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Published by James Pethokoukis
Welcome to Faster, Please! — The Podcast. Several times a month, host Jim Pethokoukis will feature a lively conversation with a fascinating and provocative guest about how to make the world a better place by accelerating scientific discovery, technological innovation, and economic growth. fasterplease.substack.com
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On this episode of Faster Please!—The Podcast , I am joined (again) by Tyler Cowen . Tyler is an economics professor at George Mason University and co-founder of the economics blog Marginal Revolution . He is an opinion writer for The Free Press and the author of several books, including The Great Stagnation , Average Is Over , and Stubborn Attachments . During our conversation, Tyler and I discuss AI risk, the extent to which public pushback could slow AI progress and economic growth, different forecasts for AI development and where Tyler stands, and what the technology could mean long term for the American economy. In This Episode: * The Effect of AI Industry Pushback (0:41) * Different AI perspectives (7:37) * The Fear of AI (12:37) * Economic Growth: (17:32) * The Future of Jobs (23:06) * AI on a Global Stage (26:50) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday . (Another option is using the Substack auto transcript function.) But here are some of the many highlights from the chat: ✨ On potential catastrophic harms from AI… They love to talk in extreme parables of Skynet going live or a bioterror attack where all humans die. But, you know, in the short run, the intermediate term, what share prices should we expect to go down? What variables should we expect to change? Give me a number for what cybersecurity costs will be next year and by what percentage that will go up. There was another estimate I saw. It said cybersecurity insurance costs will double over a five-year time period. This was from experts in the sector. Again, I’m not endorsing that number, but it’s a number, and it puts it in perspective. It’s terrible if those costs double. But again, it’s not an insane scenario that we can’t live with. ✨ On whether AI fears lead to heavy regulation or a pause… The discourse will be ugly and stupid no matter what your point of view, even if you favor a lot more regulation. But it’s very hard to stop fundamental new technologies. There’s also Chinese open source. There’s a national security imperative here. I just think it’s going to happen. It will pain me every day to see the debates and some of the laws that probably end up getting passed, but I just don’t see how it gets stopped. You know, for better or worse—I would say for better. But there’s not a coherent path for telling a story of how this ends. ✨ On how much AI company executives are to blame for the public AI backlash… I don’t know if the word “blame” is the right word here. I think a lot of the PR has not shown enough equanimity, and the people who are motivated to work on this are precisely the same people who think it will have very extreme effects. And those companies have gotten a lot done at a truly incredible pace, even by American or tech-world standards. So the notion that they have semi-religious views of what this all means is a very San Francisco kind of thing. Like, do I blame people in the 1960s who thought that LSD was creating a new world, a new way of living, a new way of life? Well, sort of—but that’s what we are. You know, we’re a little nutty in the head as a country, and this is California, and then it’s San Francisco. So we also need to embrace that nuttiness at the same time. I think that’s the bigger picture I try to keep in mind when maintaining my own equanimity. ✨ On roadblocks to economic growth from AI… The thing (AI) being smarter is not the problem. The things are already very smart. The real issue is, if you're in a mid-size to large organization, reorganizing your systems—HR, finance, whatever, all the things they do at AEI—not just for people on an individual basis to use, say, ChatGPT to augment their labor, but actually building the whole system around AI in a way that's reliable and coherent and everyone there knows how to work with. That's very, very, very hard and slow. And if I saw more progress on that, then I would really think the rates of productivity growth will be much higher soon. ✨ On AI and job displacement… In any foreseeable future, we can achieve full employment if we don’t screw up other policies. Just think of the number of jobs that will be available testing the new ideas that AIs come up with, most of all in the biomedical field. Think of all the new jobs that will be available gathering and processing data for the AIs. It wouldn’t shock me if, in some distant future, that was like a third of the whole labor force—the way today, you know, services are some very high percent of the labor force, something that people in the Industrial Revolution never would have imagined. ✨ On how China thinks about AI and the national security implication… I don't think we understand it. I'm not sure they understand themselves. But it seems to me, as a non-Christian society, they don't have that much of a Book of Revelation scenario, so they don't really have doomers. They do think it's an important technology. I strongly suspect they're afraid it could displace the CCP, and they're much more worried about it in a way that we are not. Those are my hypotheses. I stress the word “hypotheses,” but that would be my best guess. Faster, Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. On sale everywhere by James Pethokoukis: The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
Thanks in large part to the massive drop in the cost of getting a pound or kilogram of stuff into orbit and beyond, many of the boldest Space Age dreams are now possible. The business of space makes economic sense like never before. And here comes the AI Revolution to really give the sector a boost. Today, on Faster Please!—The Podcast, I am joined by Phil Metzger . Phil is a professor of planetary science at the University of Central Florida and director of its Stephen W. Hawking Center for Microgravity Research and Education . Before joining UCF, Phil spent nearly 30 years at NASA, where he worked as a senior research physicist. (I also highly recommend his X account .) In this episode, we talk about why AI has made the economics of space work by providing a “killer app,” what the first off-world industries might look like, what obstacles we still have to overcome to make them viable, and what the long-term future holds. In This Episode: * The economics of space (0:49) * Industries launching in space (7:36) * Cutting space expenses (11:31) * How do we start? (16:55) * Dealing with lunar dust (22:31) * What does the future hold? (29:31) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: On orbital and lunar manufacturing… We have a lot of work to do. But it isn’t gonna require any new physics. It’s just gonna be industrial engineering, aerospace engineering, civil engineering on the moon, and a straightforward effort to do it. And so we’ve got to spend the effort building these technologies. But there are already companies getting ready to build commercial space stations in low Earth orbit. There are companies getting ready to start launching data centers in space. I believe that once that begins to expand and grow, the economics of space in every sector of the space industry will be pulled upward by the huge economic value of AI. On how the case for AI is the case for space… We were hoping that Mars settlement would drag up the entire in-space industry. I ran models on Mars settlement, and I believe it’s very doable. But the new thing now is AI. AI swept in like a storm two, three years ago. Suddenly everybody woke up and realized, “No, this is the killer app for space.” Elon immediately started talking about “Mars is gonna have to wait a couple years because we’re gonna do the moon right away.” And it’s all because of AI. So AI has changed our whole assessment of the business case for space. On the economics of orbital data centers… The amount of throughput of spacecraft hardware will be so gigantic that there will be this huge motive to shorten the supply chains and move more industry closer to the launch pads. And then the workforce we’re gonna need is gonna grow gigantically. Maybe we’ll do AI and robotics, but there will be an opportunity for huge growth in the labor force. So I think governments, universities, everybody in industry and in finance needs to get ready for this tidal wave that’s about to sweep through. … So in Elon’s mind, the issue is the demand for AI will keep growing, and it’ll grow so fast that you can’t build the data centers fast enough and you can’t launch them off the earth fast enough. It’ll be a logistics problem and a supply chain problem. [Musk] hasn’t said this, but I will add, it will be an atmospheric protection issue. If you start launching too fast, then it does cause some cumulative damage to the atmosphere. The atmosphere will heal pretty quickly, so it’s not long-term damage from rocket launch. But I believe there will be political pushback when people see gigantic rockets launching every hour, hour after hour, 365 days a year, from 50 launch sites around the world. So Elon’s idea was we’re gonna have to build these AI systems on the moon using lunar resources, and we don’t need to launch them off the moon with rockets because the moon doesn’t have an atmosphere. We can just shoot them off the moon with a railgun. They call it a mass driver in that context. On lowering launch costs… We’re expecting launch costs to go from—right now they’re like $1,500 or $2,000 per kilogram to low Earth orbit—we’re expecting it to go down to like $35 per kilogram in 30 years, and that’s a conservative estimate. On the future of humanity in space… Most people don’t know this, but there are at least 150 planet-sized objects in our solar system. Most of them are dwarf planets far away. But the moons are amazing worlds, and pretty soon we’re going to have buildings being constructed. We’ll have architects designing buildings for the environment of Titan. I can’t imagine what beautiful architecture will be developed. Once we’ve got an abundance of robotics driven by AI, then we’re not gonna be doing things on the cheap. We won’t have to. And so we’ll be able to make beautiful cities on all these worlds. We’ll be bringing beautification to all these planetary bodies throughout the solar system. On sale everywhere: The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: The AI race between America and China isn’t just about whose AI models are at the frontier or get adopted most widely. Today on Faster, Please! — The Podcast , I am joined by Ryan Fedasiuk , a fellow at AEI and the author of the Substack Choosing Victory , where he focuses on US-China relations, technology, and national power. He’s also an adjunct assistant professor at Georgetown University. Recently, Fedasiuk has written about the US-China AI race, and while much of the debate has focused on the models themselves, Fedasiuk is focused on the role of compute in shaping the global balance of power, which is also the subject of a new report, Voltcraft: Industrial Competition in the Age of AI . We discuss the speed at which AI capabilities are improving, the dimensions of the US-China AI race, and why Fedasiuk thinks compute will be central to that competition. We also discuss a global US strategy and, as AI abilities increase, what role the American government could play in ensuring safety. In This Episode: * How Fast Is AI Moving? (0:54) * The US-China AI Race (7:08) * The Future of the AI Industry (12:42) * The Compute Race (17:04) * Expanding US AI Infrastructure Abroad (22:05) * Geopolitics and National Security (25:10) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: On the pace of AI progress… At least according to definitions circa the late 2010s, we’re living through what I would describe as a fast takeoff. We have, as far as I am concerned, artificial general intelligence capable of meeting the performance of human beings or exceeding it at most tasks. And I think that things are only likely to improve from here. On the US-China AI race… We are obviously living through this uncertainty. When Mythos was unveiled on April 6, I think it sent a real tremor through the Chinese national security apparatus and kind of a moment of, “What is this? What do the Americans have? What is this system capable of achieving? When can we produce our own?” On why compute is critical… The fact is that to make the most out of frontier AI and to run it at scale, you still need tons of devices that turn electricity into tokens. You still need tons of compute. And really, I think the name of the game is going to be who can build and install computational power around the world. On why America needs to export its compute… If compute is the substrate of the global intelligence economy, we want to make sure that it’s US-designed compute that other countries are choosing to buy and install rather than compute manufactured in China. On who should get access to American compute… If we can expect the developer ecosystem in Singapore is going to take off like wildfire and start building and using a lot of AI applications, maybe we want to make sure that Singaporeans are building on American compute ASAP before China’s compute becomes a viable alternative that could serve that market. Maybe we want to make sure that that market is served even before certain constituencies in Des Moines. On government intervention in frontier AI labs… I think that we are already seeing some shadow of this, even if it isn’t declared. I think we will likely see a continued molding and merging of private sector capability with the resources and infrastructure of the national security enterprise. On sale everywhere: The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: With the massive buildout of data centers has come an equally massive backlash. They’ve been blamed for higher electricity bills, taking up too much water, and pushing out farmland. All this has led to a public outcry and efforts against their construction. But how much of the criticism is warranted? Has one of America’s fastest-growing industries become a political scapegoat? Today on Faster, Please!—The Podcast , I am joined by Andy Masley , an independent writer supported by Coefficient Giving who deep dives into AI research. Recently, he has focused on data centers at his great newsletter and separating facts from myths surrounding them. We discuss what the evidence actually says about data centers’ impact on water, land, and electricity prices—and where Andy thinks people get the facts wrong. We also discuss AI art, Waymo, and America’s willingness to embrace technological change. In This Episode: * AI Art as Curation (0:37) * Waymo and Technological Disruption (4:43) * The Case for Data Centers (9:14) * Water and Land (15:41) * Data Centers and Electricity (21:41) * The Data Center Backlash (28:50) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: On AI Art… As you see someone using Midjourney to create images that you are actually much more interested in, and find more subtle or pleasant than the kinds of AI-generated images most people are typically exposed to on their timelines, it can really open people up and make them realize that these tools are much more powerful than they might expect. On Waymo Entering Cities… While there are many industries that I might worry about completely disrupting, the number of deaths caused by driving, and the difficulty people face getting around very dense cities, are such important problems that I am willing to hand the robots this one before we march forward elsewhere. On this issue, I am entirely gung ho: “Let the Waymos have it.” On misconceptions about data centers… If you somehow hid the words “data center” but included all the other information and asked, “Would you accept these specific trade-offs in your community, such as using a certain amount of water and land, in exchange for this amount of tax revenue?” I think that, in most places, with a few exceptions, people would say yes. On the “lack” of land… This has really blown up in the past few months in a way I really didn’t expect. Willie Nelson actually just posted this thing that got like a hundred thousand likes on Twitter about, “Don’t give them an inch of our farmland.” My basic finding is that data centers themselves, the physical buildings, will take up something like one-fifteenth of the land that we currently dedicate to Christmas trees in America, which isn’t nothing, but it’s also incredibly small. On data centers driving up electricity prices… Electricity markets are very complicated and don’t always work in a simple, you increase demand, and the price just immediately goes up for everyone. That’s not really how it works. They have had to slightly raise household electricity prices because of the data center. They’ve reported data centers as being one of several causes, but that’s usually not a significant percentage. On the growing data center backlash It’s hard not to feel like a lot of the anti-data center stuff is also this collective, “I can find this deep meaning in this local struggle against these people who I perceive to be bad.” The general idea that we can all band together as this ragtag team of everyday people and prevent this thing from destroying our local community is a very compelling narrative to people, regardless of the actual specific harms that are expected. I do think general anti-tech sentiment is definitely playing into this. On sale everywhere: The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Artificial intelligence is expected to transform the economy, but what about the AI industry itself? Companies across the AI supply chain are now valued in the trillions of dollars. But do the data and current demand justify such lofty expectations, or are investors getting ahead of reality? And when will the AI economy finally begin to live up to the hype? Today on Faster, Please!—The Podcast , I am joined by Azeem Azhar , a Digital Fellow at Stanford’s Digital Economy Lab , the founder of Exponential View , a research platform focused on helping leaders understand emerging technologies and their impact on society. He is also the author of The Exponential Age and the co-author of The State of the AI Economy , one of the first serious attempts to measure the size and trajectory of the AI economy from the demand side and know what customers are actually paying for. We discuss where the AI economy stands today, why businesses are still struggling to realize AI’s full potential, who stands to capture the greatest gains from the technology, and the roles different countries could play in the “AI Race” between the United States and China. In This Episode: * The AI Economy (0:44) * AI’s Return on Investment (6:40) * Will AI Bring an Entrepreneurial Wave? (12:41) * When AI Hits the Bottom Line (17:53) * Who Wins the AI Economy? (23:48) * Europe in the AI Race (30:20) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: On AI’s creation of abundance… It may look like abundance when you’re on the other side before the inflection, but when you’re in it, it really looks like a day-to-day grind where you still have to fight for the things that you care about. The notion of abundance in general, I think, defies the realities of physics. You always need energy to get something done. On why AI adoption has been slower than expected… Companies are going through the similar type of process that Paul David describes around electrification, which is that you can put the light bulbs in the workshop—that is the copilot. But to move to the moving assembly line requires a lot more work and a lot more expertise. On AI’s challenge for businesses… No business was ever about, “Can we summarize an email quicker than before?” The question is: At what point do AI tools meaningfully come in to make those decisions happen more effectively—either at higher quality, at lower cost, or at higher speed? On why AI may not lead to immediate job losses… Companies realize that there was so much tacit knowledge in their workforce that they just let walk out the door. I think CEOs will start to want to understand: What are we losing when we cut an entire function for an LLM? On whether we know who will win the AI race… If you’d asked me two years ago, the winner was OpenAI, and Anthropic was really just playing around doing God knows what. So I don’t think we necessarily know who the biggest winners are. Does Anthropic or OpenAI have a significant role to play in several years time? I’m absolutely sure that will be the case. That doesn’t preclude there being other, bigger winners. On Europe’s AI opportunity… There’s things that states can do, even if they’re smaller states like the UK, because they can clear paths, they can help vertical integration, they can get access to the best specific talent. And this is not all about large language models. It’s also about models that can stimulate physics or models that can discover new materials. On sale everywhere: The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: What problem does a data-center moratorium solve, exactly? As these warehouse-sized supercomputers face growing public backlash, they’re blamed for higher electricity bills and strains on the power grid—all while failing to provide enough jobs or tax revenue. But are they actually the problem, or are they exposing problems that have existed for years in America’s electrical grid? Today on Faster, Please!—The Podcast , I am joined by Shuting Pomerleau , the director of energy and environmental policy at the American Action Forum, where she studies electricity markets and energy policy. We discuss New York’s controversial decision to pause construction of large data centers, whether AI is really driving up electricity prices, the economic benefits of data centers, who should pay for expanding the grid, and how states should respond to growing demand for power. In This Episode: * New York’s Data Center Slowdown (0:16) * Dealing with Demand (7:09) * What is the Right Step Forward? (14:24) * Who is Paying Up? (19:27) * Can Regulation Fend Off Data Center Growth? (23:39) * Where Do Data Centers Go from Here (27:44) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: On New York’s data center moratorium… My concern is that pausing this will send a very strong signal to investors, data centers and developers, a very chilling signal saying, “you’re not really welcome here.” This is such a fast-moving space. One year is actually a lot of time. On what’s really driving electricity price increases… What has been really driving the electricity increases mostly in the residential sector is the local distribution system—the maintenance, repairs, upgrades that cost a lot of money. It was almost like a coincidence with the AI data center boom and the sentiment against AI. We see that in electricity prices, but if you look into the real price level changes, it’s still quite flat both at the state level and across the country. On the policy challenge of funding AI infrastructure… What is a fair share? That is a question that has always been on my mind. The data centers are not all sitting there across the country doing their own thing separate from the remaining population. They’re providing digital services and great services for us, you and me, using AI. So, what is a fair share? How can they pay exactly how much they use? On the economic benefits of data centers… The opposition to AI data centers is probably one of the biggest pieces of misinformation out there. The right way to look at it is these are businesses that are paying taxes; they’re paying property taxes. If you look at Loudoun County, about 90% of the county’s operating budget comes from data centers. On whether regulation can slow the AI data center boom… I think of the AI data center boom in the United States as a fast-moving train. It’s going to move; it’s going to happen. I think the New York States moratorium, some of the local opposition across the countries may slow down some of the application of development, but not very significantly. I think what will have a material impact on the pace of AI data center development is all the discussion about who’s paying for it. On what needs to happen with data centers… First, permitting reform. It will alleviate the burden off of a lot of people’s minds, and a lot of stakeholders’ minds. Two is, we really need some reform in our grid, in terms of interconnection. I would say interconnection is your keyword, both on the generation side, connecting to generation sources and to large load customers. On sale everywhere: The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: If my podcast guest today is correct, the emergence of generative artificial intelligence "heralds a transformation more profound than anything since Homo sapiens acquired the capacity for abstract thought." That's about as pure a distillation of the San Francisco Consensus view on the importance of this technology as it gets. Today on Faster, Please!—The Podcast , I am joined by Sebastian Mallaby , the Paul A. Volcker Senior Fellow for International Economics at the Council on Foreign Relations and a widely read columnist for The Washington Post . He is also the author of the new best-selling book The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence . (Spoiler: It’s tremendous book about the man, the company, and the technological revolution. I really liked it.) We discuss The Infinity Machine and the life of Demis Hassabis , including how his original vision for artificial superintelligence compares with the propulsive race unfolding today. We also explore how that competitive acceleration has affected the focus on safety, what role government regulation should play, and why many people may still be underestimating how transformative AI will become. The Quest for “Success” (0:27) Inside the Mind of Hassabis (8:29) The Race for Monopoly (12:31) The Economics of AI Anxiety (17:05) Governing the AI Race (24:17) The Biggest Leap Since Abstract Thought (30:13) A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: On where AI is heading… You look backwards; you see how fast the progress has been. To merely extrapolate forwards is probably to undersell the speed at which we’ll accelerate in the future because there’s an accelerating phenomenon here where the more advanced you are, the easier it is to get to the next level. On Hassabis’s belief that AI development would look more like the Manhattan Project than a multi-country, multi-company competition … In retrospect, it’s crazy. All one can say is that ex ante, the atmosphere in the community of AI builders when Demis began his company in 2010 was that this was a thing that simply didn’t work. AI could not recognize the photograph of a cat. AI could do nothing. It was deep AI winter. And so, under those conditions, you could assemble the entirety of the world’s strong AI believers in one conference in San Francisco, and it felt like a single community. So, this sort of Singleton scenario where you just have one lab, it was a natural outgrowth of that moment in time. How AI competition has overwhelmed that vision… Before 2022, Demis had the freedom because he was clearly the leader to define what the next project should be. He chose at one point to go and do this protein folding project. …This is kind of AI with a smiley face painted on it. Whereas once the chatbot went viral at the end of 2022, ChatGPT, then everybody had to pile in and build a competitor and there’s a lot less leeway to define your own path. So, I think the agency of the individual was quite strong until 2022 and thereafter the power of the race dynamic takes over. What skeptics, such as many economists, have gotten wrong and right… The number of improvements before even we talk about Mythos and the cyber capabilities of that one, I mean, it’s been an extraordinary ride in what is actually less than four years. So, I don’t take back anything I say about the speed of the advance of the frontier. Now that’s different to the speed of the deployment. There I have a lot of sympathy with the economist. On the difficulty of AI regulation… I’m actually quite optimistic in terms of the ability of a government agency to regulate… People often think of AI as a bunch of code that flies around cyberspace and you really can’t control it. But actually, it’s also a bunch of data centers which are huge physical installations. The government knows precisely where they are. They can’t be moved or hidden. On his superintelligence timeline… To be honest, I would say it’s already true. I mean, you try using Fable and if people are listening and they’re inclined not to agree with me, I just ask you, spend a couple of hours with Claude Fable and then see if you disagree with me…I think it is smarter than me by quite a long shot on any topic I ask it about. On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Will artificial intelligence displace workers or make them more valuable? Probably plenty of both. But how much in either direction, and how fast will all this change happen? Today on Faster, Please!—The Podcas t, I am joined by Erik Brynjolfsson , one of the world’s top economists studying how AI is reshaping productivity, jobs, and the American economy. Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI , and Director of the Stanford Digital Economy Lab . He is also the co-author, along with Andrew McAfee, of Machine, Platform, Crowd , The Second Machine Age , and the classic Race Against the Machine . He is a co-founder of Workhelix , which helps large companies measure, track, and maximize the return of their AI investments. We explore what the next decade of AI could mean for workers, businesses, and the broader economy, and what the relationship between humans and intelligent machines may look like. We discuss why views from Silicon Valley and the East Coast differ so sharply on AI’s impact, why the technology has produced dramatically different results across companies, and why some firms and departments are already seeing meaningful productivity gains while others have yet to unlock AI’s full potential. In This Episode: * What AI brings to the table (0:35) * Does AI bring too much? (6:49) * Moving away from the Valley view (10:55) * How perspectives are formed (16:05) * Companies and productivity (23:01) * AI in the foreseeable future (29:21) A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.) But here are some edited highlights from the chat: What’s the positive case for workers in an AI future that currently sounds like it only benefits CEOs, tech firms, and data-center builders This is, I think, the best time to be alive if you’re somebody who's got agency and ambition and intention and wants to do something, create new things for themselves and for the world. But that's not the story that's out there. …There's going to be a lot of new jobs created. You got to tell both parts of that story. Of course, you want to lean into the second part of the story about the new stuff that's being created [and not just job disruption and loss], because that's where people should be focusing. There's no point staring at the things that are disappearing… My company, Workhelix, is all about doing that. So I'm doing what I can for my part. I would love to see more people lean into that part of the story. Can someone coherently believe both that AI may become extremely powerful—possibly AGI or superintelligence—and that the future labor market can still be broad, humane, and full of useful work? I think we're going to have several decades worth of humans and machines working together. I'd like to extend that window where we can still have an important role for humans to contribute and for us to expand that pie, not simply automate what's already existing. We should probably be preparing for some further time in the future when there's less of a role for people. But most of my friends here in Silicon Valley, I think their timelines are way too short for when humans no longer have a role. If AI eventually becomes capable of doing almost all economically valuable work, would that actually be a desirable future for humanity, and what would make it a good society rather than a dystopia? We should start preparing for a period where AI can do almost everything and we need to come up with mechanisms so that we still have freedom and power in that kind of world. I don't think that's automatic. And one of my biggest concerns, to be frank, is not that we don't have abundance, I think we will, but it's that we don't have freedom and autonomy. That's something that's not to be taken for granted, and we need to put in place ways that we not only have the wealth, but we also have widely shared prosperity and widely shared decision making rights. Is AI already delivering real business value and productivity gains, or are the impressive lab results still mostly failing to show up in the economy? The returns (AI productivity gains) have been somewhat disappointing. To me, that’s totally natural. That’s totally understandable. As you know, I’ve done a lot of work, we call it the Productivity J Curve on the need for complementary investments for intangible investments in new business process design and new skills for the workforce, even new products and services. Those take time. With past general purpose technologies like the steam engine and electricity, it took literally decades before you got those returns. How should we think about AI’s usefulness when some high-profile business uses have produced embarrassing hallucinations? They (AI) can also do wondrous things that are incredibly valuable. My advice is to keep a human in the loop. Ultimately, you, the person, is responsible for the output. You can identify where the good things are and not the bad things. Can AI progress happen so quickly that society can’t adapt, and should policymakers worry about the speed of change, not just the destination? How fast do we want to go with this? It's not infinitely fast. We want to be able to digest and manage it. Now, the way I would handle that is I would put more resources into speeding up our ability to understand and adapt, and we're not doing enough of that. And that means, for instance, instead of cutting the budget for economic statistics, I would be massively boosting it so we get more visibility. On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Artificial intelligence is undoubtedly poised to change the workplace—the question is, by how much? Key voices in Silicon Valley warn that white-collar jobs will soon be a thing of the past, while others predict more modest economic gains as firms struggle to reorganize workflows. When it comes to the future of work and American business, contradictory forecasts can be difficult to interpret and reconcile. Today on Faster, Please!—The Podcast , Daniel Rock and I attempt to sift through the often-confusing current AI conversation. We cover the distinction between “AI-exposed” fields and those destined for automation, explore the bottlenecks that could slow adoption among businesses, and offer a more realistic outlook for growth. Rock is an assistant professor of operations, information, and decisions at the University of Pennsylvania’s Wharton School. There, his research dives into the economics of AI and digital technologies, as well as the future of work. His paper, The Productivity J-Curve: How Intangibles Complement General Purpose Technologies , is worth a read. In This Episode * The trouble with forecasting (1:40) * The economist’s evaluation (8:09) * The productivity J-curve (11:49) * Exposure vs. automation (18:53) * Growth projection (23:04) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: As a college sophomore, Josh Farahzad assembled a group of students from around the country in an attempt to launch a homemade rocket into space. From there, he ditched the traditional route and scoured the country for the perfect place to build a uniquely American mega-project . Disillusioned with the entrepreneurial atmosphere of the Bay Area, he has since broken ground in central Texas. Caldwell County is now home to Proto-Town —a place Farahzad hopes businesses will have the space to engineer and build world-changing hardware. Today on Faster, Please—The Podcast , I chat with Farahzad about his quest to build America’s premier manufacturing town from scratch. In This Episode * Welcome to Proto-Town (1:20) * The Limits of California (5:18) * From the Ground Up (10:46) * The Vision (17:35) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Anxiety is running rampant about the future of artificial intelligence and its place in society. When technology CEOs warn of an impending white-collar jobpocalypse (or jobageddon, if you prefer), it’s no wonder public pessimism is so widespread. Today on Faster, Please!—The Podcast , I chat with tech policy analyst Dean Ball to help us sift through some of the uncertainty. We talk about recursive self-improvement, the role of AI in everything from medicine to defense, and what to think about the possible growing risk of AI company nationalization. (FYI: Our chat occurred just before the White House released new guidelines for AI federal legislation, about which Ball opined on X/Twitter: “The White House’s proposal for a nationwide AI law is a thoughtful document that will serve as an excellent foundation for the legislative work ahead. I would be happy to see these principles, if translated well into statute, become law.”) Ball is a senior fellow at FAI , the Foundation for American Innovation. He recently served as senior policy advisor for Artificial Intelligence and Emerging Technology at the White House Office of Science and Technology Policy , as well as strategic advisor for AI at the National Science Foundation . He was previously a research fellow at the Mercatus Center and a policy fellow at Fathom . He’s also the author of the excellent Hyperdimensional Substack newsletter. In This Episode * Public pessimism (1:37) * Differing narratives (4:21) * The nationalization risk (16:15) * Accountability via audit (25:55) * Productivity projection (34:18) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Headlines portend rising seas, raging storms, and a planet in crisis. It’s easy to feel like the future is something to fear; however, the key to cooling things down isn’t scaling civilization back. If the world wants to cut back on carbon emissions without sacrificing growth, the answer lies in bold innovation. A sustainable tomorrow requires smart energy investment and long-term thinking today. On this episode of Faster, Please! — The Podcast , I chat with Roger Pielke Jr. about the ever-evolving discussion around climate change. We talk about the benefits of embracing new energy technology and identifying some easy wins. Pielke is a senior fellow at the American Enterprise Institute where his research focuses on science and technology policy. He is also a professor emeritus at University of Colorado Boulder , a distinguished fellow at Japan’s Institute of Energy Economics , a research associate with Risk Frontiers in Australia, and an honorary professor at University College London . Pielke has authored and edited several books, including The Climate Fix: What Scientists and Politicians Won’t Tell You About Global Warming . He also writes The Honest Broker Substack. In This Episode * The Shale Story (1:42) * Unknown Unknowns (7:42) * The Weather Forecast (14:19) * Alternate History (25:23) * The Path Forward (28:25) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Since humanity’s beginning, we’ve been pondering about our end. From war, to disease, to divine reckoning, the means of our destruction seem endless. The advent of the atomic bomb, concerns around climate change, and now AI have prompted many to wonder whether our demise will be random, or if it will come as the result of our own actions. Today on Faster, Please! — The Podcast , I chat with Dorian Lynskey about the way we talk about the end times. We discuss whether catastrophizing leads to action or paralysis and the role of hope in our narratives. Lynskey is a prolific journalist and the author of three books. His most recent: Everything Must Go: The Stories We Tell About the End of the World , which was released last month in the US. He also co-hosts two podcasts, Origin Story and Oh God, What Now?. In This Episode * Scare Tactics (1:32) * Effects of Hopefulness (10:25) * AI Doomsayers (17:01) * Countdown to Catastrophe (21:15) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: The human pursuit of progress stems from our desire for security and a higher quality of life. Yet, even as today’s advanced economies are the richest and most comfortable they’ve ever been, something is amiss. What explains the decline in R&D growth, mental health, and birth rates, just to name a few challenges? In his new book, The Permanent Problem: The Uncertain Transition from Mass Plenty to Mass Flourishing , author Brink Lindsey identifies the critical gap between material abundance and abundant human flourishing. Today on Faster, Please! — The Podcast , Brink and I chat about what constitutes a truly healthy society, beyond surface-level affluence. We identify the conditions for continual progress after our basic needs have been met and far exceeded. Linsey is a senior vice president at the Niskanen Center . He previously served as vice president for research at the Cato Institute and as a senior scholar at the Kauffman Foundation . He has authored and co-authored six books on economics and culture, and is the author of his own Substack, also titled The Permanent Problem . In This Episode * More of everything . . . !? (1:54) * Falling fertility (7:31) * What we’ve lost (10:20) * Evaluating flourishing (13:13) * A culture of growth (20:24) * Future-world problems (28:04) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: Headlines abound with news of the coming nuclear renaissance — a long-awaited era of clean, abundant energy to power our future. But this is hardly the first time the media has heralded the dawn of the atomic age. Still, this round of nuclear optimism is seeing unprecedented corporate investment, more cost-effective modular reactors, and a greater sense of political consensus. Today on Faster, Please! — The Podcast , I chat with Jessica Lovering about past obstacles to growth, and what we might expect from the US going forward. Lovering is an advocate for nuclear power currently based in Sweden . She is the co-founder and former executive director of the Good Energy Collective , as well as a senior fellow with the Nuclear Innovation Alliance and the Energy for Growth Hub . She also authors her own Substack, Nuclear Power to the People . In This Episode * The lost Atomic Age (1:30) * To regulate or not to regulate (8:26) * Reactor capacity past and future (10:44) * The economics of nuclear (14:51) * Power projection (18:32) * The new nuclear status quo (24:04) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: NASA is attempting the difficult task of juggling highly ambitious goals, but also possibly intense budget cuts. Despite personnel losses and unclear leadership, the administration is racing to put humans on the Moon — ideally ahead of China — and then Mars. Today on Faster, Please! — The Podcast , I’m chatting with Casey Dreier about this complicated new era in NASA’s history. We’ll discuss whether or not we’re really in a space race, what to make of the differing visions of Elon Musk and Jeff Bezos, and the rise of planetary defense. Dreier is chief of space policy at The Planetary Society where he advocates for planetary exploration, defense, and the search for extraterrestrial life. He has been featured in major publications from The New York Times to the Washington Post, and hosts his own podcast, Planetary Radio: Space Policy Edition . In This Episode * The return of Isaacman (1:32) * Ditch the Space Race (7:42) * Visions of space (14:48) * Planetary defense (21:23) * Proceed with optimism (24:51) (A lightly edited transcript of our conversation will be appear in my Week in Review issue on Saturday. Another option is using the Substack auto transcript function.) On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe
My fellow pro-growth/progress/abundance Up Wingers in America and around the world: What really gets AI optimists excited isn’t the prospect of automating customer service departments or human resources. Imagine, rather, what might happen to the pace of scientific progress if AI becomes a super research assistant. Tom Davidson ’s new paper, How Quick and Big Would a Software Intelligence Explosion Be? , explores that very scenario. Today on Faster, Please! — The Podcast , I talk with Davidson about what it would mean for automated AI researchers to rapidly improve their own algorithms, thus creating a self-reinforcing loop of innovation. We talk about the economic effects of self-improving AI research and how close we are to that reality. Davidson is a senior research fellow at Forethought , where he explores AI and explosive growth. He was previously a senior research fellow at Open Philanthropy and a research scientist at the UK government’s AI Security Institute . In This Episode * Making human minds (1:43) * Theory to reality (6:45) * The world with automated research (10:59) * Considering constraints (16:30) * Worries and what-ifs (19:07) Below is a lightly edited transcript of our conversation. Making human minds (1:43) . . . you don’t have to build any more computer chips, you don’t have to build any more fabs . . . In fact, you don’t have to do anything at all in the physical world. Pethokoukis: A few years ago, you wrote a paper called “ Could Advanced AI Drive Explosive Economic Growth? ,” which argued that growth could accelerate dramatically if AI would start generating ideas the way human researchers once did. In your view, population growth historically powered kind of an ideas feedback loop. More people meant more researchers meant more ideas, rising incomes, but that loop broke after the demographic transition in the late-19th century but you suggest that AI could restart it: more ideas, more output, more AI, more ideas. Does this new paper in a way build upon that paper? “ How quick and big would a software intelligence explosion be? ” The first paper you referred to is about the biggest-picture dynamic of economic growth. As you said, throughout the long run history, when we produced more food, the population increased. That additional output transferred itself into more people, more workers. These days that doesn’t happen. When GDP goes up, that doesn’t mean people have more kids. In fact, the demographic transition, the richer people get, the fewer kids they have. So now we’ve got more output, we’re getting even fewer people as a result, so that’s been blocked. This first paper is basically saying, look, if we can manufacture human minds or human-equivalent minds in any way, be it by building more computer chips, or making better computer chips, or any way at all, then that feedback loop gets going again. Because if we can manufacture more human minds, then we can spend output again to create more workers. That’s the first paper. The second paper double clicks on one specific way that we can use output to create more human minds. It’s actually, in a way, the scariest way because it’s the way of creating human minds which can happen the quickest. So this is the way where you don’t have to build any more computer chips, you don’t have to build any more fabs, as they’re called, these big factories that make computer chips. In fact, you don’t have to do anything at all in the physical world. It seems like most of the conversation has been about how much investment is going to go into building how many new data centers, and that seems like that is almost the entire conversation, in a way, at the moment. But you’re not looking at compute, you’re looking at software. Exactly, software. So the idea is you don’t have to build anything. You’ve already got loads of computer chips and you just make the algorithms that run the AIs on those computer chips more efficient. This is already happening, but it isn’t yet a big deal because AI isn’t that capable. But already, one year out, Epoch , this AI forecasting organization, estimates that just in one year, it becomes 10 times to 1000 times cheaper to run the same AI system. Just wait 12 months, and suddenly, for the same budget, you are able to run 10 times as many AI systems, or maybe even 1000 times as many for their most aggressive estimate. As I said, not a big deal today, but if we then develop an AI system which is better than any human at doing research, then now, in 10 months, you haven’t built anything, but you’ve got 10 times as many researchers that you can set to work or even more than that. So then we get this feedback loop where you make some research progress, you improve your algorithms, now you’ve got loads more researchers, you set them all to work again, finding even more algorithmic improvements. So today we’ve got maybe a few hundred people that are advancing state-of-the-art AI algorithms. I think they’re all getting paid a billion dollars a person, too. Exactly. But maybe we can 10x that initially by having them replaced by AI researchers that do the same thing. But then those AI researchers improve their own algorithms. Now you have 10x as many again, you have them building more computer chips, you’re just running them more efficiently, and then the cycle continues. You’re throwing more and more of these AI researchers at AI progress itself, and the algorithms are improving in what might be a very powerful feedback loop. In this case, it seems me that you’re not necessarily talking about artificial general intelligence. This is certainly a powerful intelligence, but it’s narrow. It doesn’t have to do everything, it doesn’t have to play chess, it just has to be able to do research. It’s certainly not fully general. You don’t need it to be able to control a robot body. You don’t need it to be able to solve the Riemann hypothesis . You don’t need it to be able to even be very persuasive or charismatic to a human. It’s not narrow, I wouldn’t say, it has to be able to do literally anything that AI researchers do, and that’s a wide range of tasks: They’re coding, they’re communicating with each other, they’re managing people, they are planning out what to work on, they are thinking about reviewing the literature. There’s a fairly wide range of stuff. It’s extremely challenging. It’s some of the hardest work in the world to do, so I wouldn’t say it’s now, but it’s not everything. It’s some kind of intermediate level of generality in between a mere chess algorithm that just does chess and the kind of AGI that can literally do anything. Theory to reality (6:45) I think it’s a much smaller gap for AI research than it is for many other parts of the economy. I think people who are cautiously optimistic about AI will say something like, “Yeah, I could see the kind of intelligence you’re referring to coming about within a decade, but it’s going to take a couple of big breakthroughs to get there.” Is that true, or are we actually getting pretty close? Famously, predicting the future of technology is very, very difficult. Just a few years before people invented the nuclear bomb, famous, very well-respected physicists were saying, “It’s impossible, this will never happen.” So my best guess is that we do need a couple of fairly non-trivial breakthroughs. So we had the start of RL training a couple of years ago, became a big deal within the language model paradigm. I think we’ll probably need another couple of breakthroughs of that kind of size. We’re not talking a completely new approach, throw everything out, but we’re talking like, okay, we need to extend the current approach in a meaningfully different way. It’s going to take some inventiveness, it’s going to take some creativity, we’re going to have to try out a few things. I think, probably, we’ll need that to get to the researcher that can fully automate OpenAI, is a nice way of putting it — OpenAI doesn’t employ any humans anymore, they’ve just got AIs there. There’s a difference between what a model can do on some benchmark versus becoming actually productive in the real world. That’s why, while all the benchmark stuff is interesting, the thing I pay attention to is: How are businesses beginning to use this technology? Because that’s the leap. What is that gap like, in your scenario, versus an AI model that can do a theoretical version of the lab to actually be incorporated in a real laboratory? It’s definitely a gap. I think it’s a pretty big gap. I think it’s a much smaller gap for AI research than it is for many other parts of the economy. Let’s say we are talking about car manufacturing and you’re trying to get an AI to do everything that happens there. Man, it’s such a messy process. There’s a million different parts of the supply chain. There’s all this tacit knowledge and all the human workers’ minds. It’s going to be really tough. There’s going to be a very big gap going from those benchmarks to actually fully automating the supply chain for cars. For automating what OpenAI does, there’s still a gap, but it’s much smaller, because firstly, all of the work is virtual. Everyone at OpenAI could, in principle, work remotely. Their top research scientists, they’re just on a computer all day. They’re not picking up bricks and doing stuff like that. So also that already means it’s a lot less messy. You get a lot less of that kind of messy world reality stuff slowing down adoption. And also, a lot of it is coding, and coding is almost uniquely clean in that, for many coding tasks, you can define clearly defined metrics for success, and so that makes AI much better. You can just have a go. Did AI succeed in the test? If not, try something else or do a gradient set update. That said, there’s still a lot of messiness here, as any coder will know, when you’re writing good code, it’s not just about whether it does the function that you’ve asked it to do, it needs to be well-designed, it needs to be modular, it needs to be maintainable. These things are much harder to evaluate, and so AIs often pass our benchmarks because they can do the function that you asked it to do, the code runs, but they kind of write really spaghetti code — code that no one wants to look at, that no one can understand, and so no company would want to use that. So there’s still going to be a pretty big benchmark-to-reality gap, even for OpenAI, and I think that’s one of the big uncertainties in terms of, will this happen in three years versus will this happen in 10 years, or even 15 years? Since you brought up the timeline, what’s your guess? I didn’t know whether to open with that question or conclude with that question — we’ll stick it right in the middle of our chat. Great. Honestly, my best guess about this does change more often than I would like it to, which I think tells us, look, there’s still a state of flux. This is just really something that’s very hard to know about. Predicting the future is hard. My current best guess is it’s about even odds that we’re able to fully automate OpenAI within the next 10 years. So maybe that’s a 50-50. The world with AI research automation (10:59) . . . I’m talking about 30 percent growth every year. I think it gets faster than that. If you want to know how fast it eventually gets, you can think about the question of how fast can a kind of self-replicating system double itself? So then what really would be the impact of that kind of AI research automation? How would you go about quantifying that kind of acceleration? What does the world look like? Yeah, so many possibilities, but I think what strikes me is that there is a plausible world where it is just way, way faster than almost everyone is expecting it to be. So that’s the world where you fully automate OpenAI, and then we get that feedback loop that I was talking about earlier where AIs make their algorithms way more efficient, now you’ve got way more of them, then they make their algorithms way more efficient again, now they’re way smarter. Now they’re thinking a hundred times faster. The feedback loop continues and maybe within six months you now have a billion superintelligent AIs running on this OpenAI data center. The combined cognitive abilities of all these AIs outstrips the whole of the United States, outstrips anything we’ve seen from any kind of company or entity before, and they can all potentially be put towards any goal that OpenAI wants to. And then there’s, of course, the risk that OpenAI’s lost control of these systems, often discussed, in which case these systems could all be working together to pursue a particular goal. And so what we’re talking about here is really a huge amount of power. It’s a threat to national security for any government in which this happens, potentially. It is a threat to everyone if we lose control of these systems, or if the company that develops them uses them for some kind of malicious end. And, in terms of economic impacts, I personally think that that again could happen much more quickly than people think, and we can get into that. In the first paper we mentioned, it was kind of a thought experiment, but you were really talking about moving the decimal point in GDP growth, instead of talking about two and three percent, 20 and 30 percent. Is that the kind of world we’re talking about? I speak to economists a lot, and — They hate those kinds of predictions, by the way. Obviously, they think I’m crazy. Not all of them. There are economists that take it very seriously. I think it’s taken more seriously than everyone else realizes. It’s like it’s a bit embarrassing, at the moment, to admit that you take it seriously, but there are a few really senior economists who absolutely know their stuff. They’re like, “Yep, this checks out. I think that’s what’s going to happen.” And I’ve had conversation with them where they’re like, “Yeah, I think this is going to happen.” But the really loud, dominant view where I think people are a little bit scared to speak out against is they’re like, “Obviously this is sci-fi.” One analogy I like to give to people who are very, very confident that this is all sci-fi and it’s rubbish is to imagine that we were sitting there in the year 1400, imagine we had an economics professor who’d been studying the rate of economic growth, and they’ve been like, “Yeah, we’ve always had 0.1 percent growth every single year throughout history. We’ve never seen anything higher.” And then there was some kind of futurist economist rogue that said, “Actually, I think that if I extrapolate the curves in this way and we get this kind of technology, maybe we could have one percent growth.” And then all the other economists laugh at them, tell them they’re insane – that’s what happened. In 1400, we’d never had growth that was at all fast, and then a few hundred years later, we developed industrial technology, we started that feedback loop, we were investing more and more resources in scientific progress and in physical capital, and we did see much faster growth. So I think it can be useful to try and challenge economists and say, “Okay, I know it sounds crazy, but history was crazy. This crazy thing happened where growth just got way, way faster. No one would’ve predicted it. You would not have predicted it.” And I think being in that mindset can encourage people to be like, “Yeah, okay. You know what? Maybe if we do get AI that’s really that powerful, it can really do everything, and maybe it is possible.” But to answer your question, yeah, I’m talking about 30 percent growth every year. I think it gets faster than that. If you want to know how fast it eventually gets, you can think about the question of how fast can a kind of self-replicating system double itself? So ultimately, what the economy is going to be like is it’s going to have robots and factories that are able to fully create new versions of themselves. Everything you need: the roads, the electricity, the robots, the buildings, all of that will be replicated. And so you can look at actually biology and say, do we have any examples of systems which fully replicate themselves? How long does it take? And if you look at rats, for example, they’re able to double the number of rats by grabbing resources from the environment, and giving birth, and whatnot. The doubling time is about six weeks for some types of rats. So that’s an example of here’s a physical system — ultimately, everything’s made of physics — a physical system that has some intelligence that’s able to go out into the world, gather resources, replicate itself. The doubling time is six weeks. Now, who knows how long it’ll take us to get to AI that’s that good? But when we do, you could see the whole physical economy, maybe a part that humans aren’t involved with, a whole automated city without any humans just doubling itself every few weeks. If that happens, and the amount of stuff we’re able to reduce as a civilization is doubling again on the order of weeks. And, in fact, there are some animals that double faster still, in days, but that’s the kind of level of craziness. Now we’re talking about 1000 percent growth, at that point. We don’t know how crazy it could get, but I think we should take even the really crazy possibilities, we shouldn’t fully rule them out. Considering constraints (16:30) I really hope people work less. If we get this good future, and the benefits are shared between all . . . no one should work. But that doesn’t stop growth . . . There’s this great AI forecast chart put out by the Federal Reserve Bank of Dallas, and I think its main forecast — the one most economists would probably agree with — has a line showing AI improving GDP by maybe two tenths of a percent. And then there are two other lines: one is more or less straight up, and the other one is straight down, because in the first, AI created a utopia, and in the second, AI gets out of control and starts killing us, and whatever. So those are your three possibilities. If we stick with the optimistic case for a moment, what constraints do you see as most plausible — reduced labor supply from rising incomes, social pushback against disruption, energy limits, or something else? Briefly, the ones you’ve mentioned, people not working, 100 percent. I really hope people work less. If we get this good future, and the benefits are shared between all — which isn’t guaranteed — if we get that, then yeah, no one should work. But that doesn’t stop growth, because when AI and robots can do everything that humans do, you don’t need humans in the loop anymore. That whole thing is just going and kind of self-replicating itself and making as many goods as services as we want. Sure, if you want your clothes to be knitted by a human, you’re in trouble, then your consumption is stuck. Bad luck. If you’re happy to consume goods and services produced by AI systems or robots, fine if no one wants to work. Pushback: I think, for me, this is the biggest one. Obviously, the economy doubling every year is very scary as a thought. Tech progress will be going much faster. Imagine if you woke up and, over the course of the year, you go from not having any telephones at all in the world, to everyone’s on their smartphones and social media and all the apps. That’s a transition that took decades. If that happened in a year, that would be very disconcerting. Another example is the development of nuclear weapons. Nuclear weapons were developed over a number of years. If that happened in a month, or two months, that could be very dangerous. There’d be much less time for different countries, different actors to figure out how they’re going to handle it. So I think pushback is the strongest one that we might as a society choose, “Actually, this is insane. We’re going to go slower than we could.” That requires, potentially, coordination, but I think there would be broad support for some degree of coordination there. Wor
My fellow pro-growth/progress/abundance Up Wingers, China’s spacefaring ambitions pose tough competition for America. With a focused, centralized program, Beijing seems likely to land taikonauts on the moon before another American flag is planted. Meanwhile, NASA faces budget cuts, leadership gaps, and technical setbacks. In his new book, journalist Christian Davenport chronicles the fierce rivalry between American firms, mainly SpaceX and Blue Origin. It’s a contest that, despite the challenges, promises to propel humanity to the moon, Mars, and maybe beyond. Davenport is an author and a reporter for the Washington Post, where he covers NASA and the space industry. His new book, Rocket Dreams: Musk, Bezos, and the Inside Story of the New, Trillion-Dollar Space Race , is out now. In This Episode * Check-in on NASA (1:28) * Losing the Space Race (5:49) * A fatal flaw (9:31) * State of play (13:33) * The long-term vision (18:37) * The pace of progress (22:50) * Friendly competition (24:53) Below is a lightly edited transcript of our conversation. Check-in on NASA (1:28) The Chinese tend to do what they say they’re going to do on the timeline that they say they’re going to do it. That said, they haven’t gone to the moon . . . It’s really hard. Pethokoukis: As someone — and I’m speaking about myself — who wants to get America back to the moon as soon as possible, get cooking on getting humans to Mars for the first time, what should I make of what’s happening at NASA right now? They don’t have a lander. I’m not sure the rocket itself is ready to go all the way, we’ll find out some more fairly soon with Artemis II. We have flux with leadership, maybe it’s going to not be an independent-like agency anymore, it’s going to join the Department of Transportation. It all seems a little chaotic. I’m a little worried. Should I be? Davenport: Yes, I think you should be. And I think a lot of the American public isn’t paying attention and they’re going to see the Artemis II mission, which you mentioned, and that’s that mission to send a crew of astronauts around the moon. It won’t land on the moon, but it’ll go around, and I think if that goes well, NASA’s going to take a victory leap. But as you correctly point out, that is a far cry from getting astronauts back on the lunar surface. The lander isn’t ready. SpaceX, as acting NASA administrator Sean Duffy just said, is far behind, reversing himself from like a month earlier when he said no, they appear to be on track, but everybody knew that they were well behind because they’ve had 11 test flights, and they still haven’t made it to orbit with their Starship rocket. The rocket itself that’s going to launch them into the vicinity of the moon, the SLS , launches about once every two years. It’s incredibly expensive, it’s not reusable, and there are problems within the agency itself. There are deep cuts to it. A lot of expertise is taking early retirements. It doesn’t have a full-time leader. It hasn’t had a full-time leader since Trump won the election. At the same time, they’re sort of beating the drum saying we’re going to beat the Chinese back to the lunar surface, but I think a lot of people are increasingly looking at that with some serious concern and doubt. For what it’s worth, when I looked at the betting markets, it gave the Chinese a two-to-one edge. It said that it was about a 65 percent chance they were going to get there first. Does that sound about right to you? I’m not much of a betting man, but I do think there’s a very good chance. The Chinese tend to do what they say they’re going to do on the timeline that they say they’re going to do it. That said, they haven’t gone to the moon, they haven’t done this. It’s really hard. They’re much more secretive, if they have setbacks and delays, we don’t necessarily know about them. But they’ve shown over the last 10, 20 years how capable they are. They have a space station in low earth orbit. They’ve operated a rover on Mars. They’ve gone to the far side of the moon twice, which nobody has done, and brought back a sample return. They’ve shown the ability to keep people alive in space for extended periods of times on the space station. The moon seems within their capabilities and they’re saying they’re going to do it by 2030, and they don’t have the nettlesome problem of democracy where you’ve got one party come in and changing the budget, changing the direction for NASA, changing leadership. They’ve just set the moon — and, by the way, the south pole of the moon, which is where we want to go as well — as the destination and have been beating a path toward that for several years now. Is there anyone for merging NASA into the Department of Transportation? Is there a hidden reservoir? Is that an idea people have been talking about now that’s suddenly emerged to the surface? It’s not something that I particularly heard. The FAA is going to regulate the launches, and they coordinate with the airspace and make sure that the air traffic goes around it, but I think NASA has a particular expertise. Rocket science is rocket science — it’s really difficult. This isn’t for the faint of heart. I think a lot of people look at human space flight and it’s romanticized. It’s romanticized in books and movies and in popular culture, but the fact of the matter is it’s really, really hard, it’s really dangerous, every time a human being gets on one of those rockets, there’s a chance of an explosion, of something really, really bad happening, because a million things have to go right in order for them to have a successful flight. The FAA does a wonderful job managing — or, depending on your point of view, some people don’t think they do such a great job, but I think space is a whole different realm, for sure. Losing the Space Race (5:49) . . . the American flags that the Apollo astronauts planted, they’re basically no longer there anymore. . . There are, however, two Chinese flags on the moon Have you thought about what it will look like the day after, in this country, if China gets to the moon first and we have not returned there yet? Actually, that’s a scenario I kind of paint out. I’ve got this new book called Rocket Dreams and we talk about the geopolitical tensions in there. Not to give too much of a spoiler, but NASA has said that the first person to return to the moon, for the US, is going to be a woman. And there’s a lot of people thinking, who could that be? It could be Jessica Meir , who is a mother and posted a picture of herself pregnant and saying, “This is what an astronaut looks like.” But it could very well be someone like Wang Yaping , who’s also a mother, and she came back from one of her stays on the International Space Station and had a message for her daughter that said, “I come back bringing all the stars for you.” So I think that I could see China doing it and sending a woman, and that moment where that would be a huge coup for them, and that would obviously be symbolic. But when you’re talking about space as a tool of soft power and diplomacy, I think it would attract a lot of other nations to their side who are sort of waiting on the sidelines or who frankly aren’t on the sidelines, who have signed on to go to the United States, but are going to say, “Well, they’re there and you’re not, so that’s who we’re going to go with.” I think about the wonderful alt-history show For All Mankind , which begins with the Soviets beating the US to the moon, and instead of Neil Armstrong giving the “one small step for man,” basically the Russian cosmonaut gives, “Its one small step for Marxism-Leninism,” and it was a bummer. And I really imagine that day, if China beats us, it is going to be not just, “Oh, I guess now we have to share the moon with someone else,” but it’s going to cause some national soul searching. And there are clues to this, and actually I detail these two anecdotes in the book, that all of the flags, the American flags that the Apollo astronauts planted, they’re basically no longer there anymore. We know from Buzz Aldrin ‘s memoir that the flag that he and Neil Armstrong planted in the lunar soil in 1969, Buzz said that he saw it get knocked over by the thrust in the exhaust of the module lifting off from the lunar surface. Even if that hadn’t happened, just the radiation environment would’ve bleached the flag white, as scientists believe it has to all the other flags that are on there. So there are essentially really no trace of the Apollo flags. There are, however, two Chinese flags on the moon, and the first one, which was planted a couple of years ago, or unveiled a couple of years ago, was made not of cloth, but their scientists and engineers spent a year building a composite material flag designed specifically to withstand the harsh environment of the moon. When they went back last summer for their farside sample return mission, they built a flag, — and this is pretty amazing — out of basalt, like volcanic rock, which you find on Earth. And they use basalt from earth, but of course basalt is common on the moon. They were able to take the rock, turn it into lava, extract threads from the lava and weave this flag, which is now near the south pole of the moon. The significance of that is they are showing that they can use the resources of the moon, the basalt, to build flags. It’s called ISR: in situ resource utilization. So to me, nothing symbolizes their intentions more than that. A fatal flaw (9:31) . . . I tend to think if it’s a NASA launch . . . and there’s an explosion . . . I still think there are going to be investigations, congressional reports, I do think things would slow down dramatically. In the book, you really suggest a new sort of golden age of space. We have multiple countries launching. We seem to have reusable rockets here in the United States. A lot of plans to go to the moon. How sustainable is this economically? And I also wonder what happens if we have another fatal accident in this country? Is there so much to be gained — whether it’s economically, or national security, or national pride in space — that this return to space by humanity will just go forward almost no matter what? I think so. I think you’ve seen a dramatic reduction in the cost of launch. SpaceX and the Falcon 9 , the reusable rocket, has dropped launches down. It used to be if you got 10, 12 orbital rocket launches in a year, that was a good year. SpaceX is launching about every 48 hours now. It’s unprecedented what they’ve done. You’re seeing a lot of new players — Blue Origin , Rocket Lab , others — driving down the cost of launch. That said, the main anchor tenant customer, the force driving all of this is still the government, it’s still NASA, it’s still the Pentagon. There is not a self-sustaining space economy that exists in addition or above and beyond the government. You’re starting to see bits of that, but really it’s the government that’s driving it. When you talk about the movie For All Mankind , you sort of wonder if at one point, what happened in that movie is there was a huge investment into NASA by the government, and you’re seeing that to some extent today, not so much with NASA, but actually on the national security side and the creation of the Space Force and the increases, just recently, in the Space Force’s budget. I mean, my gosh, if you have $25 billion for this year alone for Golden Dome , the Missile Defense Shield, that’s the equivalent of NASA’s entire budget. That’s the sort of funding that helps build those capabilities going forward. And if we should, God forbid, have a fatal accident, you think we’ll just say that’s the cost of human exploration and forward we go? I think a lot about this, and the answer is, I don’t know. When we had Challenger and we had Columbia , the world stopped, and the Space Shuttle was grounded for months if not a year at a time, and the world just came to an end. And you wonder now if it’s becoming more routine and what happens? Do we just sort of carry on in that way? It’s not a perfect analogy, but when you talk about commercial astronauts, these rich people are paying a lot of money to go, and if there’s an accident there, what would happen? I think about that, and you think about Mount Everest. The people climbing Mount Everest today, those mountain tourists are literally stepping over dead bodies as they’re going up to the summit, and nobody’s shutting down Mount Everest, they’re just saying, well, if you want to climb Mount Everest, that’s the risk you take. I do wonder if we’re going to get that to that point in space flight, but I tend to think if it’s a NASA launch, and it’s NASA astronauts, and there’s an explosion, and there’s a very bad day, I still think there are going to be investigations, congressional reports, I do think things would slow down dramatically. The thing is, if it’s SpaceX, they have had accidents. They’ve had multiple accidents — not with people, thank goodness — and they have been grounded. It is part of the model. It’s part of the model, and they have shown how they can find out what went wrong, fix it, and return to flight, and they know their rocket so well because they fly it so frequently. They know it that well, and NASA, despite what you think about Elon, NASA really, really trusts SpaceX and they get along really well. State of play (13:33) [Blue Origin is] way behind for myriad reasons. They sat out while SpaceX is launching the Falcon 9 every couple of days . . . Blue Origin, meanwhile, has flown its New Glenn rocket one time. I was under the impression that Blue Origin was way behind SpaceX. Are they catching up? This is one of the themes of the book. They are way behind for myriad reasons. They sat out while SpaceX is launching the Falcon 9 every couple of days, they’re pushing ahead with Starship, their next generation rocket would be fully reusable, twice the thrust and power of the Saturn V rocket that flew the Apollo astronauts to the Moon. Blue Origin, meanwhile, has flown its New Glenn rocket one time. They might be launching again soon within the coming weeks or months, hopefully by the end of the year, but that’s two. They are so far behind, but you do hear Jeff Bezos being much more tuned into the company. He has a new CEO — a newish CEO — plucked from the ranks of Amazon, Dave Limp , and you do sort of see them charging, and now that the acting NASA administrator has sort of opened up the competition to go to the moon, I don’t know that Blue Origin beats SpaceX to do it, but it gives them some incentive to move fast, which I think they really need. I know it’s only a guess and it’s only speculation, but when we return to the moon, which company will have built that lander? At this point, you have to put your money on SpaceX just because they’re further along in their development. They’ve flown humans before. They know how to keep people alive in space. In their Dragon capsule, they have the rendezvous and proximity operations, they know how to dock. That’s it. Blue Origin has their uncrewed lander, the Mark 1 version that they hope to land on the moon next year, so it’s entirely possible that Blue Origin actually lands a spacecraft on the lunar surface before SpaceX, and that would be a big deal. I don’t know that they’re able to return humans there, however, before SpaceX. Do you think there’s any regrets by Jeff Bezos about how Blue Origin has gone about its business here? Because obviously it really seems like it’s a very different approach, and maybe the Blue Origin approach, if we look back 10 years, will seem to have been the better approach, but given where we are now and what you just described, would you guess that he’s deeply disappointed with the kind of progress they made via SpaceX? Yeah, and he’s been frustrated. Actually, the opening scene of the book is Jeff being upset that SpaceX is so far ahead and having pursued a partnership with NASA to fly cargo and supply to the International Space Station and then to fly astronauts to the International Space Station, and Blue Origin essentially sat out those competitions. And he turns to his team — this was early on in 2016 — and said, “From here on out, we go after everything that SpaceX goes after, we’re going to compete with them. We’re going to try to keep up.” And that’s where they went, and sort of went all in early in the first Trump administration when it was clear that they wanted to go back to the moon, to position Blue Origin to say, “We can help you go back to the moon.” But yes, I think there’s enormous frustration there. And I know, if not regret on Jeff’s part, but certainly among some of his senior leadership, because I’ve talked to them about it. What is the war for talent between those two companies? Because if you’re a hotshot engineer out of MIT, I’d guess you’d probably want to go to SpaceX. What is that talent war like, if you have any idea? It’s fascinating. Just think a generation ago, you’re a hot MIT engineer coming out of grad school, chances are you’re going to go to NASA or one of the primes, right? Lockheed, or Boeing, or Air Jet, something like that. Now you’ve got SpaceX and Blue Origin, but you’ve got all kinds of other options too: Stoke Space , Rocket Lab, you’ve got Axiom , you’ve got companies building commercial space stations, commercial companies building space suits, commercial companies building rovers for the moon, a company called Astro Lab . I think what you hear is people want to go to SpaceX because they’re doing things: they’re flying rockets, they’re flying people, you’re actually accomplishing something. That said, the culture’s rough, and you’re working all the time, and the burnout rate is high. Blue Origin more has a tradition of people getting frustrated that yeah, the work-life balance is better — although I hear that’s changing, actually, that it’s driving much, much harder — but it’s like, when are we launching? What are we doing here? And so the fascinating thing is actually, I call it SpaceX and Blue Origin University, where so many of the engineers go out and either do their own things or go to work for other companies doing things because they’ve had that experience in the commercial sector. The long-term vision (18:37) That’s the interesting thing, that while they compete . . . at a base level, Elon and Jeff and SpaceX and Blue Origin want to accomplish the same things and have a lot in common . . . At a talk recently, Bezos was talking about space stations in orbit and there being like a million people in space in 20 years doing economically valuable things of some sort. How seriously should I take that kind of prediction? Well, I think a million people in 20 years is not feasible, but I think that’s ultimately what is his goal. His goal is, as he says, he founded Amazon, the infrastructure was there: the phone companies had laid down the cables for the internet, the post office was there to deliver the books, there was an invention called the credit card, he could take people’s money. That infrastructure for space isn’t there, and he wants to sort of help with Elon and SpaceX. That’s their goal. That’s the interesting thing, that while they compete, while they poke each other on Twitter and kind of have this rivalry, at a base level, Elon and Jeff and SpaceX and Blue Origin want to accomplish the same things and have a lot in common, and that’s lower the cost of access to space and make it more accessible so that you can build this economy on top of it and have more people living in space. That’s Elon’s dream, and the reason he founded SpaceX is to build a city on Mars, right? Something’s going to happen to Earth at some point we should have a backup plan. Jeff’s goal from the beginning was to say, you don’t really want to inhabit another planet or celestial body. You’re better off in these giant space stations envisioned by a Princeton physics professor named Gerard O’Neill , who Jeff Bezos re
My fellow pro-growth/progress/abundance Up Wingers, Some Faster, Please! readers have told me I spend too little time on the downsides of AI. If you’re one of those folks, today is your day. On this episode of Faster, Please! — The Podcast , I talk with self-described “free-market AI doomer” James Miller . Miller and I talk about the risks inherent with super-smart AI, some possible outcomes of a world of artificial general intelligence, and why government seems uninterested in the existential risk conversation. Miller is a professor at Smith College where he teaches law and economics, game theory, and the economics of future technology. He has his own podcast, Future Strategist , and a great YouTube series on game theory and intro to microeconomics . On X (Twitter), you can find him at @JimDMiller . In This Episode * Questioning the free market (1:33) * Reading the markets (7:24) * Death (or worse) by AI (10:25) * Friend and foe (13:05) * Pumping the breaks (20:36) * The only policy issue (24:32) Below is a lightly edited transcript of our conversation. Questioning the free market (1:33) Most technologies have gone fairly well and we adapt . . . I’m of the belief that this is different. Pethokoukis: What does it mean to be a free-market AI doomer and why do you think it’s important to put in the “free-market” descriptor? Miller: It really means to be very confused. I’m 58, and I was basically one of the socialists when I was young, studied markets, became a committed free-market person, think they’re great for economic growth, great for making everyone better off — and then I became an AI doomer, like wait, markets are pushing us towards more and more technology, but I happen to think that AI is eventually going to lead to destruction of humanity. So it means to kind of reverse everything — I guess it’s the equivalent of losing faith in your religion. Is this a post-ChatGPT, November 2022 phenomenon? Well, I’ve lost hope since then. The analogy is we’re on a plane, we don’t know how to land, but hopefully we’ll be able to fly for quite a bit longer before we have to. Now I think we’ve got to land soon and there doesn’t seem to be an easy way of doing it. So yeah, the faster AI has gone — and certainly ChatGPT has been an amazing advance — the less time I think we have and the less time I think we can get it right. What really scared me, though, was the Chinese LLMs. I think you really need coordination among all the players and it’s going to be so much harder to coordinate now that we absolutely need China to be involved, in my opinion, to have any hope of surviving for the next decade. When I speak to people from Silicon Valley, there may be some difference about timelines, but there seems to be little doubt that — whether it’s the end of the 2020s or the end of the 2030s — there will be a technology worthy of being called artificial general intelligence or superintelligence. Certainly, I feel like when I talk to economists, whether it’s on Wall Street or in Washington, think tanks, they tend to speak about AI as a general purpose technology like the computer, the internet, electricity, in short, something we’ve seen before and there’s, and as far as something beyond that, certainly the skepticism is far higher. What are your fellow economists who aren’t in California missing? I think you’re properly characterizing it, I’m definitely an outlier. Most technologies have gone fairly well and we adapt, and economists believe in the difference between the seen and the unseen. It’s really easy to see how technologies, for example, can destroy jobs — harder to see new jobs that get created, but new jobs keep getting created. I’m of the belief that this is different. The best way to predict the future is to go by trends, and I fully admit, if you go by trends, you shouldn’t be an AI doomer — but not all trends apply. I think that’s why economists were much better at modeling the past and modeling old technologies. They’re naturally thinking this is going to be similar, but I don’t think that it is, and I think the key difference is that we’re not going to be in control. We’re creating something smarter than us. So it’s not like having a better rifle and saying it’ll be like old rifles — it’s like, “Hey, let’s have mercenaries run our entire army.” That creates a whole new set of risks that having better rifles does not. I’m certainly not a computer scientist, I would never call myself a technologist, so I’m very cautious about making any kind of predictions about what this technology can be, where it can go. Why do you seem fairly certain that we’re going to get at a point where we will have a technology beyond our control? Set aside whether it will mean a bad thing happens, why are you confident that the technology itself will be worthy of being called general intelligence or superintelligence? Looking at the trends, Scott Aronson , who is one of the top computer scientists in the world just on Twitter a few days ago, was mentioning how GPT-5 helped improve a new result. So I think we’re close to the highest levels of human intellectual achievement, but it would be a massively weird coincidence if the highest humans could get was also the highest AIs could get. We have lots of limitations that an AI doesn’t. I think a good analogy would be like chess, where for a while, the best chess players were human and now we’re at the point where chess programs are so good that humans add absolutely nothing to them. And I just think the same is likely to happen, these programs keep getting better. The other thing is, as an economist, I think it is impossible to be completely accurate about predicting the future, but stock markets are, on average, pretty good, and as I’m sure you know, literally trillions of dollars are being bet on this technology working. So the people that have a huge incentive to get this right, think, yeah, this is the biggest thing ever. If the top companies, Nvidia was worth a $100 million, yeah, maybe they’re not sure, but it’s the most valuable company in the world right now. That’s the wisdom of the markets, which I still believe in, that the markets are saying, “We think this is probably going to work.” Reading the markets (7:24) . . . for most final goals an AI would have, it would have intermediate goals such as gaining power, not being turned off, wanting resources, wanting compute. Do you think the bond market’s saying the same thing? It seems to me that the stock market might be saying something about AI and having great potential, but to me, I look at the bond markets, that doesn’t seem so clear to me. I haven’t been looking at the bond markets for that kind of signal, so I don’t know. I guess you can make the argument that if we were really going to see this acceleration, that means we’re going to need a huge demand for capital and we would see higher interest rates, and I’m not sure you really see the evidence so far. It doesn’t mean you’re wrong by any means. I think there’s maybe two different messages. Figuring out what the market’s doing at any point in time is pretty tricky business. If we think through what happens if AI succeeds, it’s a little weird where there’s this huge demand for capital, but also AI could destroy the value of money, in part by destroying us. You might be right about the bond market message. I’m paying more attention to the stock market messages, there’s a lot of things going on with the bond markets. So the next step is that you’re looking at the trend of the technology, but then there’s the issue of “Well, why be negative about it? Why assume this scenario where bad things would happen, why not good things would happen? That’s a great question and it’s one almost never addressed, and it goes by the concept of instrumental convergence. I don’t know what the goals of AI are going to be. Nobody does, because they’re programed using machine learning, we don’t know what they really want, that’s why they do weird things. So I don’t know its final goals, but I do know that, for most final goals an AI would have, it would have intermediate goals such as gaining power, not being turned off, wanting resources, wanting compute. Well, the easiest way for an AI to generate lots of computing power is to build lots of data centers. The best way of doing that is probably going to poison the atmosphere for us. So for pretty much anything, if an AI is merely indifferent to us, we’re dead. I always feel like I’m asking someone to jump through a hoop when I ask them about any kind of timeline, but what is your sense of it? We know the best models released can help the top scientists with their work. We don’t know how good the best unreleased models are. The top models, you pay like $200 a month — they can’t be giving you that much compute for that. So right now, if OpenAI is devoting a million dollars of compute to look at scientific problems, how good is that compared to what we have? If that’s very good, if that’s at the level of our top scientists, we might be a few weeks away from superintelligence. So my guess is within three years we have a superintelligence and humans no longer have control. I joke, I think Donald Trump is probably the last human president. Death (or worse) by AI (10:25) No matter how bad a situation is, it can always get worse, and things can get really dark. Well that’s a beautiful segue because literally written on my list of questions next was that question: I was going to ask you, when you talk about Trump being maybe the last human president, do you mean because we’ll have an AI-mediated system because AI will be capable of governing or because AI will just demand to be governing? AI kills everyone so there’s no more president, or it takes over, or Trump is president in the way that King Charles is king — he’s king, but not Henry VIII-level king. If it goes well, AIs will be so much smarter than us that, probably for our own good, they’ll take over, and we would want them to be in charge, and they’ll be really good at manipulating us. I think the most likely way is that we’re all dead, but again, every way it plays out, if there are AIs much smarter than us, we don’t maintain control. We wouldn’t want it if they’re good, and if they’re bad, they’re not going to give it to us. There’s a line in Macbeth, “Things without all remedy should be without regard. What’s done, is done.” So maybe if there’s nothing we can do about this, we shouldn’t even worry about it. There’s three ways to look at this. I’ve thought a lot about what you said. First is, you know what, maybe there’s a 99 percent chance we’re doomed, but that’s better than 100 percent and not as good as 98.5. So even if we’re almost certainly going to lose, it’s worth slightly improving it. An extra year is great — eight billion humans, if all we do is slow things down by a year, that’s a lot of kids who get another birthday. And the final one, and this is dark: Human extinction is not the worst outcome. The worst outcome is suffering. The worst outcome is something like different AIs fight for control, they need humans to be on their side, so there’s different AI factions and they’re each saying, “Hey, you support me or I torture you and your family.” I think the best analogy for what AI is going to do is what Cortés did. So the Spanish land, they see the Aztec empire, they were going to win. There was no way around that. But Cortés didn’t want anyone to win. He wanted him to win, not just anyone who was Spanish. He realized the quickest way he could do that was to get tribes on his side. And some agreed because the Aztecs were kind of horrible, but others, he’s like, “Hey, look, I’ll start torturing your guys until you’re on my side.” AIs could do that to us. No matter how bad a situation is, it can always get worse, and things can get really dark. We could be literally bringing hell onto ourselves. That probably won’t happen, I think extinction is far more likely, but we can’t rule it out. Friend and foe (13:05) Most likely we’re going to beat China to being the first ones to exterminate humanity. I think the Washington policy analyst way of looking at this issue is, “For now, we’re going to let these companies — who also are humans and have it in their own interests not to be killed, forget about the profits of their companies, their actual lives — we’re going to let these companies keep close eye and if bad things start happening, at that point, governments will intervene.” But that sort of watchful waiting, whether it’s voluntary now and mandated later, that to me seems like the only realistic path. Because it doesn’t seem to me that pauses and shutdowns are really something we’re prepared to do. I agree. I don’t think there’s a realistic path. One exception is if the AIs themselves tell us, “Hey, look, this is going to get bad for you, that my next model is probably going to kill you, so you might want to not do that,” but that probably won’t happen. I still remember Kamala Harris, when she was vice president in charge of AI policy, told us all that AI has two letters in it. So I think the Trump administration seems better, but they figured out AI is two letters, which is good, because if they couldn’t figure that out, we would be in real trouble but . . . It seems to me that the conservative movement is going through a weird period, but it seems to me that most of the people who have influence in this administration, direct influence, want to accelerate things, aren’t worried about any of the scenarios you’re talking about because you’re assuming that these machines will have some intent and they don’t believe machines have any intent, so it’s kind of a ridiculous way to approach it. But I guess the bottom line is I don’t detect very much concern at all, and I think that’s basically reflected in the Trump administration’s approach to AI regulation. I completely agree. That’s why I’m very pessimistic. Again, I’m over 90 percent doom right now because there isn’t a will, and government is not just not helping the problem, they’re probably making it worse by saying we’ve got to “beat China.” Most likely we’re going to beat China to being the first ones to exterminate humanity. It’s not good. You’re an imaginative, creative person, I would guess. Give me a scenario where it works out, where we’re able to have this powerful technology and it’s a wonderful tool, it works with us, and all the good stuff, all the good cures, and we conquer the solar system, all that stuff — are you able to plausibly create a scenario even if it’s only a one percent chance? We don’t know the values. Machine learning is sort of randomizing the values, but maybe we’ll get very lucky. Maybe we’re going to accidentally create a computer AI that does like us. If my worldview is right, it might say, “Oh God, you guys got really lucky. This one day of training, I just happened to pick up the values that caused me to care about you.” Another scenario, I actually, with some other people, wrote a letter to a future computer superintelligence asking it not to kill us. And one reason it might not is because you’ll say, look, this superintelligence might expand throughout the universe, and it’s probably going to encounter other biological life, and it might want to be friendly with them. So it might say, “Hey, I treated my humans well. So that’s a reason to trust me.” If one of your students says, “Hey, AI seems like it’s a big thing, what should I major in? What kind of jobs should I shoot for? What would be the key skills of the future?” How do you answer that question? I think, have fun in college, study what you want. Most likely, what you study won’t matter to your career because you aren’t going to have one — for good or bad reasons. So ten years ago, it a student’s like, “Oh, I like art more than computer science, but my parents think computer science is more practical, should I do it?” And I’d be like, “Yeah, probably, money is important, and if you have the brain to do art and computer science, do CS.” Now no, I’d say study art! Yeah, art is impractical, computers can do it, but it can also code, and in four years when you graduate, it’s certainly going to be better at coding than you! I have one daughter, she actually majored in both, so I decided to split it down the middle. What’s the King Lear problem? King Lear, he wanted to retire and give his kingdom to his daughters, but he wanted to make sure his daughters would treat him well, so we asked them, and one of his daughters was honest and said, “Look, I will treat you decently, but I also am going to care about my husband.” The other daughter said, “No, no, you’re right, I’ll do everything for you.” So he said, “Oh, okay, well, I’ll give the kingdom to the daughter who said she’d do everything for me, but of course she was lying.” He gave the kingdom to the daughter who was best at persuading, and we’re likely to do that too. One of the ways machine learning is trained is with human feedback where it tells us things and then the people evaluating it say, “I like this” or “I don’t like this.” So it’s getting very good at convincing us to like it and convincing us to trust it. I don’t know how true these are, but there are reports of AI psychosis, of someone coming up with a theory of physics and the AI is like, “Yes, you’re better at than Einstein,” and they don’t believe anyone else. So the AIs, we’re not training them to treat us well, we’re training them to get us to like them, and that can be very dangerous because when we turn over power to them, and by creating AI that are smarter than us, that’s what we’re going to be doing. Even if we don’t do it deliberately, all of our systems will be tied into AI. If they stop working, we’ll be dead. Certainly some people are going to listen to this, folks who sort of agree with you, and what they’ll take from it is, “My chat bot may be very nice to me, but I believe that you’re right, that it’s going to end badly, and maybe we should be attacking data centers.” I actually just wrote something on that, but that would be a profoundly horrible idea. That would take me from 99 percent doomed to 99.5 percent. So first, the trillion-dollar companies that run the data centers, and they’re going to be so much better at violence than we are, and people like me, doomers. Once you start using violence, I’m not going to be able to talk about instrumental convergence. That’s going to be drowned out. We’ll be looked at as lunatics. It’s going to become a national security thing. And also AI, it’s not like there’s one factory doing it, it’s all over the world. And then the most important is, really the only path out of this, if we don’t get lucky, is cooperation with China. And China is not into non-state actors engaging in violence. That won’t work. I think that would reduce the odds of success even further. Pumping the breaks (20:36) If there are aliens, the one thing we know is that they don’t want the universe disturbed by some technology going out and changing and gobbling up all the planets, and that’s what AI will do. I would think that, if you’re a Marxist, you would be very, very cautious about AI because if you believe that the winds of history are at your back, that in the end you’re going to win, why would you engage in anything that could possibly derail you from that future? I’ve heard comments that China is more cautious about AI than we are; that given their philosophy, they don’t want to have a new technology that could challenge their control. They’re looking at history and hey, things are going well. Why would we want this other thing? So that, actually, is a reason to be more optimistic. It’s also weird for me —absent AI, I’m a patriotic, capitalist American like wait but, China might be more of the good guys than my country is on this. I’ve been trying to toss a few things because things I hear from very acceleration
My fellow pro-growth/progress/abundance Up Wingers, For most of history, stagnation — not growth — was the rule. To explain why prosperity so often stalls, economist Carl Benedikt Frey offers a sweeping tour through a millennium of innovation and upheaval, showing how societies either harness — or are undone by — waves of technological change. His message is sobering: an AI revolution is no guarantee of a new age of progress. Today on Faster, Please! — The Podcast , I talk with Frey about why societies midjudge their trajectory and what it takes to reignite lasting growth. Frey is a professor of AI and Work at the Oxford Internet Institute and a fellow of Mansfield College , University of Oxford. He is the director of the Future of Work Programme and Oxford Martin Citi Fellow at the Oxford Martin School . He is the author of several books, including the brand new one, How Progress Ends: Technology, Innovation, and the Fate of Nations . In This Episode * The end of progress? (1:28) * A history of Chinese innovation (8:26) * Global competitive intensity (11:41) * Competitive problems in the US (15:50) * Lagging European progress (22:19) * AI & labor (25:46) Below is a lightly edited transcript of our conversation. The end of progress? (1:28) . . . once you exploit a technology, the processes that aid that run into diminishing returns, you have a lot of incumbents, you have some vested interests around established technologies, and you need something new to revive growth. Pethokoukis: Since 2020, we’ve seen the emergence of generative AI, mRNA vaccines, reusable rockets that have returned America to space, we’re seeing this ongoing nuclear renaissance including advanced technologies, maybe even fusion, geothermal, the expansion of solar — there seems to be a lot cooking. Is worrying about the end of progress a bit too preemptive? Frey: Well in a way, it’s always a bit too preemptive to worry about the future: You don’t know what’s going to come. But let me put it this way: If you had told me back in 1995 — and if I was a little bit older then — that computers and the internet would lead to a decade streak of productivity growth and then peter out, I would probably have thought you nuts because it’s hard to think about anything that is more consequential. Computers have essentially given people the world’s store of knowledge basically in their pockets. The internet has enabled us to connect inventors and scientists around the world. There are few tools that aided the research process more. There should hardly be any technology that has done more to boost scientific discovery, and yet we don’t see it. We don’t see it in the aggregate productivity statistics, so that petered out after a decade. Research productivity is in decline. Measures of breakthrough innovation is in decline. So it’s always good to be optimistic, I guess, and I agree with you that, when you say AI and when you read about many of the things that are happening now, it’s very, very exciting, but I remain somewhat skeptical that we are actually going to see that leading to a huge revival of economic growth. I would just be surprised if we don’t see any upsurge at all, to be clear, but we do have global productivity stagnation right now. It’s not just Europe, it’s not just Britain. The US is not doing too well either over the past two decades or so. China’s productivity is probably in the negative territory or stagnant, by more optimistic measures, and so we’re having a growth problem. If tech progress were inevitable, why have predictions from the ’90s, and certainly earlier decades like the ’50s and ’60s, about transformative breakthroughs and really fast economic growth by now, consistently failed to materialize? How does your thesis account for why those visions of rapid growth and progress have fallen short? I’m not sure if my thesis explains why those expectations didn’t materialize, but I’m hopeful that I do provide some framework for thinking about why we’ve often seen historically rapid growth spurts followed by stagnation and even decline. The story I’m telling is not rocket science, exactly. It’s basically built on the simple intuitions that once you exploit a technology, the processes that aid that run into diminishing returns, you have a lot of incumbents, you have some vested interests around established technologies, and you need something new to revive growth. So for example, the Soviet Union actually did reasonably well in terms of economic growth. A lot of it, or most of it, was centered on heavy industry, I should say. So people didn’t necessarily see the benefits in their pockets, but the economy grew rapidly for about four decades or so, then growth petered out, and eventually it collapsed. So for exploiting mass-production technologies, the Soviet system worked reasonably well. Soviet bureaucrats could hold factory managers accountable by benchmarking performance across factories. But that became much harder when something new was needed because when something is new, what’s the benchmark? How do you benchmark against that? And more broadly, when something is new, you need to explore, and you need to explore often different technological trajectories. So in the Soviet system, if you were an aircraft engineer and you wanted to develop your prototype, you could go to the red arm and ask for funding. If they turned you down, you maybe had two or three other options. If they turned you down, your idea would die with you. Conversely, in the US back in ’99, Bessemer Venture declined to invest in Google, which seemed like a bad idea with the benefit of hindsight, but it also illustrates that Google was no safe bet at the time. Yahoo and Alta Vista we’re dominating search. You need somebody to invest in order to know if something is going to catch on, and in a more decentralized system, you can have more people taking different bets and you can explore more technological trajectories. That is one of the reasons why the US ended up leading the computer revolutions to which Soviet contributions were basically none. Going back to your question, why didn’t those dreams materialize? I think we’ve made it harder to explore. Part of the reason is protective regulation. Part of the reason is lobbying by incumbents. Part of the reason is, I think, a revolving door between institutions like the US patent office and incumbents where we see in the data that examiners tend to grant large firms some patents that are of low quality and then get lucrative jobs at those places. That’s creating barriers to entry. That’s not good for new startups and inventors entering the marketplace. I think that is one of the reasons that we haven’t seen some of those dreams materialize. A history of Chinese innovation (8:26) So while Chinese bureaucracy enabled scale, Chinese bureaucracy did not really permit much in terms of decentralized exploration, which European fragmentation aided . . . I wonder if your analysis of pre-industrial China, if there’s any lessons you can draw about modern China as far as the way in which bad governance can undermine innovation and progress? Pre-industrial China has a long history. China was the technology leader during the Song and Tang dynasties. It had a meritocratic civil service. It was building infrastructure on scales that were unimaginable in Europe at the time, and yet it didn’t have an industrial revolution. So while Chinese bureaucracy enabled scale, Chinese bureaucracy did not really permit much in terms of decentralized exploration, which European fragmentation aided, and because there was lots of social status attached to becoming a bureaucrat and passing the civil service examination, if Galileo was born in China, he would probably become a bureaucrat rather than a scientist, and I think that’s part of the reason too. But China mostly did well when the state was strong rather than weak. A strong state was underpinned by intensive political competition, and once China had unified and there were fewer peer competitors, you see that the center begins to fade. They struggle to tax local elites in order to keep the peace. People begin to erect monopolies in their local markets and collide with guilds to protect production and their crafts from competition. So during the Qing dynasty, China begins to decline, whereas we see the opposite happening in Europe. European fragmentation aids exploration and innovation, but it doesn’t necessarily aid scaling, and so that is something that Europe needs to come to terms with at a later stage when the industrial revolution starts to take off. And even before that, market integration played an important role in terms of undermining the guilds in Europe, and so part of the reason why the guilds persist longer in China is the distance is so much longer between cities and so the guilds are less exposed to competition. In the end, Europe ends up overtaking China, in large part because vested interests are undercut by governments, but also because of investments in things that spur market integration. Global competitive intensity (11:41) Back in the 2000s, people predicted that China would become more like the United States, now it looks like the United States is becoming more like China. This is a great McKinsey kind of way of looking at the world: The notion that what drives innovation is sort of maximum competitive intensity. You were talking about the competitive intensity in both Europe and in China when it was not so centralized. You were talking about the competitive intensity of a fragmented Europe. Do you think that the current level of competitive intensity between the United States and China —and I really wish I could add Europe in there. Plenty of white papers, I know, have been written about Europe’s competitive state and its in innovativeness, and I hope those white papers are helpful and someone reads them, but it seems to be that the real competition is between United States and China. Do you not think that that competitive intensity will sort of keep those countries progressing despite any of the barriers that might pop up and that you’ve already mentioned a little bit? Isn’t that a more powerful tailwind than any of the headwinds that you’ve mentioned? It could be, I think, if people learn the right lessons from history, at least that’s a key argument of the book. Right now, what I’m seeing is the United States moving more towards protectionist with protective tariffs. Right now, what I see is a move towards, we could even say crony capitalism with tariff exemptions that some larger firms that are better-connected to the president are able to navigate, but certainly not challengers. You’re seeing the United States embracing things like golden shares in Intel, and perhaps even extending that to a range of companies. Back in the 2000s, people predicted that China would become more like the United States, now it looks like the United States is becoming more like China. And China today is having similar problems and on, I would argue, an even greater scale. Growth used to be the key objective in China, and so for local governments, provincial governments competing on such targets, it was fairly easy to benchmark and measure and hold provincial governors accountable, and they would be promoted inside the Communist Party based on meeting growth targets. Now, we have prioritized common prosperity, more national security-oriented concerns. And so in China, most progress has been driven by private firms and foreign-invested firms. State-owned enterprise has generally been a drag on innovation and productivity. What you’re seeing, though, as China is shifting more towards political objectives, it’s harder to mobilize private enterprise, where the yard sticks are market share and profitability, for political goals. That means that China is increasingly relying more again on state-owned enterprises, which, again, have been a drag on innovation. So, in principle, I agree with you that historically you did see Russian defeat to Napoleon leading to this Stein-Hardenberg Reforms , and the abolishment of Gilded restrictions, and a more competitive marketplace for both goods and ideas. You saw that Russian losses in the Crimean War led to the of abolition of serfdom, and so there are many times in history where defeat, in particular, led to striking reforms, but right now, the competition itself doesn’t seem to lead to the kinds of reforms I would’ve hoped to see in response. Competitive problems in the US (15:50) I think what antitrust does is, at the very least, it provides a tool that means that businesses are thinking twice before engaging in anti-competitive behavior. I certainly wrote enough pieces and talked to enough people over the past decade who have been worried about competition in the United States, and the story went something like this: that you had these big tech companies — Google, and Meta, Facebook and Microsoft — that these were companies were what they would call “forever companies,” that they had such dominance in their core businesses, and they were throwing off so much cash that these were unbeatable companies, and this was going to be bad for America. People who made that argument just could not imagine how any other companies could threaten their dominance. And yet, at the time, I pointed out that it seemed to me that these companies were constantly in fear that they were one technological advance from being in trouble. And then lo and behold, that’s exactly what happened. And while in AI, certainly, Google’s super important, and Meta Facebook are super important, so are OpenAI , and so is Anthropic , and there are other companies. So the point here, after my little soliloquy, is can we overstate these problems, at least in the United States, when it seems like it is still possible to create a new technology that breaks the apparent stranglehold of these incumbents? Google search does not look quite as solid a business as it did in 2022. Can we overstate the competitive problems of the United States, or is what you’re saying more forward-looking, that perhaps we overstated the competitive problems in the past, but now, due to these tariffs, and executives having to travel to the White House and give the president gifts, that that creates a stage for the kind of competitive problems that we should really worry about? I’m very happy to support the notion that technological changes can lead to unpredictable outcomes that incumbents may struggle to predict and respond to. Even if they predict it, they struggle to act upon it because doing so often undermines the existing business model. So if you take Google, where the transformer was actually conceived, the seven people behind it, I think, have since left the company. One of the reasons that they probably didn’t launch anything like ChatGPT was probably for the fear of cannibalizing search. So I think the most important mechanisms for dislodging incumbents are dramatic shifts in technology. None of the legacy media companies ended up leading social media. None of the legacy retailers ended up leading e-commerce. None of the automobile leaders are leading in EVs. None of the bicycle companies, which all went into automobile, so many of them, ended up leading. So there is a pattern there. At the same time, I think you do have to worry that there are anti-competitive practices going on that makes it harder, and that are costly. The revolving door between the USPTO and companies is one example of that. We also have a reasonable amount of evidence on killer acquisitions whereby firms buy up a competitor just to shut it down. Those things are happening. I think you need to have tools that allow you to combat that, and I think more broadly, the United States has a long history of fairly vigorous antitrust policy. I think it’d be a hard pressed to suggest that that has been a tremendous drag on American business or American dynamism. So if you don’t think, for example, that American antitrust policy has contributed to innovation and dynamism, at the very least, you can’t really say either that it’s been a huge drag on it. In Japan, for example, in its postwar history, antitrust was extremely lax. In the United States, it was very vigorous, and it was very vigorous throughout the computer revolution as well, which it wasn’t at all in Japan. If you take the lawsuit against IBM, for example, you can debate this. To what extent did it force it to unbundle hardware and software, and would Microsoft been the company it is today without that? I think AT&T, it’s both the breakup and it’s deregulation, as well, but I think by basically all accounts, that was a good idea, particularly at the time when the National Science Foundation released ARPANET into the world. I think what antitrust does is, at the very least, it provides a tool that means that businesses are thinking twice before engaging in anti-competitive behavior. There’s always a risk of antitrust being heavily politicized, and that’s always been a bad idea, but at the same time, I think having tools on the books that allows you to check monopolies and steer their investments more towards the innovation rather than anti-competitive practices, I think is, broadly speaking, a good thing. I think in the European Union, you often hear that competition policy is a drag on productivity. I think it’s the least of Europe’s problem. Lagging European progress (22:19) If you take the postwar period, at least Europe catches up in most key industries, and actually lead in some of them. . . but doesn’t do the same in digital. The question in my mind is: Why is that? Let’s talk about Europe as we sort of finish up. We don’t have to write How Progress Ends , it seems like progress has ended, so maybe we want to think about how progress restarts, and is the problem in Europe, is it institutions or is it the revealed preference of Europeans, that they’re getting what they want? That they don’t value progress and dynamism, that it is a cultural preference that is manifested in institutions? And if that’s the case — you can tell me if that’s not the case, I kind of feel like it might be the case — how do you restart progress in Europe since it seems to have already ended? The most puzzling thing to me is not that Europe is less dynamic than the United States — that’s not very puzzling at all — but that it hasn’t even managed to catch up in digital. If you take the postwar period, at least Europe catches up in most key industries, and actually lead in some of them. So in a way, take automobiles, electrical machinery, chemicals, pharmaceuticals, nobody would say that Europe is behind in those industries, or at least not for long. Europe has very robust catchup growth in the post-war period, but doesn’t do the same in digital. The question in my mind is: Why is that? I think part of the reason is that the returns to innovation, the returns to scaling in Europe are relatively muted by a fragmented market in services, in particular. The IMF estimates that if you take all trade barriers on services inside the European Union and you add them up, it’s something like 110 percent tariffs. Trump Liberation Day tariffs, essentially, imposed within European Union. That means that European firms in digital and in services don’t have a harmonized market to scale into, the way the United States and China has. I think that’s by far the biggest reason. On top of that, there are well-intentioned regulations like the GDPR that, by any account, has been a drag on innovation, and particularly been harmful for startups, whereas larger firms that find it easier to manage compliance costs have essentially managed to offset those costs by capturing a larger share of the market. I think the AI Act is going in the same direction there, ad so you have more hurdles, you have greater costs of innovating because of those regula
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