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The new "science golden age" looks suspiciously like a grift
This edition of PN is made possible by paid subscribers. Become one ⬇️ 🚀 Subscribe to PN 🚀 The Trump regime is trying to usher in a “Golden Age in Science” where scientific research is run like a Trump family business, artificial intelligence is all that matters, profit is everything, and expertise is for chumps. Or at least that’s the vision emerging from a new 123-page “Report to the President” from Michael Kratsios, the Director of the White House Office of Science and Technology Policy. In a normal administration, you could probably skip over learning about a science policy document — but this is no normal administration, and this is no normal science policy document. Instead, it’s Trump’s attempt to end the actual golden age of science that was kicked off in 1945 with the publication of '“The Endless Frontier” by Vannevar Bush. [ ](https://www.publicnotice.co/p/harmeet-dhillon-portland) [ Harmeet Dhillon demonstrates danger of posting through it ](https://www.publicnotice.co/p/harmeet-dhillon-portland) Liz Dye · Jul 29 [ Read full story ](https://www.publicnotice.co/p/harmeet-dhillon-portland) Bush, at President Franklin Delano Roosevelt’s request, laid out a vision of federal support for science that centered on funding universities to do basic research, saying that as long as those institutions were “vigorous and healthy and their scientists are free to pursue the truth wherever it may lead, there will be a flow of new scientific knowledge to those who can apply it to practical problems in Government, in industry, or elsewhere.” Kratsios’s report is supposed to be the successor to Bush’s, and even apes the format, but this isn’t really a document about science policy. It’s one about ending science research as we know it in favor of shoveling cash at big tech companies. While it might be temping to wave this away as an aspirational policy document, Trump has already begun to shift billions in research funds to AI projects and has taken a hatchet to science funding across the board. He’s teed up the shift Kratsios proposes here, and since neither Congress nor the Supreme Court seem inclined to stop him from doing whatever he wants with taxpayer money, it isn’t doomering or hyperbole to say we are staring down the barrel of a world where the only “science” the government intends to support is for-profit tech and defense advancements. Science for dummies Part of how you can tell this isn’t a document about science is that you are immediately faced with an absolute wall of business-speak of the worst sort. Did you know we must “tightly couple our science and technology enterprises to ensure that groundbreaking ideas invented in the United States are rapidly prototyped, tested, manufactured, and scaled domestically”? You do now! That tone isn’t surprising, because Kratsios has no background in science or research. He has a bachelor’s in political science and spent most of his career working for Peter Thiel at Thiel Capital. After a stint in the first Trump administration as Chief Technology Officer, where he also did not do any science, Kratsios was tasked in 2020 with overseeing the Pentagon’s research budget for a brief time, a position that, as the Wall Street Journal put it, “carries a great deal of power in terms of dictating investment allocations.” He spent the Biden years at Scale AI as “head of strategy,” which you will also note is neither a science nor a research job. But Scale AI has contracts with the Department of Defense and, as the Washington Post wrote in 2023, “has been aggressively pitching itself as the company that will help the US military in its existential battle with China.” And now you have the necessary background for our new golden age, which is going to be a golden age of taking money away from scientists and universities and giving it to companies like Scale AI. A note from Aaron: Working with brilliant contributors like Lisa takes resources. If you aren’t already a paid subscriber, please become one to support our work. 👇 Subscribe In a genuinely galling move, Katsios spends a lot of time slamming universities because of “administrative burdens on researchers” and the “well-documented inefficiencies” in academia, the message being that those damn universities waste the time of amazing researchers with paperwork. Except it’s the administration that is attempting to impose inefficient, unnecessary burdens on those researchers with a proposed Office of Management and Budget rule that would require senior political appointees to approve every science grant and ensure it aligns with Trump’s personal priorities. Researchers would also have to seek prior government approval to attend conferences or publicize their research. And political appointees could terminate any grant at any time if they feel like it. [ ](https://www.publicnotice.co/p/omb-rule-science-funding) [ The murder of expertise ](https://www.publicnotice.co/p/omb-rule-science-funding) Lisa Needham · Jun 11 [ Read full story ](https://www.publicnotice.co/p/omb-rule-science-funding) But when it comes to AI, we’re going to “restore permissionless innovation.” The report proposes to give lucky ducky innovators a pile of cash up front for grants lasting five years or longer, saying “awards should be fully-funded in year one, with all resources earmarked upfront, to minimize administrative burdens and reduce pressure for researchers to generate intermediate results to secure continued funding.” Yeah, wouldn’t want to have to make the Very Special Tech Bros show results or get money parceled out year by year like the plebes at universities. The report also proposes that we should have “fast grants” with “simplified applications requiring just a few pages of writing” because that is definitely how science works, just writing a bit off the dome and getting some cash. [ ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) [ Tech elites are turning AI into ChatGPTrump ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) Paul Waldman · July 29, 2025 [ Read full story ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) Oh, and we should have prizes and challenges, where multiple teams compete. The proposed prizes include incentives like “advance procurement commitments,” which means the federal government agrees to buy whatever is being developed. There’s also “regulatory fast-tracking,” making sure all those great AI thingies don’t have to wait too long to be unleashed on a market. These aren’t incentives to do science. These are incentives to develop things with tax dollars that you can then sell to the government for more tax dollars. In the suggestion that might be the most unhinged, the most very opposite of how both government and research work, our new age will include “golden tickets.” That’s where an individual reviewer in an agency — yes, just one person — can “recommend unconventional proposals that may not pass consensus-driven review panels” because somehow this will “surface valuable ideas too divisive for committees and attract higher quality reviewers by empowering them to exercise independent scientific judgment.” Yes, that’s saying that a random person in an agency should be able to shovel money at some idea even if it can’t pass a review panel, and that the opportunity to do so will attract better employees because apparently all the best minds in science are chomping at the bit to be allowed to unilaterally fund AI research based on their personal vibes. But that isn’t at all how actual science works, and actual scientists aren’t unhappy about review panels. They work collaboratively, not siloed in some secret race to create the next AI chatbot. A 2020 study by the National Bureau of Economic Research examined whether peer review panels succeed in helping agencies predict which research ideas will best advance science. Peer review was better at finding “diamond in the rough” applicants than a single government program official. How about that. Trump always wants a taste It’s painfully clear that what Katsios is trying to do here is turn the whole of government science research and funding into the sort of “move fast and break things” paradigm that is the hallmark of big tech. That method works to line the pockets of tech executives, but it isn’t science. There’s a reason the government funds different kinds of research than private tech companies do. What Katsios either fails to understand or deliberately ignores is that the federal government is the main funder of basic research precisely because it can take a long time for it to bear fruit and is not intended to be profitable as such. “Basic” here does not mean rudimentary or simple, but rather research that is designed to advance scientific knowledge instead of achieving a specific outcome or product. Big tech companies don’t widely fund that sort of research, because it doesn’t make them money. Industry dollars account for only about 20 percent of basic research funding, while the government contributes around 60 percent. Big tech companies and startups do, however, rely on and benefit from that basic research. A 2019 study found that 35 percent of all patents obtained by venture-backed startups from 1976 to 2016 cited federally supported research. Basic research funded by the NIH contributed to every single drug the FDA approved from 2010 to 2016. [ ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) [ The Saudi nuclear farce and governance by whim and meme ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) Noah Berlatsky · Jul 27 [ Read full story ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) Unlike inventing the next ChatGPT, actual science is slow and iterative. Government funding of research that, on its face, would not appear to lead to any big new invention is actually what underpins those big new inventions. If the government hadn’t funded research into desert lizard venom, we wouldn’t know about semaglutide, and we wouldn’t have Ozempic. If research scientists hadn’t studied how bees optimize nectar foraging within a colony, we wouldn’t have the algorithm that assigns internet traffic across computer servers. The report assiduously ignores that a symbiotic, profitable relationship between the federal government, universities, and private companies already exists. Each federal research dollar generates about $1.50 to $3.00 in long-term economic benefits. The $36 billion the government awarded in NIH research funding in fiscal year 2025 generated $94 billion in new economic activity. Trump’s proposed new golden age is intended to strip those billions from universities, resulting in the dismantling of departments and the shuttering of labs. But Katsios has a plan for that: Universities should just become trade schools to churn out no-show STEM degrees! “Require universities and community colleges to embed practical technical training and externships into STEM curricula. Let hands-on experience and industry credentials count toward degrees.” It’s quite the feat to, on the one hand, strip schools of funding for scientific research while simultaneously demanding to control what is taught and, oh, also, colleges need to give degrees to employees at big tech companies just for being employees at big tech companies. The new golden age would also exacerbate the brain drain Trump’s policies have already caused. Nature found that in the first quarter of 2025, US scientists submitted 32 percent more job applications abroad than in 2024. A March 2025 poll by Nature of roughly 1,600 scientists found that over 1,200 said they were considering leaving the country. Europe is investing $565 million to make it a “magnet for researchers” and provide funding for scientists who relocate. The new golden age is also about embedding Trump’s xenophobia into the fabric of American science. The report complains of an “over-reliance on foreign students” that “creates a security challenge our institutions are ill-equipped to address” and that a “reliance on foreign talent sidelines American students.” This is, to put it mildly, nonsense. International students pay far higher tuition and often receive no financial aid. Their tuition dollars are what make it possible for American students to get financial aid. In the 2023-2024 school year alone, international students contributed $43.8 billion to the US economy and supported over 378,000 jobs. Those 1.1 million international students accounted for only six percent of the total number of students in higher education that year, but were such an economic engine that for every three international students, one job was created or supported. It’s impossible not to wonder whether part of Trump’s loathing for universities and their leading role in scientific research is that there is no way for him to corrupt the process and make money off it in the same way he can by giving millions and billions to Silicon Valley types who, in turn, give him millions in cash. There’s no way for his children to invest in universities that the government then gives no-bid contracts to, fattening the family’s bottom line, which is pretty much the Trump sons’ current venture capital practice. Trump and Katsios’s view of science is narrow, cramped, and wrong. We’re not in a crisis, we’re not a failed state. We’ve been a science superpower for decades, and the only thing that threatens that is Donald Trump. Tech bros are already living high off the hog in this administration. They don’t need any more of our tax dollars. Hopefully this new golden age never comes to pass. Thanks for reading Public Notice. This post is public so feel free to share it. Share We’ll be back with more tomorrow. If you appreciate today’s PN, please do your part to keep us free by signing up for a paid subscription. 🚀 Subscribe to PN 🚀 Thanks for reading, and for your support.
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Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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The politics and possibilities of ‘AI could kill us all’
I found myself fielding a lot of questions from folks who don’t usually talk much about AI this week. They were mostly along the lines of “do you think AI is going to kill us all?” or “how worried should I be about this human extinction stuff?” Motivating this wave of existential anxiety was the highly dramatic resignation of now-former Anthropic employee Jacob Coxon, who quit his job while publicly sharing his concerns about safety practices at the leading AI companies. More specifically, he declared that almost everyone at OpenAI and Anthropic, the leading AI labs, believes that there’s a decent chance the AI systems they’re producing could “kill us all” by the end of the decade, and the companies aren’t “acting responsibly.” Other AI safety researchers, including those still employed at Anthropic and OpenAI, all confirmed that yes, Coxon is correct, they’re all worried that their company’s products might prove fatal for the entire human race as well. Two more AI employees quit and joined the chorus. Now, the warning shot has been covered everywhere from the Wall Street Journal to Fox News to WIRED to the Guardian. I understand why all this sounds scary, but my response to those who have asked, has generally been: No, you don’t need to worry that AI will kill go rogue and kill us all. But that does not mean we shouldn’t worry, period. Far from it. The companies building AI products are now among the wealthiest and most powerful entities in the world. And they very much are building enormous engines of mass surveillance, extraction, and automation. They can and have allowed their AI systems to carry out felony hacks of other companies, provided the government with autonomously generated kill lists, and sold enterprise technology to firms around the world that is explicitly intended to replace jobs. But “AI” has done these things not because they have all-powerful minds of their own but, ultimately, because people have programmed, calibrated, and directed them to. In other words, it’s not AI we need to worry the most about. It’s the people and corporations working to profit from and gain power by deploying it. The technology is sophisticated, sure, and, especially in cybersecurity contexts, potentially disruptive to plenty of existing systems. But if we want to stop this predatory, profiteering, and extractive form of AI—and it’s worth noting that at this point, most people do—we absolutely can. A quick note/plea: I can only do all of this with the support of readers like you, so if you find value in independent, critical tech journalism and analysis, please consider becoming a paid subscriber. I want to keep as much of this un-paywalled as absolutely possible, and I need your support to do so. Many thanks to all who continue to chip in, it’s very much appreciated. Onward. Subscribe First, some context: AI industry folks like Coxon have been discussing the prospect that AI will destroy humanity for years. Even before the beginning of the AI boom, many working in the industry, from the executive staff to the rank and file researchers, have obsessed over ‘x-risk’, or the possibility that AI will become sentient or semi-sentient and move one way or another to exterminate humanity. This is ostensibly why Elon Musk bankrolled OpenAI in the first place, and is the grounds for his lawsuit alleging OpenAI betrayed its charter by abandoning its nonprofit status to become a commercial enterprise. Many AI engineers and startup founders have calculated their own personal p(doom), or the probability that the tech product they are working on will initiate an actual humanity-extinguishing apocalypse. 10-25% p(doom)s are pretty common. (I wrote about a long weekend I spent among these folks at one of their top conferences here, to give you an idea of their ideas and belief systems.) Sam Altman himself drove the narrative of the imminent rise of a super-powerful AI, or artificial general intelligence, to prominence by declaring that the technology he was building posed an existential risk to humanity. This idea is in other words baked into the DNA of modern AI companies and into the products they engineer. It’s also why the industry is routinely accused, by myself and others, of “doom marketing,” ie, using such proclamations and framings to impart the sheer power of the technology behind your product onto investors, partners, consumers, and the state. Some ambient understanding of this tendency may have helped prevent the press from repeating the claim every time one of the x-risk folks made it over the years, including when they quit their jobs at AI companies while doing so. Yet Coxon’s AI x-risk alarm has broken containment, as they say, and has exposed the idea to the mainstream media once again. This has happened, I think, for a number of reasons. First, when AI CEOs were going around saying AI might kill everyone in 2023, their companies were well-capitalized, but not nearly to the extent they are today. They were more easily dismissed as eccentric geeks of the sort that Silicon Valley is supposed to produce. Now, not only do their products have much greater market saturation, but people are more aware of a) the often unethical ways the executives and companies comport themselves b) the other adverse impacts of AI that have already arrived, and c) the physical cost of building these systems, particularly when it comes to data centers. Finally, toss in the constant stream of headlines in 2026 about AI models going rogue or potentially upending the world’s infrastructure. [ ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) [ With the backlash to data centers, Flock and AI glasses, a mass opposition to big tech is underway. Silicon Valley is in denial. ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) Brian Merchant · Aug 27 [ Read full story ](https://www.bloodinthemachine.com/p/with-the-backlash-to-data-centers) As a result, general sentiment was already overwhelmingly skeptical of AI and its overlords, so the table was pretty well set for someone to come along and say ‘just a reminder, the companies that are building the product that promises to take your job and leave a data center in your neighborhood also think that it might exterminate the human race’ and get a strong reaction from the media and the public. The question is: Should we understand this stuff as hype, strategically fanned to feed already swelling valuations? Is it doom marketing? Genuine cause for alarm that the infrastructure that undergirds modern life is facing a novel threat vector? Or reason to be sincerely worried that AI companies are amassing too much power, and that their products are genuinely dangerous? The answer, I’m afraid, is “yes.” Let me try to untangle all of this a bit, because arguments over how to navigate this moment are extremely heated right now, as they are essentially arguments over how to approach the politics of AI at a moment where there may be a unique opening for action. Those sympathetic to AI alignment concerns and x risk arguments—which increasingly includes a faction on the left who want to shut down AI over safety concerns—argue that people who dismiss AI as all hype have their heads in the sand. Meanwhile, critics of the x-risk narrative—which I might note includes a cadre of investors and tech folks on the right, too—argue that it is hype and can be used to further inflate valuations or for pursuing regulatory capture. The challenge is that many feel that dismissing AI as just hype or purely marketing also diminishes the urgency of taking on the AI industry as the novel and threat to labor, civil society, and the public sphere it is; others that by accepting the industry’s own terms, we imbue it with undue power and enable it. But let’s be clear: Declarations that AI is so powerful it might end the world are a kind of marketing, and anyone who says otherwise is just as off base as those who say that’s all that it is. If you don’t think that these sub-trillion dollar companies have higher-ups who noticed that when their founders talked about super-powerful dangerous AI they got spikes in media interest and phone calls from investors, or that those founders didn’t notice that themselves, and lean into certain narratives to advantage their products, then I don’t know what to tell you. Not all marketing is a Pepsi ad campaign with a celebrity spokesperson. Yet confronting the AI industry should certainly be considered an urgent project. Do I share the x-riskers’ fear that out-of-control, super-intelligent AI agents might end the world? I do not. I’ve read a good deal of AI extinction literature over the last couple of years and I have not come across a credible, step-by-step documentation of how exactly AI might move from self-recursively improving AI to killing every single human on the planet. Most of what I’ve read seems predicated on a sort of reverse Ian Malcolmian logic: Eventually, it becomes smart enough and AI, uh, finds a way (to exterminate humanity). Are the AI companies also overstating and dramatizing what their products can do? Yes, pretty much all the time. They’re in a knife fight for market share and are trying to win customers and government contracts before their investors come knocking. And yet. They have concentrated enormous amounts of compute, have extensive data collection and surveillance capacities, have inserted themselves into the state in numerous capacities, including military kill chains, and they are being run by egomaniacal billionaires who are rushing to monopolize an entire new market. The way I think about their most recent frightening-seeming exploits is not as a “swarm of rogue AI agents building a new civilization” or whatever, but as corporations with a hitherto-unimaginable amount of compute at their disposal recklessly experimenting with automation programs, completely unafraid of any repercussions because we have been unwilling or unable to impose any on them. So I think we should welcome any newfound imperative to hold them accountable. I also think that ultimately, the AI critics, Pause AI folks, leftists, doomers, effective altruists, and, honestly, most of the public, want a lot of the same things: legitimate checks and limits on AI companies’ power, actual transparency into what the companies are doing, and for more of AI to be under public control. (Ultimately, a lot of what animates the x-risk fears are rooted in just that: a fear of losing control.) How we move towards all this, exactly, is the question of the day, and while there’s much to be argued over in terms of building a viable political platform to challenge the AI industry’s power, I do want to cast a vote against one particular approach. Not to pick on Chris here, as there are a lot of folks out there proposing stuff like this, and the policy ideas actually stem from AI safety groups. But not only would this be challenging to land politically (I can already hear the ‘China will beat us to AGI’ calls) and all but unworkable in practice, this approach is a) far too limiting, b) fails to address the current harms caused by AI and c) would likely only wind up serving Anthropic and OpenAI; it’s what regulatory capture looks like in action. Full model transparency is the floor for what should be on offer here. If these companies believe their products will kill us, the very least we deserve is to know how. It’s in the public interest. I would also argue we might get a lot of the way to stopping any currently envisioned robot apocalypse simply by forcing AI companies to assume liability, and to submit to the full brunt of the law. If Sam Altman were liable to be charged with a felony for approving the hack on Hugging Face1, if Google was forced to pay defamation fines each time it published a false and injurious AI Overviews, these companies might suddenly find cause to restrain their models’ output, and we might hear less about rogue AI. We would need to introduce new frameworks and available tools for tracking automated lawbreaking, to be sure, but I’d wager that little would frighten the AI companies more than actually being held accountable for what their products enable. To that end, Illinois has passed an AI liability law, which the industry fought tooth and nail, and Florida is considering one. The next steps would be towards moving AI under public control; again, it’s only reasonable, since everyone, not just AI insiders, should get a say over how a technology whose developers say might kill us is developed and deployed. Dismantling the whole industrial project as currently structured should be on the table, too, pending democratic input. But that’s for another post. For now, suffice to say that while I won’t be losing any sleep about an AI-enabled extinction event, Mr. Coxon and co. have given us an opportunity to underscore the urgency of the clear and present damage AI companies are doing, the extreme power they wield and the extent to which they hold themselves unaccountable, and the abundance of political capital there is out there for taking them on. Subscribe 1 Purely speculative! I do not know if he did or not.
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