AI Sovereignty Wars, Palantir-Nvidia Deal, SCOTUS Birthright Ruling, Newsom's CA Budget Lie

David Friedberg: The way I see this evolving is very much in line with what Alex Karp suggested on CNBC. If you go back a couple of years, I think we all assumed there was going to be this large hub, large spoke model for AI model development and deployment, meaning there would be these very large clusters, these large clusters would be ultimately capital advantaged. So those who had the most capital, which is why everyone's raised 10s and hundreds of billions of dollars, would be able to train models. And then there would be these large spokes, these large clusters for deploying those models with inference. So everyone's using the neo clouds and the hyperscalers and whatnot to run models. And then maybe they've got their own proprietary data layer that sits in front of that. But I think what everyone's realizing is they're better off developing their own weights and their own models using either an open source basis or there might be some intermediary business model that evolves, meaning there will end up being several large hubs that do all of the core foundational model development, then smaller hubs, meaning like clusters for training, that enterprises will use to train and develop their own proprietary advantaged models using their own data. And then there will be these much more distributed spokes because I think everyone's also realizing the value of on-prem. By putting a set of servers and building a cluster in your own data center or even in your own enterprise IT closet, you can run a lot of the workflows that you're using AI for your enterprise locally. And so I think the model is shifting where we're going from large hubs, large spokes, to large hubs, medium hubs, and then a distributed spoke model, where there will still be neo-clouds and hyperscalers that are being used for inference, but people will also have their own inference instances that they're going to run for their own enterprise setting.

David Sacks: If you want to think about the AI stack for a minute, at the most basic level, there's three layers to the stack. It's basically the chips, it's the models, and then it's the applications. Right now we have in the middle layer, at the model layer of the stack, you've got two dominant companies. You've got Anthropic and OpenAI. We know that Anthropic's around 60-something billion of ARR. OpenAI is at 40-something billion of ARR. As far as we know, no one else is really generating meaningful revenue at the model layer. So we already have, let's say, an emerging duopoly situation. We have Anthropic pushing for a regulatory capture agenda that would probably enshrine that duopoly situation at a regulatory level because they're pushing for a safety agenda where Dario explicitly says that these other models are not safe, you shouldn't have access to them. So you've kind of got that situation. You've got the market producing duopoly, you've got the government now potentially leaning not to bust up the duopoly, but maybe to enforce it. So that's sort of the emerging situation at the model layer. And so if you're an application at the top of the stack, like Palantir, or you're a chip company at the bottom of the stack, that's the last thing you want. You want a competitive model layer. Why? Because if you're an application, you don't want to be beholden to 1 model provider, right? You want to have a choice. And if you're an enterprise, you want to have a choice because you don't want to have to give up all of your proprietary knowledge. And if you're a chip company, you don't want a monopsony buyer situation. So I think really the whole ecosystem in a way, the chip companies, developers, applications, enterprises, everybody has an incentive for a competitive layer of the stack at the model layer. And really the only companies who don't are Anthropic and OpenAI, because obviously they want to dominate, they want to be a duopoly.

Chamath Palihapitiya: This is what I've been saying with, when I was going on my Open Claw, which now is like Hermes and some other products. Everybody in your organization is going to have a Mac Studio or a Dell with a massive amount of RAM, and you're going to spend $10, $20,000 per employee on local compute so that they can token max to a retard maxing level. Who cares what they do on their local computer? Who cares? Let them rip. And then you're going to give them a laptop that connects to it, and it syncs so you can control it. It's literally going to be a server per individual in your company. That's the way to model this in your brain. Everybody has their own language model that they're crafting 100% of the time as they work. And it's all local, so you don't have data leaks. Never trust anybody because there's too much at stake. You cannot risk your entire business. You might as well be part of the vanguard and start investing here. You're going to slow down to speed up is what's going to happen. Whether you're using 8090, Abacus, or any of these other solutions or rolling your own, you must have AI sovereignty, you must have intelligent sovereignty, or you're just giving your business over to your company's competitors.

David Friedberg: I think one of the things that this reveals quite clearly is that there is no buttered, slippery slide to job loss. I think everyone is realizing that, you know, I was wondering where you were going with that buttering it up. So is Truman. But I think, the idea that everyone had in their head two years ago, the narrative that was formed and everyone clutched onto, and by the way, you will not see the media reverse on this narrative. I've realized the importance here, if you're a reporter or you're a journalist and you come up with some story that says something that's happening or is going to happen or has happened or is about the future, you destroy your own credibility if the narrative shifts, you cannot ever let go of the narrative. And I think this is one of the things that we can kind of acknowledge is going on with this job loss narrative problem, is that even as all the data comes out, as all of the reconfiguring of how enterprises are using AI, as it's revealing to all of them that they're actually not just going to cut costs, but they're going to grow revenue, and it's going to be a kind of clunky way of getting there, and it's not going to happen overnight. It's not the slippery slope to job loss, to nihilism. Everyone's going to kind of wake up and be like, wait, this reality that we all thought we were living in is not really the reality that we are living in. We're living in a reality where AI is clunky. It takes some putting together. It's a little more complicated than we thought. It's definitely going to deliver value, but it's not about just turning off all the jobs and letting the genius AI solve all my enterprise problems and scale me into infinity without humans. And I think that that's a big kind of narrative shift. And you will not see the media accept that their narrative is wrong. Because as soon as they have to acknowledge that they were wrong in what they were saying about job loss and all the other senators and people that are proclaiming job loss, job loss, job loss. By the way, the reason they're making that proclamation is so that they can step in and control AI and they can drive their systems of socialism, which is what they're all looking to deploy. But if they had to come in and say, look, the data doesn't map to the narrative, their credibility is done. So they'll double down on it and they'll double down on it. And I'm telling everyone that's listening, look at the data. There is no job loss with AI. It is an absolute scam to tell the world that AI is taking away jobs and destroying jobs and the world is shifting. It is clunky. It is valuable. It is going to take some time and it is going to create far more jobs than it is destroying.