The first time we're releasing a 405 billion parameter model, by far the most sophisticated open source model that I think anyone has put out. I was a little shocked by how directly you called out Apple and their closed approach. Can you kind of expand on that and where Apple has been a blocker for meta? Yeah, I think it's a little bit soul crushing when you go build features that are what you believe is good for your community and then you're told that you can't ship them because some company wants to put you in a box so that they can better compete with you. Can you talk more about your long term vision for what you think is going to happen with AI and possibly AGI in the future? All right, Mark, thanks so much for doing this. Obviously, huge AI announcement from Meta today. Can you give us the rundown on everything released and why it's important? Yeah, sure. So, I mean, the big release today, first of all, happy to be doing this big fan of what you do. The big release today is Lama 3.1 and we're releasing three models. The first time we're releasing a 405 billion parameter model. So it's by far the most sophisticated open source model that I think anyone has put out. And it really kind of is competitive with some of the leading closed models and in some areas is even ahead. So I'm really excited to see what people do with that, especially now that we're making it so that the community policies around Lama allow people to use it as a teacher model to distill and fine tune and basically create whatever other models they want with it. In addition to that, we've distilled the 405 billion parameter model down to make newer and updated and now leading for their size 70 billion and 8 billion parameter models. They also have really good performance, really good kind of cost per performance ratios. So I'm really excited to see what everyone does with this. Yeah, I mean, taking a step back, I think this is a pretty big moment for open source AI. Yeah, I've been reflecting on this and I kind of think it's, you know, I've thought for a while that open source AI was going to become the industry standard. And I thought that it would basically follow the path that Linux did, where, you know, if you if you just go back to before Linux was popular there, you know, there are all these companies that their own closed versions of Unix. And at the time, you know, there's nothing that was sort of that sophisticated that had ever been done as an open source project. And people thought, hey, no, this is like the closed model of development is the only way to do something that's this advanced. And at first, Linux kind of got its foothold because it was cheaper, because developers could customize it in different ways. And then over time, as the ecosystem built out, it, you know, got more scrutiny. So it actually became the more secure one, it became the more advanced one. There were more partners that basically built more capabilities. And in the case of Linux, more drivers, and things like that, that basically ended up making it have more capabilities as well than any closed source Unix. So I think that this moment with Lama 3.1 is kind of like that inflection point, where I think Lama has the opportunity to become the open source AI standard for open source to become the standard, the industry standard for AI. And even in the places where it's not yet ahead on performance, it leads on on kind of cost and on on customizability and on the ability to take the model and fine tune it and do all the things that you want with it. So I think that those are just huge advantages that we're going to see developers take. And we're focusing on building out this partner ecosystem. And there are going to be all these different capabilities that get built out around it. So yeah, really excited to talk about all that. Yeah, I've seen all the benchmarks. It looks incredible. Obviously, the first ever open source frontier model for 405b. Are there any specific real world use cases that you're really excited about to see people build with the model? Well, the thing that I'm most excited about is seeing people use it to distill and fine tune their own models, right? It's, I mean, like you're saying, I mean, this is the first open source frontier level model, but it's not the first frontier level model. So there have been other models that sort of have that capacity and yeah, people are going to want to do inference directly on the 405 because it's, you know, by our estimates, it's going to be at 50% cheaper, I think, than GPT-40 to do that directly. And so I think that obviously makes a difference to a lot of people. But the thing that I think is really new in the world with this is the, because it's open weights, the ability to take the model and distill it down to whatever size that you want, to use it for synthetic data generation, to use it as a teacher model. You know, so our vision for the future, it's not just, okay, it was never that there's going to be one singular thing. I think this is like open AI sort of as this vision that they're going to build kind of one big AI. Anthropic does too. Google does too. It's never been our vision. Our vision is that there should be lots of different models. I think every startup out there, every enterprise governments, they all kind of want to have their own custom models. And yeah, when the closed ecosystem was so much better than open source, it was just better to take the vanilla closed thing off the shelf, because even though you could customize open source, there was still some gap between the performance that you could get. But now we don't see that anymore. Right now, as open source basically closes the gap, I think you're just going to see this wide proliferation of models where people now have the incentive to basically customize and build and train exactly the right size model for what they're doing, train their data into it. They're going to have the tools to do it because of a lot of the partner integrations that the companies like Amazon are doing with AWS or Databricks or different folks like that, who are building these whole suites of services for distilling and fine-tuning open models. So I think that that's going to be the thing that's new here. And that's really exciting is how far can that get pushed? And that's a completely new capability in the world because there hasn't been an open source or open weight model of kind of this sophistication that's ever been released before. Yeah, it's a really big deal. And how are you educating developers to use these tools? And more broadly, does Meta have like a plan or strategy to educate the rest of the world on open source and why it really matters? Yeah. So I'd say before Lama 3.1, our approach, I mean, the reason that Meta fundamentally is investing in this is we basically want to know that we have access to a leading model. Because of some of our history of kind of how mobile worked and things like that, we didn't want to be in a position where we had to rely on some competitor for this kind of fundamental technology. So we built it for ourselves. And before Lama 3.1, you know, we kind of had this instinct that if we made it open source, there would be a community that would grow around it. And that would actually extend the capabilities and make it more valuable for everyone, including us. Because at the end of the day, this isn't just a technology, it's an ecosystem, right? That you're developing. So in order for this to end up being a useful thing for us, there also needs to be a broad ecosystem. One of the big changes that I think we see with Lama 3.1 is instead of just building it for ourselves and throwing it over the wall and letting developers use it, this time we're really taking a much more proactive stance on building partnerships and making sure that there's this whole ecosystem of companies that can do interesting things with the model and can serve developers in ways that we're not going to. We're not a public cloud provider, right? I mean, we're not AWS or Google or Azure, right? So developers aren't going to come to us to build their stuff. But we want to make sure that all those public clouds are well-equipped to do this. That goes for some of the more basic functionality, like just hosting the models and serving inference. But we also want to make it set some of the new stuff that's going to be possible with this, like distillation and fine-tuning, that there's specific services that are set up for that. And, you know, a lot of those services haven't been that well built out before, because that's not really a thing that you could do as well with closed models. So there was specific work that we had to do with those partners to enable that. At the same time, I think that there are also going to be folks like Grok, right, who are doing really interesting work on really kind of ultra-low latency inference. And I'm really excited to get this in their hands. And they're building something for launch that basically is going to enable that too. And then there's this whole set of enterprise companies. So folks like Dell or, you know, Scale.ai or Deloitte or Accenture, who work with all the enterprises around the world on technology deployments. And I think those are going to be a lot of the folks who help build kind of custom models for, you know, whether they're large enterprises or governments. A lot of folks who basically want to have a model that's their model, that they can train their custom data into it. But they, you know, a lot of companies don't want to send their data over an API to, you know, Google or OpenAI. It's not because those companies have any particular privacy issue, but it's the same reason why people like encryption and WhatsApp, right? People just want kind of secure by design architectures where their data can kind of stay with them. And I think that there's going to be a whole market around things like that that get built out too. So I'm pretty excited about all of this. But yeah, we're taking a much more proactive position this time and helping to build out the ecosystem because I think that that's how this grows and just becomes more valuable for everyone. Yeah, I love how close you are to the developer community. Just me, myself being in the community, I know for a fact that people really want these private and local models. So moving on to your letter. So alongside of the meta AI announcements, you published a letter. And the first portion really focused on why open source is good for developers. And it felt like really spot on. Can you talk more about like what the broad societal implications are of open source AI? Yeah, I mean, my view is that open source is a really important ingredient to having a positive AI future. And that there are all these awesome things that AI is going to bring in terms of productivity gains and creativity enhancements for people. And hopefully it'll help us with research and things like that. But I think open source is an important part of how we make sure that this benefits everyone and is accessible to everyone. It isn't something that's just locked into a handful of big companies. At the same time, I actually think that open source is going to end up being the safer and more secure way to develop AI. I know that there's sort of a debate today about is open source safe. And I actually take the different position on it. It's not only do I think it's safe, I think it's safer than the alternative of closed development. And, you know, I sort of break it down into, you know, there are lots of different kinds of harm. So you can't just talk about one type of thing. But on this, I think that there's, there's unintentional harms. So the system goes off the rails in some way that people didn't intend. And then there's intentional harms where you have like some bad actors trying to use the system to do something bad. When it comes to unintentional harms, which I think, by the way, it's worth noting that like most of the sci-fi scenarios that people worry about of AI just going rogue are kind of unintentional. I actually think that open source should be safer on that because it's, it will have more scrutiny. It'll have more transparency. And I think all the developers who use it with all the llama guard and the safety tools that it comes with, there's going to be so much scrutiny and testing and pressure on those that my guess is that it will have kind of just like traditional open source software, any kind of issues with it, I think will be ironed out and fixed a lot quicker than with when the closed models. So I think you've got, you've got that on, on kind of unintentional harm, which is why I think most of the discussion around safety for open source revolves around intentional harm. It's okay. It's open. It's out there. How are you going to stop bad actors from, from doing it, doing bad things with it? There, I think you basically want to probably divide the problem into kind of smaller actors, like an individual or, or, or some kind of smaller group that's trying to create some, some, some mayhem and larger actors who are more sophisticated and have huge amounts of resources like big nation states. I think it's kind of a different mix for the two of those. You know, for the smaller actors and my view on this is that, you know, the way that we've, that I think that having a balance of power on this is super important. You know, what we've done in managing our social networks is we have all these kind of bad actors who are trying to do kind of bad stuff on our networks and the way, and a lot of times they deploy AI systems to do that. And the way that we stop them and identify them is by having more sophisticated AI systems that have more compute to go find what they're doing. So I think that this is actually pretty similar to the way that governments and law enforcement essentially maintain order in society. It's like, yeah, you have a bunch of rogue people who might be committing crimes, but you know, generally the police forces and the militaries are much better funded, have more resources. And I think that that's basically going to be true here. As a matter of fact, I think what you want is for open source to be widely deployed, which I think that there's sort of a risk if it's closed, that that's not the case, but when it's open, you're going to have all these big institutions that have a ton of resources that they can basically deploy these systems in a way that I think will check bad actors. Then you get to, um, the question of, of basically, you know, folks like China or like large sophisticated actors. And one of the questions that you sometimes hear debated is like, okay, if you're open sourcing the really advanced models, how do you make it so that, that it doesn't get to, to, to China, or they're not going to use that against us. And, um, and that's sometimes an argument that people have for, Hey, you should lock down development. But I think that that's sort of missing a few things. One is that in order for this all to work, the U S has to have an advantage in the first place or the West and, and, and kind of, our advantage is basically open and decentralized innovation, right? It's not just a small number of big companies or labs, it's startups and universities and individuals hacking on things who are already parts of companies. And that's a big part of it. And you don't want to shut that down. So, and I think if you do, you, you, you increase the chance that we don't even lead in the first place, but then I think you get to the, the issue, which is okay. Like China or not even China, any government, um, you know, the, the, the, all the risks of, of kind of stealing the models and an espionage. I mean, a lot of the models fit on, you know, a hard drive that you can, you know, quickly put in your backpack or whatever. And it's, um, I, I just think we need to be realistic about how likely it is that we can secure, um, and not, not just not us, but like any of the tech companies can secure any of these, um, models long-term against very sophisticated efforts to do that. Yeah. So my own fear is that if we lock down development, we end up in a world where basically you have a small number of companies, plus all the adversaries who can steal the model are the only ones who have access, but all the startups, all the universities, all the individual hackers are kind of just left out and don't have the ability to do this. So my own view is that a realistic aim that we should hope for is, um, is that we use open source to basically develop the leading and most robust ecosystem in the world. And that we have an expectation that our companies work closely with our government and allied governments on national security. So that way our governments can persistently just be integrating the latest technology and have a, you know, whatever it is, a six month advantage, eight month advantage on our adversaries. And I think that that's, you know, I don't know that, that in this world you, you get a 10 year permanent advantage, but I think, uh, a kind of perpetual lead actually will make us more safe, um, in one where we're leading than the model that others are advocating, which is okay. You have a small number of closed labs. They lock down development. We probably risk being in the lead at all. Like probably the other governments are, are, are, are getting access to it. Uh, it's that that's my view. I actually think on, on both these things, spreading prosperity for, for, um, more evenly around the world, making it so that there can be more progress and on safety, I think we're basically just going to find over time that open source leads, um, look that there are going to be issues, right? It's like, we'll have to mitigate the issues. We're going to test everything rigorously. We do, we work with governments on all this stuff. We'll continue doing that. Um, but that's my view of, of kind of where the equilibrium, I think will settle out given what I know today. Yeah. Let's talk about more on the benefits of open source AI. Um, so another thing you mentioned in your letter is that open source AI can accelerate innovation and economic growth. Um, how is this already happening and how do you see this happening more in the future? Yeah. I mean, I think that there's a version of this, which AI will do no matter how it's developed. Um, and then there's a version of this that I think benefits from open source specifically. So I think that AI has more potential than any other single technology that's being developed right now to increase productivity, accelerate the economy, um, make it set kind of every person has the ability to be more creative and, and, and produce more interesting things. And I think that that's all going to be great. I also think, I hope that it'll help out with science and, um, medical research and things like that. Um, there are a lot of folks today though, who don't necessarily have access to the ability to fine tune or build their own state of the art models. So they're sort of limited to what these large labs do. Um, and like I just said, I think, um, you know, one of the defining aspects of our culture around innovation as a sort of a country or society is like, it's not just big companies that do it, right? There's all these startups and hackers and academics and people in university. And I think you want to give all of those folks access to state of the art models that they can build on top of not just that they can run, which is what they have today with, with the closed vendors, but that they can build on top of and tweak and distill down to smaller models that they can run on their laptop or their phone or whatever other device they're building. And I think that that's just going to unlock a ton of progress. There's also a version of this where there are, you can look at it by, um, you know, nation too. Um, you know, so it's not just that startups might not have the resources or universities might not have the resources to go train their own, um, you know, large scale foundation models now or in the future. But, um, but there are a lot of countries that aren't going to have the ability to do that because I mean, you know, pretty soon these things are going to cost many billions of dollars to train. And, um, I think that having the ability for different countries and entrepreneurs and different countries and businesses to use it to serve people better and just do better work is going to be something that, that basically like lifts all boats around the world. And, um, it just has a massive kind of equalizing effect. So I know that that's really positive. And, you know, that's one of the places where we get the most, um, thanks for this is not actually the tech community, but it's just, it's like different developing countries or other countries that want to have access to the technology and do stuff with it. Um, but, but wouldn't necessarily have the tech field themselves to produce something that is state of the art that their business can build on top of, but once they have it, it's actually pretty easy to go train your own thing. So, um, so that's, uh, that's, that's a pretty neat part of this. Yeah. I love it. Um, one other thing I want to touch on from the letter is I was a little shocked by how directly you called out Apple and their closed approach. Um, can you kind of expand on that and where Apple has been a blocker for meta and potentially others? Yeah. I mean, my point in there is more, it's a little more philosophical on how it's affected my own kind of approach towards things and, um, and psychologically sort of affected how I think about building stuff. Um, I actually don't know how they're going to approach AI. Um, you know, they do some open development, they do some closed development. Um, you know, by the way, I think it's worth noting, like, I don't actually consider myself to be an open source zealot. I just think that in this case, um, I think that open models are going to be the standard and I think that that's going to be good for the world, but we do open development, we do closed development. So I get it right. And I'm not saying that Apple's necessarily going to be on the wrong place on this, um, for AI. But if you look back over the last 10 or 15 years, um, it has been a formative experience for us is building our services on top of platforms that are controlled by our competitors. And for a number of different incentives, they, they absolutely, from my perspective, apply different rules to kind of limit what we can do. And, and yeah, they have all these taxes and, you know, we, at some point we, we did, um, you know, we've done some analysis that we, we think we'd be way more profitable, um, if it weren't for some of these arbitrary rules. And, and I think a lot of other businesses would be too, but. You know, honestly, the, the money part, I think, um, it's annoying, but for me, it's not the biggest thing. It's, you know, I think it's a little bit soul crushing when you go build features that are, that you, that are what you believe is good for, for your community. And then you're told that you can't ship them because some company wants to put you in a box so that they can better compete with you. And my concern for AI at this point, isn't actually Apple. It's more the other companies and how that would evolve. And I think to some degree, it's not even that I'm not even saying that they're like bad people. It's, it's, um, I think that there's just a physics and incentive structure to the system where, you know, if you build a closed system, then eventually there are all these forces on you that, that sort of kind of push you to, to, to kind of clamp down on things. And, um, I, I think that it will be a healthier ecosystem if it's developed more like the web, but, um, but more capable. And I think that, you know, because of how mobile developed where the closed model won, right. And it's like Apple, I think has, has really reaped most of the benefits, um, in terms of, you know, they, there might be more Android phones out there, but like Apple gets like almost all the profits of, of, for mobile phones. I think there's a bit of recency bias bias because these are, these are long cycles, right? I mean, the, the iPhone, I mean, it came out in 2007, right? So we're almost 20 years into this thing. It's a long cycle. Um, but it's easy to forget the fact that the closed model doesn't always win. Um, if you go back to PCs, um, now I know a lot of people have, especially if you're using the Linux analogy, you know, people don't necessarily consider Windows to be maximally open, but compared to the, um, the Apple approach of, of kind of coupling your operating system with, um, with the device, the windows approach was a more open ecosystem and it won. And part of my hope for the next generation of platforms, which includes both AI and the work that we're doing in augmented and virtual reality is to, you know, meta wants to be on the side of building the open ecosystems. And it's not just that we want to build something that's an alternative to the closed ecosystem. I want to restore the industry to the state where the open ecosystem is actually the one that is leading. Um, so I think it's possible. I think we'll, uh, you know, I think we're, we're making good steps on that in both AI and AR and VR. Um, but, but that's something that I just personally and philosophically care about given the, the kind of limits on, on creativity that I, I've felt have, have sort of been applied to, to our industry by the closed model of, of mobile development over the last 10, 15 years. I want to go deeper on that point that you just mentioned of restoring the industry to the state where the open ecosystem is the one that's leading. Um, and so obviously right now we have llama 3.145b just came out. It's competitive and it even beats some of the best closed models across key benchmarks, which is insane in its own right. Uh, but in your letter, you also mentioned that llama four is expected to become the most advanced model of the industry. Are there any specific things about llama four that you're really excited about? Oh man. I mean, it's, um, you know, we're just doing 3.1 for, for, for llama now. I think it might be a little early to talk about llama four, but, um, but we've got the compute cluster set up. Um, we've got a bunch of the data set up. We, we kind of have a sense of what, what the, the architecture is going to be and, and, and I've run a bunch of research experiments to, to kind of max that out. So I, I do think that, um, llama four is going to be another big leap on top of llama three. I think we have, um, a bunch more progress that we can make. I mean, this is the first dot release for llama. Um, there's more that I'd like to do, um, including launching the, uh, the, the multimodal models, um, which we, we kind of had an unfortunate setback on, on, on that. Um, but, but I think we're going to be launching them probably everywhere outside of the EU, um, uh, hopefully over the next few months, but, um, yeah, probably a little early to talk about llama four, but, but it is going to be awesome. And it has been one of the interesting things in running the company is basically planning out the compute clusters and data trajectories for not just llama four, but you know, the next, um, probably four or five versions of llama, because I mean, these are long lead time investments to build out these data centers and, and the power around them and, um, and the chip architectures and the networking architectures. So all this stuff. Um, so yeah, I, I, I realize that's, that's a bit of a non-answer for now, other than just some general excitement, but, um, I don't know, let's, uh, I, I think llama three deserves at least, um, you know, llama 3.1 deserves at least a week of, of kind of just processing, um, you know, what we've put out there, uh, before we get into talking about the future. That's fair. And I completely agree that the next couple of weeks are going to be wild just with 3.1. Uh, but it's still super exciting, obviously to hear that Meta is already getting everything ready for llama four. Um, on that front though, can you talk more about your long-term vision for what you think is going to happen with AI and possibly AGI in the future? Yeah. I mean, I'm happy to talk about it both from a technical perspective and a product perspective, but since we've mostly talked about the models so far, maybe I'll start with, um, with the products. So our vision is that there should be a lot of different AIs out there and AI services, not just kind of one singular AI. Um, and that really informs the open source approach. It's, you know, it, it also informs the product roadmap. So yeah, we, we have Meta AI, um, Meta AI is doing quite well. My goal was for it to be the most used AI assistant in the world by the end of the year. I think we're well on track for that. We'll probably hit it, hit that milestone. Um, you know, a few months before the end of the year. Um, and, and look on that, you know, I think we just have the ability in the business model to basically build in the most advanced models in the world and offer it to everyone for free. So I think that that's a, you know, a, a kind of a huge advantage. Um, it's really easy to use from all of our apps. Um, so I'm pretty excited about how that's going. So that's, yeah, we have the basic assistant. Um, and I, and I think that that's going to be a big deal, but even more than that, a lot of what we're focused on is giving every creator and every small business, um, the ability to create AI agents for themselves, um, making it so that every person on our platforms can create their own AI agents that they want to interact with. And if you think about it, these are just huge spaces, right? So there are hundreds of millions of small businesses in the world. And one of the things I think is really important is basically making it. So with a relatively small amount of work, um, a business can basically, you know, a few taps, um, stand up an AI agent for themselves that, uh, can do customer support sales, communicate with all their people, uh, all their customers. I kind of think that every business in the future, just like they have an email address and a website and the social media presence today, I think every business is going to have a, um, an AI agent that their customers can talk to in the future. And we want to enable that for all of those. So that's, it's going to be hundreds of millions, um, maybe billions of what kind of small business agents, similar deal for creators. Um, there are more than, it's more than 200 million people on our platforms who consider themselves creators who basically use our platform, um, in a way that is primarily for, you know, building a community, um, you know, putting out content, feel like it's, it's kind of like a part of their job is, is doing that. And they all have this basic issue, which is that there aren't enough hours in the day to engage with their community as much as they'd like. And likewise, I think that their communities would generally want more of their time, but, um, but again, not enough hours in the day. So I just think it's a, there's going to be a huge unlock where basically every creator can pull in all their information from social media, can train these systems, um, to reflect their values and their business objectives and what they're trying to do. And then people can, can interact with that. It'll be almost like this almost artistic artifact that creators create that, um, that people can, can, can kind of interact with in different ways. And then, and that's not even getting into all the different ways that I think people are going to be able to create, you know, different AI agents for themselves to do different things. So I think we're going to live in a world where there are going to be hundreds of millions of billions of different AI agents, eventually probably more AI agents than there are people in the world. And, um, and that people are just going to interact with them in all these different ways. So that's part of, you know, that's the product vision. Um, obviously there's a lot of business opportunity in that that's where we want to go make money. So we don't want to, we're not going to make money from selling access to the model itself. Um, cause again, we're not a public cloud company. We will make money by building the best products. An important ingredient to the best products is building is having the best models, which having the best kind of ecosystem around open source will help us do. So that's why it's kind of all aligned for us and why this is going to end up, I think being really valuable for us to build the, the highest quality products that we can, um, and, and have the best business results by, by kind of building out this open source community. But, but it's also why it's all philosophically aligned, right? We don't, we just don't believe that there's going to be kind of one big AI, whether it's a product or a model that everyone uses. We kind of fundamentally believe in having this broad diversity and different set of models and that, you know, every business and, um, you know, people are just going to want a lot of their own stuff that they're going to make. And I think that that's kind of going to be interesting. It's going to be a lot of what makes this interesting. Yeah. I think it's really interesting to see how Matt is integrating the tech directly into his products and obviously giving a frontier level AI model for free to billions of users is a huge deal. Um, but on that, I have a final question around skepticism and for context in the 1990s, skepticism around the internet was everywhere, but eventually it became almost irrational to oppose it. And it kind of feels like we're in a similar trajectory with skepticism around AI right now. Do you think we're kind of at that early stage and there'll be a point where anti-AI sentiment will be viewed similar to the internet today? And what kind of factors do you think will be crucial in changing that perspective? I think there are different ways of, uh, different ways that people can be worried about something. I mean, it's, um, I mean, financially, one thing that I'm quite aware of is the internet, um, had a big bubble burst before it succeeded. And, you know, so all the people who were very long on the internet, um, were eventually right, but sometimes things take a little longer to develop than you think. And you just need to have the commitment to see that through. And, um, that's something that I'm aware of because yeah, I mean, I, I, I'm really excited about, you know, all the unlocks that we're going to get from llama three and then llama four and then llama five. And I think that's going to translate into better products, but realistically, um, it's hard to know in advance when something is good enough that you're going to have a product that billions of people use. And then when it's ready to, to kind of be a large business. And I mean, look, we're all spending, you know, a lot of capital and, and, uh, on, on basically training these models. So I think that people are going to be probably losing money for quite a while. Uh, but, but I don't know, maybe, maybe, maybe that'll all happen quicker. I'm not, it's, it's, it's hard to know exactly. Um, the other part of this that I think you are more getting at is people's concern about what it means for their livelihoods. And on that, this is one of the reasons why I think the open source approach, the approach of, um, lots of different models out there that are kind of personalized and customized to, to every business and every creator and every person. Um, I think that's important because if this develops in a way where it's just, uh, you know, a small number of companies that build the products and benefit and people use the products and maybe they like talking to, to, you know, an AI assistant and that's valuable for them. But. You know, if, if that, if this doesn't in some way help lift all boats, then I think you end up eventually getting a backlash. And part of what I've spent some time thinking about after just looking at how the kind of web 2.0 stuff developed is in the next generation of technologies around AI around AR and VR, how do we create not just a kind of thriving set of products, um, and, and kind of economic kind of productivity gains, but how do we have like a better and more sustainable political economy around it where there's just way more people who are, who, who feel like they're, they're kind of bought in or benefiting from this, um, in supportive of, of, of the system. And, you know, I, I thought we did that reasonably well with social media, but, um, but I, you know, just looking at some of the feedback and some of the response from, from the world, um, I think that it's going to be important to do that even better with AI and some of the new technologies in order to, to, uh, mitigate some of the concerns that people are going to have about what this is going to mean for their livelihoods and, and jobs and their lives. Yeah. I don't think anyone could have said it any better. Um, but Mark, it's been amazing speaking with you. Thanks so much for doing this. And thanks for everything you and Matt are doing for the AI community. Happy to do it. And, um, I'm really looking forward to seeing what people build.. 
You are given a transcript from a YouTube video. Your task is to create 15 segments from this transcript that can be used for YouTube Shorts.Cut out fragments with interesting moments and conversations

Instructions:

1.Do Not Modify the Text: Extract segments exactly as they appear in the transcript. Do not change, correct, or rephrase any words, even if they contain errors or numbers written in letters.

2.Continuous Text Only: Each segment must be a continuous passage. Do not combine or merge parts from different sentences or sections.

3.Length Constraint: Ensure each segment is short enough to fit into a 1-minute YouTube Short when spoken.

4.Meaningful Content: Each segment should represent a complete and coherent thought or idea.

5.Formatting:

	Begin each segment with "/No/No/" followed by the segment number.
	Provide a catchy and relevant Title for each segment.
	Include the Text of the segment exactly as it appears in the transcript.
	Response Format:

	/No/No/
	1. TITLE: [Insert Title Here]
	   TEXT: [Insert Text Here]
	
	/No/No/
	2. TITLE: [Insert Title Here]
	   TEXT: [Insert Text Here]
	
	...

	/No/No/
	10. TITLE: [Insert Title Here]
	TEXT: [Insert Text Here]
	
	
Additional Notes:

Engaging Titles: Make sure the titles are engaging and accurately reflect the content of each segment.