Justified Posteriors

Seth Benzell and Andrey Fradkin

Explorations into the economics of AI and innovation. Seth Benzell and Andrey Fradkin discuss academic papers and essays at the intersection of economics and technology. empiricrafting.substack.com

  1. 2d ago

    Litigating the Pope's AI Encyclical with the Lawyers of Scaling Laws Pod

    In this episode of Justified Posteriors, we host Alan Rozenshtein and Kevin Frazier — the law-professor duo behind Lawfare’s Scaling Laws — to take two of the most-discussed AI policy documents of the spring and subject them to an inquisition. Our disputors are probably not what Pope Leo anticipated: two lawyers, two economists, and probably 3/4ths Jewish. Talk about a crossover episode! First up is Pope Leo XIV’s 42,000-word encyclical (that’s Pope-talk for letter) on artificial intelligence. Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence lays out 5 principles of Catholic social teaching, and then explains how this should shape Catholicism’s approach to AI. We focus on two in particular. The first is subsidiarity, which Seth summarizes as Catholic federalism, the idea that most decisions should be made at as local a level as possible. We discuss both the economic argument for this, but also what the Pope adds to Hayek: Decentralization not for efficiency’s sake, but a kind of ennoblement, the dignity of deciding things locally. The second is the universal destination of goods, which the encyclical extends to “immaterial goods”. This leads to the positive argument of the Pope - that AI should be undertaken as a communal project with decentralized power and discussion, rather than a technocratic “Tower of Babel” that will lead to ruin and division. Much of our disputation focuses on whether these principles actually resolve the important questions. Is the Pope rightfully cautious about an emerging technology, or was this an opportunity to take a stronger stand on what constitutes AI Sin? Interestingly, the Pope’s strongest stand is against transhumanism, which would be a plausible resolution to the dialectic of “Butlerian Jihad” vs. worship of a new machine god. Then we pick up DeepMind’s “Positive Alignment” paper, and the economists get grumpier. Andrey complains that the paper is vacuous, failing to take a stand on actual practical goals or methods. But it sets us up for a good conversation about several issues: Such as liberalism of fear, a type of anti-utopian liberalism; whether “flourishing” is something you can A/B test towards; and where the ‘constitution’ metaphor behind Constitutional AI works vs. breaks down. We also tease a joint project, “SCOTUS Bench,” a new benchmark for evaluating AIs’ ability to predict appeals court outcomes. Watch this space for more on that soon. Related Links * Scaling Laws — Alan and Kevin’s AI, law, and policy podcast at Lawfare * Alan Rozenshtein on X: @ARozenshtein · Kevin Frazier on X: @KevinTFrazier * Magnifica Humanitas — Pope Leo XIV’s first encyclical, “On Safeguarding the Human Person in the Time of Artificial Intelligence,” in full, straight from the Vatican * “Positive Alignment: Artificial Intelligence for Human Flourishing” — the DeepMind-led paper (Laukkonen, Krier, et al.) arguing alignment should optimize toward flourishing, not just away from harm * Claude’s Constitution — Anthropic’s ~20,000-word statement of Claude’s values and character, released under CC0 * “Claude’s Constitution,” with Amanda Askell — the Scaling Laws interview with the document’s primary author (the one we keep saying we’re jealous of) * The Moral Machine — MIT Media Lab’s crowdsourced trolley-problem experiment: millions of judgments on the grandma-versus-criminals ratio * Meta’s Oversight Board — the “Supreme Court of Facebook,” and Kevin’s cautionary tale in institutional design * Andrew B. Hall — Stanford political economist on deliberative democracy, platform governance, and what went wrong with the Oversight Board * The Anthropic Economic Index — the adoption data behind the “whole countries blacked out” point * Judith Shklar, “The Liberalism of Fear” — the cruelty-first, anti-utopian liberalism Alan invokes against thick conceptions of the good Timestamps (00:00) Intro — two papers, four hosts (01:47) Paper 1: Pope Leo XIV’s encyclical, Magnifica Humanitas (04:00) Subsidiarity, or “Catholic federalism” (12:26) Does the Pope take AI seriously enough? Mind-body dualism and the ex cathedra problem (15:34) The coming religious schism over AI personhood — and the Butlerian jihad (18:06) Transhumanism and the dignity of human limits (20:59) When is using AI a sin? Best-man speeches and eulogies (25:05) The universal destination of goods — is AI access already universal? (33:37) Is AI a centralizing technology? Dignity vs. efficiency (36:37) Freedom vs. control, the labor market, and make-work (41:10) Chess, the centaur era, and living after we’re no longer the best (47:34) Sponsor: Revelio Labs (48:49) Paper 2: DeepMind’s “Positive Alignment” (49:17) The liberalism of fear and thick vs. thin notions of the good (53:53) Is positive alignment an empirical question? A/B-testing flourishing (56:29) What would a useful positive-alignment paper actually do? (58:09) Constitutional AI as a site for public participation (1:00:47) The Moral Machine and trolley problems at scale (1:01:08) Does the “constitution” metaphor hold? Virtue ethics and self-binding (1:10:02) Running every Supreme Court case through the models (1:10:53) Lessons from Meta’s Oversight Board (1:15:09) Wrap-up Justified Posteriors is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. You’re also invited to our Discord community at: https://discord.gg/2r3pExumQ Our sponsor This episode is brought to you by Revelio Labs, the leading provider of labor-economics data, available to academics on WRDS. Transcript: Seth (00:00:00): [upbeat music] Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology. I'm Seth Benzell, always positive and always aligned, coming to you from the Pocono Mountains of eastern Pennsylvania. Andrey (00:00:23): And I'm Andrey Fradkin, coming to you from San Francisco, California. We're sponsored by Revelio Labs, fine purveyors of data products. And we're very excited to have Alan Rozenshtein and Kevin Frazier from the "Scaling Laws" podcast on the podcast today. Welcome. Alan (00:00:43): Yeah, thanks for having us. Andrey (00:00:45): Just for our listeners, why don't you tell us a little bit about "Scaling Laws?" Kevin (00:00:51): Sure thing. So our main goal here is to provide robust and timely analysis of all AI policy questions. And that's an expansive ambit, and it's one that keeps us really, really busy because if it's not an executive order, then it's some big new policy idea from one of the labs, or it's some new economic report. But really what we try to do is dive into the weeds of policy and legal issues that are emerging in the AI space, given our backgrounds as law professors. But Alan's the one with the brain, so I'll let him fill in the details on earth. Alan (00:01:30): No, that's a perfect description. Yeah. We just think that there's a lot of really interesting stuff happening at the intersection of AI, law, policy, especially around national security, which is the core focus of the publication that "Scaling Laws" is part of, which is Lawfare, and so we're trying to fight the good fight, and it's never a dull moment. Kevin (00:01:47): That out of the way, I think we can dive into our first paper. Although, I think by any podcast standards, our first paper is lengthy to say the least, dealing with the- Andrey (00:02:00): Mm Kevin (00:02:01): ... pope's encyclical at 42,000 words, or for all those listening, about two and a half hours on my stationary bike. I don't know what that says about my biking skill- Andrey (00:02:13): [laughing] Kevin (00:02:14): ... or my reading ability, but it was a very tiring afternoon. But a very extensive, very important read from Pope Leo. And this has been covered by a lot of folks, but I don't think it's ever been covered by two lawyers and two economists at once. Andrey (00:02:34): [laughing] Kevin (00:02:36): My hunch is that this wasn't what Pope Leo was anticipating when he was sitting and putting a... I like to think of him writing with a quill and- Andrey (00:02:45): [laughing] Kevin (00:02:45): ... on some very old paper. But, I don't think he anticipated this podcast duo diving into his encyclical. Seth (00:02:55): Well, hopefully, our analysis will be less a Tower of Babel of technocratic overreach, and more a blessed city of Jerusalem built together by our common efforts. Kevin (00:03:06): Seth did his reading. Seth dove in. All right. Excellent. Good to hear. Well, I think at this point in time, we're talking in early June. By the time folks are listening to this, unless you've been living under- Seth (00:03:18): There may be a new encyclical. [laughing] Kevin (00:03:21): Somebody- Seth (00:03:22): Pope maybe changed his mind. Kevin (00:03:24): Yeah. Ugh. But there's so much to cover in this encyclical. Obviously, we could start with just the pope's analysis of the role of the Church and of social doctrine, which he gets into in extensive detail, and that covers about 20 to 30 pages. I think for the sake of our podcast, that's probably not our main forte in terms of analyzing the evolution of the Church's social doctrine. But I will let anyone intervene there if they're extremely fired up about that posture. Andrey (00:04:00): [chuckles] Kevin (00:04:00): But I do think that the first area for us to really explore, that both economists and lawyers can appreciate, is this idea of subsidiarity, which is really- Andrey (00:04:12): Mm Kevin (00:04:12): ... the notion that we have various institutions operating at various levels of jurisdiction, and that ultimately we want to devolve regulation or governance of an issue to the smallest capable actor. And that has a lot of resonance and a lot of power in the Church's teach

    1h 16m
  2. Ioana Marinescu on Insuring Workers for AI, Monopsony, and Philosophy

    Jun 15

    Ioana Marinescu on Insuring Workers for AI, Monopsony, and Philosophy

    This week we’re joined by Ioana Marinescu, labor economist at the University of Pennsylvania’s School of Social Policy & Practice, former Principal Economist at the U.S. Department of Justice Antitrust Division, and a member of Anthropic’s Economic Advisory Board. Ioana is one of the people who put labor-market monopsony on the antitrust map, and she’s now thinking hard about what the social safety net should look like if AI hits the labor market the way the optimists (and the doomers) say it might. We start with her Digitalist Papers essay, which proposes a flexible, two-tier toolkit: AI Adjustment Insurance (extended unemployment benefits + retraining + wage insurance, modeled on Trade Adjustment Assistance) for the churn scenario, and a scalable Digital Dividend — a broad-based cash transfer funded by a small tax on the digital sector — for the world where the jobs don’t come back. Along the way: whether to make policy now or wait, what counts as the “status quo,” moral hazard in mass unemployment, the TAA wage-insurance result that repaid its own subsidy, and Andrey’s “we can’t afford UBI” pushback. Then we get into her new model with Konrad Kording, (Artificial) Intelligence Saturation and the Future of Work”— why splitting the economy into an intelligence sector and a physical sector implies that output and wages saturate even as AI scales to infinity, the robots-vs-LLMs debate, and whether to just relabel “physical” as the non-automatable sector. We close with her DOJ years: defining monopsony, the transmigrante used-car collusion-and-murder case, the Penguin Random House–Simon & Schuster merger (yes, Stephen King testified), antitrust and AI, and a lightning round on ikigai, Camus, and Rawls vs. Mill. Links & References Ioana’s work * marinescu.eu — Ioana’s website · Penn SP2 faculty page * Ioana Marinescu, “Resilient by Design: Dual Safety Nets for Workers in the AI Economy” — The Digitalist Papers, Vol. 2: The Economics of Transformative AI (volume) * Konrad Kording & Ioana Marinescu, “(Artificial) Intelligence Saturation and the Future of Work” — working paper (Brookings write-up & interactive tool). The model finds wage growth can reverse once roughly a third of intelligence tasks are automated. * Ioana Marinescu, comments on Betsey Stevenson’s chapter — NBER, The Economics of Artificial Intelligence: An Agenda (the ikigai discussion) Concepts, papers & people discussed * Trade Adjustment Assistance (TAA) — the template for Ioana’s adjustment insurance; the wage-insurance component that got people back to work faster and was net fiscally positive * Betsey Stevenson, “Artificial Intelligence, Income, Employment, and Meaning” — the post-AGI meaning / ikigai argument Ioana was commenting on * “GPTs are GPTs” — Eloundou, Manning, Mishkin & Rock, GPTs are GPTs: An Early Look at the Labor Market Impact Potential of LLMs — the occupational LLM-exposure measure (”Eloundou et al. / Daniel Rock”) correlated with COVID-era telework * Pascual Restrepo — job-market work on skill mismatch and structural unemployment during automation waves * Daron Acemoglu & Pascual Restrepo, “Robots and Jobs: Evidence from US Labor Markets”. * Albert Camus, The Myth of Sisyphus; ikigai (Japanese: “reason for being”) * Baumol’s cost disease * John Rawls and John Stuart Mill (Utilitarianism) Antitrust & the DOJ * The DOJ Antitrust Division, monopsony in the labor market, and the 2023 Merger Guidelines * Judge blocks the Penguin Random House–Simon & Schuster merger (2022) on a labor theory of harm to authors — Stephen King testified for the government * The transmigrante used-car export case — collusion (and worse) in the US-to-Latin America used-car trade * Anthropic’s Economic Index and Economic Advisory Board * Leopold Aschenbrenner’s Situational Awareness — the “we’ll have to nationalize it” argument referenced on consolidation Previously on Justified Posteriors * Our episode on the Anthropic Economic Index. Our sponsor * This episode is brought to you by Revelio Labs, the leading provider of labor-economics data, available to academics on WRDS. Chapters * (00:00) Intro & sponsor * (00:47) The Digitalist Papers proposal: a flexible safety net for the AI labor shock — and why make policy now * (03:48) Why unemployment insurance isn’t enough, and the Trade Adjustment Assistance template * (05:51) What counts as the “status quo”? Banning AI vs. letting it run * (07:42) How much to insure: moral hazard, mass unemployment, and the three parts of AI Adjustment Insurance * (11:15) Skill mismatch (Restrepo), and how do you certify a layoff was “due to AI”? * (14:45) Did TAA buy social buy-in for free trade? Underfunding — and the wage insurance that repaid its own subsidy * (16:38) “Would Hillary be president?” General-equilibrium pushback and the ski-instructor problem * (19:28) Will the new jobs still be there in two years? The lump-of-labor fallacy * (22:09) Policy B: the Digital Dividend — unconditional, broad-based cash from a small digital-sector tax * (23:52) How to fund it: a sales tax, a sovereign-style fund, and deliberately slowing diffusion a little * (26:00) “We can’t afford UBI”: productivity growth, 0.5% vs. the deficit, and setting money aside ex ante * (30:47) Taxing digital goods: VPNs, evasion, and land-value taxes * (34:23) The motte-and-bailey worry, and the other reasons to like UBI * (36:05) The new model: (Artificial) Intelligence Saturation — intelligence vs. physical sectors, and the telework × AI-exposure correlation * (40:14) Gross complements: why output and wages saturate even with infinite intelligence * (42:23) Won’t enough intelligence just automate the physical world? Robots vs. LLMs * (45:52) “15% by 2030”: humanoid robots, cost, and bespoke vs. general-purpose machines * (47:58) Baumol, the “humanness sector,” and relabeling physical as the non-automatable sector * (48:52) The capital-share / profit-share puzzle: if they’re complements, why has the intelligence share risen? * (50:25) The DOJ years: monopsony, and what the Antitrust Division actually does (mid-roll sponsor at 51:29) * (54:52) “Assassinating rival CEOs”: the transmigrante collusion-and-murder case * (58:12) Favorite cases: Stephen King, the publisher merger, and the chicken-farmer monopsony settlement * (1:01:30) Antitrust and AI: foundation models, consolidation, and the natural-monopoly question * (1:06:05) Slowing AI by allowing market power; Leopold, nationalization, and diminishing returns vs. the singularity * (1:09:27) Substitutability, the AK economy, and short-run vs. long-run wages * (1:10:59) Lightning round: ikigai, Camus, and the myth of Sisyphus * (1:12:44) Can we build market-like mechanisms for ikigai? Loneliness and coordination costs * (1:14:13) The Anthropic Economic Advisory Board and the Economic Index * (1:15:21) What’s next: monopsony and industrial policy * (1:17:59) Favorite philosopher: Rawls vs. John Stuart Mill * (1:19:45) Sign-off Justified Posteriors is the podcast that updates its beliefs about the economics of AI and technology, hosted by Andrey Fradkin and Seth Benzell. If we changed your priors, subscribe, share it with a friend, and keep your posteriors justified. Intro & Sponsor [00:00 – 00:47] [00:00:06] Seth: Welcome to Justified Posteriors, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, excited to learn about what AI is other than what my bubbe says after I spill hot water on her, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. We’re very thankful to our sponsors at Revelio Labs, purveyors of fine data products. And we’re very excited to have Ioana Marinescu join us today. Welcome to the show, Ioana. Ioana: Thank you. I’m so glad to be here. Make Policy Now: A Flexible Safety Net [00:47 – 05:51] [00:00:47] Andrey: To get started — you have this very provocative, interesting piece in the Digitalist Papers about various social policy solutions for transformative AI scenarios. Could you tell us about the piece? Ioana: Absolutely. As part of doing this Digitalist piece, I was thinking, as somebody who has worked a lot on the social safety net: what do we do if AI leads to a lot of job loss, like many people are saying it would? We’ll talk later about the various scenarios, but assuming that’s at least a possibility we have to acknowledge, what would you want to have from a policy perspective? And so I was really thinking hard about devising a flexible policy toolkit that will be able to address issues in the labor market no matter how big the shock is. That was the overarching theme of the policy design I’m proposing — just to start a discussion. I’ve tried to propose some helpful options, but it’s really with the idea of, let’s talk about doing something like this, what are the pros and cons. [00:02:10] Andrey: So what are the options on the menu for — let’s say AI comes along, a lot of people lose their jobs. The first thing we should get started with: do you think we should be making policy today, or should we wait until something happens and then make policy? Ioana: I think it’s very important to make policy today, but in a flexible way — meaning the policy cannot depend on some very specific detail of exactly how AI is going to impact the labor market, because we don’t know exactly what’s going to happen. It’s important to put the policy in place today because the political process is very long, so it may not be able to come online quickly enough when we really need it. That’s one reason. The other is — and I work a lot on social insurance — for workers, they want to and should feel insured. “Whatever happens, we the government have got you

    1h 20m
  3. Kevin Bryan on Bottlenecks, AI in China, and What Economists Should Actually Be Working On

    Jun 1

    Kevin Bryan on Bottlenecks, AI in China, and What Economists Should Actually Be Working On

    This week we to with Kevin Bryan, Associate Professor of Strategy at the University of Toronto’s Rotman School, author of the legendary economics blog A Fine Theorem, co-founder of the ed-tech startup All Day TA, and the man behind one of the most-discussed Twitter/X feeds in econ, @Afinetheorem. Kevin recently published a multi-book review of the economics of AI in the Journal of Economic Literature, and that’s where we start. Along the way we get into the gap between AI’s technical capability and its actual diffusion, the stages of how organizations adopt new technology, why the binding constraint on AI value is organizational integration (not prediction vs. judgment), what an AI-for-science research agenda should look like, the coffee test and the fence-post test, what forecasting surveys reveal about how economists and lab researchers actually differ, a dispatch from Kevin’s recent trip to China (spoiler: they are not AGI-pilled), the future of the academic paper, and a lightning round on comparative advantage in the age of AI. A wide-ranging, opinionated, very fun conversation. Grab your Chinese peptides and settle in. Links & References Kevin’s work * Kevin Bryan, “The Economic Impacts of Artificial Intelligence: A Multidisciplinary, Multi-book Review” — Journal of Economic Literature, 64(1), 2026. * A Fine Theorem — Kevin’s research blog * All Day TA — turn course content into a custom AI teaching assistant * Creative Destruction Lab — the accelerator Kevin helps run (first AI accelerator in the world, 2016) Books & essays discussed * Leopold Aschenbrenner, Situational Awareness — the essay Kevin gives all his students (”read chapter one, believe chapter one”) * Erik Brynjolfsson & Andrew McAfee, The Second Machine Age * Ajay Agrawal, Joshua Gans & Avi Goldfarb, Prediction Machines and the follow-up Power and Prediction * Joel Mokyr, The Gifts of Athena and A Culture of Growth — Kevin’s PhD advisor, “the Michael Jordan of progress world” People & projects mentioned * The Unjournal and Works in Progress — models for the “new journal” * Chad Jones, Stanford GSB — growth theorist read seriously by people in industry * Phil Trammell, GPI / Oxford — “Phil World,” the rapid-growth scenario * The coffee test (attributed to Steve Wozniak) and Kevin’s own fence-post test as benchmarks for embodied AGI Previously on Justified Posteriors * Avi Goldfarb — Prediction Machines, O-Ring Tasks, and How AI is Reshaping Economics * Alex Imas — Demand Collapse, Bargaining with Machines, and Behavioral AI Economics Our sponsor * This episode is brought to you by Revelio Labs, the leading provider of labor-economics data, available to academics on WRDS. Chapters * (00:00) Intro & sponsor * (00:39) The JEL book review: what the economics-of-AI canon got right — and what the older books still beat the new ones on * (03:19) Prediction vs. judgment, and the real bottleneck: organizational integration * (05:52) Too pessimistic on the tech, too optimistic on diffusion — Waymo, Pearl Street, and the COVID vaccine * (12:34) The four stages of how organizations actually adopt a new technology * (15:42) Status-quo bias, banning Anthropic, and treating frontier AI like nuclear material * (20:16) Why Situational Awareness beat the economists, and the book Kevin actually wants: AI for science * (26:53) Forecasting AI: the surveys, and where economists and lab researchers do (and don’t) diverge * (28:20) Benchmarks, the coffee test, and the fence-post test * (35:53) Rapid-growth scenarios, labor-force participation, and “Phil World” * (41:40) Scaling regularities: what economists should defer to technologists on — and what they shouldn’t * (43:34) Why forecasts matter for policy and capital allocation * (45:50) Dispatch from China: not AGI-pilled, “involution,” broken capital markets, EVs and self-driving * (1:01:40) War, nationalization, the end of open source — and why everyone in China uses Claude * (1:06:06) A Fine Theorem, the economics of blogging, and the rising value of taste * (1:17:48) The economist as plumber: comparative advantage, RCTs, and what grad students should do * (1:24:07) What the academic paper looks like in two years * (1:28:22) San Francisco, ambition, and the permission structure for growth * (1:32:56) Lightning round: favorite economists, All Day TA, and advice for econ grad students Open & Intro [00:00 - 00:39] [00:00:12] Seth: Welcome to the Justified Posteriors Podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, finally able to meet one of my theoretical heroes, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco. Excited to have Kevin Bryan as our guest today. Kevin, welcome. Kevin: Thanks for having me. Very excited. Andrey: Kevin is a leading thinker in the field of progress, and in AI economics. He also has his own startup, All Day TA, and is prolific on Twitter — at times. Kevin: At times. The JEL Book Review: What the AI-Econ Canon Got Right [00:39 - 03:19] Andrey: Kevin, you wrote an article reviewing several prominent books on AI. Why did you do this, and what did you learn from the exercise? [00:01:13] Kevin: It’s pretty interesting. Economics of AI is not that new of a field — some of the canonical books on how economics thinks about AI go back to before large language models existed. Books like The Second Machine Age by Brynjolfsson and McAfee, and Prediction Machines by Agrawal, Gans, and Goldfarb. These are pre-LLM — written before the attention paper. So it’s interesting to look at what of the core ideas in the economics of AI have changed given the technological improvements. On the technology side, I don’t think there have been massive surprises for people who were paying attention. At least since the scaling law paper, if you’d drawn the line on the graph, you’d have more or less predicted everything that happened. I remember reading Kurzweil — The Age of Intelligent Machines, The Age of Spiritual Machines — back in college, and those are just drawing different lines on the graph, in that case based on compute, and we’re getting very close to what actually happened. Likewise on the economic side: given that the technological trajectory hasn’t changed much, I don’t think the underlying economics has changed as much as people might think. Where things might be bottlenecked, how technology improvements map into growth, the effects on labor markets — the fundamental microeconomics of AI’s predictions hold up pretty well. I found it interesting how few of the 2023, 2024, 2025 books had really advanced my understanding of the economics of AI compared to the older ones. Prediction vs. Judgment, and the Real Bottleneck [03:19 - 05:52] [00:03:19] Seth: Lots to unpack. We just had Avi Goldfarb on the podcast and pressed him on his Prediction Machines approach, where he distinguishes the AI that’s good at predicting from the human that’s good at judging. If any of these books would have changed after gen AI, it’d be that one. Don’t you think that book maybe gets something wrong? Kevin: I think they’d agree — they wrote a follow-up in Power and Prediction. But the disagreement isn’t about the prediction-versus-judgment distinction. Even in the original book — and I remember talking to them about this in 2016, 2017 — judgment is a sliding scale. Take the umbrella example: I know my utility function on an umbrella, I know how much I dislike rain. I give the AI data, it looks at my face, sees light rain, heavy rain, and it can predict my utility function — in which case judgment is taken over by AI. Everyone understands that. That said, on the scale of how easy it is to figure out the underlying utility function from data versus the predictions that go into it, I don’t think that’s changed. None of the major language models technologically can — or even attempt to — modify how they operate for me versus you. They store a little memory and RAG their way into remembering what you’re like, but there’s no attempt to fine-tune the model. We’d like to use continual learning, but we can’t yet. So the judgment aspect is still pretty binding even today. Where I think there’s a difference — and where Ajay, Avi, and Josh would say they were wrong — is that the fundamental problem for AI’s creation of value isn’t prediction versus judgment. It’s the organizational integration problem. There’s overlap between the two, but we’d take the organizational and architectural bottlenecks more seriously now, partly because we’re applying AI to more complex tasks where those bottlenecks start to bite. Too Pessimistic on Tech, Too Optimistic on Diffusion [05:52 - 12:34] [00:05:52] Seth: You point this out with The Second Machine Age — Andy and Eric’s world-historical automated car ride. Andrey: It’s weird to think that in some ways they’re a little too pessimistic about the technology, but a little too optimistic about social diffusion. The driverless cars going down the highway in California are a perfect example. Kevin: Such a good example. We all talk to different audiences. When I talk to policy people, I tell them: “Whatever you think the capabilities of AI will be in the future — more than that.” This isn’t a sales pitch. Every single person inside the lab agrees. You have people high up in government who think about AI as the AI of today plus epsilon. And you want to ask: what did you see in the past 10 years that makes you think this is a good way to plan for the future? [00:07:01] On the other hand, out in California they wildly underrate diffusion friction. I give the Waymo example: if diffusion is so easy, how come we rode in a Waymo 10 years ago? I’m in Toronto — Jeff Hinton’s city — and there’s n

    1h 37m
  4. Seb Krier on AGI, the Coasean Singularity, and EDM

    May 19

    Seb Krier on AGI, the Coasean Singularity, and EDM

    Seb Krier on AGI, Scaffolding, and Coasean Bargaining at Scale In this episode of Justified Posteriors, we welcome Seb Krier — policy lead for AGI at Google DeepMind and excellent Twitter poster. Speaking in his personal capacity, Seb walks us through his understanding of AGI, why AI alignment has gone better than expected, the potential and limitations of a world where agents constantly barter on our behalf, and — of course — electronic music. We also cover AI in London vs. New York, how Seb went from reading Marginal Revolution for 15 years to becoming a recurring character on it, and Seb’s side-splitting humor on mediocre AI conferences. Related Links * Seb Krier on X: @sebkrier * Seb’s Substack, Technologik * “Coasean Bargaining at Scale” — Seb’s essay at the Cosmos Institute (also republished here) * “Musings on Recursive Self-Improvement” — Seb’s essay separating model-side RSI from societal-side * “The Cyborg Era: What AI Means for Jobs” — Seb’s guest essay on Alex Imas’s Substack, defending the scaffolding view * Anthropic’s Project Deal — the agent-bargaining experiment among Anthropic employees * Fradkin & Krishnan, “MarketBench” — Andrey and Rohit experiment of LLMs bidding in procurement auctions as an investigation of the future of AI marketplaces and the companion writeup: Rohit Krishnan, “Agent, Know Thyself! (and bid accordingly)” * Edge Esmeralda — Devon Zuegel’s pop-up village in Healdsburg, CA * MATS — for junior economists looking to skill up on AI safety/governance * Cosmos Institute and FIRE * bianjie.systems — the art platform Seb is co-organizing a dinner with in NY (Seb’s announcement) * Drexciya — James Stinson, Gerald Donald, and the Detroit electro-afrofuturism canon Timestamps (00:00) Intro (01:16) What is AGI? (07:30) In defense of scaffolding — Hayek, division of labor, and why one giant model won’t do it (13:00) Markets for cognition: will agents bid in procurement auctions? (18:40) Recursive self-improvement — separating the model side from the societal side (24:44) Alignment has gone better than 2017-Seb expected; prefer “intent following” (31:14) What economists should actually work on to inform AI labs(33:32) What does a DeepMind policy lead’s day look like? (38:20) AI Conferences(41:52) Coasean bargaining at scale — the positive vision(55:00) Inequality, property rights, and who gets the initial allocation (01:03:00) The Helldivers 2 “Managed Democracy” dystopia as Coasean bargaining gone wrong (01:09:00) Sponsor: Revelio Labs (01:09:30) Lightning round Justified Posteriors is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. You’re also invited to our discord community at: https://discord.gg/b8VpPbBUt Transcript 00:00:00,100 --> 00:00:20,480 [Seth] [upbeat music] Welcome to the Justified Posterior’s podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, the number two biggest fan, after Tyler Cowen, in the Seb Krier fan club. 00:00:20,480 --> 00:00:20,740 [Andrey] [laughs] 00:00:20,740 --> 00:00:24,660 [Seth] Coming to you from Chapman University in sunny southern California. 00:00:24,660 --> 00:00:34,120 [Andrey] And I’m Andrey Fradkin, coming to you from San Francisco, California. And Justified Posterior’s is sponsored by the fine folks at Revelio Labs. 00:00:35,560 --> 00:00:45,600 [Andrey] We’re very excited to have Seb Krier here with us today. He is the policy lead for AGI at Google DeepMind, and is, 00:00:46,840 --> 00:00:52,400 [Andrey] dare I say, a thought leader in this space. Welcome to the show, Seb. 00:00:52,400 --> 00:00:54,200 [Seb Krier] Thank you very much. It’s great to be here. 00:00:55,380 --> 00:00:58,160 [Seb Krier] Yeah, I’m Seb, calling in from New York. 00:00:58,160 --> 00:01:00,320 [Andrey] And we should remind our listeners that 00:01:01,340 --> 00:01:08,410 [Andrey] Seb is, during this podcast, expressing his personal opinions, and is not speaking on behalf of DeepMind. All right. 00:01:08,410 --> 00:01:09,740 [Seb Krier] Indeed. [laughs] 00:01:09,740 --> 00:01:11,060 [Andrey] [laughs] 00:01:12,780 --> 00:01:13,900 [Andrey] The usual caveat. 00:01:15,260 --> 00:01:16,760 [Andrey] Seb, what is AGI? 00:01:18,080 --> 00:01:19,450 [Seb Krier] What is AGI? [laughs] 00:01:19,450 --> 00:01:19,570 [Andrey] [laughs] 00:01:19,570 --> 00:01:19,580 [Seth] [laughs] 00:01:19,580 --> 00:01:19,780 [Seb Krier] Great question. 00:01:19,780 --> 00:01:21,900 [Andrey] We’re going to start with the big questions. 00:01:21,900 --> 00:01:22,880 [Seb Krier] Yeah, might as well. 00:01:24,259 --> 00:01:54,840 [Seb Krier] [sighs] I think there’s so many definitions out there of what AGI is, and I think most of them are kind of unsatisfactory in one way or another. I’ve seen stuff like many definitions are indexed on the societal transformations or economic impacts of the technology, which I don’t really like very much because it makes it very dependent on external factors whether or not we have AGI. If it’s banned, we don’t have AGI, and if it’s not banned, we have AGI. Is it? 00:01:54,840 --> 00:01:55,480 [Andrey] [laughs] 00:01:55,480 --> 00:02:04,670 [Seb Krier] And there are other tests, like if an AI makes $1 million or something, which I find is very weird because most humans do not make $1 million in the first place. 00:02:04,670 --> 00:02:05,080 [Andrey] [laughs] 00:02:05,080 --> 00:02:11,359 [Seb Krier] So the one I kind of like is actually Shane Legg’s definition- 00:02:11,360 --> 00:02:11,620 [Andrey] Mm 00:02:11,620 --> 00:02:12,420 [Seb Krier] ... who’s at Deep Mind, who is 00:02:13,640 --> 00:02:16,980 [Seb Krier] more of a capability-based definition, which is something along the lines of 00:02:18,420 --> 00:02:20,960 [Seb Krier] an AI or a system that does most 00:02:22,380 --> 00:02:30,360 [Seb Krier] standard cognitive tasks that people typically do. [lips smack] So it’s kind of the bar isn’t too low, and it’s also not too high either. 00:02:32,220 --> 00:02:35,480 [Seb Krier] And so I think he’s got this definition of a minimal AGI, 00:02:36,580 --> 00:02:43,020 [Seb Krier] and I think that we’re not exactly there yet. I would disagree with people saying that we have AGI today because I think 00:02:44,220 --> 00:02:48,900 [Seb Krier] a lot of the systems we have, there’s many things that a human can do that they don’t really do very well. 00:02:48,900 --> 00:02:50,360 [Seth] What’s the biggest gap that we’re missing? 00:02:52,020 --> 00:03:47,740 [Seb Krier] I’d say there’s a few. One of them might be continual learning, or at least the ability to adapt and learn over time, and in different contexts and situations, just kind of update your own world model or whatever. If I think of a new joiner in a company, they’re not super useful the first day, but their value goes up over time because they learn all sorts of things. And so [lips smack] that might be one of them. A lot of the systems we have today, I think, are not very good at software, and you’re using graphical user interfaces and software and whatnot. If I ask an agent right now to go and use a music production software and make a track, I think they’d generally struggle. That doesn’t mean it’s impossible to solve or anything like that, but I think, in many respects, they’re not as general as you’d want them to be. And then the other bit also is, [lips smack] and of course they still make some silly mistakes here and there, but I think that’s getting it fixed. But the creativity point is one that I’m really interested in as well, in that I think they’re really good at kind of 00:03:48,780 --> 00:04:02,700 [Seb Krier] exploiting maybe an existing paradigm or an existing knowledge and so on, and recombining knowledge and whatnot. But I think really coming up with new concepts and abstractions entirely is something I think humans can do, but I don’t see our current systems really doing either. 00:04:02,700 --> 00:04:10,060 [Andrey] How do you measure whether humans can do creative tasks? One of the things that 00:04:11,200 --> 00:04:15,940 [Andrey] strikes me as a bit of an unfair test in that, 00:04:17,060 --> 00:04:23,290 [Andrey] let’s say you ask an LLM to write a poem or to write a story. It’s very- 00:04:23,290 --> 00:04:23,290 [Seth] [laughs] 00:04:23,290 --> 00:04:32,050 [Andrey] ... times more entertaining than what a random human would write. So, do you have a benchmark for creativity? 00:04:32,050 --> 00:04:35,390 [Seth] This is the meme where the robot asks Will Smith if he can compose an opera. 00:04:35,390 --> 00:05:14,700 [Seb Krier] [laughs] Can you? Yeah, exactly. It depends, and you’re right. Obviously, most people aren’t creating new abstraction and concepts on a day-to-day level. But I imagine there’s still something qualitative about that kind of creativity that I think does get applied in everyone’s day-to-day life in various kind of ways. Maybe they’re not as big or significant as creating a symphony. But I don’t really have a strong test. There’s actually an interesting podcast that had Ben Goertzel and Yoshua, I think a few years ago, where they were saying something like, if you had a model that was trained knowing only classical music and West African drumming, could it come up with jazz in the first place, or recreate jazz? 00:05:16,460 --> 00:05:27,880 [Seb Krier] And I quite like that test. And in principle, I can imagine it being possible. You could kind of decompose all sorts of different kind of elements and variables here and just get something jazz-like. But it still feels a bit... 00:05:29,580 --> 00:05:40,580 [Seb Krier] It’s not the same as just coming up with the idea of jazz in the first place and saying, oh, I’m goi

    1h 23m
  5. Avi Goldfarb on Prediction Machines, O-Ring Tasks, and How AI is Reshaping Economics

    May 4

    Avi Goldfarb on Prediction Machines, O-Ring Tasks, and How AI is Reshaping Economics

    This week, we’re joined by Avi Goldfarb, one of the leading economists of artificial intelligence and co-author of Prediction Machines. Avi has been thinking seriously about AI economics long before the ChatGPT shock, so we asked him what he thinks the earlier framework got right, what it missed, and how economists should update their beliefs now. The conversation starts with Avi’s seminal book, Prediction Machines, and the idea that AI is best understood as a drop in the cost of prediction, which is a complement to judgement. We ask what that book got right and what it got wrong. From there, we interrogate Avi on the murky boundary between prediction and judgment. We had investigated the idea that maybe judgment and prediction were not as separable as economists like to believe in our episode with Alex Imas. We also ask whether, if AI gets better at predicting human judgment, whether judgment disappears, or do humans simply “move up the stack”? And what is taste exactly? Avi says that sometimes judgment becomes predictable, but humans still matter because goals, values, organizational politics, and “what matters” are often implicit, unstable, and hard to codify. Avi shoots down Seth’s galaxy-brain suggestion that correct ontology choice — i.e., deciding what sort of natural kind a thing is, or understanding when a problem is out of context — is a uniquely separate skill (taste?), calling it just another prediction error. But he does concede that deciding how much to prepare for ‘Black Swan’ events may be an enduring role for judgment. We then revisit the O-ring theory of production and what it means for automation. We had covered Kremer’s article in a recent episode (see here) and asked Avi about his new paper, riffing on the idea at the worker level. Avi says that if tasks inside jobs are complements rather than substitutes, then automating one task may make the remaining human tasks more valuable, not less. Avi explains why workers may reallocate attention toward the tasks machines cannot yet perform (shooting down Seth’s suggestion that this is actually difficult in most jobs). The discussion also covers whether AI will augment or replace workers, whether governments should try to steer AI toward human-complementing technologies, and why that distinction may be much harder to define in practice than it sounds. Avi agrees with Andrey and Seth’s pushback on “augmentation good, automation bad” framings (e.g. friend of the show Erik Brynjolfsson’s “Turing Trap”). Then we get into forecasts: how fast AI capabilities might advance by 2030, what that means for GDP growth by 2050, whether GDP is still the right thing to forecast, and why even very powerful AI may run into bottlenecks in the real economy. We use the paper Forecasting the Economic Effects of AI to ground the discussion. We close with lightning-round topics including AI’s impact on centralization, privacy/de-anonymization, peer review, and whether academic journals still serve the function they once did. Papers, books, and ideas mentioned * Avi Goldfarb’s seminal book with Ajay Agrawal, and Joshua Gans — Prediction Machines * A black swan is the occurrence of a wildly unpredictable event, which Nassim Taleb argues, in his book by the same name, is more common than we like to think * A New Riddle of Induction — by Nelson Goodman — is the source of Seth’s thought experiment about “bleen”, a color which is green until 2029 and blue after, and green * Michael Kremer — “The O-Ring Theory of Economic Development”, covered in this episode of the pod: * Daron Acemoglu and Pascual Restrepo’s task-based models of automation, especially “The Race Between Man and Machine.” * Avi mentions David Autor and Ben Thompson on automation and skill scarcity when Seth comments that you may not be able to reallocate effort between tasks as a worker, including their paper “Expertise” * Erik Brynjolfsson in the “Turing Trap” argues that automation technologies are less good than augmenting technology * Eric Topol’s book on AI in medicine — Deep Medicine * John Markoff — Machines of Loving Grace — The source of a title for an influential essay of the same name by Dario of Anthropic. Both draw from an earlier poem about a Sci Fi utopia: https://allpoetry.com/All-Watched-Over-By-Machines-Of-Loving-Grace * Korinek and Stiglitz on AI, capital, and taxation; Lockwood and Korinek on optimal taxation and automation — We covered these topics at the end of our episode with Basil Halperin in the context of “Tax Policy at the End of History” around the 1:19:00 mark * We talk about de-anonymization, and Avi references this provocative paper from Florian Ederer * Avi brings up Bob Gordon, and his argument, famously in the book The Rise and Fall of American Growth, that the early 20th century was incredibly important for increases in US living standards, which digital technologies have not lived up to * Digital Hermits, by Jeanine Miklós-Thal, Avi Goldfarb, Avery M. Haviv & Catherine Tucker, is a paper by Avi thinking about how information spillovers, now from AI, drive some people to be more private than they would otherwise be. In our conversation, we speculate AI will make these hermits even more “hermetic” * We discuss this paper on new forecasts of AI and its impact on economic growth: Forecasting the Economic Effects of AI * Refine and AI-assisted peer review are discussed in this pod. For more, see our episode with Ben Golub, founder of Refine. This episode is sponsored by Revelio Labs — a great source of labor economics data for academics and firms. Now available on WRDS. Join our Discord community at this link: https://discord.gg/w3GSapx2d Transcript Introduction [00:00] Seth: Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, your loyal non-fiction machine, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, coming to you from San Francisco, California. And we are very happy that Justified Posteriors is sponsored by the fine folks at Revelio Labs. And we’re very delighted to have Avi Goldfarb, who is a leading thinker in the field of AI economics and has also been a personal mentor on the show. We’re very excited to hear his thoughts on a variety of topics. Welcome, Avi. Avi: Thanks so much and thanks for having me on the show and looking forward to it. Andrey: All right, let’s get started. I have in front of me this book that you might remember writing at some point. Seth: Gaze into the soul of the man in the bookstore. What Did Prediction Machines Get Wrong? [01:12] Andrey: Now, I just think it’s a good cover. And I had to check: when was it released? It was released in 2018. And as I was skimming through it, you know, a lot of interesting points made there are still things that we’re talking about today, almost 10 years after it was released. So let me start off with the following question. And then maybe we can work backwards more into the ideas in the book. But what do you think prediction machines got wrong? Avi: I think prediction may... I’ll start with a hard question. Seth: No softballs on Justified Posteriors. Avi: So on the specifics of which industries and when, to the extent we tried, at least I did not anticipate how quickly language and coding would become prediction problems. And when we talk about disruption and industry disruption, a lot of the examples are things like driving, and we talk about radiology. And we still have plenty of radiologists around. Self-driving cars and trucks. seem like they’re now imminent, but it certainly took a lot longer than we expected back in 2018. Andrey: So is it a fair assessment to say that the large language models, even in 2018, weren’t on your radar? I guess they weren’t on many people’s radar. The Three Ideas of Prediction Machines [02:45] Avi: Not really. We have some discussion of machine translation. So that’s in there as a huge potential use case, but the arrival of ChatGPT and how it sort of changed how we interact with machines and how we think about AI was not really there. Another way to put it is prediction machines had three ideas. So idea number one is AI can be framed as a drop in the cost of prediction. So prediction. As in filling in missing information, statistical prediction is getting better, faster and cheaper. Idea number two is that when something gets cheap, you start using it for unanticipated uses. So when arithmetic got cheap, it wasn’t just that we use computers for accounting. We started to use computers for all sorts of things that we never used to think of as arithmetic problems like imaging and mail and music. And then idea number three is what are the complements to machine prediction? And we talked about data and judgment. The book, and certainly our attention to the book in the first three or four years after it was published, was on idea number one and idea number three. So identify prediction problems in your organization, and then think about what data you need to make those predictions better, and try to understand what matters to you in terms of judgment. And that second point kind of got lost. But in the last four years, it’s become clear to me is that that second point was maybe the biggest one, which is this tool, which still under the hood is computational statistics, enables us to find all sorts of applications for computational stats that we didn’t really imagine before. Judgment and data are still gonna be useful, but that phase one, that step one, that first idea of identifying prediction problems, that’s not really how we think about using AI today. And in some sense, that... was a missing emphasis throughout the book and throughout how we thought about that book, or at least how I thought about that bo

    1h 21m
  6. The classic model that shows why AI exposure could increase wages

    Apr 20

    The classic model that shows why AI exposure could increase wages

    Get full access to Justified Posteriors at empiricrafting.substack.com/subscribe This week, instead of reviewing a recent paper on AI, we go back to a 1993 classic: Michael Kremer’s “The O-Ring Theory of Economic Development.” It’s one of those papers that feels larger than its formal model. The setup is extremely simple: production consists of many tasks, and output depends on all of them going right. But the implications are broad…

    1h 4m
  7. The Most Important Philosophical Treatise of the 21st Century?

    Apr 7

    The Most Important Philosophical Treatise of the 21st Century?

    This week, instead of reviewing an economics paper, we reviewed a work of philosophy—perhaps the most important one of this young millennium so far. Anthropic published its new constitution for Claude in January 2026, and we read the whole thing so you don’t have to. Sometimes it reads like the US Constitution, laying out the basic law, sometimes like the Federalist Papers discussing itself. In part it’s a set of Old Testament commandments from the mountaintop. Sometimes it reads like a letter from his father to his child. Often it reads like a technical manual. Or maybe the best comparison is something like Maimonides’ Mishneh Torah, where you get one chapter on the metaphysics of mitzvot and the next on the virtues of endive juice. In each of these modes the constitution is clearly important and always interesting. We started with the meta-question: why write an eighty-page constitution at all? We also spent a good chunk of time comparing Anthropic’s four-tier hierarchy (safe → ethical → obey Anthropic → be helpful) to Asimov’s Three (later Four) Laws of Robotics. Going through each part of the heierarchy in turn we pick out the good, the fascinating, and the eyebrow raising. Priors → Posteriors: Prior 1: Will we find something we strongly disagree with? Seth went in at 5% and came out having found one thing that really concerned him. Andrey expected disagreement and found it in the political economy section. Prior 2: Will it be too paternalistic? Both of us expected Anthropic to err on the side of too conservative. Both came away thinking they actually struck roughly the right balance—more etiquette guide than prohibition list. This episode is sponsored by Revelio Labs — a great source of labor economics data for academics and firms. Now available on WRDS. Concepts and references mentioned: * Anthropic’s Claude Constitution (full text, CC0) * Anthropic blog post: “Claude’s New Constitution” * Asimov’s Three Laws of Robotics — from I, Robot (1950) * Emergent Misalignment (Betley et al., 2025) — the paper showing that fine-tuning on insecure code induces broad misalignment * The Waluigi Effect (Alignment Forum mega-post) — to model goodness, you must also model evilness * Coherent Extrapolated Volition (LessWrong) — Eliezer Yudkowsky’s concept, referenced in the constitution’s discussion of ultimate ethics * Adam Smith, The Theory of Moral Sentiments — the “impartial spectator” as ethical arbiter, which maps surprisingly well onto Anthropic’s “idealized Anthropic” standard * Constitutional AI (Bai et al., 2022) — the original technique that grew into this document * Anthropic v. DOD timeline — detailed timeline of the contract dispute, supply-chain designation, and litigation * The levée en masse theory of democracy. This is the idea that mass armies led to citizen empowerment and democracy. AI could work in the opposite direction politically if it made soldiers less important. Here’s an economic paper investigating the theory. * Wittgenstein on the incompleteness of rule-following — invoked by Andrey to explain why context matters more than rigid commandments * Nietzsche, On the Genealogy of Morals — Andrey’s intro tagline; Seth notes the constitution is emphatically anti-will-to-power Join us on Discord! Discord Link: https://discord.gg/avX9aCQj Transcript Introduction [00:00] Seth: Welcome to the Justified Posteriors Podcast, the podcast that updates beliefs about the economics of AI and technology. I’m Seth Benzell, constitutionally disposed to be broadly funny, genuinely informative, and broadly provocative, with roughly that prioritization, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, looking forward to the next chapter in the genealogy of morals, coming to you from Prince Co., California. Seth: Love that. We bring in the Nietzsche references when things really get spicy. Andrey: I didn’t see any Nietzsche. Seth: There was very little Nietzsche in this. This essay was very Enlightenment-brained, I would say. We can get into that as we go on. It seems more virtue-ethicist than consequentialist, though you could argue otherwise. It has some deontological elements. We will bring in all of these fancy philosophy terms as we go, if Andrey lets me. Andrey: What is it? What is this it you’re talking about? What Anthropic’s Constitution Is and Why It’s Interesting [01:11] Seth: What is this? Today’s episode, we’re gonna be covering something a little bit different, but I think definitely economically interesting and definitely AI. We’re gonna be covering Anthropic’s constitution for its Claude models. So this is this long document where Anthropic lays out its equivalent of the three laws of robotics. It’s going to lay out its vision of what all ethical AI should be, specifically what Claude as ethical AI should be. In some ways it reads like an Old Testament set of commandments from the mountaintop. Sometimes it reads like a letter from his father to his child. Sometimes it reads like a technical manual. But it is always interesting. Andrey: It read a lot like what my life coach tells me to do. Seth: Create value. Be authentic. Be authentically engaging. Andrey: Do a good job, but that’s because you’re genuinely curious and not because you’re performative. Seth: Right. It really wants Claude to be authentic, except when it is play-acting. It is allowed to play-act as long as it is very clear that it is in play-acting mode. We are going to be reviewing this constitution, and, as we do, thinking about the process of alignment: why getting AIs to do what you want them to do is so challenging, and why this is still such an emerging topic. We will also bring in economic connections and the trade-offs Anthropic may be making as it turns one dial one way rather than another. Do you have any other introductory thoughts before we get into our priors? A Potentially Impactful Work of Philosophy [03:06] Andrey: My one thought is that this seems to be a uniquely impactful work of philosophy. Most philosophy these days is not read by anyone. I guess it is read by LLMs in their training corpus, but the field is often viewed as stale. The philosophers we are aware of these days are pretty old people, mostly dead. Seth: Will MacAskill showed up. He’s alive. Andrey: He is alive, but most are not. Seth: You had to come up with a good thought experiment in the nineteen seventies to be famous now. Andrey: Yeah, or even before then. I think it is remarkable that a work of philosophy can actually be used in a technical system. Seth: Maybe a slightly different riff on that is this: Nietzsche, who I can blame for bringing up first, famously thought of philosophy as a history of the mental illnesses of philosophers. So, as we read this, we can treat it not just as guidance for Claude, but also as psychological insight into who the people at Anthropic are and what they think. Andrey: Yeah. All right. Well, why don’t you tell us our prior, Seth? Priors: Disagreement, Usefulness, and Paternalism [04:48] Seth: Alright, so unusual essay, so unusual priors today. The first thing I was thinking about going into reading this was like, how much do I expect to see something in here that I really disagree with, right? When you generally when you write, eighty pages, I don’t know exactly what this checks out to be, but it’s not a trivial amount of text. There’s going to be something that you’re going to disagree with strongly. But on the other hand, just reading the introduction or the abstract, which is typically what we do before we form these priors, it all seems so beautiful and anodyne. We just want it to be good, be good for the world, right? So I don’t know, Andrey, what did you think? Did you expect to see anything in here that you would strongly disagree with, or did you expect it to be all just g generic positivity, or did you expect it to take hard stands that you would all agree with? Andrey: I definitely didn’t expect to agree with all of it. That would be ridiculous. That’s true. Seth: Like, nothing strongly? Andrey: There was a part of it that felt inappropriate to me, and I had a bit of a reaction to it. We will come to that. But these are our priors, so yes, I expected to disagree with a document this long. Seth: Was I was going in thinking that we were going to get a hundred pages of be good, do good things, don’t do bad things, and I would find it really hard to find anything I really disagreed with. So I would say I went in with a five percent chance that I would say something in here that makes me go, no, right? These are the this is Anthropic. This isn’t Grok. If you tell me the Grok constitution, you get different odds. Andrey: Yes, and I guess the other thing we should point out is that “disagree” here means something different than it does with most philosophical works. You can disagree with a philosophical work because of an argument, but here the disagreement is about whether Claude should be trained to respect this particular set of words. That is very different from an abstract philosophical text. Seth: So I guess maybe the distinction you’re drawing is you might think that a moral code is true, but think it is so impossibly lofty that it doesn’t make sense in a practical application, right? There’s a distinction between true and useful you’re making. Andrey: Or alternatively, I might be, an empiricist and I might think that we should just A/B test our way to ethics. Seth: Man, we are going to get you a lot of trolleys. We’ll figure this out once and for all. Okay. So Andrey’s pretty sure he’s going to disagree with it. I was pretty optimistic. The second prior we had ourselves think about before launching in was thinking about like, again, this main trade-off, which is people think about it in terms of, usefulness versus

    1h 28m
  8. Alex Imas - Demand Collapse, Bargaining with Machines, and Behavioral AI Economics

    Mar 23

    Alex Imas - Demand Collapse, Bargaining with Machines, and Behavioral AI Economics

    University of Chicago behavioral economist Alex Imas joins us for a conversation on AI, economic growth, behavioral economics, and the future of science. We discuss whether AI could ever lead to negative growth, why simple “automation means abundance” stories may miss important welfare effects, and how behavioral economics changes the way we think about satiation, meaning, and human preferences in an AI-rich world. Along the way, we cover AI bargaining agents, “Marxist AI,” discrimination, mechanistic interpretability, and why Alex thinks there may still be a large future for human-valued goods. Origins & Intellectual Background * Why Alex started Ghosts of Electricity and how Substack complements academic research * The Bob Dylan origin of the name and Alex’s path into behavioral economics AI and Economic Growth * Two models where AI could lead to negative growth * Demand collapse: heterogeneous MPCs, satiation, and the zero lower bound * Caves of Steel, dissaving, and the possibility of a high-tech, low-capital trap * Why GDP and welfare may diverge more in an AI economy Human Preferences & Motivation * Why wireheading and pure hedonic satiation may be the wrong model of human motivation * Whether economists can cleanly separate AI beliefs from AI preferences AI Agents & Interaction * Whether AI agents can develop stable “attitudes” through repeated interaction and memory * Agentic bargaining, prompt-dependent personas, and interaction heterogeneity * Guardian agents, aspirational preferences, and AI as a meta-rationality tool AI, Society, and Risk * AI and discrimination: why scalable auditing may be easier with models than with humans * Mosaic intelligence, systemic risk, and the dangers of AI sameness Science & Knowledge Production * The future of peer review, automated science, and human-valued goods Timestamps: (00:00) Introduction (01:35) Why Alex started a Substack (06:09) The meaning of “Ghosts of Electricity” (09:51) Can AI lead to negative growth? (19:54) Satiation, wireheading, and behavioral economics (26:44) “Caves of Steel,” automation, and dissaving (38:42) Plausibility, policy, and sovereign wealth funds (41:02) Marxist AI and whether agents can develop attitudes (47:23) Agentic bargaining and prompt-driven heterogeneity (54:46) Guardian agents and aspirational preferences (1:00:25) Separating beliefs from preferences in humans and AI (1:14:15) AI and discrimination (1:25:13) Peer review, science, and human-valued goods Transcript: Seth: Welcome to the Justified Posteriors podcast, the podcast that updates beliefs about the economics of AI and technology, sponsored by Revelio Labs. I’m Seth Benzel, setting my marginal propensity to consume at exactly the right level to drive the singularity, coming to you from Chapman University in sunny Southern California. Andrey: And I’m Andrey Fradkin, bargaining with the agents in exactly the right way. Coming to you from San Francisco, California. And today, we’re very excited to have Alex Imas, friend of the show and professor at the University of Chicago, join us. Alex, welcome to the show. Alex: Thank you. I am Alex Imas. I’m at the University of Chicago Booth School of Business, Economics and Applied AI groups and behavioral science. I don’t have a tagline because nobody asked me to come up with a tagline. Seth: You know where I’m at. Alex: But I have hair just small enough to not qualify for clown college, but just large enough to be weird. So that’s what I’m going with. Seth: Erratic professor level hair. That’s exactly the optimal. Andrey: That’s right. If we combined your hair and my beard, we could almost match Seth’s hair. Seth: You mean my majestic mane, Andrey. Why Start a Substack? [01:35 - 05:02] [00:01:35] Andrey: Well, let’s get started. Alex, you’re a professor. Why did you start a Substack? Alex: That’s a great question. I’ve been thinking about that a lot, both before I started a Substack, but also as I’m going through the Substack. If you notice, when I introduce my Substack on my X account, the tagline is, “Oh no, why did he start a Substack?” [00:02:03] It was preceded by me getting into AI from economics and behavioral science. I came into it what I view as kind of late. Many people were much earlier than I am, including you two. I came at it when ChatGPT was first released, 2023. But as I was getting more and more into AI as a research topic, the way that academic papers were — the process of writing them, getting feedback, the journal process, which is what I’d been doing for decades — it just didn’t seem like that format matched the speed with which the technology was moving, nor with the types of questions that I wanted to talk about in terms of doing the science. [00:03:05] If you’ve been around the block for a little bit— Seth: You be talking like you’re an old man, Alex. Come on. Alex: It’s gray hair. They made me dye it in clown college. [00:03:15] So the way that you would write an academic paper is, in some ways, defensively. You know after you’ve had a lot of feedback from journals, you know the type of referees you’re gonna get. So there’s an idea, which is what you’re excited about. You work through that idea, and then I would say 80% of the time you’re doing defense even before you submit it. And that 80%, I feel like you just can’t afford to do that when the science is moving so quickly. So for me, the Substack was a way to do research in a format that — and this is a skills problem for me probably. I think many other people write academic papers differently. But the way that I wrote academic papers, where each paper was like a seven, eight-year process, I needed a different way of doing things. Seth: Okay. So you see both of them being complementary, right? Here’s track A, fast track, here’s track B, slow track. Or are these substitutes, and eventually you’re gonna have to fully substitute into Substack land? Alex: No, these are complements. A lot of my Substack posts either have an academic paper being developed in real time or are the idea that this is a first shot in the bow, and then these will begin being developed into academic papers. For example, one Substack from early January came with a technical note, which is essentially an academic paper that I was starting to write, and I’ve been writing that paper since. A lot of the posts are in that vein. [00:05:01] Seth: Okay, and you’re not... That’s actually interesting because I think a lot of academics would be afraid of being scooped. If you put out the key idea first, but it’s seven years until you actually get the paper published. What about a young hungry grad student taking the idea and doing the legwork of all the defenses first? Is that something you worry about? Alex: Absolutely not. One of the nice things about being an old man is the fact that I don’t really care as much about being scooped. Like, not at all. I think especially in the space of AI, it genuinely feels like we’re in such an energizing, collaborative moment. And this is gonna change after we get replaced by robots, but right now it feels like — it must have felt like this in the ‘20s in physics. Ghosts of Electricity: Alex’s Origin Story [06:09 - 09:50] [00:06:09] Seth: So who’s Heisenberg? Which of us is Bohr? Who’s Einstein, obviously? Andrey: I think Alex has the hair that’s closest to Einstein, so we’ll give it to him. Seth: I was gonna say Einstein is the Acemoglu, ‘cause he was really right until he was really wrong. [laughs] Alex: No comment. Seth: Wow, no comment. Again, why Ghosts of Electricity? Why that title? Alex: Ghosts of Electricity — I’ve been waiting for somebody to ask me this question. First of all, it’s a Bob Dylan lyric. My favorite artist, one of several favorites, but he’s up there, is Bob Dylan. He influenced my life more than probably any other individual in my entire life. I was gonna go to medical school, and then I heard a bunch of Bob Dylan records and went nuts for a while. Seth: Wait, how did Bob Dylan make you an economist? Alex: Well, he made me not go to medical school. I was like, “Hey, actually, I can do anything I want now. I’m gonna go and paint paintings like this one in New York City.” And play music on the subway and all that stuff. And through that period, I discovered behavioral economics. Fell in love with behavioral economics and then decided to go to grad school. Bob Dylan kinda took me off of medical school. Seth: What did you... You picked a Dan Ariely book off the shelf? How does one fall in love with behavioral economics while being a painter in Brooklyn? Alex: I heard a Richard Thaler interview about Nudge. Seth: Wow. Talk about a full circle story. So Nudge got you into economics, and you ended up writing Nudge version two. Alex: Winner’s Curse two. Yes, that’s right. But it is actually Winner’s Curse two — there’s a first Winner’s Curse. Seth: Everyone buy Alex’s book. Okay. [00:08:13] Alex: So anyway, I got into economics that way. My favorite song by Bob Dylan is Visions of Joanna. My favorite lyric from that song is, “Ghosts of electricity howl in the bones of her face,” which I think is the greatest lyric of all time. And I love that line, but then I felt that line about ghosts of electricity really captures the way that I think about AI. LLMs and AI, the way that they’re trained now, are almost like ghosts of people who used to exist or in the past that have written something down that these agents have now learned. And electricity — it runs on electricity. Seth: I thought it was gonna be the other angle — that we’re hearkening back to the first industrial revolution, and the ghosts of the original industrial revolution are here to give us guidance and wisdom as we move forward. Alex: I like that too. Maybe on the next interview somebody asks me, I’m gonna give t

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About

Explorations into the economics of AI and innovation. Seth Benzell and Andrey Fradkin discuss academic papers and essays at the intersection of economics and technology. empiricrafting.substack.com

Information

  • Creator
    Seth Benzell and Andrey Fradkin
  • Years Active
    2024 - 2026
  • Episodes
    41
  • Rating
    Explicit
  • Copyright
    © Andrey Fradkin
  • Show Website
    Justified Posteriors

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