race and how it plans to turn its models, products, and distribution into an advantage. Tune in to hear his candid explanation of what went wrong with Llama, why the best AI products will use multiple models, and what it will take for consumer agents to break through. We also cover Meta’s AI glasses, the future of augmented reality, employee tracking and training programs, AI companions, and the painful process of adapting a company to a technological revolution. Hit play for a revealing conversation about Meta’s AI comeback and the products that could shape how we interact with computers. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

Meta-Chief Technology Officer Andrew Bosworth joins us to talk about the company's AI efforts and why it's building its own new AI glasses. That's coming up right after this. In the face of ongoing disruption and opportunity, TMT leaders need to deliver tangible results, not just ideas. When pace and performance matter most, PWC combines market insights and deep sector experience with AI, cloud, and emerging tech to accelerate your transformation and drive measurable ROI. from strategy to execution. PWC can help you anticipate what's next,

outpace disruption, and compete. For more information, visit pwc.com. 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. Bob, 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. 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 need 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. And you have that. And we do have that, I think, as well. So yeah, There's two stories here. The first one is, you know, I think, you know, we go back to Lama 1, Lama 2, Lama 3.

We really were, you know, kind of at the forefront and advancing things. And you, of course, know this. We, the Facebook, Fundamental A.I. Research Group goes back a decade more. I mean, that's where I actually first got queued into what was going on with AI is when M. The AI messaging 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. Meadow was on it very early. 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 Lama 4 together. Sorry, when we were pulling Lama 3 together, we had really pulled in all the research, all the, every, we pulled at 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 path-finding and

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, 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, to deliver that model. Well, that meant when it came time for Lama 4, we didn't have any of the path finding 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 we're behind in a bunch of these critical technologies that have been used to continue the pace of progress. This is a pretty public disappointment, I think, a year ago for us, and 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. AI is a bet that's foundational to the entire company. And so we're to change how we're thinking about this. And this is such a cliche, but I don't have a better word for it. ghost 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 your latest model. Which is not our frontier model, but it's the latest model that we've released to the public. 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 wise. We've built the team I really believe in. We've got all the compute in 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 open eyes. 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 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 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 want to, they don't care if it's 4.7 or 4.8. Like you don't care what Oracle, if I'm using Oracle or SQL databases. is 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 need 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 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-trained 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 Lama 3, 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 when you're using these harnesses, whether it's, you know, open code, cloud code, codex. Using these harnesses, they're shopping underneath to lots of different models depending on the tasks. So they might be

going to a multimodal model. You know, if you're using Gemini, it'll farm tasks out to nanobanana if it's trying to do image generation. So we've really, we really, moved past this world with 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. Because if you think about human tasks, I really believe,

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 like human tasks don't have infinite intelligence demands. 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 of performance and price and value. Yeah, you said a couple of 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. 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 report, Syria 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. So 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 price and a performance that makes, that matters here. And there's a latency that matters here. 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.

But Apple didn't spend that much money. It was like a billion dollars to Google. And it's too related to, I don't know what the experience is going to be yet. I don't have access to it, so we'll find out. I also, for us at least, we're talking about personal superintelligence, 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. We're not seeing this as, 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. You know, we've always tried to model ourselves after pioneers like Xerox Park 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,

and that's, hence our work on neural interface, 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. 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.

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? So I think that we are working We are working on having incredible models, and 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 from Google or from Anthropic or from Open AI,

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 and 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 that 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 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 strategic construct of having a model and having it be an absolute leading state of the art model.

And that's super important. But 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 Anthropical Open AI or Google only have one of those things. 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

merging with AI. I still want to have a very clear separation between things. I'm going. I know. We'll keep it, 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. And 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 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 not have to look. So 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 get back to us. You've really tremendously improved the bit rate.

This is that Doug Engelbart when he left NASA to, you know, and he's a lot. 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 it.

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. Okay. And so the way that it manifests could be

in this personal assistant, right? That knows your context. Yeah. 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 we'd 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. So if you think about what OpenAI is, we just had Greg Brockman on the show. And what OpenAI is trying to do is trying to create this, you know, super app that will get things done for you and understand you and really help you out, you know, 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 revision, 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 Anthropica is doing and that Open AI appears to be increasingly pursuing, concentrating things under Greg, is an enterprise business where they're building these harnesses that, and that's where the money is, and I understand that they need money. So it's an important place to start where it's like, it's actually very much to attach to the enterprise. That's where all the revenue is as a practical

matter. And I get that. That's like, 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. I think their major focus is definitely on these like work use cases. 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. And I think the real question, I don't know that the AIs become indistinguishable from one other at all. I think there's a real question of, actually, you framed it yourself. 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 that involves that. So if you haven't this 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. But you saw with opening I, you're right, they definitely pivoted 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 this idea, you would imagine that, like, consumer AI would be very appealing to people from an entertainment standpoint, 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. And it's not that people often misunderstand the hype cycle. They think how there's the hype cycle for those who don't know. You know, 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 high cycle isn't that the technology that's fake. It's just that people willing to go through a bunch of hoops to make it work are 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. It's also, you've made the user interface, you know, 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 to change their habits. You're asking them to change how they deal with computers kind of in a pretty dramatic way. And they mostly don't like it? It's not going to, it's not the, you have to lead with value. What are we do? 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 agenic work.

You know, so like many other people in our industry, I was very early on in December with Pi and then my claw, 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. And so when I think about, hey, I built one for my, my wife and I. 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. 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 delegate. Yeah, and then I go to the agent. So that's the past through. It's good path. 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. So 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. And 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. Right. Where do you stand on AI companions? because, you know, 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 Anthropica 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. And 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, in the agenic 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. And 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. 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 really 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. You know, there's, 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 can be, you know, all encompassing. and in its best case, fulfilling. And time spent is like a pretty important metric, although how you feel after you spend that time is also important. Time well spent. Time well spent. And maybe, you know, that gets replaced by people spending time with, I mean, ultimately it's like how do you engage with something on your computer.

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 to... I'm not judging. No, I agree. Yeah. With technology, my instinct is that for the overwhelming majority of people, the major benefit of AI is going to be increased time for human contact for people that they care about, people they love.

And, you know, I talked to this a lot in the context of augmented reality, for example. You know, even just 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. And 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 replaceive technology.

I 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 my prediction on how the overwhelming majority of people will 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 and authentic human moments. But I'm sure the entire distribution, will exist. Yep. And of course, the AI glasses are kind of core to that vision. Yeah, that's right. So we'll talk about that right after this. Hi, everyone, Alex Cantewicz 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 Rosker, former White House

CEO Teresa Payton, Michelin's Group Chief Data and AI officer Ambica Roger Gopal, 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. You can watch the full documentary

at the link in the show notes. Here we go. Someone's already claiming this is our year. Someone else said that last year too A round of Jameson, ginger, and lime arrives at the table. Smooth enough for kickoff, smooth enough for extra time. New friends pulling up a stool.

Debates about whether that was a handball. Cheers rising like a roar around the room. Because match days are about the shared moments. How did Jameson to your match day lineup? Jameson, it's what you bring. Please enjoy our products responsibly. Hey, y'all. It's Kelly Clarkson with Wayfair. Ever order furniture online and wonder what if?

What if it doesn't hold up? That sofa was four days old. You should have ordered from Wayfair. With Wayfair, there's no what if. Just style you love and quality you can trust. Visit Wayfair.ca. Wayfair, every style, every home. And we're back here on Big Technology podcast with Andrew Bosworth, Boz, the CTO of Meta.

Boss, great to see you again. Thank you for taking the time to speak with me. If we go to the white shot, we can see we're here in New York at a moment where you and your team are releasing three new pairs of meta-designed glasses. It's something we've been debating on the show is sort of is your phone the AI device or is it a wearable. And we've had this moment again going back to Apple where it looks like they're preparing to release a version of Apple intelligence that actually works that knows your context to a degree and might be able to get things done for you. And then we see sort of the opposite side is the Snapchat specs release, which got a lot of people saying maybe we don't. I mean, those were so bad that people were just, you don't have to comment on. I'll say it.

I can't comment. I haven't seen them. I haven't seen it myself here. Let's just say, I'll just, my comment reflects what the market did. Evan Spiegel wore them out to some presentation. I think Snap stock went down like 6% immediately. It's just what happened. Well, this will be the first video of me wearing, I don't think it's going to happen to you.

Let the market decide. Yeah. But I'd love to hear your thoughts on your, obviously, meta, has invested a lot in this. You believe it's a compelling use case. if I were to say maybe we don't need AI glasses we can just use our phone

what would you say it makes you feel the other side of that yeah phones are great I mean I love phones I have two of them I think they're wonderful devices

I the glasses from the very beginning the question we asked ourselves was this exact question we said okay phones are great what is something that you wish

you could get access to it's on your phone without having to take your phone to your pocket and we came up with camera and audio it's just very simple It's like, cool, if I could just do that.

The AI has been this tremendous tailwind where actually it unlocks a much larger swath of potential capability over time than what the phone can do just through, you know, Bluetooth connections. And so, yeah, it's much more promising now than it looked two years ago or three years ago. Two or three years ago, this looked like,

hey, at some point you have to put a display on this and it has to become a standalone system and it has to have all this, you know, kind of accessories attached to it. Now it actually looks like there's a totally, enough room in the market for a big range of wearable devices. Glasses, certainly, probably not just glasses, probably a lot of other things. People don't want to wear glasses. They want to

wear different things. And some of those devices are just going to be input and output to your phone. That's cool. Like, your phone's great. And if it's just making your life more efficient in terms of how it's doing input and output, that's awesome. Some of them will be more complete. So for the the Metter A Band Display Glasses, for example, we just launched a vibe coded platform for it. And so anybody who wants to can go literally just build whatever app you want for the glasses. Now, right now, you kind of build the app and you put them on the glasses. But in the future, there's no reason that couldn't just be you wearing the glasses in real time, telling the glasses what app you want right now and having it on the fly build that app for you.

Interesting. You know what I'm saying? And so I think we are headed towards a very cool zone where it's a little less like app garden specific. you're still going to have these content homes. Content continues to be an evergreen and important thing as it has been on TV, as it has been on social media, as it has been everywhere. So there's still going to be places where media

that you want to reach lives. And those are, there's look kind of like apps or channels or whatever, like of a better term. But there's a long tale of things. Like, why does my toaster need an app? Let me ask you this in seriousness. Like my toaster has an app. I don't think it needs one.

I don't want that. Right. I just want to tell my AI agent get me the toast that I want. It's the same toast I have every day. Just get it for me. I don't want to have to go do whatever the thing is. What does your toaster app?

Does it let you toast remotely? I refuse to install it. I refuse. I refuse. I refuse. I absolutely won't do it. And so... You have to stand up for something.

Listen, that's a line. There's a line that nobody, you know... I think you can actually... I have to admit, sometimes, like, it's so cool that you can have a specific app to control every aspect of the thing. And I respect that. And I'm a tech guy, right? So I like the fidgety nature of it. But it's like literally at this point, it's kind of gotten out of hand.

What I really just wanted to tell an intelligent system, hey, get me the thing that I want. And it can do that for me. And we see an early form of this. You know, our partnership with Spotify, you ask the glasses to play music. If you have a Spotify account linked, it goes and gets the music you want. And it's like, yeah, this is great. This is what I wanted. I didn't want to have to go through a bunch of steps to do this.

So for me, at least, the way I'm thinking about this is not that phones are great. and they're going to continue to be great. I don't think the appy thing is the way the future is going to look. I think the future is going to be valuable services that are provided to you and you getting access to those services the way that you want when you need it

and paying money to the people who provide those valuable services all negotiated, either in advance or on demand. Yeah. I really believe in this. I saw you had the, I was on the meta-AI app today and I saw there's a garment connector to the glasses.

And for me, you know, as I'm training, I'd love to be able to say, while I'm building up to this like half marathon, meta AI, find me a 5K in my area in this window and sign me up. Totally. And to do that as I'm on a run. So I don't need to spend an hour figuring it out on my own. Agree completely.

And taking it a higher level, you know, your meta AI ideally would already know that you're training and you have a goal that you're trying to reach. And it's tied into all the pieces that matter, your nutrition and your, you know, it's like, that's like, that's the direction we want to get this thing. There's a lot of steps between now and then, but that is where we're going. The Orion glasses, we talked about those last time.

Yeah. Where do those stand? Those are the full AR experience. Full AR glasses. Yeah. So Orion was such a important moment for us, you know, having had this AR vision for us a long time, finally gave us the device that we could use to start to play with the software on. And even though we couldn't get the price to be one that we felt comfortable launching as a

consumer product, we did intent when we designed it and developed it, it was a consumer design and intention. And so the product itself is like, is quite wearable, quite workable. Like I have a pair at home. We use it to test the software. So we've continued to iterate in the software and we've made so much more progress in the software. Not just because AI has gotten better, but that makes a huge difference to what that software is, but also because you have Orion to develop on, which makes a big difference. So yeah, we continue to be very focused on the entire spectrum. You know, we've hinted here that you know in addition to display glasses and camera glasses you know there's a whole range of glasses that maybe below that in the price range well there also maybe there i really still believe in

full AR as a future for the space um i think we're going to continue to take the same approach we have so far and the same reason we didn't launch Orion um it's not just enough that it does all this functionality has to look great has to be comfortable enough that you want to wear it um has to be at a price point that a reasonable person would say, yeah, this is a good value. So how far away is that? I'm not going to say exact number. I will say, I like the progress we're making. Measured in years or months?

I'm not going to answer that. All right, that's fair. I appreciate the hustle. Have to ask. I know you do. Some of my reticences is, you know, people who have been in companies like ours know this, we're constantly looking at vehicles and like asking ourselves, is this the one? Is it ready yet?

You know, is this the one? Man, we're getting into the zone. It's pretty exciting. Okay, cool. Let's talk about metaculture for a moment. You're running this Applied AI division. That's right. Which has been the subject of some reporting. I run the agentic transformation accelerator. Right.

One of the groups in that is the AAI team, yeah. Okay, I'm just going to read the quote from Wired. One employee told Wired, it's literally the gulag. You have zero purpose in life all of a sudden. You barely interact with anyone. You just have these tasks every week, apparently talking about how, you know, employees there have been put on some like AI puzzles that they they have to try to accomplish that helps train the AI. What's going on there? I'm not sure this person's ever Googled what a

gulag was like and how similar or not it is to a six-figure software job in Silicon Valley. It doesn't seem like it, but the fact that they would say that. Setting aside the hyperbole. Okay. Yeah. So we've been spending a lot of time on this internally. It's a hugely important topic for us. You've been covering us a long time, so you know this. Like, this is a company that goes into lockdown. Like when we have an urgent opportunity ahead of us, we like do this. We did it with mobile. We did it with video.

We did it with stories. We've done it. And it's not that they, every one of these things pivots the entire company, but their moments were like, wait, if we put exquisite effort on something right now, we think there's a tremendous opportunity for us in the market. And in this case, we saw that. We really feel like, you know, when we came out with Muse Spark, and I want to be careful, like, Mew Spark is a great model and we're really excited about it.

And it's what coding had not been a focus for us on the model, but it actually was a better out of the box at coding than we had expected it to be. And we found early on through experiments that like actually giving it just a relatively modest number of trained kind of expertly guided examples and we could post train the model, we could dramatically improve its competitiveness. and so when you start to like run the numbers and the math and this you're like oh this is an incredible opportunity for us to build a coding model that not only allows us to have independence to how we operate the company but also something that we think is going to be valuable both inside

of if you you know give users AI that's able to code that's obviously one of the very very powerful tools that's kind of become very common in these AI systems over the last year and then also for us to be able to make the model itself more wide. available over time. So we basically saw this huge opportunity, such a big opportunity, that we pivoted kind of on a dime and brought a lot of people across the company, thousands of people out into this AAI organization to do these expert traces. We absolutely need their expertise. It doesn't work if you do a bad job. It turns out if you use a bad piece of coding to train the model, you do some damage to it. Yeah, they have to. They don't want to reinforce failure.

They have to be well done. They have to be expertly guided. Now, we did it very quickly. And as a consequence, it did not have a lot of structure. It did not have great communication around it. I've been on record. Actually, it's not true. I wasn't on record. I was leaked.

It was leaked. Calling it atrocious. You said maybe not the worst it's ever been in 20 years here, but it's up there. It's definitely up there. That actually was not a quote for me. And I don't know where that. You didn't say that. I didn't say that.

Okay. But I've said things like it. I'm fine with it. And so the degree to which it's a big company, the degree to which we saw this urgent opportunity. and made the change that I think strategically was absolutely the right change but did not do the work

to kind of go to each person and be like, let me talk to you about what this is and why we need it and why it's important. Knowing that they had other work that they were excited about that they were putting on pause to come do this work.

But that is something our company does when we feel like we see these unbelievable opportunities that exist in moments of time. And so, yeah, we are like navigating this change that's happening in the industry is happening inside every company as well. And it's like nothing we've ever seen.

You said you let out with this. It's like nothing we've ever seen before in our careers. And I think that is giving people pause. And so it raises the bar on me and other leaders do a much better job than we have done communicating what's going on. Why is it happening? How does it affect you? How do we see it playing out long term?

Make sure they understand that the role they're playing is one that we consider very critical, very important. those we wouldn't have made that change, obviously. Can we talk about the tracking briefly? Yeah. I actually, you know, I understand. If I was an employee, I don't think I'd be a fan of it. But I actually sort of made the case for why you might be doing it on our show recently.

And now that we're sitting next to each other, let's talk about it. Because, so basically the reports have been that meta has started to track some keystrokes and the way that employees type and basically use that as a way to train model. And my perspective on this was as model training moves into reinforcement learning, where I think Scale AI where Alexander Wayne came from said most of their training is reinforcement learning now, as opposed to pre-training, which we talked about previously. As the technology moves into reinforcement learning, it's very valuable for these models to learn how to accomplish tasks in what's typically called as gyms or like different areas,

that different like simulations of real world activity that they go in. in and try to accomplish. And so am I right in thinking that this program is basically a just massively scaled up version of that where the models watch employees work through their tasks and then learn how to accomplish tasks on their own? Yeah, well, there's two parts to this. The first one is you're absolutely right. Reinforcement learning is playing a much bigger role in today's kind of AI than people had maybe predicted two or three years ago that it would. It's not just that though. There's also the long tail is long. like the long tale of human knowledge and behavior is very long.

And most of it, as much as for all the text, for the entire corpus of text on the internet, most of the stuff that we know is still not on the internet. It's like in our heads, it's experience, it's built over time, it's behaviors that are second nature to us. And so this system was in some ways, I thought quite genius, you've got employees who need to change nothing about how they go about their day, can go about it as they always have, and in doing so, produce this corpus of view. unique data, in this case, design, and how do humans use computers? AIs are actually still really

weirdly bad at just using computers. Like, it's like, it's a surprisingly hard problem that is not well solved. And that's where all the energy is going with computer use and agentic. That's all computers. And you can, you can ramp up, you know, the intelligence on the front end for sure, and then try to distill down from that. But we do think having this data has the potential of making people's lives easier. It's not even about the content. The content, the thing that was a challenge to communicate, and again, we did a poor job, was not even about the content of the thing that you're doing. It's about how is the computer able to understand what's happening inside this digital interface,

which is the way we access a lot of our tools, like in the world today. The second thing is, and so I think this data set is interesting, but we won't know. It's like a long-running data set. So the second part of this is you're still, for a long-tail expert training, you're better off doing work like we are doing with our applied AI team, the AI team. Like that is a relatively small number of really well-documented, you know, tasks that can post-trained a model. This is a difference thing.

This is like very long-running. Once we have like a year of data, you have something that's potentially interesting to bring to bear on the model. I do want to add, we've also made a bunch of changes to the program since the launch. We've added a 30-minute break. Unlimited pausing, people can opt out for a bunch of reasons. So we've made a bunch of changes to the program for people who had concerns about it. So you are posting a lot of your old blog posts to Substack.

Yeah. And I've been getting them in my email and reading them. And there was a very interesting one that I read recently talking about how you were doing some biology research and the doctor said the pain is rehab. You need that pain in order to be able to heal. You write, at some point, you have to be able to heal. some point you have to be able you have to embrace the pain to make real progress given uh two otherwise equal stories humans remember the story that evoked stronger emotion emotion is how our

brain triages memories sometimes it has sometimes it has to hurt for your brain to prioritize it um shout out to bs80 a class my my neuro bio class at harvard AI is or evolutionary bio AI is taking away a lot of the pain right like big part of what humanity is doing with AI right now is a lot of the painful parts of our work, we're giving it to AI. If that goal is accomplished, where do we find the pain? So I love this. And a very small aside, one of the things I did is I assigned my agent the task of bringing my blog posts over to Substack so at some point I could do both. I didn't realize until very recently that it wasn't any belitted list.

It would just strip out. My agent did not understand a loaded list. So we have a long ways to go on agents. Okay. Phase one. The pain is the rehab came. Yeah, there was a question we were studying the neurobiology that would occur during withdrawal from drug use. And a student asked, hey, we have all these symptoms.

Why don't we just give people a pain medicine? And the professor was like, you don't understand. The pain is the medicine. Like experiencing desire to pursue drugs, drug-seeking behavior, and then having it be immensely painful, is the way you reprogram your brain to, like, overcome the drug-seeking behavior. And if you get rid of the pain, then the person is never going to do it.

This is a productive form of pain. By the way, I would argue AI, all these paroxysms happening, not just at meta, but at every company, is the pain I'm talking about. That is the pain that there is no way out but through, and you have to figure out the path through it to figure out what works and what doesn't work,

and it's just gritty. We do have lots of other types of pain in our society that have nothing to do with real value being created. This comes up a lot, And education is a good example. I remember being told, I'm sure you were when I was in school, hey, you can't use a calculator in this test. You will not have a calculator with you as you go about your day in the real world.

Bullshit. I have at least three calculators on my person at all the time. Not to mention I can just ask my glasses, math problems. I'm filthy with calculators. It turns out doing a math problem, doing a math test without a calculator is a certain kind of pain, not a particularly useful kind. doing a harder math test that requires critical thinking with a calculator is probably the more valuable way to do that thing, right? So I do think it's important to align the pain that we're experiencing

with the value we're trying to create in the world. I think like learning to integrate AI, you could avoid that pain. You skip it. You don't do it. You and I both know that puts you at real risk. You're going to fall behind people who, you know, are able to do AI and want to do the same job as you. You're going to fall behind other companies that have integrated AI, either economically, or in the products that you offer. You know, there's this, Cheryl always had this, Cheryl Sandberg has this great quote, which is that companies don't usually fail

by setting tough goals and missing them. They fail by setting easy goals and hitting them all the way down. And so, like, I think you could easily avoid the pain today by just going to, yeah, we're just not going to do it. We're just going to let it happen, and then we'll figure it out later on. So I think there is productive pain

and unproductive pain and maybe a little bit of judgment to know which one's which. Boss, it's really always a pleasure to speak with you. Thanks so much for coming on this. Thanks for having me. All right, everybody. Thanks so much for listening and watching and we'll see you next time on Big Technology Podcast.