💥 My chat (+transcript) with economist Eli Dourado on creating a fantastic future
Faster, Please! — The PodcastShow Notes
Eli Dourado is on a mission to end the Great Stagnation, that half-century period of economic and technological disappointment that began in the 1970s (what I refer to in my 2023 book, The Conservative Futurist, as the Great Downshift). If we want to turn the page on this chapter of slow progress and deserved skepticism, we’re going to have to accept some creative destruction. Dourado believes that the courage to embrace major change is key to meeting our potential. Today on Faster, Please! — The Podcast, I talk with Dourado about the future of the US job market and energy production in a world of AI. Dourado is chief economist at the Abundance Institute, and author of his own Substack newsletter. In This Episode * The dawn of a productivity boom? (1:26) * Growing pains of job market disruption (7:26) * The politics of productivity growth (15:20) * The future of clean energy (23:35) * The road to a breakthrough (30:25) * Reforming NEPA (35:19) * The state of pro-abundance (37:08) Below is a lightly edited transcript of our conversation The dawn of a productivity boom? (1:26) Pethokoukis: Eli, welcome to the podcast. Dourado: Thanks for having me on, Jim. I would like to think that what we are experiencing here in the 2020s is the beginnings of an extended productivity boom. We have some good economic data over the past year and a half. I know this is something that you care about, as I do . . . What's your best guess? I think the seeds of a boom are there. There's plenty of low-hanging fruit, but I'd say the last few quarters have not been that great for TFP growth, which is what I followed most closely. So we actually peaked in TFP in the US in Q4, 2021. Now what is that, what is TFP? Total factor productivity. So that's like if you look at inputs and how they translate into outputs. Capital, labor . . . Capital and labor, adjusting for quality, ideally. We've gotten less output for the amount of inputs in the last quarter than we did at the end of 2021. So slight negative growth over the last three years or so, but I think that you're right that there is room for optimism. Self-driving cars are coming. AI has immense potential. My worry with AI is other sociopolitical limits in the economy will hold us back, and you kind of see the news breaking today as we're recording this, is there's a strike at the ports on east coast, and what's at issue there is are we allowed to automate those jobs? Are the owners of the ports allowed to automate those jobs? And if the answer ends up being “no,” then you can say goodbye to productivity gains there. And so I really think the technology is there to do a lot more to kick off a productivity boom, but it's the sociopolitical factors that are slowing us down. And I definitely want to talk about those sociopolitical factors, and the port strike is hopefully not a harbinger. But before I leave this topic, I suppose the super bullish case for productivity is that AI will be so transformative, and so transformative throughout the economy, both automating some things, helping us do other things more efficiently, and creating brand new high-productivity things for us to do that we will have maybe an extended 1990s, maybe more, I might hope? What is your bullish case, and does that bullish case require what they call artificial general intelligence, or human-level, or human-level plus intelligence? Is that key? Because obviously some people are talking about that. Can we have an important productivity boom from AI without actually reaching that kind of science-fictional technology? I don't actually think that you need one-to-one replacement for humans, but you do need to get humans out of the loop in many, many more places. So if you think about the Baumol effect, the idea here is if there are parts of the economy that are unevenly growing in productivity, then that means that the parts of the economy where there is slow productivity growth, perhaps because you have human labor still being the bot
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My guest this episode is Stephanie Wakefield. Stephanie is an assistant professor of Urban Planning and Environmental Design at Florida Atlantic University. She is also the author of a wonderful recent article in Palladium magazine titled “Frontier Panic,” which examines how the apocalyptic thinking surrounding the climate change movement has now been transferred to artificial intelligence. Today’s conversation will discuss this article as well as some of her other scholarship, diving into how an excessive focus on resilience could hold us back from building a world shaped by innovation and adaptation to a changing environment. In this episode * From climate panic to AI panic (0:53) * Who gets to govern AI? (6:15) * Discovery, experimentation, and the frontier (11:06) * Why we fear change (20:28) * Climate adaptation as experiment (23:02) * Aesthetics, infrastructure, and the future (26:17) 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 parallels between AI and climate change… It’s as if you took climate change documentaries and transposed them onto AI. It’s very much the same script and lens. You see people saying our children might not make it to high school because of AI, that we’re all going to die. And, like with climate change, this discourse of apocalyptic projections based on models and media is tied to what are then said to be the appropriate responses. So it’s a discourse that authorizes a particular set of voices to make a claim to governing this new phenomenon, this new technology, and this new environment that we’re in. With climate change, that led to this concept of resilience, which we could talk about more. And now it’s underwriting the idea of pacing the frontier or the AI pause—slowing or even forbidding the development of frontier models. On who gets to govern AI… In these moments, it is often the case that some figures step in to be the moral authority and position themselves as the ones with the right answer, to guide the mass of the population. And I personally find that to be a really cynical operation because a lot of these discourses are deployed in the name of humanity and the good of humanity, or even ordinary Americans concerned about data centers or something. And I think they actually play on the fears of these people—that they’re using them as a tool in this broader play for power, rather than actually being concerned with them. On fear surrounding AI… I think there are certain factions within society that are obsessed with that (what can go wrong) and forwarding that way of thinking and trying to spread it. It’s a contagion, it’s a virus, like other ones that we have experienced. It spreads through mimetic contagion, through repetition, through feedback loops. We are in a cybernetic society, and if you are trying to influence the culture and the discourse and the mood, you understand that. And you are an operator of feedback, trying to push the swarm, let’s say, in a particular direction and not others. But I would say I don’t think that’s just us in general. These moments of technological inflection—the same was the case in the early 20th century—always involve multiple factions and perspectives. Every time we’re in this space of upheaval, it’s actually like a war, really, a war situation or a game situation. It’s a contest for putting forward other visions and other attitudes. On the AI frontier… The famous Turner essay on it—it’s kind of cliché to bring it up, but actually reading it again is amazing. What he’s describing is a specific experience in a material world, in a real reality, of certain people that was complex, it was fraught, it involved conflict, yes, but also experimentation—an experience that ultimately reshaped those people who had it, who forged out into the unknown. The frontier spirit is one of an indivi
My guest this episode is Ramez Naam. Ramez has spent years thinking, writing, and investing around technological progress in energy and artificial intelligence. He is the founder and managing partner of Planetary VC, where he invests in emerging technologies across areas including clean energy, electrification, and AI. He is also the author of The Infinite Resource, More Than Human, and the Nexus science-fiction trilogy. Today’s conversation will focus on the pace of AI progress, the roadblocks that could stand in the way, and what all of this could mean for long-term economic growth. In this episode * The state of AI growth (00:38) * Roadblocks to an intelligence explosion (06:11) * AI in the short term (13:34) * AI’s economic impact (19:00) * Balancing AI safety and exploration (22:04) * What will power the AI economy? (31:04) The state of AI growth (00:38) AI capabilities are likely to be jagged for the foreseeable future. I believe in superintelligence, I believe it’s already here. We already have superintelligence playing Go. We’re crossing into superintelligence in formal math. That doesn’t mean we’ll have super capable persuaders, politicians or city planners. Pethokoukis: Where are we right now? As we’re recording this, OpenAI just released all these mathematical proofs and people are being wowed by that. Wherever you think we are at in the world of AI progress, does that result change your opinion? Naam: It’s amazing. It’s very impressive. I’m not sure I thought we would be here right now. Math is a very special domain. I think about the different types of domains that AI is good or bad at in terms of how highly verifiable they are. And AI first got really good at things like chess and then the game of Go, and now parts of math, formal math, because these have some interesting characteristics. You can generate an unlimited amount of data, and AI is very data hungry. You can get to a very firm yes or no with one-hundred percent accuracy. You can verify it as absolutely correct or not correct. But like human persuasion, politics, policy, and so on you can’t. You can do all of that at the speed of compute. You’re limited only by the speed of your computing to figure out if you won the game of Go or if the math proof is correct, which is not how it is in biological research or new material science, which are messy real world. So math is a domain that is pretty far over on the highly verifiable, symbolic, perfect training data, absolutely correct yes or no. All of it happens in software. Where something like curing aging is messy. We don’t have the training data at all. We’re going to need new instruments, new science, you need experiments, and your lag to verify anything is years, decades. And so that’s something that AI is not going to be as inherently good at, in my view. I made you leap over my first question, which is sort of where we are. There are certainly people who think that, by the end of 2027, there will be no debate that everything is changing in a profound, rapid way that changes all aspects of life. And then there are those who say “that doesn’t seem to be happening right now.” The world is a messy place. The world of carbon based life forms is a messy place. And whatever the capabilities in the lab of this technology, how it emerges into the real world is going to be a much slower and uneven process. On those two poles, where are you? Yeah, I mean I’d say look, I’m an AI optimist and I’m also an AI realist. And I think there’s two separate issues we’re dealing with in your question and the one before that. One is speed of diffusion, right? And if you look at expectations of how much impact AI will have on the economy, and you ask people in AI labs, some of them say it’s going to take us to 20 percent annual growth. I mean there’s one chart out there that says it’ll get us to 100 percent annual growth. Then you ask economists Move the decimal point over. Yeah, just get rid of it even. You ask economists and
My guest this episode is Scott Strand. Scott is Head of Strategy, Operations, and Special Projects at Google, where he founded the Google AI Economy program as well as Google ATLAS. Recently, Scott co-authored two papers, “Mapping Gemini’s Usage in the Economy” and “AI in Science: Early Insights.” Today’s conversation will discuss these two papers, diving into the role AI could play in benefiting society through everyday life, scientific progress, and long-term economic growth. In this episode: * Is AI a general purpose technology? (0:40) * Gemini’s usage in the economy (4:20) * What will it add up to? (9:44) * AI and entry level jobs (15:34) * AI beyond the workplace (19:13) * AI and the future of science (22:15) Faster Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
On this episode of Faster Please!—The Podcast, I am joined by Dalibor Rohac. Dalibor is the director of global research at GLOBSEC and a longtime senior research fellow in international relations at the University of Buckingham. Recently, he wrote the book Unshackled, which focuses on how economic power is inseparable from geopolitical strength. In our conversation, Dalibor and I discuss his new book and what growing strains within the Western alliance could mean for its economic and geopolitical future. We also talk about Europe’s defense posture and its struggle to remain competitive in the global technology race, as well as why trade within the Western alliance is critical to maintaining long-term Western prosperity. In this episode: * What defines the West (0:43) * The changing West (6:04) * The disappearing secret ingredient (12:54) * A change in nationalism? (18:25) * European regulation (22:56) * Europe’s economic future (26:10) 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 for now here are some of the many highlights from the chat: On defining the West… For me, the West is not defined geographically, it’s not defined in ethnic terms, it’s not defined in cultural terms. It’s defined through adherence to a certain set of institutions that people like Acemoglu and Robinson would call inclusive economic and political institutions, and different scholars would have different names for them. But I think we see as meaningful the distinction between autocracy and liberal democracy, between free markets and state capitalism or a planned economy. And I think countries that do have those sorts of institutions that have enabled the rise of the West—places like the UK and the United States—and the opening up of our political systems, they can be in Asia. There’s Japan, there’s South Korea, there’s Australia, and I think they are very much part of the West as much as we are here in Europe or in the United States. On the potential of a changing West… I think we can deal with troubled democracy in Hungary or democratic backsliding in Turkey. Turkey would be one of those cases where there has been a significant shift away from the Western alliance and away from democracy. But if we see a similar shift sustain itself in the United States, I think the world will be in a lot more trouble, even relative to the status quo. On the West’s trajectory… I see all these controls that you’re looking at blinking in the wrong direction. And I think there is some urgency with which we have to create intellectual, political coalitions to defend some of these things. We might need to learn things, relearn things the hard way, but I very much hope that it won’t be necessary and that people will understand what is at stake. I think the most immediate pressure point is the federal budget. If you are spending more on financing your debt than on the Department of Defense, you’re placing yourself in a very poor position to fight a major conflict. On Europe’s diverging defense mindset… A place like Poland, which has been spending consistently between 3.5 and 5 percent of GDP on defense, which has a dynamically growing economy that has now joined the G20 group of most advanced industrial nations of the world, which surpassed Japan in per capita GDP last year. Unfortunately, the further west you go, I think the more complacency you see. I was recently at a conference in Austria, beautiful place, lots of young German-speaking people, both from Austria and Germany, and I had the sense that people wanted to be left in their illusions. Much of the rhetoric reminded me of the stuff you would hear at conferences 10 years ago about multilateralism and just getting along and solving the climate issue and preserving the advances of the welfare state. There just wasn’t the same sort of urgency that you hear when you go further east. On Europe’s posit
On this episode of Faster Please!—The Podcast, I am joined by Christian Catalini. Christian is a research scientist and the founder of the MIT Cryptoeconomics Lab. Recently, Christian co-authored “Some Simple Economics of AGI” In our conversation, we focus on the AI bottleneck of verification, rising safety concerns, and how to address and solve these problems while maintaining growth and the US advantage over China. In this episode: * Red light or green light? (0:37) * AI risk and safety (8:07) * The bottleneck: verification (15:28) * Markets and AI safety (22:48) * AI labs and regulations (26:17) 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 slowing AI industry growth… Definitely not a red light for the entire space. I think that would be a very bad idea. That said, concerns have been raised by the labs themselves. So I think the next natural step is to send some real evaluators in and see what’s actually going on. We’ve seen a report from METR. I think they’re a talented team, but they have a very particular bias and a very particular type of expertise in this. I would love to see traditional security engineers and cyber experts looking at what happened inside OpenAI, what is happening inside Anthropic. Is there something we should be concerned about? Or are these companies just racing and potentially being a little bit reckless with their deployments? ✨ On catastrophic AI risk… Could someone, on the bio side, use the information from an LLM to create something more dangerous than attempts that have been happening over the last few decades, where people were just ill-informed? They got 80 percent of the way. Could they get to 100 percent of the way and hurt a substantial number of people? Yes. Would it be a pandemic? I think that’s the big stretch. Once you do those massive jumps, I think that’s where we’re in the land of science fiction. And honestly, it’s harmful because we keep anthropomorphizing these agents when there are very benign explanations for what they’re doing. In fact, it’s the training that the labs are doing with reinforcement learning that’s leading exactly to the outcomes you’re seeing. ✨ On the verification bottleneck… The act of verification is the actual bottleneck. Because with AI, you can just throw compute at anything that’s been measured and therefore can be automated. But if you can’t trust the output, if there are important unmeasured dimensions of that problem that the AI doesn’t have access to, well, then you’re in trouble. I think companies today, including the labs—and maybe this is why we’re seeing the security incidents—have this temptation to keep advancing and rapidly progressing, potentially not doing all the proper verification. ✨ On regulating AI… I don’t think we need to come up with draconian, very tight regulatory bodies for mature markets, right? Airlines, financial services—how long did it take us to fine-tune that regulation so that we got it right? Decades. And in fact, it’s still a work in progress. I think here we need to be thoughtful. I think we can start with better measurement. Let’s surface the evidence. Let’s have neutral evaluators and inspectors. Again, we have METR, but we need probably 10 different organizations with very different philosophies, different skill sets, that can go in and provide us a neutral third-party assessment. By the way, neutrality also means funding. If the funding for these institutions comes from the labs, well, I don’t know what to make of it, right? Maybe academia has a role to play. I’m not sure, but we really urgently need better measurement. ✨On the labs’ role in AI safety… The antitrust concerns don’t really apply in the format of safety. Of course, if you’re timing model releases and sort of handicapping the industry in different ways, that could be an antitru