Sneha

The AI lobby has entered our race to bankroll my opponent, Scott Wiener. That's because he worked with them to water down AI regulations in California. As his reward, he gets a huge super PAC. I'm not taking any corporate PAC or lobbyist money, and in Congress, I’m going to end this kind of legalized bribery. AI oligarchs want to control your future. I will fight to put people back in control.

The AI lobby has entered our race to bankroll my opponent, Scott Wiener. That's because he worked with them to water down AI regulations in California. As his reward, he gets a huge super PAC. I'm not taking any corporate PAC or lobbyist money, and in Congress, I’m going to end this kind of legalized bribery. AI oligarchs want to control your future. I will fight to put people back in control.

The AI lobby has entered our race to bankroll my opponent, Scott Wiener. That's because he worked with them to water down AI regulations in California. As his reward, he gets a huge super PAC. I'm not taking any corporate PAC or lobbyist money, and in Congress, I’m going to end this kind of legalized bribery. AI oligarchs want to control your future. I will fight to put people back in control.

On initial read this plan struck me as similar to OpenAI's "Industrial Policy for the Intelligence Age Paper," but reading them both again the similarities are more striking than I expected. You would really think that if OpenAI really believed in making these policies happen, they would support Bores's candidacy, or at the least not back a Superpac spending millions to attack him. (The OpenAI funded Superpac network even frequently puts out content about how concerns about job loss are a doomer hoax!) Similarities: 1. Citizens get stake in AI profits Bores: "if AI dramatically increases productivity and concentrates wealth, the American people have a stake in those gains" OpenAI: "Create a Public Wealth Fund that provides every citizen—including those not invested in financial markets—with a stake in AI-driven economic growth" 2. Change tax code to favor labor Bores: "If AI can substitute for labor rather than complement it, then our tax code is actively subsidizing job elimination. We encourage companies to invest in AI by making it cheaper through tax breaks, while taxing the wages of the workers being displaced." OpenAI: "As AI reshapes work and production, the composition of economic activity may shift—expanding corporate profits and capital gains while potentially reducing reliance on labor income and payroll taxes. This could erode the tax base that funds core programs... Policymakers could rebalance the tax base by increasing reliance on capital-based revenues... and by exploring new approaches such as taxes related to automated labor." 3. Trigger based safety nets Bores: "The program would be tied to clear economic triggers—such as sustained declines in labor force participation, wage compression in affected sectors, or rapid increases in AI-driven productivity without corresponding job growth—to ensure it activates based on real-world conditions, not political discretion." OpenAI: "Define a package of temporary, expanded safety nets... that activates automatically when these metrics exceed pre-defined thresholds. When disruption rises above those levels, support would scale up; as conditions stabilize, it would phase out."

I thought this was a great piece, not least because it captured so much of my own journey into taking AI seriously. In the early days of Encode, we were squarely focused on immediate AI harms and in fact I was actively giving workshops internally about how our members should avoid being psyopped by the catastrophic risk sideshow (as I then perceived it). Friends asked me if I’d read the AI safety canon and whether it was shaping my work, and I was completely dismissive. It wasn’t until I actually sat and reflected on why I was so averse to thinking about the most extreme AI scenarios that I realized I didn’t have very good object-level reasons - it just felt weird and uncomfortable to entertain such alien futures for more than a few seconds, and at the time there was basically no social proof for this outside Berkeley. These days I laugh when critics of AI safety say that people should stand strong against doomerism and that if you dwell on AI risk you must be a loser devoid of imagination and hope. That literally sounds like something I would say on any other topic - I am a devout, lifelong, in-my-bones optimist, and taking bad outcomes seriously has felt like a constant battle against my natural disposition. I’m honestly pretty embarrassed by how I initially approached thinking about AI. But I’m also proud of the work Encode has done since my mind opened up, and I know a lot of smart people are on the other side of this bridge I only relatively recently crossed. If that’s you, I really implore you to use the latest models, see for yourself, and ask: “What if AI actually is world-historic? Are there specific capabilities or arguments that could convince me this is true, or am I just not open to being convinced? How would my other assumptions about the future change under the premise that AI is world-historic?” As Dylan also acknowledges, I am totally sympathetic to how hard this is (especially if others around you are still AI skeptics); but when I did this exercise, it completely reoriented my life and work.

I thought this was a great piece, not least because it captured so much of my own journey into taking AI seriously. In the early days of Encode, we were squarely focused on immediate AI harms and in fact I was actively giving workshops internally about how our members should avoid being psyopped by the catastrophic risk sideshow (as I then perceived it). Friends asked me if I’d read the AI safety canon and whether it was shaping my work, and I was completely dismissive. It wasn’t until I actually sat and reflected on why I was so averse to thinking about the most extreme AI scenarios that I realized I didn’t have very good object-level reasons - it just felt weird and uncomfortable to entertain such alien futures for more than a few seconds, and at the time there was basically no social proof for this outside Berkeley. These days I laugh when critics of AI safety say that people should stand strong against doomerism and that if you dwell on AI risk you must be a loser devoid of imagination and hope. That literally sounds like something I would say on any other topic - I am a devout, lifelong, in-my-bones optimist, and taking bad outcomes seriously has felt like a constant battle against my natural disposition. I’m honestly pretty embarrassed by how I initially approached thinking about AI. But I’m also proud of the work Encode has done since my mind opened up, and I know a lot of smart people are on the other side of this bridge I only relatively recently crossed. If that’s you, I really implore you to use the latest models, see for yourself, and ask: “What if AI actually is world-historic? Are there specific capabilities or arguments that could convince me this is true, or am I just not open to being convinced? How would my other assumptions about the future change under the premise that AI is world-historic?” As Dylan also acknowledges, I am totally sympathetic to how hard this is (especially if others around you are still AI skeptics); but when I did this exercise, it completely reoriented my life and work.

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

I thought this was a great piece, not least because it captured so much of my own journey into taking AI seriously. In the early days of Encode, we were squarely focused on immediate AI harms and in fact I was actively giving workshops internally about how our members should avoid being psyopped by the catastrophic risk sideshow (as I then perceived it). Friends asked me if I’d read the AI safety canon and whether it was shaping my work, and I was completely dismissive. It wasn’t until I actually sat and reflected on why I was so averse to thinking about the most extreme AI scenarios that I realized I didn’t have very good object-level reasons - it just felt weird and uncomfortable to entertain such alien futures for more than a few seconds, and at the time there was basically no social proof for this outside Berkeley. These days I laugh when critics of AI safety say that people should stand strong against doomerism and that if you dwell on AI risk you must be a loser devoid of imagination and hope. That literally sounds like something I would say on any other topic - I am a devout, lifelong, in-my-bones optimist, and taking bad outcomes seriously has felt like a constant battle against my natural disposition. I’m honestly pretty embarrassed by how I initially approached thinking about AI. But I’m also proud of the work Encode has done since my mind opened up, and I know a lot of smart people are on the other side of this bridge I only relatively recently crossed. If that’s you, I really implore you to use the latest models, see for yourself, and ask: “What if AI actually is world-historic? Are there specific capabilities or arguments that could convince me this is true, or am I just not open to being convinced? How would my other assumptions about the future change under the premise that AI is world-historic?” As Dylan also acknowledges, I am totally sympathetic to how hard this is (especially if others around you are still AI skeptics); but when I did this exercise, it completely reoriented my life and work.