Mark Zuckerberg — AI will write most Meta code in 18 months

27:25I mean one of my core guiding principles and designing products is like People are smart right they know what is valuable in their lives Everyone's in a while You know something can something bad happens in a product and you want to make sure that you design your products well to minimize that. But if you think that someone is doing is bad, and they think it's really valuable, most of the time in my experience, they're right, and you're wrong, and you just haven't come up with the framework yet for understanding why the thing that you're doing is valuable and helpful in their life. Yeah, so that's kind of the main way that I think about it.

28:03I do think that people are going to use AI for a lot of these social tasks. already one of the main things that we see people using that AI for is kind of talking through difficult conversations that they need to have with people in their life. It's like, okay, my, you know, my, I'm having this issue with my girlfriend or whatever, like, help me have this conversation or like, I need to have this hard conversation with my boss at work. Like, how do I have that conversation? That's pretty helpful. And then I think as the personalization loop kicks in, and the AI just starts to get to know you better and better, I think that will just be really compelling.

28:46One thing just from working on social media for a long time is, there's the stat that I always think is crazy. The average American I think has, I think it's fewer than three friends. Three people have made consider friends. And the average person has demand for, meaningfully more. I think it's like 15 friends or something, right? I guess there's probably some point where you're like, all right, I'm just too busy. I can't deal with more people. But the average person wants more connectivity, connection than they have. So, you know, there's a lot of questions that people ask of stuff like, okay, is this going to replace kind of in -person connections or real life connections?

29:29and my default is that the answer that is probably no. I think that there are all these things that are better about kind of physical connections when you can have them, but the reality is that people just don't have the connection and they feel more alone a lot of the time than they would like. So I think that a lot of these things that today there might be a little bit of a stigma around. And I would guess that over time, we will find the vocabulary as a society to be able to articulate why that is valuable and why the people who are doing these things are, like, why they are rational for doing it and how it is adding value for their lives.

30:12But also, I think that the field is very early. So I mean, it's like, I think, you know, they're a handful of companies and stuff. We're doing virtual therapists. Yeah. There's virtual girlfriend type stuff, but it's very early. The embodiment in the things is pretty weak. A lot of them, you open it up and it's just like an image of the therapist or the person you're talking to or whatever. Sometimes there's some very rough animation, but it's not like an embodiment. You've seen the stuff that we're working on in reality labs where you have the codec avatars and it feels like it's a real person.

30:47I think that's where it's going. You'll be able to basically have like an always on video chat where it's like, and also the AI will be able to, you know, the gestures are important too. Like more than half of communication when you're actually having a conversation is not the words that you speak. It's all the non -verbal stuff. Yeah. I did get a chance to check out Orion the other day and I thought it was super impressive. And I'm mostly optimistic about the technology just because generally, As you mentioned, like libertarian about if people are doing something, probably they think it's good for them.

31:20Although, I actually don't know if it's the case that if somebody is using TikTok, they would say that they're happy with how much time there's something on TikTok or something. So I'm mostly optimistic about it. Also, in the sense that we're going to be living in this future world of AGI. We need to be in order to keep up with that humans need to be upgrading our capabilities as well with tools like this. And just generally, it can be more beauty in the world if you can see studio -gibli everywhere or something. I was worried that one of the flagship use cases that your team showed me was I'm sitting at the breakfast table and on the periphery of my vision is just a bunch of reels that are scrolling by maybe in the future my eagre girlfriend is on the other side of the screen or something.

31:55And so I am worried that we're just removing all the friction between getting totally a reward hacked by our technology. Yeah, how do we make sure, like, I don't know, this is not what ends up happening in five years? I mean, again, I think people have a good sense of what they want. That experience they saw was, there was a demo just to show multitasking and holograms. So I mean, I agree that, like, I don't think that the future is like you have stuff that's trying to compete for your attention in the corner of your vision all the time. I don't think people would like that too much. So it's actually one of the things as we're designing these glasses that we're really mindful of is like Probably the number one thing that glasses need to do is get out of the way and be good glasses, right?

32:48As an aside, I think that's part of the reason why the Rayban meta product has done so well is like all right It's great for listening to music and taking phone calls and taking photos and videos and the AI is there when you want it. But when you don't, it's like a great, good looking pair of glasses that people like and it kind of gets out of the way well. I would guess that that's going to be a very important design principle for the augmented reality future. Right, the main thing that I see here is, you know, I think it's kind of crazy that for how important the digital world is in all of our lives, the only way we can access it is through these like physical, you know, digital screens, right?

33:40It's like you have like a phone, you have your computer, you can put a big TV, it's like this huge physical thing. It just seems like we're at the point with technology where the physical and the digital worlds should really be fully blended. And that's what the holographic overlay is allow you to do. But I agree, I think a big part of the design principles around that are gonna be, okay, you'll be interacting with people and you'll be able to bring digital artifacts into those interactions and be able to do cool things is very seamlessly. If I want to show you something here, here's a screen, here it is, I can show you, interact with it, it can be 3D, we can play with it.

34:27You want to play a card game or whatever, it's like, all right, here's a deck of cards, we can play with it, it's two of us are here physically, you have a third friend who's just hologramming in, they can participate too. But I think that in that world, people are going to be, just like you don't want your physical space to be cluttered. It's sort of like, you know, it just kind of has like a, it wears on you psychologically. I don't think people are going to want the digital kind of physical space to feel that way either. So I don't know, that's more of an aesthetic and and and one of these norms that I think we'll have to get worked out.

35:04But, but I think I think we'll figure that out. Going back to the AI conversation, you're mentioning how big of a bottleneck the physical infrastructure can be related to other open source models like DeepSeek and so forth. DeepSeek right now has less compute than a lab like Meta and you could argue that it's competitive with a lot of models. If China is better at physical infrastructure, industrial, scale -ups, getting more power and more data centers online, how worried are you that this will, they might beat us here? I mean, I think it's like a real competition. I mean, I think that you're seeing the the industrial policies really play out.

35:47Where, yeah, I mean, I think China's bringing online more power. And because of that, I think that the US really needs to focus on streamlining the ability to build data centers and build and produce energy or I think we will be at a significant disadvantage. At the same time, I think some of the export controls on things like chips, I think you can see how they're clearly working in a way, because there was all the conversation with DeepSeek about how they did all these very impressive low -level optimizations. And the reality is they did, and that is impressive, but then you ask why did they have to do that when none of the American labs did it?

36:31And it's like well, because they're using partially nerfed chips that are the only thing that NVIDIA has allowed to sell in China because of the export controls. So deep seek basically had to go spend a bunch of their calories in time doing low -level infrastructure optimizations that the American labs didn't have to do. Now, they produced a good result on text, right? It's like, I mean, deep seek is text only. So the infrastructure is impressive, the text result is impressive. But every new major model that comes out now is multimodal, right? It's image, it's voice, and there isn't. And now the question is why is that the case?

37:19I don't think it's because they're not capable of doing it. I think that they basically had to spend their calories on doing these infrastructure optimizations to overcome the fact that there were these export controls. But when you compare like Lama 4 with deep seek, I mean, our reasoning model isn't out yet. So I think that the kind of R1 comparison isn't clear yet. But we're basically like effectively same ballpark on all the tech stuff is what DeepSeek is doing, but with a smaller model. So it's much more efficient per the kind of cost per intelligence is lower with what we're doing for Lama on text.

37:55And then all the multimodal stuff we're effectively leading at and it just doesn't even exist in there stuff. So I think that the Lama 4 models, when you compare them to what they're doing, are good. And I think generally people are going to prefer to use the Lama 4 models. But I think that there is this interesting contour where like, it's clearly a good team that's doing stuff over there. And I think you're right to ask about the accessibility of power, the accessibility of compute and chips and things like that. because I think the kind of work that you're seeing the different labs do and play out, I think is somewhat downstream of that.

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39:19Radar currently runs millions of checks per week. And when you plug Radar into your own product, you immediately benefit from the millions of training examples that Radar has already seen through other top companies. Previously, building this level of advanced protection in -house was only possible for huge companies. But now with WorkOS Radar, advanced security is just an API call away. Learn more at workos .com slash Radar. All right, back to Zach. So Sam Altman recently tweeted that OpenAI is going to release a open source, a sort of reasoning model. I think part of the tweet was that we will not do anything silly, like say that you can only use it if you have less than 700 million users.

40:03DeepSeek has the MIT license, whereas Lama, I think a couple of the contingencies in the Lama license require you to say, build with Lama on applications using it, or any model that you train using Lama has to begin with the word Lama. What do you think about the license? Should it be less onerous for developers? I mean, look, we've basically pioneered like the open source LLM things. So I mean, I don't consider the license to be onerous. I kind of think that when we were starting to push on open source, it was this big debate in the industry of like, is this even a reasonable thing to do? like can you do something that is safe and trustworthy with open source?

40:48Like, will open source ever be able to be competitive enough that anyone will even care? And basically when we were answering those questions, which a lot of the hard work that, I think a lot of the teams at Meta, although there are other folks in the industry, but really the Lama models were the ones that I think broke open this whole open source AI thing in a huge way. We were very focused on, okay, if we're going to put all this energy into it, then at a minimum, if you're going to have these large cloud companies like Microsoft and Amazon and Google turn around and sell our model, that we should at least be able to have a conversation with them before they do that around basically, okay, what kind of business arrangement should we have?

41:36But our goal with the license isn't, we're generally not trying to stop people from using the model. We just think like, okay, if you're like one of those companies or if you're Apple, just come talk to us about what you want to do and let's find a productive way to do it together. So I think that that's generally been fine. Now, if the whole open source part of the industry evolves in a direction where there's a lot of other great options and if the license ends up being a reason why people don't want to use Lama, then I don't know, we'll have to reevaluate the strategy, whether what it makes sense to do at that point.

42:15But I just don't think we're there. That's not in practice a thing that we've seen companies coming to us and saying, we don't want to use this because your license says that if you reach 700 million people, you have to come talk to us. So at least so far, it's a little bit more of something that we've heard from open source purists. Is this as clean of an open source model as you'd like it to be? And look, I think that debate has existed since the beginning of open source with all the GPL license stuff versus other things. And it's like, OK, does it need to be the case that anything that touches open source can have to be open source or can people just take it and use it in different ways?

43:03and I'm sure they'll continue being debates around this. But I don't know, if you're spending many, many billions of dollars training these models, I think asking the other companies that are also huge and similar in size and can easily kind of afford to have a relationship with us to talk to us before they use it, I think it seems like a pretty reasonable thing. If it turns out that you, other models are also, there's like a bunch of good open source models, so that part of your mission is fulfilled. and maybe other models are better at coding. Is there a world where you just say, look, opens the ecosystem is healthy, there's plenty of competition.

43:41We're happy to just use some other model whether it's for internal software engineering at meta or deploying to our apps. We don't necessarily need to build with Lama. Well, again, we do a lot of things. So it's possible that, you know, I guess let's take a step back. The reason why we're building our own big models is because we want to be able to build exactly what we want. And none of the other models in the world are exactly what we want. If they're open -source, then you can take them, and you can find to them in different ways, but you still have to deal with the model architectures and they make different size trade -offs around that affect the latency and inference cost of the models.

44:24And it's like, okay, the scale that we operate at, that stuff really matters. Like, we made the Lama Scout and Maverick models certain sizes for a specific reason, because they fit on a host and we wanted certain latency, especially for the voice models that we're working on, that we want to just basically have pervade and be across everything that we're doing from the glasses to all of our apps, to the Meta AI app and all this stuff. So I think that there's a level of control of your own destiny that you only get when you build the stuff yourself. That said, there are a lot of things that AI is going to be used in every single thing that every company does.

45:02When we build a big model, we also need to choose which things, which use cases internally we're going to optimize for. So does that mean that for certain things, we're not going to think that maybe cloud is better for building this specific development tool that this team is using. I had called and used that. Fine. Great. I don't think we don't want to fight with one hand tied behind our back. We're doing a lot of different stuff. You also asked, would we maybe, would it not be important because other people are doing open source? I don't know. On this, I'm a little more worried because I think you have to ask, for anyone who shows up now and is doing open source now that we have done it, there's a question which is, would they still be doing open source if we weren't doing it?

45:49And I think that there are a handful of folks who see the trend that more and more development is going towards open source. And they're like, crap, we kind of need to be on this train or also we're going to lose. We have some closed model API. And increasingly a lot of developers, that's not what they want. So I think you're seeing a bunch of the other players start to do some work in open source. But it's just unclear if it's dabbling or fundamental for them in the way that it has been for us. And, you know, a good example is like what's going on with Android. Right? It's like Android start off as the open source thing.

46:27There's not really like any open source alternative. Like I think over time Android has just been kind of getting more and more closed. So I think if you're us, you'd kind of need to worry that if we stopped pushing the industry in this store, direction that like all these other people maybe are only really doing it because they're trying to kind of compete with us in the direction that we're pushing things and You know they've already have their revealed preference for what they would build if open source didn't exist and it was an open source, right? so So I just think we need to be careful about relying on that continued behavior for the future of the technology that we're going to build at the company.

47:11I mean, another thing I've heard you mention is that it's important that the standard gets built around American models like Lama. I guess I wanted to understand your logic there because it seems like what's certain kinds of networks. It is the case that the Apple App Store just has a big contingency around what it's built around. But it doesn't seem like, you know, you, if you build some sort of scaffold for deep -seek, you couldn't have easily just switched it over to Lama 4, especially since between generations. Lama 3 wasn't, MOE, Lama 4 is. So things are changing between generations and models as well.

47:43So what's the reason for thinking things will get built out in this contingent way on a specific standard? I'm not sure what do you mean by contingent. Or as in like it's important that people are building for Lama rather than for LLMs in general, because that will determine what the standard is for the future. Sure, well, look, I mean, I think these models encode values and ways of thinking about the world. And we had this interesting experience early on where we took an early version of Lama and we translated it. I think it was, it might have been into French or some other language. And the feedback that we got, I think it was French.

48:22From French people was this sounds like an American who learned to speak French. Like it doesn't sound like a French person. It's like, what do you mean? It doesn't not speak French well. It's like, no, it speaks French fine. It's just like the way that it thinks about the world is like, seems slightly American. So I think there's like these subtle things that kind of get built into it. Over time, as the models get more sophisticated, they should be able to embody different value sets across the world. So maybe that's like a very kind of, not particularly sophisticated example, but I think it sort of illustrates the point.

49:00And some of the stuff that we've seen in testing some of the models, especially coming out of China, is they sort of have certain values encoded in them. And it's not just like a light fine tune to get that to feel the way that you want. Now, the stuff is different. So I think language models, or something that has a kind of like a world model embedded into it, have more values. reasoning, I think, is, I mean, I guess there are kind of values or ways to think about reasoning, but one of the things that's nice about the reasoning models is their train unverifiable problems. So, do you need to be worried about, like, cultural bias if your model is doing math?

49:44Probably not. I think that that's, you know, I think it's like the chance that, like, like some reasoning model that was built elsewhere is like going to kind of incept you by like solving a math problem in a way that's, that's, DVS seems low. There's also a different issue is I think we're on coding, which is the other verifiable domain, which is, you know, I think you kind of need to be worried about like waking up one day and like does a model that I have some tie to another government, like, can it embed all kinds of different vulnerabilities in code that then like the intelligence organizations associated with that government can then go exploits.

50:27And now you sort of like, all right, like in some future version where you have, you know, some model from some other country that we're using to like secure or build out a lot of our systems. And then all of a sudden you wake up and it's like everything is just vulnerable to in a way that country knows about, but you don't. Or it turns on of vulnerability at some point. Those are real issues. What we've basically found is, I'm very interested in studying this because I think one of the main things that's interesting about open source is the ability to distill models. Most people, the primary value isn't just taking a model off the shelf and saying, okay, I met a built this version of Lama, I'm gonna take it, and I'm gonna run it exactly in my application.

51:14It's like, no, well, your application isn't doing anything different if you're just running our thing, you're at least gonna fine -tune it or try to distill it into a different model. And when we get to stuff like the behemoth model, like the whole value in that is being able to basically take this very high amount of intelligence and distill it down into a smaller model that you're actually gonna run. But this is like the beauty of distillation. And it's like one of the things that I think has really emerged as a very powerful technique in the last year since the last time we sat down is, and I think it's worked better than most people would predict, as you can basically take a model that is much bigger and take probably like 90 or 95 % of its intelligence and run it in something that's 10 % the size.

51:55Now, do you get 100 % of the intelligence? No, but like 95 % of the intelligence at 10 % of the cost is like pretty good for a lot of things. The other thing that's interesting is now with this like more varied open source community where you, it's not just Lama, you have other models, you have the ability to distill from multiple sources. So now you can basically say, okay, Lama's really good at this, like maybe the architecture's really good because it's fundamentally multimodal and fundamentally more inference friendly and more efficient, but like, let's say, this other model's better at coding.

52:30Okay, well, you can distill from both of them and then build something that's better than either of them for your own use case. So that's cool, but you do need to solve the security problem of knowing that you can distill it in a way that is safe and secure. And so this is something that we've been researching and have put a lot of time into, and what we've basically come to is that, look, anything that's kind of like language is quite fraught because there's like a lot of values embedded in that. So unless you don't care about having the values from whatever the model is that you you probably don't want it to still to straight, like, language world model.

53:08On reasoning, I think you can get a lot of the way there by limiting it to verifiable domains, running kind of code, cleanliness and security filters, like whether it's like the LamaGuard open source or the code shield open source things that we've done, that basically allow you to incorporate different input put into your models and make sure that both the input and the output are secure. And then just a lot of red teaming to make sure that you just have people or experts who are looking at this as a guard, is this model doing anything that isn't what I want after distilling from something. And I think with the combination of those techniques, you can probably distill on the reasoning side for verifiable domains quite securely.

53:57That's something I'm pretty confident about. and it's something that we've done a lot of research around. But I think this is a very big question. Is like, how do you do good distillation because there's just so much value to be unlocked, but at the same time, I do just think that there is some fundamental bias in the different models. Speaking of value to be unlocked, what do you think the right way to monetize AI will be? Because obviously digital ads are quite lucrative, but as a fraction of total GDP, it's small, and comparison to like all the remote work, like even if you can increase its productivity and not replace work, that's still worth tens of trillions of dollars.

54:33So is it possible that ads might not be it? Or yeah, how do you think about this? I mean, like we were talking about before, there's going to be all these different applications and different applications tend towards different things. Ads is great when you want to offer people a free service, right? Because it's free, you need to cover it somehow. Ads is like, okay, it's ads solves this problem of like, a person does not need to pay for something, and they can get something that is like amazing for free, And also, by the way, with modern ad systems, a lot of the time people think that the ad's ad value to the thing.

55:04If you do it well, right? It's, you know, you need to be good at ranking and you need to be good at having enough liquidity of advertising inventory. So that way, you know, if you only have five advertisers in the system no matter how good you are at ranking, you may not be able to show something to someone that they're interested in. But if you have a million advertisers in the system, then you're probably going to be able to find something pretty compelling if you're good at picking out the different needles and the haystack that that person's going to be interested in. So I think that definitely has its place, but there are also clearly going to be other business models as well, including ones that just have higher costs, so it doesn't even make sense to offer them for free.

55:48Which by the way, there have always been business models like this. There's a reason why social media is free and ad supported. But then if you want to watch Netflix, or ESPN or something, you need to pay for that. It's OK, because the content that's going into that, they need to produce it, and that's very expensive for them to produce. And they probably could not have enough ads in the service in order to make up for the cost to produce in the content. So basically, you just need to pay to access it. Then the try -off is fewer people do it. It's like they're talking about hundreds of millions of people using those instead of billions.

56:20So there's kind of a value you switch there. I think similar here. Not everyone is going to want a software engineer or a thousand software engineering agents or whatever it is. But if you do, that's something that you are probably going to be willing to pay, thousands or tens of thousands or hundreds of thousands of dollars for. So I think that this just speaks to the diversity of different things that need to get created. is they're going to be business models at each point along the spectrum. And it met a, yeah, for the consumer piece, we definitely want to have a free thing. And I'm sure that will end up being ad supported.

57:01But I also think we're going to want to have a business model that supports people using arbitrary amounts of compute to do really even more amazing things than what it would make sense to be able to offer the free service. And for that, I'm sure we'll end up having a premium service. But I mean, nothing are basic values on this, so we want to serve as many people in the world. Lambda is the cloud for AI developers. They have over 50 ,000 Nvidia GPUs ready to go for startups, enterprises, and hyper -sailers. Compute seems like a commodity though, so why use Lambda over anybody else? Well, unlike other cloud providers, Lambda's only focus is AI.

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58:19Head to lambda .ai slash the work hash for a free trial of their inference API featuring the best open source models like deep seek and lama four at the lowest prices in the industry. All right, back to Zuck. How do you keep track of, you've got all these different projects some of which we've talked about today, I'm sure there's many I don't even know about. as the CEO overseeing everything. There's a big spectrum between like, going to the Lama team and here's the hyper parameters you should use to just giving like a mandate, like go make the AI better, and there's many different projects. How do you think about the way in which you can best deliver your value add and oversee all these things?

58:58Well, I mean, a lot of what I spend my time on is trying to get awesome people onto the teams, right? I mean, it's, so there's that, and then there's stuff that cuts across teams. It's like, all right, you build MetaAI and you want to get it into WhatsApp or Instagram. It's like, okay, then I'm now going to get those teams to talk together. And then there's a bunch of questions like, okay, I was, you know, it's like, okay, do you want the thread for MetaAI and WhatsApp to feel like other WhatsApp threads, or do you want it to feel like other kind of like AI chat experiences? There's like different idioms for those.

59:35And so I think that there's like all these interesting questions that sort of need to get answered around like how does this stuff basically fit into all of what we're doing. Then there's a whole other part of what we're doing, which is basically pushing on the infrastructure. If you want to stand up a gigawatt cluster, then first of all, that has a lot of implications for the way that we're doing infrastructure buildouts. It has sort of political implications for how you engage with the different states where you're building that stuff. It has financial implications for the company in terms of, all right, there's a lot of economic uncertainty in the world.

1:00:09Do we go double down on infrastructure right now? And if so, what are their trade offs do we want to make around the company? I mean, those are things that it's tough for other people to really make those kind of decisions. And then I think that there's this question around like taste and quality, which is like, when is something good enough that we want to ship it? And I do feel like in general, I'm the steward of that for the company, although we have a lot of other people I think have good taste as well, who are also filters for different things. But yeah, I think that those are basically the areas.

1:00:49But I think AI is interesting because more than some of the other stuff that we do, it is more of research and model led, been really product led. Like you can't just like design the product that you want and then try to build the model to fit into it. You really need to like design the model first and like the capabilities that you want and then you get some emergent properties, then it's going to build some different stuff because this kind of turned out in this way. And I think at the end of the day, like people want to use the best model, right? So that's partially why, you know, when we're talking about building the most personal AI, the best voice, the best personalization, and also a very smart experience with very low latency.

1:01:36Those are the things that we basically need to design the whole system to build, which is why we're working on full duplex voice, which is why we're working on the personalization to both have good memory extraction from your interaction with AI, but also be able to to all the other meta systems and why we design the specific models that we designed to have the kind of size and latency parameters that they do. Speaking of politics, there's been this perception that some tech leaders have been aligning with Trump, you and others have donated to his inaugural event and we're on stage with them and I think you settled like a lawsuit which resulted in them getting $25 million.

1:02:15I wonder what's going on here? Does it feel like the cost of doing business with the administration or what's the best sort of thing about this? I mean, my view on this is like, he's the president of the United States. Our default as an American company should be to try to have a productive relationship with whoever is running the government. I would do this, like we've tried to offer to support previous administrations as well. I've been pretty public with some of my frustrations with the previous administration, how they basically did not engage with us or the business community more broadly, which I think frankly, I think is going to be necessary to make progress on some of these things.

1:02:56Like we're not going to be able to build the level of energy that we need if you don't have a dialogue and they're not prioritizing trying to do those things. But fundamentally, I mean, I think a lot of people want to write this story about what direction or people going. I just think we're trying to build great stuff. We want to work with, we have a productive relationship with people. And that's how I see it. And it is also how I would guess most others see it. But obviously I can't speak for them. You've spoken out about how you've rethought some of the ways in which you engage and defer to the government in terms of moderation and stuff in the past.

1:03:42How are you thinking about AI governance? Because if AI is as powerful as we think it might be, the government will want to get involved. What is the most productive approach to take there? What's the government thinking about here? Yeah, I guess in the past, I probably just... I mean, most of the comments that I made, I think we're in the context of content moderation. Right where, you know, it's been an interesting journey over the last 10 years on this, where there have obviously been an interesting time in history. There have been novel questions raised about online content moderation. Some of those have led to, I think, productive new systems getting built, like our AI systems to be able to detect nation -states trying to interfere in each other's elections.

1:04:25I think we will continue building that stuff out and that I think has been that positive. Right. I think other stuff, we went down some bad paths. I just think the fact -checking thing was not as effective as community notes, because it's not an internet scale solution. There weren't enough fact checkers. People didn't trust the specific fact checkers. You want a more robust system. I think what we got with community notes is the right one on that. I point on this was more that I think historically, I probably deferred a little bit too much to either the media and their critiques, or the government on things that they did not really have authority over, but just as a central figure.

1:05:14Like I think we tried to build systems that where maybe we could not have to make all of the content moderation decisions ourselves or something. And I guess I just think part of the growth process over the last 10 years is just, okay, we're a meaningful company, we need to own the decisions that we need to make, we should listen to feedback from people, but shouldn't defer too much to people who do not actually have authority over this because at the end of the day, we're in the seat and we need to own the decisions that we make. So I think it's been a maturation process and some ways painful, but I think we're probably a better company for.

1:05:58Will tariffs increase the cost of building data centers in the US and shift buildouts to Europe and Asia? It is really hard to know how that plays out. I think we're probably in the early innings on that and it's very hard to know. Got it. What is your single highest leverage hour in a week? What are you doing in that hour? I don't know. I mean, every week is a little bit different. And it's probably got to be the case that the most leveraged thing that you do in a week is not the same thing each week or else by definition you should probably spend more than one hour doing that thing every week.

1:06:34But yeah, I don't know. It's part of the fun of both, I guess, this job, but also the industry being so dynamic is like things really move around. Right? And the world is very different now that it was at the beginning of the year, then it was six months into the middle of last year. I think a lot has sort of really advanced meaningfully, and a lot of cards have been turned over since the last time that we sat down. I think that was about a year ago, right? Yeah, yeah. I guess you were saying earlier that recruiting people is a super high -level thing you do. It's very high -level. Yeah. What would be possible if you talked about these models being mid -level software engineers by the end of the year, What would be possible if a sales offer productivity increased like 100x in two years?

1:07:20What kinds of things could be built that we can't build right now? What kinds of things? Well, it's, that's an interesting question. I think one theme of this conversation is that the amount of creativity that's gonna be unlocked is gonna be massive. And if you look at the overall arc of kind of human society and the economy over 100 or 150 years, it's basically people going from being primarily agrarian and most of human energy going towards just feeding ourselves to that has become a kind of smaller and smaller percent in the things that take care of like our basic physical needs or a smaller and smaller percent of human energy, which has led to two impacts.

1:08:10One is more people are doing kind of creative and cultural pursuits. And two is that more people in general spend less time working and more time on entertainment and culture. I think that that is almost certainly going to continue as this goes on. This isn't like the one to two -year thing of what happens when you have a super powerful software engineer, but I think over time, you know, if you, like everyone is going to have these superhuman tools to be able to create a ton of different stuff. I think you're going to get this incredible diversity. Part of it is going to be solving like things that we hold up is like, these like hard problems like solving diseases or like solving different things around science or just like different technology that makes our lives better.

1:09:01But But I would guess that a lot of it is going to end up being kind of cultural and social pursuit and entertainment. I would guess that the world is going to get a lot more, like a lot funnier and like weirder and corkier in a way that like the memes on the internet have sort of gotten over the last 10 years. And I think that that adds a certain kind of richness and depth as well that in kind of funny ways I think it actually helps you connect better with people because now like, I don't know, it's like all day long, I just find interesting stuff on the internet and like send it in group chats to the people I care about who I think are going to find it funny and it's like, like the media that people can produce today to express very, very nuanced specific cultural ideas, I don't know, it's cool and I think that'll continue you to get built out.

1:09:56And I think it does advance society in a bunch of ways, even if it's not like the hard science way of curing a disease. But I guess this is sort of if you think about it like the like meta social media view of the world is like, yeah, I think people are going to spend a lot more time doing that stuff in the future. And it's going to be a lot better and it's going to help you connect because it's going to help express different ideas. Because the world's going to get more complicated, but like our technology or or cultural technology to kind of express these very complicated things in like a very kind of funny little clip or something are gonna just get so much better.

1:10:32So I know that's all great.

1:10:37I don't know, next year for, I tend to, I mean, just one other thought that I think is interesting to cover is I tend to think that it for at least the foreseeable future. this is going to lead towards more demand for people doing work not less. Now people have a choice of how much time they want to spend working. But I'll give you one interesting example of something that we were talking about recently. We have like three, almost three and a half billion people use our services every day. And one question that we've struggled with forever is like how do we provide customer support? Right, today, you can write an email, but we've never seriously been able to contemplate having voice support, where someone can just call in.

1:11:27And I guess that's maybe one of the artifacts of having a free service, right, is the revenue per person's not so high that you can have an economic model that people can kind of call in. But also, I'm with three and a half billion people using your service every day. I mean, there'd be a massive number of people, like the biggest call center in the world type of thing. But it would be like $10, $20 billion, some ridiculous a year to kind of staff that. So we've never really kind of thought too seriously about it because it was always just like, no, there's no way that this kind of makes sense.

1:11:59But now, as the AI gets better, you're gonna get to this place where the AI can handle a bunch of people's issues. not all of them, right? Cause maybe 10 years from now or something it can handle all of them. But when we're thinking about like a three to five year time horizon, it'll be able to handle a bunch kind of like self driving cars can handle a bunch of terrain, but in general they're not like doing the whole route by themselves yet in most cases, right? It's like people thought truck driving jobs were gonna go away. There's actually more truck driving jobs now than there were like when we started to talk to them at self -driving cars, in whatever it was almost 20 years ago.

1:12:42And I think for going back to this customer support thing, it's like, all right, it wouldn't make sense for us to staff out,

1:12:52calling for everyone, but let's say the AI can handle 90 % of that. Then like, and then if you can't handle it, then it kicks it off to a person. Okay, now like if you've gotten the cost of providing that service down to one tenth of what it would have otherwise been, then, all right, maybe now that actually makes sense to go do. That would be kind of cool. So, the net result is like, I actually think we're probably going to go higher and more customer support people, right? It's like the common knowledge or like the kind of common belief that people have is that like, oh, this is clearly just going to automate jobs and like all these jobs are going to go away.

1:13:27I actually just, that has not really been how a history of technology has worked. It's been, you know, you can, you like create things that take away 90 % of the work and that leads you to want more people, not less. Final question, who is the one person in the world today who you most seek offer advice? Oh, man. Well, I feel like it's part of my style is I like having a breadth of advisors. So it's not just one person, but we've got a great team. I think there's people at the company, people on our board.

1:14:09There's a lot of people in the industry who are doing new stuff. There's not like a single person, but it's fun. And also as when the world is dynamic, I'm just having a reason to work with people you like on cool stuff. To me, that's what life is about. Yeah. All right, great note to close on. Awesome. Thanks for doing this. Yeah, thank you. I hope you enjoyed this episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it. Send it to your friends, your group chats, Twitter, wherever else. Just let the word go forth. Other than that, super helpful if you can subscribe on YouTube and leave a five star review on Apple Podcasts and Spotify.

1:14:49Check out the sponsors in the description below. If you want to sponsor a future episode, go to doarkesh .com slash advertise. Thank you for tuning in. I'll see you on the next one.