I think where it's all leading to is an effort to ban opensource models. There's a lot of breadcrumbs leading here. If you look at a lot of the rhetoric around how models need to have guard rails and that with open source models, the guardrails can be removed and therefore they're dangerous. You see this rhetoric already in Anthropics blog posts. Any threat that they describe, they kind of go out of their way to take that shot at open source models. I think again they're trying to create ideas or put predicate facts in the public record to justify an action later on.

Sacks frames Anthropic's safety rhetoric as a deliberate lobbying strategy to build a legal and political foundation for banning open-source AI competitors.

I think it would be a tragic mistake if the government were to take action against the open source ecosystem. That would do nothing but hurt America's position in this AI race. It would backfire badly. And I think that the key point here is that regardless of what you think about distillation, you cannot punish American developers for it. So, you know, you can't say that American companies and American developers can't use Chinese contributions to the public domain. That's just cutting off our nose to spite our face.

Sacks articulates the core policy argument that restricting American access to open-source AI, regardless of its origin, would harm U.S. competitiveness more than it would protect it.

The tell on this, the way that you know that this whole distillation thing is fake is because if stopping distillation was their primary objective, Anthropic would push to ban Chinese access to American models, not American access to Chinese models.

Sacks offers a falsifiability test for Anthropic's stated motivations, arguing that the direction of their lobbying reveals the true goal is competitive protection, not national security.

Tamatha has been saying for a while, why don't you KYC your customers? Well, they know that if they KYC their customers, it'll slow their growth. So instead, what they're saying is, hey, ban our competitors. Well, that's ridiculous. I mean, they're in the best position to stop the distillation. I think that they're negligent about doing that. or I mean if they really think it's that big a threat, they should use a few points of their 90% gross margins to do that. What you don't do is then say that American developers cannot use everything that's in the public domain.

The panel argues Anthropic is making a deliberate business decision to forgo basic security hygiene in order to protect growth metrics, then externalizing the cost onto the broader ecosystem.

Distillation is when you fire up a model and you ask it a question and you observe it and you take its output and you use that in training of your own model. Now multiply that behavior by tens of millions and what you exfiltrate is essentially trillions of questions and answers. And Sax is right. If you really care about distillation, you implement KYC. You force people to make an account, not just with a username and a password, but with some form of identification, maybe with a bounded credit card. There's all kinds of steps that you can take that would frankly slow things down in terms of revenue traction, but would solve the distillation problem on its face.

Palihapitiya provides a clear technical definition of AI distillation and explains precisely why it is a solvable problem that Anthropic is choosing not to solve.

These models are getting commoditized much faster than anybody thought. And how do we know this? Because there is no meaningful sustained advantage once a model publishes their performance criteria. What you see is literally within weeks other models some open some closed some open weight who are able to match and in some cases exceed the performance. So I think what's happening here is a handful of American companies have realized whoa this value that we are seeing today may not be sustainable in a 5 and 10 year period.

Palihapitiya identifies rapid commoditization of foundation models as the core strategic threat facing closed AI labs, reframing the regulatory battle as a valuation defense.

The real business model is not in the foundational model anymore. It's at the application layer above and it's in the infrastructure below whether that's the cloud or whether that's chips. And so I think in the absence of regulatory intervention and in the absence of the United States government stepping in to put their thumb on the scale, what will happen is that as people learn about how value is changing, they're going to put more value in the application layer and more value in the infrastructure layer. That is bad for closed frontier labs, especially when they're mispriced 25 to 50x the open alternative.

Palihapitiya maps the AI value chain and argues that market forces, absent regulatory distortion, will naturally shift economic returns away from foundation model providers.

If anybody gets involved, so we should just not get involved. What it sounds like is you're saying that American enterprises will pay a token tax if the government gives anthropic and open AI a government enforced duopoly and enterprises are no longer free to use open source like the rest of the world. Yes. We will put ourselves on an island. We'll be on an island of overly expensive AI.

The hosts crystallize the economic argument against a ban: restricting open source creates a mandatory AI cost premium for American businesses relative to global competitors.

Fundamentally if you look back on the internet in the early days Netscape made a proprietary browser and a proprietary server software, the Netscape software, and that they went public, and they were the first to do this, and it was super valuable and profitable. That company ended up getting crushed because of open source. The Mozilla Foundation was formed to create an open-source web browser called Firefox, and then Google ended up hiring everyone and made it Chrome, but it was still open source. The Apache Foundation set up the first HTTP server as an open-source product. Rather than having to pay Netscape or Microsoft or Oracle for their server software, anyone with a computer could download the Apache software and make a web server and be on the internet and create a website. And what ended up happening is the value acrewed to the internet.

Friedberg draws a detailed historical parallel between the open-source browser wars and the current AI moment, arguing that open models will diffuse value broadly just as open internet infrastructure did.

We have to acknowledge that it's incredible how fast the value capture at this segment of the market has basically evaporated. I've never seen it in my 25 years in Silicon Valley where a sector of the economy can absorb hundreds and hundreds of billions of dollars and then you think that there's going to be economic pricing power many decades into the future and it effectively evaporates in months. Months.

Palihapitiya makes an unusually stark claim about the speed of value destruction in AI foundation models, suggesting the investment cycle is unlike anything in prior tech history.

Think about the strategy as well for China. If you think about the global economy of the last 50 years, the US has acrewed so much value by being at the core of the knowledge economy and effectively a services economy. And in that sense through the development of intellectual property of IP of knowledge and then the conversion of one bit to another bit we've been able to derive trillions of dollars in GDP. Meanwhile we outsourced manufacturing and created a sleeping giant in China where they have this incredible manufacturing capacity.

Friedberg reframes the open-source AI debate as a long-run geopolitical strategy by China to commoditize America's knowledge economy advantage while holding dominance in physical production.

What Anthropic did is they pirated all these books from LibGen and they trained on them. And the reason why they got in trouble is cuz they basically took stolen books. They didn't even pay for one copy of them. But if they had paid for just one copy of each book, they could not have been nailed for piracy. they would have been potentially under fair use... It is still Anthropic's position and it's OpenAI's position that they should be able to train on all these books under fair use if they buy one copy... they believe they should be able to train on every creator's output in the world... However, they say that the one type of content that you should never be able to train on is their output. That is currently their position. It's completely hypocritical.

Sacks exposes a direct logical contradiction in Anthropic and OpenAI's simultaneous legal positions — claiming broad fair use rights for themselves while arguing Chinese distillation of their outputs constitutes IP theft.

I suggest just like the self-regulatory group that we talked about last week, all the AI companies should get together, take 10% of your revenue, put it in a pool, and keep paying the people and getting permission from them so you can get updates on the content so you get the next book so you get the next New York Times story, the next Reuters story.

This proposes a concrete industry-wide licensing framework as an alternative to costly litigation over AI training data.

They have now basically confessed to their entire product being stolen. And it seems to me that why wouldn't the content creators now assert that they're entitled to own 100% of anthropics revenue? It seems to me that this could be a bridge too far that you know that that they're so good at regulatory capture. They're so good at making these arguments and getting the government involved to create new regulations to protect them. But I wonder if this was just a little bit too cute.

This highlights a potentially catastrophic strategic blunder by Anthropic — framing AI training as IP theft may have legally undermined their own entire business model.

if this IP theft thing sticks that all derivative works of Chinese models are tainted now too. So that means the whole startup ecosystem is now at risk.

The argument reveals that Anthropic's IP theft framing could have sweeping collateral damage on the entire AI startup ecosystem, not just Chinese competitors.

Do you know what Google's 25-year average return on invested capital has been since going public? 32%. Okay. This is when you are a machine and a group of people and a business model that compounds money at 32% over 20 year average. You give these guys the benefit of the doubt. These are not people that are flying fast and loose. They are methodically investing in their edge.

A 32% average ROIC over 25 years is a powerful data point that reframes Google's massive capex spending as disciplined capital allocation rather than reckless investment.

The best thing that can happen to them is 500 different models proliferate and they support all of them because they will make so much money at the silicon layer. They'll make so much money as the cloud provider and they'll find a bunch of apps including YouTube and other things to make money from because you use the AI to target ads better or to help make better content etc etc.

This articulates why AI model fragmentation paradoxically benefits Google most — as infrastructure provider, it profits regardless of which models win.

the minute that you sign up to be a web provider, Jason, and a cloud service provider, what you're really signing up for is 59s of reliability and uptime. And that is just extremely expensive. Getting to the first two nines, you know, 99% uptime. Getting to the third nine, 99.9 probably cost you in the billions. Getting to the fourth nine costs the tens of billions, but getting to that fifth nine costs hundreds of billions. And that takes a real investment and real technical skill, and there's only three games in town.

The exponential cost of each additional 'nine' of uptime explains why cloud infrastructure is a natural oligopoly nearly impossible for new entrants to replicate.

fundamental to the foundation of the United States of America was this idea of private property rights? Because if you think about where everyone that came to America was coming from, there were these tyrannical governments, monarchies or whatever, where some overlord or some cabal could decide at any point to take the things that you have. You had no private property rights as an individual. They could come in, they're like, 'That farm is my farm. You're actually a surf. I'm the lord.' And it was that stasis that drove so many to come to the United States and say we want a place where individuals, one person can say, I own something and no one can take it from me. Private property rights are the foundations of liberty in America.

Friedberg grounds the NYC eviction debate in foundational political philosophy, arguing that undermining landlord rights echoes the very tyranny America was founded to escape.

anarchy is a temporary state. It's always in between one state and another. All anarchies end up in tyranny. Groups of people fight each other. They're all stealing from each other. Everyone just goes and takes and gets what they want. And eventually people coalesce. They form groups. And those groups become the more powerful groups. And the powerful groups end up winning and they become the tyranny over the mass. And that is why all anarchies eventually evolve into tyranny. So anarchy and tyranny are one and the same.

Friedberg's historical-political argument that anarchy always precedes tyranny provides an intellectual framework for why incremental erosions of property rights are genuinely dangerous.

I think we also have to stop and consider what this means for the other residents in these buildings. I mean, first of all, if the landlords aren't making income because they got a bunch of delinquent tenants in the building, they can't now pay for upkeep and maintenance. And so, these buildings become more dilapidated, and that affects the other tenants... these DSA types are always these like highly educated and often affluent types and they can afford to have luxury beliefs about public spaces because they never use them, right? They don't use the bus or the subway or parks. And so when they get taken over by homeless drug addicts, they always defend the addicts as opposed to the middle class.

Sacks reframes anti-eviction policy not just as a landlord issue but as one that most harms the working-class tenants it purports to help — a powerful second-order effects argument.

If you want to solve the housing problem, anybody with any basic understanding of economics would just say, well, increase the supply and the price will go down. And it actually doesn't matter which supply you add. It doesn't matter if it's luxury units or multifamily or single family. As long as there's more housing and there's transportation to get to it and to move people in and out, it'll be fine. As I like I'm sitting here in Tokyo, like they figured this out a long time ago. Just build up and put more units in. They figured it out in Texas, Florida, Nevada. The only people who can't seem to figure this out, you know, is like New York, LA, and San Francisco just happens to be liberal elite enclaves.

The supply-side housing argument, backed by Tokyo and Austin as real-world examples, directly challenges why rent control advocates ignore the most proven solution to housing affordability.