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Biggest LBO Ever, SPAC 2.0, Open Source AI Models, State AI Regulation Frenzy

All-In with Chamath, Jason, Sacks & Friedberg

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  • Gaming As The Internet’s Anchor
    • Video games are the internet’s anchor pillar of usage and can be bigger than social media.
    • Taking EA private lets it invest in AI, distribution, and long-term value away from quarterly pressure. Transcript: Jason Calacanis EA is being taken. What is your arm length, Jake-Al? David Friedberg Do you have a good arm length? My wingspan? Jason Calacanis My wingspan technically is enough to kick your ass with one hand tied behind my back. That’s actually what it is. David Sacks Rain Man, David Sack. We open source it to the fans and they’ve just gone crazy. Love you, Ben. Jason Calacanis This is Queen of King. I’m going all in. Okay, EA is being taken private in the largest take private deal in history. $55 billion. Man, that just stacks up to, let’s seexas power company in 2007 hca healthcare 33 billion this is a large deal investors in the take private include saudi’s pif silver lake and friend Of the pod jared kushner’s affinity partners 210 bucks a share 25 premium on the stock kushner’s largest LP and Affinity, as you know, Saudi PIF as well. The PIF has invested over $900 billion. You know many of the things, Lucid Motors, Live Golf, the SoftBank Vision Fund, Uber back in the day, Newcastle, the Premier League. Electronic Arts obviously is in the video game business. They were founded at sequoia’s office in 1982 in san mateo shout out to our guy rulolf botha who joined us for the all-in summit their headquarters still in redwood city madden nfl the Sims oh that’s why you have the background the sims this week need for speed pretty insane deal here chamath and this is a high watermark for private equity. Anyway, you look at it and the PIF loves games. They are the biggest shareholder in Nintendo, Savvy Games, Scopely. I mean, they just keep buying games. What are your thoughts here on this deal happening right now? Chamath Palihapitiya I really like it. Let me give you the bull case and then let me give you what the bear case would have to believe. The thing to remember is that video games is the anchor pillar of usage across the entire internet. Last week at our poker game, we had Matt Bromberg join in just for dinner, who’s the CEO of Unity, and Alex Blum, who’s the COO of Unity. And one of the stats that they shared with us at dinner was it’s about 3 billion DAO play games, which is just an incredible, incredible stat. So in many ways, it’s much bigger than social networking and social media or as big. And in that, EA is sort of this 800-pound gorilla. But I think the problem is that they’ve always been these gatekeepers. And I think that there’s a risk and a chance that these gatekeepers get eroded away. Specifically, who I’m talking about are folks like Microsoft and Xbox. And at the point that this company is going private, there’s some really interesting things that are happening. So Xbox, I think the day after the EA deal got announced, decided to hike prices 50% to their subscription service. And what happened over the subsequent few days is that so many people tried to cancel that the site went down. So what are you seeing happening? You have distribution gatekeepers trying to raise prices and take share. And then you have the original IP owners who have not had a well-funded way of fighting back in a category that is basically as important and frankly, more important than social media. So I think if you take an asset like this private, it allows you to take your time to clean up the OPEX model, figure out who does what, be able to use the best of all these next gen tools, and Then be able to find ways of finding distribution outside the scope of Xbox and PlayStation so that you can take more of your share. If you do those things, this is a multi-hundred billion dollar asset. And in that, I think it could be just an enormous win. So I think it’s very smart. What’s the bear case? I think the bear case is extending a theme that I’ve talked about here a few times, which is, I think the value of patents, and by extension, and copyrights are going to go away. And in that, there’s going to be a spectrum where certain content IP holders lose and other ones win. I think gaming is on the winning side, to be honest. And I think content studios in general, like traditional content, the Disneys, the Hulus, the Netflixes are on the losing side. But the bare case would be that these tool chains allow the number of games to be built to increase by two, three, four orders of magnitude, and that they are distributed by other places Like the social media sites. I just think that that’s a pretty low probability. So on balance, I think that Jared and Egon did a killer deal. I really like it. (Time 0:01:26)
  • PE’s Growth Breeds Diminishing Returns
    • Private equity exploded with cheap leverage but now suffers from too much capital chasing deals.
    • When distributions dry up, capital concentrates to the few PE firms that actually return cash to LPs. Transcript: Chamath Palihapitiya And it just keeps growing. I think private equity is totally screwed. I don’t think Silver Lake or Affinity or this deal are screwed, but I think private equity in general is totally ours. Jason Calacanis All right. Well, it’s gotten huge just since 2015 and tripling in size. So why is this, I guess, my question for the gentlemen here and for the audience, why is private equity becoming so large? And what impact does that have on society if people can’t put EA into their retirement account? They can’t put Stripe into their retirement account. If we take all the great companies and we start to privatize them, SpaceX, let’s say, it never goes public, what impact does that have on people’s retirement accounts? Okay, look, I think the history of this is important. Chamath Palihapitiya There was a longstanding belief that the best way to generate the best risk-adjusted return, what does that mean? That means to manage through periods where the stock markets go down and to manage through periods of volatility. The best way to do that was to have what’s called a 60-40 allocation, 60% to bonds and 40% to equities. Over many years, especially when we artificially suppressed rates at zero through Obama, a lot of people started to move their allocations away from 60-40 and they started to make More and more investments further out on the risk curve. The biggest beneficiaries of that were venture capital, private equity, and hedge funds. The thing with private equity is that because rates were zero, they had an infinite amount of borrowing capacity, had very little downside to them. And so they were able to manufacture returns much faster than venture capital and hedge funds could. So as a result, you had an initial group of people that were defining the asset class, making a ton of money. And then you had all these fast followers that said, well, if they’re doing it, I can do it too. So far, so good. But then always what happens is then you have this flood of laggards that just flood the zone. And it’s these laggards that make it very difficult to generate returns, because they start overpaying for assets, they start mismanaging and undermanaging the assets that they Do own. And so where we are is that private equity has seen a very consistent way of returning money to help improve that 60-40 portfolio. As a result, they got a lot of money, but then that created a lot of competition. And so that’s why you see this hockey stick graph, Jason. And when you see that kind of graph, it doesn’t matter what asset class it is, the returns go to zero. And so we’ve seen this in venture capital, we’ve seen this in hedge funds, and we’re now going to see this in private equity. Jason Calacanis Too much money going in, to be clear what you’re saying, Shema, means you kind of index it, right? Chamath Palihapitiya There’s no And so again, I’ve said in any of these alternative asset classes, there is only one thing you should always ask. If you had to have one critical question, what are your distributions? Don’t show me your IRR. What is your DPI? The distributions on your paid in capital. And if the answer is zero, then it is a very challenged asset class. And what I will tell you in private equity is that over the last four or five years, distributions have been few and far between. So I think what’s going to happen is that the money is going to come out of private equity and it’s going to get concentrated into the few companies that know what they’re doing, of which Silver Lake has generated over, you know, the last 15, 20 years, 10s and 10s of billions of dollars of distributions, they are just an exceptionally well run organization, they’ve Done these huge buyout deals successfully before. So we need to go through that in PE. (Time 0:12:51)
  • Design SPACs With Sponsor Skin In The Game
    • Use SPACs as a competitive, lower-cost route to public markets with aligned sponsor incentives.
    • Structure sponsor compensation so founders only earn value after stock milestones to protect public shareholders. Transcript: Chamath Palihapitiya Look, there are three ways to go public. There’s the traditional way IPO. There’s the direct listing. And then there’s the reverse merger of the SPAC. Up until I floated IPO A in 2018, I think it was, the first way was really the only way. I was involved in two direct listings, Slack and Coinbase. And in both of those, what I learned is that it has the same vagaries as the traditional IPO. So in the traditional IPO, you go to a bank, they underwrite you, they act as a gatekeeper, and they take 6%, 7%, 8% fees as a result. And then they allocate what is essentially underpriced stock to their best customers. Then you see a one-day pop, maybe a two or three-day pop. All of those customers tend to unload. And then the stock tends to drift down. So the IPO is expensive, and it typically is mispriced. The direct listing, you have a different dynamic, which is the first trade is always the highest trade. And then it just goes straight down. That happened with Slack, and it happened with Coinbase. So Spotify would be in that group as well. Yeah, with Slack, I remember like, I was like offside a billion dollars and I was like, well, I’m never letting this happen again. And so when I had the Coinbase thing, I sold it the first day and I texted Brian. I said, this is not a directional indication of your company. It’s the dynamics of the direct listing because I learned it the hard way that the time to sell is on day one. So where does the SPAC come in? (Time 0:17:53)
  • Limit Retail Exposure To SPACs
    • Avoid retail exposure to high-risk SPAC deals and keep allocations small if you participate.
    • Treat many SPAC targets like venture-stage bets, not mature public equities. Transcript: Chamath Palihapitiya Resilient revenue, maybe rugged revenue? I think it’s the latter. But I think it’s also important to note that this time around, I’ve tried to really minimize retail exposure to this. I don’t think that retail is well-suited right now to have these things. My honest advice is avoid, maybe not all SPACs, but definitely my SPAC, just avoid it. I think that there is more than enough liquidity on the institutional side for us to do an interesting deal, but it fits in our portfolio and our construction, which is a very different Risk model. (Time 0:26:20)
  • AI Needs Owner Operators To Succeed In Buyouts
    • AI can transform mature, commoditized industries if owners execute with the right people and capital.
    • Owner-operated buyouts are likelier to realize AI productivity gains than typical PE portfolio companies. Transcript: David Friedberg Of your portfolio would be my advice before we move on can i just make one comment? And I’d like your guys to know about the private equity stuff, because Chamath made a comment that private equity is big. But I think one of the things to take note of in this take private of EA, and we talked about it as the theme of AI empowering EA to kind of transform the business. And Jared’s brother, Josh, has at Thrive been executing a roll up of CPA accounting firms that he’s then applying AI to, to reinvent that business. Oh, is he really? Yeah. Jason Calacanis Oh, I should talk to him because we have an investment in a company called TaxGPT.com that is basically like co-pilots with AI for accountants that’s doing spectacular. David Friedberg So what he’s done is he’s bought these kind of traditional accounting firms at some multiple of EBITDA, and then he can transform the business with AI and really create a new opportunity. And I’ve said, like, I think this is one of those few moments in history, where there really is an opportunity to beat the market and make money in the public markets. If you can be thoughtful and selective about the companies that stand to benefit from an AI execution strategy. Because in all of these traditional kind of markets where you have competition, everything’s commoditized and the market is mature, it’s very hard for any of these players to differentiate Product, service, and obviously, you know, unit economics. But with AI, it’s completely transformative and has transformative potential in nearly every industry. So as a public market investor, if you can identify those opportunities, select them where the management team has the right leadership in place to execute against this, you could Make real money. The problem is most of these companies are not led by folks that understand AI or software first. And so I think there’s an opportunity for more buyouts. They’re not going to be of the $55 billion scale. It’s worse than that. (Time 0:27:40)
  • First-Hand Play With Sora
    • Chamath tried Sora (a short-form AI video app) and saw an AI-generated tennis clip that illustrated rapid content creation.
    • He noted the app is clunky now but will improve quickly over a year or two. Transcript: Chamath Palihapitiya That has the same principle. I asked Bromberg and Alex about exactly this at dinner. What was their take? He said, it’s just really, really hard to get these things to actually be legitimate engines at the scale of what Unity offers for the quality of game that needs to be made for it to work. Jason Calacanis The interim step is going to be the assets in it are created by AI. That’s what I’ve seen a lot of startups doing. So you want to make a character, you know, you drop in characters and they can be done in real time. I think you’re exactly right. David Friedberg The whole thing is Unify and Unity as the rendering engine. And the AI sits on top. And the AI basically can render objects, can render concepts, can render structure, can render the direction that you as an engineer would typically provide to the Unity or Unified 3D engine. And that’s going to unlock not just in video games, but also in film. (Time 0:33:20)
  • Open-Source Models Shift Economics
    • Chinese open-source models like DeepSeek and Kimmy are driving cost and performance pressure globally.
    • US adopters can run forks domestically to capture cost savings while hosting data on US infrastructure. Transcript: Jason Calacanis Some red meat for you. DeepSeek, the Chinese LLM, just dropped their latest model, 3.2 EXP. It’s faster, it’s cheaper, and it has a new feature called DSA, DeepSeek Sparse Attention, which makes it faster to do training and inference at larger tasks. The key takeaway is it can reduce API costs by up to 50%. The new model charges $0.28 per million inputs, $0.42 per million outputs. Claude, which is a leading model from Anthropic that a lot of developers use, a lot of startups use, is like $3.15, so 10 times, 35 times more expensive. Obviously, people are cutting their prices pretty quick. But, Sachs, this is your wheelhouse as our czar of crypto and AI for the United States of America. What are your thoughts here on the continued execution of the Chinese government with DeepSea? David Sacks Well, I want you to hear Freeber’s thoughts on this because he was paying attention to this, weren’t you? David Friedberg Yeah, I mean, I think there’s a total re-architecture underway. And we’re at the earlier stages of cost per token in terms of dollar and energy. My understanding is there’s actually a lot of work going on with US labs right now on a similar kind of track that’s going to result in similar results. Maybe they’re a little bit ahead of the curve, but we should really pay attention to the curve. I think, you know, what are the models say in terms of energy demand, in terms of cost per token, if these architectural changes really do drive down 10x, 100x, 1000x, 10,000x over the Coming months. Jason Calacanis And this is open source. So just so everybody understands, it’s available on AWS, it’s available on GCP, at least 3.1 is I don’t know if 3.2 is available there now. But I’m hearing from a lot of startups, I don’t know if you’re hearing this in the field, Chamath, that they’re testing it and playing with it, in some cases using it because it’s so much Cheaper. Are you seeing that? Chamath Palihapitiya We are a top 20 consumer of Bedrock. So let me tell you what it looks like on the ground. We redirected a ton of our workloads to Kimi K2 on Grok because it was really way more performant and frankly just a ton cheaper than OpenAI and Anthropic. (Time 0:45:02)
  • Plan For Migration Costs Between Models
    • Evaluate switching to cheaper open-source models but budget time for refactoring and fine-tuning.
    • Plan weeks to months for safely migrating inference workloads between model providers. Transcript: Chamath Palihapitiya We are a top 20 consumer of Bedrock. So let me tell you what it looks like on the ground. We redirected a ton of our workloads to Kimi K2 on Grok because it was really way more performant and frankly just a ton cheaper than OpenAI and Anthropic. The problem is that when we use our coding tools, they route through Anthropic, which is fine because Anthropic is excellent, but it’s really expensive. The difficulty that you have is that when you have all this leapfrogging, not easy to all of a sudden just like, you know, decide to pass all of these prompts to different LLMs, because They need to be fine tuned and engineered to kind of work in one system. And so like the things that we do to perfect code gen or to perfect back propagation on Kimmy, or on anthropic, you can’t just hot swap it to deep speed, all of a sudden it comes out and it’s That much cheaper. It takes some weeks, it takes some months. So it’s a complicated dance and we’re always struggling as a consumer. What do we do? Do we just make the change and go through the pain? Do we wait on the assumption that these other models will catch up? Jason Calacanis Yeah. People are making tools now. And by the way, I can’t just go to my engineers. To make it easier to switch between them. Chamath Palihapitiya No, and this weekend, a different company with a huge model came to us and gave us the preview of their next gen model. Okay, and it’s incredible. But then when I sit on Monday morning with my team, and I’m like, okay, what do we do? We don’t know what to do. Do we cut it? Do we move over and say, great, we’ll refactor all these workloads to run on on this new model. It’s a really hard problem. And it’s getting worse the more complicated tasks that we undertake. (Time 0:46:52)
  • Open Source As A Competitive Check
    • Open-source models act as a check on big tech by enabling deployment on local infrastructure.
    • The US leads closed frontier models while China currently leads many high-performance open-source releases. Transcript: David Sacks I think this is actually a really interesting topic, this topic of open source. I’m a big fan of open source software because it’s a check on the power of big tech in a way. What we’ve seen in the past and the history of technology is that these major categories end up getting dominated by one or two big tech companies and they have all the power and control. And open source provides an alternative path, right? Because the community of open source developers just puts things out there and then you can take it and run it on your own hardware and you’re not dependent, right? It’s a path to sort of software freedom, if you will. So, so far, so good. I think the thing that is now tricky about this is that all the leading open source models are from China these days. China has made a really big push on open source. Obviously, DeepSeek is an open source Chinese model. That was the first big one. Kimmy is one, Quen from Alibaba. And so I think that if you want the US to win the AI race, then we’re all kind of of two minds about this. On the one hand, it’s good that there are open source alternatives to the closed source proprietary models. On the other hand, they’re all coming from China. Now, there were some American efforts that have been important. So Meta, most notably, has invested billions of billions of dollars in Llama. But the release of Llama 4, I think, was considered disappointing by a lot of people. And now there’s statements by Meta that they might be backing away from open source and just going proprietary. OpenAI released an open source model, but it’s nowhere near their frontier. And there are some startups that are trying. So there’s one called Reflection that looks promising is developing an open source American model. But so far, this is maybe the one area in AI where the US is behind China’s sort of open source models. I’d say every other part of the stack, closed models, chip design, chip manufacturing, semiconductor manufacturing equipment, every other part of the stack, even data centers, I would say we’re ahead. (Time 0:48:55)
  • AI’s Energy Demand Is A Binding Constraint
    • AI’s growing compute demand will stress local grids and can raise electricity rates materially.
    • Firms and cloud providers must consider energy impacts as a real constraint on AI adoption. Transcript: Chamath Palihapitiya Meaning you saw, I think this week where the residents of Indianapolis were able to reject or get their city to reject a billion dollar data center that Google was going to build near Indianapolis, largely because of concerns of price inflation around electricity. And what this energy CEO told me is, look, the next five years are baked. And if we don’t find some compelling solves, and I’ll tell you what the two ideas were, but if we don’t find some compelling solves, electricity rates will double in the next five years. Now, if you think about how then consumers will view the use of AI, and then if you think about companies like us and others trying to use the cheapest version so that we are minimally impacting The downstream cost of these things, because it will become an energy problem. This is a very complicated thing. (Time 0:52:14)
  • Mitigate Data-Center Power Impact
    • Consider grid-side strategies: cross-subsidies, home batteries, and peak-shedding to mitigate data-center impact.
    • Use short-term gas and longer-term nuclear investments as the energy transition bridge for AI demand. Transcript: Jason Calacanis It’s a bad look because you’re saying your energy is doubling and this could take your jobs, right? Yeah, it’s terrible. Whether you believe that’s true or not, that is the perception of the public. Chamath Palihapitiya There are two off-ramps that he suggested, which I think are worth considering. Off-ramp one is what’s called a cross subsidy, which is essentially to say that they pay a rate card, which they can absorb with all their free cash flow, materially higher than what Other rate payers would pay in that geographic area. So the homeowner, his or her electricity costs stay flat to down, the data center costs are higher. And it’s the Metas, the Googles, the Apples, the Amazons who have hundreds of billions of free cash, they absorb it. That was idea number one. And idea number two is to start to set up some mechanism so that they can install things like batteries every single home in and around these data centers to allow those homes to have a Jason Calacanis Better chance of actually absorbing some of this inflation without having to pay it. That’s a really good idea. And this is playing out sacks in Virginia in a major way because that’s where data center alley is. And 40% of the energy in Virginia now is going to data centers. This is becoming acute. So what are your thoughts here, Zar? David Sacks Well, Chris Wright spoke to this pretty well at the All-In Summit in terms of what we have to do. I mean, there’s no question that AI is going to create a huge need for power over the next five or 10 years. I think on a five to 10-year timeframe, the answer is probably nuclear, or at least that’s a big part of it. But nuclear takes at least five years. Within the next five years, it’s probably gas, natural gas. But the issue there is there’s a huge backlog for gas turbines, basically the engines that burn the gas to create power. And there’s like a two to three year backlog for those to spin those up. So the question is, what do you do in the next few years? And I think Chris Wright talked to this, and I’ve heard this from other energy executives, which is we just need to squeeze more out of the grid. If we were to shed just 40 hours a year of peak to say backup generators, diesels, things like that, you could get an extra 80 gigawatts out of the grid. This is what one energy executive told me. The reason is because they build the grid and they have regulations on it based on the peak, right? Which is basically the coldest day in winter or the hottest day in summer. And the same way that you, you know, you build your church for Easter Sunday, the rest of the year it runs at 50%. Same thing with the grid. And so if they could just reduce the peak 40 hours, if they could shed that load to backup to generators, to diesel, things like that, then they could run the grid to squeeze an extra 80 Gigawatts out of it. And I think that’s the bridge over the next few years that we need to then get a lot more gas and then eventually some nuclear as well. (Time 0:53:30)
  • AI Will Proliferate, Not Centralize
    • AI will decentralize widely — consumers and enterprises will run models locally and vertically.
    • Treating AI like a proliferating consumer good, not a weapon, better frames national-security policy. Transcript: David Sacks New world. Yeah, you bring up an interesting point. In the early years of this AI revolution, I’m talking about 2023, 2024. I mean, this has started in the last three years. There was this analogy that AI was like nuclear weapons. You hear the Doomer crowd, the advocates saying this, that like AI was this really threatening technology. And they would even say things like GPUs are like plutonium, you know, things like that. And I think that model of the world is just wrong, right? Because what we’re seeing is, and Jensen actually had a pretty good line about this. He says, nobody needs nuclear weapons. Everyone needs AI. And it’s true. Every consumer, every business is going to want to run AI. A lot of them are going to want to run it on their own infrastructure. Consumers are going to want to run it on their own phone. You’re going to have an AI that’s highly personalized to you. And so everyone’s going to have AI. It’s not like a nuclear weapon where we want to stop all proliferation. AI is first and foremost a consumer product that is going to proliferate. And so the question is, bearing that in mind, how do you then create an appropriate response for the national security risks? But this idea that we’re just going to stop AI and only have two or three companies who have it, which I think was the view a few years ago among policymakers. Yeah, it’s ridiculous to even think that now. (Time 1:03:08)
  • Push For One Federal AI Framework
    • Seek federal preemption to avoid 50 divergent state AI regimes that would fragment the US market.
    • Push Congress to set national standards rather than leave patchwork state oversight that burdens startups. Transcript: Jason Calacanis There is a lot of, and maybe we’ll get into Wikipedia as well, but there’s a lot of states that are starting to look into regulating AI. California SB 53, the Transparency in Frontier Artificial Intelligence Act, is working through the system. It’s going to serve as a template possibly for other states. It was introduced in January as an alternative to the more sweeping bill, the SB 1047. This will require AI developers to conduct extensive safety tests before rolling out the models. It got a lot of pushback from tech, obviously, and Newsom ultimately vetoed it. But this new law focuses only on the most advanced large frontier models that we just talked about. And it requires companies to release a framework for knowing how they’re approaching safety issues, including standards and best practices, whatever that means, and however safety Is defined. These are models, I guess in this definition, that have half a billion in annual revenue. I don’t know how they picked that out, but it requires these companies to release transparency reports before deploying. So they’re going to be like the App Store, I guess, if this gets through, to approve frontier models with updates. Oh, that sounds great. You got to go to the government to release a new model. Your thoughts, David Sachs? Yeah, I mean, look. America’s czar of AI. David Sacks I think it’s very concerning. There’s a regulatory frenzy happening at the states right now. Just to be very clear about what happened in California, there was an original bill, SB, was it 1047? That was incredibly obtrusive. (Time 1:05:13)
  • State Laws Focus On Oversight, Not New Harms
    • Many state AI bills create oversight regimes rather than direct liability for harms covered by existing law.
    • That oversight can enable intrusive approvals and ideological shaping of model outputs (e.g., DEI rules). Transcript: David Sacks The problem is that you’ve got to multiply this by 50 states. Got 50 different states, each with their own reporting regime, which is going to be a trap for startups. They’ve all got to figure this out about what they’re supposed to report on, what the deadlines are, who to report to. I mean, this is like very European style regulations, actually maybe even worse than the EU, because the EU tried to basically harmonize to get to one authority. We’re going to have 50, they’re going to have one. But the other problem is that this is just the camel’s nose under the tent. So even in California, Scott Wiener, who’s the legislator who did SB 1047, now he did this, he’s got a block of legislators and they have 17 more AI regulation bills that they want to pass. So this is just the beginning. And if you want to see where this is going, okay, look at Colorado, we should talk about this Colorado bill, this has already been passed into law. It’s called SB 24-205, Consumer Protections for Artificial Intelligence. It was passed all the way in May of 2024. So it was one of the first to pass, even though they didn’t really know what they were trying to regulate. No one’s quite sure how to implement it. But what the law does is it bans something they call algorithmic discrimination. Okay. And algorithmic discrimination is defined as unlawful differential treatment or disparate impact based on protected characteristics. So things like age, race, sex, disability. If any of those factors drive an AI decision and it results in a disparate impact, then both the developer of the AI model and the deployer, which means basically the business that’s Using it, can be in violation of this law. And they can be prosecuted by the Colorado Attorney General. Let me give you a practical application. So let’s say that you got someone like a mortgage loan officer who’s reviewing applications. Okay. And let’s say they don’t even discuss race. It’s not on the form. Okay. They’re just using race neutral criteria, like a credit rating or financial holdings, something like that. If the result of their decision nevertheless had a disparate impact on a particular protected group, its decisions could be found to be discriminatory. And moreover, the developer of that model could be liable, even though their model just gave an answer that under the circumstances was truthful. (Time 1:10:22)
  • Fragmented State Rules Threaten Scale
    • Fifty different state AI rules would cripple national scale and innovation, repeating past fragmentation mistakes.
    • Federal preemption preserves a seamless national market that enabled US internet dominance. Transcript: David Sacks Law to build in a new DEI layer into the models to basically somehow prevent models from giving outputs that might have a disparate impact on protected groups. So we’re back to woke AI again. And I think that’s the whole point. That’s the whole point of this Colorado law. Chamath Palihapitiya Let’s get Shamath in on this discussion. Shamath, I think that this is really, really dumb what’s happening. If you have 50 sets of rules, what you will have are some conservative versions of AI. You’ll have some progressive leaning versions of laws. These 50 series of laws will essentially just render this industry impotent and incapable of maximizing itself and actually doing what’s necessary to drive productivity and GDP On behalf of the country. There is no conceivable way as freebrook said that anybody in sacramento or little rock or you know name your estate capital, will have the intellectual wherewithal to get to an answer As good as the federal government will and as Sachs will, just to be totally honest with everybody. So what should happen here is that there needs to be a complete moratorium and the federal government should be given the time to figure out what the framework should be so that there Is a one size, one set of rules. Now, if that doesn’t happen, and this is allowed to stand, there is a perfect example of where this has happened before. And that is in the car market. Because in the car market, what happened was, there is a complete set of rules in California for emissions that is entirely different than the rest of the country. And you can look and see what it did. Now, that’s just two sets of rules. And what the- Would you argue that’s been a good thing? Let me finish. Okay. And so what these two sets of rules, going from one set of rules to two, what did it do? It drove most of these companies to go towards barely break even or massively money losing. It has been something that the entire industry has been fighting back on for now 10 plus years. Now, can you imagine instead of two sets of rules, you have 50? I think you know what the economic consequences will be. (Time 1:12:45)