What Markets Tell Us About AI Timelines (with Basil Halperin)

Summary

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Future of Life Institute Podcast

Episode: What Markets Tell Us About AI Timelines (with Basil Halperin)

Date: September 1, 2025
Host: Gus Docker (FLI)
Guest: Basil Halperin (Economist, University of Virginia)

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Overview

This episode explores what economic indicators—particularly real interest rates and other market signals—may reveal about timelines for transformative artificial intelligence (AI). Basil Halperin, economist and author of a prominent essay on AI timelines and market efficiency, joins host Gus Docker to break down how financial markets process existential and economic risk from advanced AI, why interest rates are an especially useful signal for predicting transformative AI scenarios, and what the (frankly, surprising) absence of such signals suggests about AI futures.

Halperin provides rigorous, accessible explanations of core economic mechanisms, market hypotheses, and why explosive growth or catastrophic risk from AI would, in theory, already be visible in forward-looking economic data. Together, they also probe the limitations of using market indicators for AI forecasting and discuss what economic theory can—and can’t—tell us about the future of advanced AI.

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Key Discussion Points & Insights

1. How Economic Markets Reflect (or Don't Reflect) the Prospect of Transformative AI

(Highlights: 00:00–14:00, 40:53–44:50)

• Markets Aggregate Beliefs:
  Financial markets, in theory, incorporate all available information—including the probability and impact of economic shocks like transformative AI. Interest rates, stock prices, and other prices should reflect not just average beliefs, but the beliefs of those with enough capital (“the marginal trader”) to move markets.

• Interest Rates and AI:
  If markets expected either:

  • Aligned Advanced AI (driving explosive economic growth), or
  • Unaligned AI (posing existential risk of human extinction),
    long-term real interest rates should already be significantly elevated.

  “If markets were expecting transformative AI to be coming in the next, say, 30 years…either of those possibilities would result in high long term real interest rates. And…we don’t see particularly high real interest rates.” — Basil Halperin [02:02]

• Why Interest Rates?
  They reflect forward-looking savings/consumption demand and respond to expectations about future prosperity (or ruin). This makes them especially suitable for AI prediction, compared to wages (which are more backward-looking) or equities (which behave differently under aligned/unpaired AI outcomes).

  “Interest rates are useful because the effect of aligned AI and unaligned AI goes in the same direction on interest rates, unlike equities or other asset prices.” — Basil Halperin [40:53]

2. The Efficient Market Hypothesis (EMH) as It Applies to AI

(Highlights: 05:05–13:13)

• EMH Basics:
  Markets incorporate all public information. If traders believed transformative AI (with massive upside or risk) was imminent and underpriced, they’d act—shifting prices until there’s no “free lunch” left.

  “Even if you have some insight, it’s a good benchmark to trust markets to get things approximately right…Markets are good information aggregators.” — Basil Halperin [07:55]

• Limits to Arbitrage:
  EMH is imperfect, especially for long-term, low-probability, or “weird” future events. When payoffs are far away, or capital is limited, mispricings can persist.

  “This no arbitrage…is harder for things that take a long time to pay off…Theoretical and empirical evidence [shows]…limits to arbitrage…are more severe with arbitrages that take a longer time.” — Basil Halperin [10:58]

3. Is the “AI Future” Priced In?

(Highlights: 16:08–24:33)

• Diffusion of AI Awareness:
  Information about AI progress is gradually spreading through Wall Street and finance. Some hedge funds (e.g., Leopold Aschenbrenner’s Situational Awareness) are explicitly trading on short AI timelines and backing this thesis financially.

• Recent Market Moves:
  Stocks like Nvidia have skyrocketed since ChatGPT, but there’s little evidence that long-term real interest rates (after controlling for inflation expectations) are moving in a way that matches what a “transformative AI is near” world would predict.

• Who Moves Markets?
  Markets reflect beliefs of those making marginal trades, often the most informed/capitalized, not the “average” market participant.

4. Why Stock Prices Are Harder to Interpret for AI Timelines

(Highlights: 24:33–28:36)

• Aligned vs. Unaligned AI:
  In an aligned scenario, some stocks (e.g., Nvidia, MSFT) could soar; but, an unaligned AGI would wipe out all stocks.
  “Aligned advanced AI would plausibly raise profits…unaligned AI would…exterminate humanity.” — Basil Halperin [24:50]

• Public vs. Private Companies:
  The most transformative AI tech companies may not be public, making the signal murkier.

• Disconnect with Underlying Wealth:
  Even with explosive economic growth, company profits might not directly follow if there are e.g., profit cap agreements (like OpenAI’s) or nationalizations.

• Discounting:
  The effect of growth is tempered by discounting future cash flows at higher interest rates, making the present value of future windfalls less obvious.

5. Core Objections: New Goods, Bottlenecks & Inequality

(Highlights: 28:36–37:30, 54:35–60:00)

• “New Goods” Objection:
  Might people want to save more (lowering today’s rates) if future goods are unimaginably better?
  Halperin: Good objection, but historically higher growth associates with higher rates; AI would have to be radically different and new goods alone aren't enough.

• Bottlenecks or Concentrated Gains:
  If only the ultra-rich benefit (e.g., all gains go to Sam Altman), mass precautionary saving or inequality could blunt the signal in rates for a time.

  “It’s hard to get away from the idea that there will be skyrocketing inequality in a truly transformative AI scenario. But…inequality might still be consistent with everyone being better off.” — Basil Halperin [58:54]

• Political Economy and Redistribution:
  The degree of redistribution will affect how broad-based the economic effects of AI are and, therefore, how visible they are in aggregate indicators like interest rates.

6. Alternative Indicators: Wages, Labor Share, Capex, and Beyond

(Highlights: 61:15–65:53)

• Other Economic Indicators:
  While interest rates are prized for forward-looking qualities, stock prices, capital expenditures, labor share of income, and unemployment rates also provide useful clues, especially broken out by sector.

  “There’s a lot of capital expenditure. I just moved to Virginia. Basically the ground’s covered in wires from all the data centers.” — Basil Halperin [65:53]

• Potential “Moonshot” Metrics:

  • Track exact tasks AI can do (from government ONET database, regularly updated)
  • Decompose which tasks are being automated/added/lost over time

• Difficulty of Benchmarking:
  Economic impact lags behind benchmark achievement; actual workflows and real-world tasks are messier, require coordination, and take time to reengineer.

7. Why Impressive Benchmarks Haven’t (Yet) Moved the Economic Needle

(Highlights: 72:33–80:09)

• Diffusion & Workflow Lags:
  Key point: Economic effects of a new tech are often delayed by adoption lags, workflow inertia, and slow reorganization of production.

• Benchmarks vs. Real Work:
  Many AI benchmarks are narrow and not representative of the complicated, coordination-heavy, or ongoing tasks that drive the real economy.

  “Benchmarks are these narrowly defined tasks that don’t really capture the breadth of what a worker does every day. Like work is pretty complicated.” — Basil Halperin [77:38]

8. Tasks, “Horizons,” and the Limits of Automation

(Highlights: 80:09–85:16)

• Short vs. Long-Horizon Tasks:
  LLMs may be great at short, well-scoped tasks, but struggle with anything requiring sustained, error-correcting work over weeks/months.

• Chaining Tasks:
  Success in a many-minute or month-long task is not just “do more short tasks,” because errors multiply.

9. Open Questions in Economics of AI

(Highlights: 85:16–94:40)

• Where Are the Bottlenecks?

  • Will AI cause explosive growth everywhere, or will energy, land, or hard-to-automate sectors bottleneck expansion?

• Recursive Self-Improvement and the “Singularity” Feedback Loop:

  • Will automating AI research produce a runaway “intelligence explosion,” or will diminishing returns keep overall growth constrained?
  • The empirical question: Are “ideas getting harder to find” or easier in AI research?

• Relevance of Microeconomic Theory:

  • Mechanism design, agent theory, stress testing, insurance models—many econ ideas could help analyze (or govern) advanced AI.

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Notable Quotes & Memorable Moments

• “If markets were expecting transformative AI…either of those possibilities would result in high long term real interest rates. And…we don’t see particularly high real interest rates.”
  — Basil Halperin [02:02]

• “Markets are good information aggregators, particularly forward looking financial markets.”
  — Basil Halperin [07:55]

• “The effect of aligned AI and unaligned AI goes in the same direction on interest rates, unlike equities or other asset prices.”
  — Basil Halperin [40:53]

• “Benchmarks…are these narrowly defined tasks that don’t really capture the breadth of what a worker does every day. Like work is pretty complicated.”
  — Basil Halperin [77:38]

• “It’s hard to get away from the idea that there will be skyrocketing inequality in a truly transformative AI scenario. But…inequality might still be consistent with everyone being better off.”
  — Basil Halperin [58:54]

• “If a model can do a one minute task, why can’t it do two one minute tasks in a row?…If you can do a one minute task with 50% probability…two will be 25%…That’s why models are worse at longer horizon tasks.”
  — Basil Halperin [81:08]

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Timestamps for Key Segments

• What are real interest rates and why do they matter for AI?
  [02:02–05:05]
• Efficient Market Hypothesis explained
  [05:05–10:58]
• Limits of market efficiency for long-horizon bets
  [10:58–13:13]
• How much is the AI risk priced into markets today?
  [16:08–24:33]
• Why stock prices aren’t simple signals for transformative AI
  [24:33–28:36]
• New goods & diminishing returns arguments
  [28:36–37:30]
• Inequality, redistribution, and bottleneck scenarios
  [54:35–60:00]
• Other indicators worth tracking (capex, labor share, etc.)
  [61:15–65:53]
• Shortcomings of AI benchmarks for measuring real economic effects
  [72:33–80:09]
• Task horizon, error accumulation, and limits of current automation
  [80:09–85:16]
• Open research questions in the economics of AI
  [85:16–94:40]

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Tone & Style

The conversation is rigorous but accessible, engaging both for technically literate listeners and for those newer to economics or AI policy. Halperin brings a blend of skepticism, humility, and data-driven reasoning, frequently noting limitations and flagging compelling “counterarguments” to his own position.

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Final Thoughts

The episode makes a persuasive case: If truly transformative (good or bad) AI is imminent and broadly believed, forward-looking economic indicators should already reflect it. The absence of massive moves in interest rates, especially, is a sign that markets either don’t believe in near-term AI takeoff, or that key information is too concentrated, secret, or misprocessed for prices to budge. Still, as Halperin admits, there are real limits: rapid shifts, hidden bottlenecks, and sectors or actors who could “soak up” most benefits or risks unseen. Ultimately, economic signals are a crucial—but not foolproof—tool for separating signal from hype in the AI forecasting debate.

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For researchers, policymakers, and investors hoping for actionable indicators of AI timelines, Halperin recommends closely watching real interest rates, capital expenditures of tech giants, and evolving wage/labor data—while remaining cautious about the limitations and inherent lag of all economic signals.

Transcript

[Basil Halperin]

So within macro, I think the big question is, will AI lead to a speed up in economic growth or will it get bottlenecked by certain sectors or areas? The effect of aligned AI and unaligned AI goes in the same direction on interest rates, unlike equities or other asset prices. It's hard to get away from the idea that there will be skyrocketing inequality in a truly transformative AI scenario. But skyrocketing inequality might still be consistent with everyone being better off. Coordination with other people is not something that AIs as they exist today in helpful, harmless chatbots can really help with. It's very plausible that benchmarks are these narrowly defined tasks that don't really capture the breadth of what a worker does every day.

[Gus Docker]

Welcome to the Future of Life Institute podcast. My name is Gus Docker and I'm here with Basil Halperin. Basil, welcome to the podcast.

[Basil Halperin]

Thanks, Gus, for inviting me on. Excited to be here.

[Gus Docker]

All right, could you give a little background on yourself to start with?

[Basil Halperin]

Yeah. So I just joined the University of Virginia as an assistant professor in the economics department after finishing a postdoc at Stanford. My background is that I did a PhD at MIT. In past lives I worked as a data scientist at Uber and maybe relevant to today's conversation, my first job out of college was at a quant hedge fund researching and trading inflation linked bonds.

[Gus Docker]

Interesting. Perfect, right? So the theme for today's conversation is actually the intersection of economics and AI and specifically what we can learn about AI risk and AI timelines from economic indicators. We could say you have this fantastic essay on AI timelines and the efficient market hypothesis and interest rates, what we can learn from interest rates when we're trying to predict when we might get advanced AI. So I'll link that paper in the show notes, but could you sketch out the basic idea here or the basic conundrum?

[Basil Halperin]

Yeah. So this paper, which was joint with, or is joint with Trevor Chow and Zach Mazlish. The argument in one sentence is that if markets were expecting transformative AI to be coming in the next, say 30 years, either aligned AI going to rapidly accelerate economic growth, or unaligned AI that was going to lead to existential risk of the kinds that I know your listeners are very familiar with, either of those possibilities would result in high long term real interest rates. And looking at markets, looking at real interest rates today, we don't see particularly high real interest rates. So I break that down, maybe starting with what, what are real interest rates?

[Gus Docker]

Yeah, that. That would be useful, I think.

[Basil Halperin]

Yeah. So when you open up the Wall Street Journal, the Financial Times, interest rates are not usually, if things are going well, the sort of thing on the front page you see stock prices. Interest rates though, are this very important price in the economy. They affect a lot of other prices. So if you do see interest rates in the newspaper, you'll see a nominal interest rate. So when the US Government borrows money, it typically issues these nominal loans that pay back in dollar terms. So if it issues a 10 year bond at I think the current rate, something like 1.6% last I checked, that means that in 10 years it has to pay back the amount of the loan plus 1.6% interest on that loan in dollar terms. Real interest rates are different from those nominal interest rates in that they adjust for inflation that occurred over that period. So they're sort of the real thing, so to speak. So why look at real interest rates to think about transformative AI well, interest rates clear the market. The way economists think about real interest rates is that they clear the market in the supply and demand for saving and borrowing. So if I want to borrow, then I need to take out a loan. The real price, the real cost to me of that loan is the real interest rate. Meanwhile, what does the lender get in return for lending to me? They get the real interest rate. So if I really want to borrow, if everyone in the economy really wants to borrow, that would push up interest rates in order for the markets to clear. How does that come back to AI? Well, if we all expect to be super rich next year, if I'm going to earn a lot more money next year than I am this year, there's much less reason for me to save today. That lower supply of savings, so to speak, speaking a bit loosely, pushes up interest rates. Similarly, if you all expect to be dead next year, no reason to save today, that would push up interest rates. That's the argument in an abstract level, can get into more concrete things like do we see people on AI Twitter talking about taking out large long term loans, talking about not investing in their 401k. We can get into that. That's the high level abstract argument.

[Gus Docker]

Yeah, and this rests on the market for interest or. Yeah, the market for, for loans and the market that sets the price of interest, the market that sets interest rates being efficient in general. So maybe we could explain the efficient market hypothesis and what it is that we are. So if we are saying that the market is wrong, what does that actually mean?

[Basil Halperin]

Yeah, so the efficient market hypothesis, which is sort of what we're leaning on or hinting at in our argument is this idea that financial markets reflect all available information. So the price of General Motors stock reflects all available information in the world, so to speak, about future profits. That's because stocks reflect expectations about future profits because those are paid out as dividends to shareholders of equities. Similarly with interest rates, I just explained how interest rates reflect the supply and demand for savings. Therefore, we would hope that if markets are financially informationally efficient, they will reflect correctly the market participant beliefs about future consumption, savings decisions about future economic growth. The idea for markets being efficient is sort of essentially supply and demand, essentially just no arbitrage that if you knew with certainty that General Motors was going to have really high profits next year and that was not reflected in stock prices, you'd immediately want to go out, buy a bunch of GM stock, hold on to that, earn the dividends next year. That's harder if you're unsure. That's harder if you sort of don't have the capital to invest in GM enough to move prices up to the correct level. But that's, that's the sort of basic idea underlying this. Markets are good information aggregators, particularly forward looking financial markets.

[Gus Docker]

Yeah, the basic idea here is something like if you think, you know, if you think you have a piece of information that the market is not incorporated, you then have an incentive to, to use that information to try to earn money. And because that incentive is quite powerful and because there's so many people looking to earn money and to price assets correctly, the assets tend to reflect all of the information. Also because people now have an incentive to seek out new information. So yeah, I think just a quick point here. Why is it that when my uncle, say, hears about ChatGPT and invests in Apple or Nvidia or some kind of tech stock and he beats the market. Why isn't that a refutation of the efficient market hypothesis?

[Basil Halperin]

Yeah, so I think even if you're someone who thinks I have information about one asset to buy to beat the market, you're still a believer in sort of market efficiency for 99.9% of assets, 99.9% of the time. So even if you have some insight, it's a good benchmark to trust markets to get things approximately right, sort of as this sort of outside view perspective, there are a number of reasons why someone might be able to beat the market if you just look at historical data. Number one, there might be selection bias. No one talks about all the money they lost on bad cryptocurrencies. In the late 2010s they talk about all the money they made in bitcoin. Another reason is that you can earn excess returns in financial markets by taking excess risk. So for example, investing in bonds has historically a very low return, whereas stocks have a higher average return. Does that mean that there's a market failure here or sort of market inefficiency, that you can always just invest in stocks and get like a 7% annual real return historically something like that, versus like a 2% roughly historical annual real return on bonds? No, that's not a missing arbitrage there. It's that stocks are riskier. If you invest in stocks, sometimes stocks go down a lot and you would like to avoid those big drawdowns ex ante beforehand. Investing investors need compensation in order to bear that risk. Maybe, for example, you're most likely to be unemployed right in the middle of a recession. And those are also periods when stock markets are down. This correlation between when you want money the most, when your margin utility of consumption is highest, in econ speak, that correlation with stock market returns means that stocks are risky and is one way that people can beat the market. There also are genuinely people who have information that hasn't been incorporated into financial markets yet, and they are getting compensated for bringing that information to the market. So sometimes I think it might be better to talk about market efficiency in terms of, is the market for information perfectly competitive? Are people getting compensated for going out and doing the costly work of acquiring information, processing information and bringing that information, incorporating it into prices?

[Gus Docker]

Yeah, yeah, I guess. One, one question here is to ask whether markets are actually good at pricing in so to speak, the possibility of either extreme growth from AI or existential risk from AI. These could be very low probability events. These events could be far out. And, and what do we know about how markets price in such information or such possibilities?

[Basil Halperin]

Yeah, so I think there are two lenses I would think about this from. So one is that a lot of important asset prices are incorporating expectations or require incorporating expectations about things very far in the future. So the average duration of the stock market, I don't have that number off the top of my head, unfortunately, but it's certainly greater than 10 years, maybe, maybe even more than 20. That is to say that like the average cash flow, roughly speaking of stocks, is like at least 10 or 20 years out in the future. So markets, market participants have to be doing far, far, far future, or you know, maybe not far future by the standards of the FLI podcast, but far future by the standards of like Contemporary media discourse, forecasting. So that's one thing. A second thing though to say is that, yeah, there is a lot of evidence that markets are worse when it comes to things further in the future. And that's because this no arbitrage that I emphasized as important for financial market efficiency is harder for things that take a long time to pay off. So if you've paid attention to prediction markets around elections, this is something that you'll have seen where four years before a US Presidential election, there'll be a lot of crazy odds on people who are never going to win the election. And the reason those odds can persist is because you would have to hold on to a short position against some crazy person who's never going to win the presidency for four years. And there's a high opportunity cost of holding onto that trade because you could be doing other things with your money over that time, or because there's random fluctuations in the market over that time and at some point you could get blown out in general, Limits to arbitrage. This is the technical term for things that can prevent arbitrage errors from correcting market mispricings. Limits to arbitrage. There's a good amount of theoretical and empirical evidence that this is more severe with arbitrages that take a longer time to pay off.

[Gus Docker]

I mean, so you mentioned your background in finance, right? I know some people who have profited tremendously from the COVID pandemic, from Nvidia, from predicting the Trump tariffs and so on. And you probably know many more of these people than I do. So it seems that there are pockets of people with special knowledge, people who are extra super special smart, say that know something that, that others don't. Could it be the case that there are insiders that know something about AI progress that others don't, or that the broader market is not incorporating?

[Basil Halperin]

It absolutely could be the case. I, I am still wary of extrapolating too much from anecdotes because again, no one hears about the anecdotes where my first investment, funnily enough, when I was like 14, for my bar mitzvah, my dad gave me a few hundred dollars play money to invest in the stock market so I could learn how to Invest. I, being 14 years old, had no idea what to invest in. And so I just go to him, I'm like, what should I do with this money? He's like, well, I read this article in the newspaper that said TSMC is a good investment. This was 2008 or 2007. And so I put the few hundred dollars in TSMC and it like went up a little bit at first. And then the 2008 financial crisis happens. Stock market plunges. I hold onto it for a few months, expecting a recovery. He keeps telling me to hold on. I'm like, no, I got to sell out. So I looked at this a few years ago. I sold out the week that the market bottomed. So that, that's, that's the average investor for you, me, at age 15 or whatever. Um, and of course, if I'd held on to tsmc, today would probably be doing a lot better. Um, that's so. So I think that really is an important point that anecdotes are hard to extrapolate from. That said, a second point is that, again, I think the right way to think about what we should expect in markets, in financial markets, is that you should be compensated for doing real work. So if you spend 24 hours a day reading AI Twitter, spending time on less Wrong, et cetera, et cetera, reading papers on Arxiv, maybe most plausibly really doing the hard work of trying to understand what's going on in the AI industry, then you do deserve compensation for that. And we should expect that you will achieve some alpha, reflecting the opportunity cost of your time. That's one important point. The third point just is there can be instances where someone has to go collect the information to trade and have that reflected in financial markets. So there can be instances where you happen to be someone who collects some alpha, some excess returns, because you had the information first. That is possible, yeah.

[Gus Docker]

Do you have a good sense of whether ideas about either explosive growth or existential risk from AI has spread into kind of mainstream Wall street institutions? Is it the case that the highly informed people there with all of the compute and all of the data and so on, have they heard the arguments and rejected them, or have they perhaps not heard the arguments?

[Basil Halperin]

So the filtering process, the process of information diffusion is happening, happening pretty quickly. Still ongoing is my read. I haven't seen like a definitive survey. So some things I can say are like, we wrote the initial version of this essay published in January 2023, so two months or six weeks after ChatGPT was released. Since that time, Nvidia has gone up like a thousand percent. Microsoft has doubled or tripled or something. Real interest rates kind of interestingly at the third year horizon at least, have gone up a percentage point. And maybe I should say when I first started thinking about this issue and debating with very bullish friends in San Francisco, whether transformative AI was coming soon at that point, real interest rates. This is like the depths of COVID 2021, 2020. Real interest rates in the US, in the UK, in the developed world were negative. So if the US government issued a 30 year bond for $100 after 30 years it would only have to pay off like 98 or $99. It's like a negative 1% interest rate, negative 1.5%. Over the succeeding four years, real interest rates rose by 2 to 3 percentage points, which is kind of a large move. That's probably not because of AI. It's probably because central banks around the world have been raising interest rates to fight inflation. That's sort of a separate issue. We can get into short run macro and inflation if we want. I love that stuff. Maybe less relevant for your listeners, but definitely kind of interesting that interest rates rose over this time and have continued to rise further since we published this post. So that's some context and additional context to say or a reason to bring that up is that I think this market space perspective was particularly useful a few years ago, prior to ChatGPT, because there was such a smaller fraction of the world thinking about these issues, such a smaller amount of information processing by humans trying to argue how long until transformative AI develops. And so having this look at interest rates was particularly useful then. Still useful today, but even more so then. Anyway, coming back to your question about is this information diffusing through markets or through the financial industry? So certainly two or three years ago there are very few people thinking about it today there are more. So famously Leopold Aschen Brenner, who is actually sort of a conversation with him inspired the argument with him, debate with him inspired this whole essay, has since launched a hedge fund, Situational Awareness had an essay last summer that made a big splash and just in the Wall Street Journal this week has reported that his fund with Carl Schulman made a ton of money trading on these ideas. Leopold, in his interview with Dwarkesh Patel directly cited my work with Zach and Trevor on interest rates, saying yes, he expects interest rates to rise eventually as bond market traders wake up and he has like $1.5 billion in his fund is reported. So 1.5 billion in situational awareness alone is maybe a niche part of the market. Maybe that'll grow. I guess 47% return that was reported is already growing his fund. But then the industry more broadly. There's certainly like Goldman Sachs or other investment bank reports, bare minimum thinking about how quickly is the data center industry growing, thinking about what could the impact of AI be on long run growth? The latest numbers I've seen are that if you look across investment banks consultancies, on average 10 year growth forecasts are more or less unchanged. But there are individual forecasters in the financial industry who are much more bullish and much more aligned with the man on the street, the woman on the street in San Francisco, so to speak.

[Gus Docker]

I mean, how would we even know? You discussed an increase in interest rates since 2020, but this has probably very little to do with AI. If we saw interest rates increase, how would we know why they were increasing?

[Basil Halperin]

Yeah, so I don't think there's a great definitive way, but there are some sort of consistency checks we can look at. So I mean, one thing you can do is sort of build a full model of interest rates, trusting that you're able to forecast the path of interest rates well, you understand the determinants of interest rates. Well, I'm skeptical that macro financial models have that much predictive power in general. Hence why I want to look at markets and look for giant changes in interest rates, which is what this transformative AI perspective would predict. That said, you could still look at other prices, other things in the economy to try to understand is the change in interest rates caused by AI expectations. So for example, the effect of transformative AI on stock prices is plausibly harder to interpret for various reasons we can get into, but it's very plausible that certain stocks would benefit strongly from expectations of transformative AI, for example Nvidia, tsmc. And indeed we have seen those go up a bunch over the last few years. So that's kind of interesting. I should say that's in the case of aligned transformative AI. Unaligned transformative AI would obviously wipe out not just Taiwan, the United States, but the entire world and send those stock prices to zero at some point. So that only helps us corroborate an increase in interest rates due to transform of AI or sorry, aligned transform of AI. You can also look at these surveys of financial market participants, of bank analysts and see if their expectations are changing. That said, and actually this is a very important point that I think a lot of people misunderstand. Financial market prices do not reflect consensus views, consensus expectations necessarily. They're not meant to track the average level of expectations like a forecasting aggregator like Metaculus more or less tracks average expectations of participants. Financial market prices and all prices reflect the financial market prices reflect the marginal unit of capital, so to speak. They reflect the views of the marginal trader, the marginal trader being the person who is just at the indifference point between buying or selling this asset. So it's their beliefs that matter. So if there's someone who has very strong beliefs about AI, there will be the person who's disagreeing with others and is trading the asset. Put loosely, and there are lots of good reasons to believe that that marginal trader is more informed than the average person. Because if you have particularly strong beliefs, then you must have, or it's plausible that you have better reasons for those beliefs. The people who have spent 10,000 hours reading the Bioinkos report or whatever maybe are more willing to make bets in a certain direction and maybe they also.

[Gus Docker]

Have access to more capital. I think, unfortunately there are many amateur traders that have strong beliefs and not a lot to kind of back up those beliefs, but maybe those people don't have a lot of capital and so they can't really move the market a lot.

[Basil Halperin]

Totally, totally. So now I've lost track of where we were on the question, but. Oh yes, I was explaining that we could look at surveys of financial market participants to see if their beliefs have changed. But that said, surveys are not definitive because the average belief does not determine the market price. The marginal, the. The belief that the marginal trader does.

[Gus Docker]

Yeah, yeah. Maybe you can talk a bit about why it's not straightforward to interpret stock prices or equity prices. It's not, it's not just that. We can, we can look at. Yeah. Why is it that stock prices aren't a perfect indicator of AI timelines?

[Basil Halperin]

Yeah, so there are a couple reasons, and I'll say that stock prices are not necessarily uninformative. It just. You might need to make additional assumptions to interpret them. So one thing already discussed is the unaligned versus aligned distinction where aligned advanced AI would plausibly raise profits of companies a lot, push up stock prices, whereas unaligned AI would push profits down by exterminating humanity. That's one issue. The second issue is that you can only invest in publicly traded companies. For example, OpenAI is not publicly traded, of course. Microsoft has a 49% share, I believe. So if you want to look at stock prices to interpret the effects of AI, maybe it'd show up in Microsoft. But other companies, maybe this is less the case. A related issue is that it's not obvious that advanced AI would indeed, in the align case lead to higher profits. So OpenAI, at least historically, has had this $100 billion profit cap promising that any profits above 100 billion would be rebated to humanity or something like that. And so advanced AI might not even lead to a higher valuation of OpenAI. Again, historically, that seems to be in process of being changed. Or there's been talk of this windfall clause that if, just like OpenAI's $100 billion profit cap, if AI really leads to some massive windfall, then companies could commit to give that windfall to humanity or something. Or coming back to Leopold Aschen Prenner's work, perhaps there's been talk of nationalization of companies and then these companies wouldn't earn profits. Final reason why stocks are hard to interpret, potentially that's the most economically interesting, is that higher growth rates for the economy, higher expected growth rates for the economy, that is, do not necessarily lead to higher stock prices. The reason for this is kind of subtle. So stock prices reflect the present discounted value of future dividends, the present discounted value of future profits. That's the way that's the most successful framework for thinking about equity prices.

[Gus Docker]

And when you, when you say discounted, what do you mean?

[Basil Halperin]

Yeah. So if you have a company that exists, staying tomorrow, it's going to exist that the stock price is going to reflect the value of any dividends it pays out to you today and the value of any dividends it pays out to you tomorrow. But not just the sum of those two. You discount the value of the profits it pays out to you tomorrow by the interest rate that you could earn in the meantime by putting your money in the bank, earning some interest rate. So exactly to your point, stocks reflecting the present discounted value of future profits means that although transformative AI could push up future profits, as the whole thesis of our blog post and paper argue, it will also raise interest rates. And so it depends on will future profits go up by more then future interest rates go up. That in turn depends on this very important parameter in economics, the elasticity of intertemporal substitution, which reflects how people trade off consumption today versus consumption tomorrow. I can go into more of that. It depends on whether this elasticity is above or below 1. And the literature is not settled on that. Famously or sort of famously, macroeconomists think it's below one. Financial economists think it's above one. The estimates in our paper suggest below one. But it's a hard parameter to estimate.

[Gus Docker]

This is actually kind of maybe an interesting objection to your thesis. The argument or the objection goes something like this. So if we have advanced AI, we might expect to have products and services that are of much higher quality, say in five years than we have today. And so you would Expect people just to save money and thereby driving interest rates lower because, say, you can. You can buy an amazing virtual reality headset in five years, or you can buy medicine that can extend your lifespan in five years and so on. And so this might drive saving and thereby lower interest rates. Is this, is this incorporated into your argument? Or how would you think about this?

[Basil Halperin]

Yeah, so basically, I think this is one of the two or three best arguments against the whole thesis. But I still, in my best guess, think it's not powerful enough to outweigh all the other factors.

[Gus Docker]

Do you mind restating perhaps the best version of the argument that I just tried to give?

[Basil Halperin]

Yeah, totally. And I think this will give a good opportunity to get more technical on the argument that we're making. So the precise reason why higher future growth, traditionally we think of that as leading to higher interest rates today, is that if we're going to be rich in the future, the marginal utility of consumption in the future is lower marginal utility of consumption, meaning that a dollar in the future is worth less to me than is today because I have diminishing marginal utility, where diminishing marginal utility means that going from earning an income of $100 to $1,000 is a bigger gain than going from a million dollars to a million and $900. Because if I only have $100 a month, going to $1,000 a month means that I can get more basic necessities. If I go from a million dollars a month to a million and $900 a month, that doesn't. That doesn't do that much to me.

[Gus Docker]

Yeah.

[Basil Halperin]

So if money is less valuable in the richer future because of diminishing marginal utility, there's again the argument that there's less reason to save in the future. I'd rather have that dollar today. So this counter argument about new goods in the future, I think of Phil Trammell as having prominently argued for this. It's really good points, a really understudied point in general in economics. He has ongoing work with Chad Jones, I think, to flesh out these thoughts. And I think it's a really interesting conceptual idea. The argument is exactly as you said, Gus, that if we're going to have these amazing goods in the future that don't exist today, then potentially that dollar in the future still is worth more than it is today, even if I'm going to be rich in the future, because there's not that much I can do with the dollar today. The example that Phil gives is that if you're a member of Genghis Khan's Golden Hoard. If you had an extra dollar, like what are you going to do? Buy another horse or something? Versus today, if you have an extra dollar, there's like all this cool stuff. You can buy something like that. So there's not this diminishing marginal utility because of these new goods. And that seems very plausible. The reason why I don't think it overturns the argument is a couple of things or two things. One is that if you look historically looking at economic growth, and this is what we do in the paper, you do just see this strong positive relationship between higher growth and higher interest rates. We have some nice data that I think is sort of a contribution to normal macroeconomic literature away from AI on this relationship between RNG, real interest rates and growth, showing that historically, across 60 different countries, a number of decades higher growth and higher interest rates just really are pretty correlated. So that's one thing that historically the invention of new goods has not outweighed the traditional diminishing margin utility mechanism. The second is it's plausible that AI will be different, that AI will lead to all these new goods. That again, all your listeners are very familiar with life extension, et cetera. Amazing things I'd love to have access to. That's all true. That does give a motivation to save for the future, depressing interest rates. At the same time, we'll still be super rich because we'll have transformative AI leading to rapid economic growth. And so I will be rich enough to hopefully afford life extension, so on.

[Gus Docker]

Even if you're not saving, you mean you will still be rich enough to basically afford all of the goods that are available in this potentially amazing future?

[Basil Halperin]

Yeah, that's what I think is really most plausible. But all that said, I think this is like one of the two or three best arguments against the whole thesis. But overall, not convinced yet.

[Gus Docker]

Does AI change anything here? So you mentioned that this, this is not a phenomena we've seen in the past, but maybe AI is different in that you would expect these, these great products and services sooner just because the rate of innovation could be higher. So maybe if you, if you don't have to wait 30 years, you have to wait three years in order to, to enjoy better goods and services. You are more inclined to actually save the money, or it is more rational perhaps for you to save the money.

[Basil Halperin]

So what I would say is that it would be great if there was more historical work looking at how much of growth came from new varieties of goods versus more of horses in the golden horde. Someone should just do that decomposition. And to my knowledge, there's not any definitive work on that or really much work on that at all. Then you could think about how could the future be different, affected by the channels you describe. Like maybe AI in particular is sort of biased towards new varieties rather than more of the same. And again, I think that's extremely plausible. Is it enough to overcome the fact that in this transformative AI world that we're considering, we're having 30% overall income growth? I don't know. 30% annual growth is a lot.

[Gus Docker]

Yeah. How much is that actually? You know, maybe you could put that in perspective for our listeners because, you know, maybe the difference between 3% and 30% doesn't sound incredible, but it really is. So maybe you could, you could say something about how extreme a 30% yearly growth rate in the economy might be.

[Basil Halperin]

Yeah, great question. So to expand on that, the, the transformative AI align scenario we're considering is 30% growth. The reason for that is that's roughly a 10x increase in GDP growth compared to what we see today, which is about 3%, as you say. That number comes from the existing literature, Tom Davidson's work. I think you've had Tom on the show. Yeah, we are looking historically, prior to the Industrial revolution, something like 0.3% GDP growth is what we saw. So there was an order of magnitude increase in GDP growth from before to after the Industrial Revolution, maybe similarly around the agricultural revolution. And so maybe in AI, the AI world we love talking about ooms orders of magnitude, maybe there'll be another order of magnitude increase in growth around transformed AI. So 30% growth, that's a lot, a lot more than 3% that we see on average today and even a lot more than like the really fast growth episodes that you might think of in history. So China had this astounding sustained growth episode from the reform and opening up period to around 2010. Things have slowed down a bit since then. Still remarkably fast, but their remarkable growth rate was sustained. Three decades of 10% annual growth, 10% versus 30%. Still a large gap to put 30% in perspective. I don't know, you can use Moore's Law as one benchmark perhaps, where Moore's Law we think of as this astoundingly fast thing, computing power, doubling every year historically. So 30% isn't quite as fast as Moore's Law, but it's like nearly there. Moore's law is like 40%, 44, 42% annual growth, something like that historically. So 30% would be like the economy as a whole is growing as fast as the incredible progress, nearly as fast as incredible progress we've seen in computing systems over the last 60 years.

[Gus Docker]

Yeah, yeah. So life would change rapidly and kind of tremendously under 30% growth rates.

[Basil Halperin]

Yes. So if we have 2 or 3% growth historically in the developed world, in the post war era, then that's like what, a 36 year doubling time for incomes. So once a generation your income doubles.

[Gus Docker]

Yeah.

[Basil Halperin]

30% growth means every two, two and a half years your income's doubling. A totally different world.

[Gus Docker]

Yeah, yeah, totally. It's actually surprising to me to go back to something you mentioned earlier, that we don't have more research on the question of whether most growth comes from kind of new inventions or most growth, whether it comes from kind of more production of already existing things. That, that's, that seems like a massively important and interesting and kind of deep question that we should, we should know more about.

[Basil Halperin]

I totally agree. I can speculate that one reason why it's hard is that it's hard to think about the introduction of new goods because it breaks a lot of things, both economically and philosophically. So like, would you rather live in the year 1500 without vaccines or today is, it's, it's much harder to make that comparison versus would you rather live today versus 1980 with approximately the same set of goods or something? Because you're comparing preferences over different, non totally overlapping sets of goods. That sort of just breaks a lot of basic microeconomic theory. So again, Phil Trammel, Chad Jones, I think are doing some very cool work on this. Hopefully they'll inspire others.

[Gus Docker]

Yeah, yeah. I mean, just thinking about this for the first time, it seems to me like new goods and services are introduced all the time. The kind of set of goods and services that's available to me right now right in front of me is very different from the one that my, say, dad had access to 30 years ago. And it's so, isn't there, isn't there kind of a. Yeah, I mean, what, what do we do with the fact that this is already happening, that new goods and services are continually being introduced. So in, in some sense economists must, must be thinking about this problem just because it's, it's a reality.

[Basil Halperin]

So I think if you, if you talk with Phil, he would say that economists are not thinking hard enough about this issue and sort of slip it under the rug. So one way you can slip it under the rug is by only making local changes or, sorry, making local comparisons. Local in the sense of comparing you today to you two years ago. Because for over two years in the modern era, at least two years, there's not that much change happening. Those are pretty comparable. And if you read in the footnotes or whatever of your favorite econ textbook, you'll see notes that comparing over different decades is a lot slipperier because of new good introduction, how that affects price indices, construction, how that affects this adjustment from nominal to real nominal GDP versus real gdp. So this is a known issue. There's just not a great way around it beyond taking these local changes and sort of extrapolating them, at least as far as I'm aware of.

[Gus Docker]

Yeah, got it, got it. Okay, so if we go back to the question of kind of economic indicators for AI timelines, we've talked about interest rates and maybe summarize again for us, why is it that interest rates is like the thing to focus on? Why is that a great indicator? Why in particular is that a number that incorporates a lot of information?

[Basil Halperin]

Yeah. So one way of framing this is that Paul Cristiano had this blog post over a decade ago saying three implications of advanced AI. And the three implications he lists are, number one, growth will speed up, Number two, wages will fall. Number three, humans won't control or sort of set the future. Thinking about alignment so plausibly with advanced AI that is superior to human that humans at all tasks, wages will be driven down to zero. An issue with looking at wages to understand, to forecast a capabilities is that wages will not get driven down to zero until we sort of have those capabilities at hand. Interest rates on the other hand are forward looking. Financial market prices in general are forward looking. So like the US government issues 30 year bonds regularly, those incorporate expectations about future savings decisions over the next 30 years. UK government issues 50 year bonds. I think Austria has a 100 year nominal bond, maybe Argentina does too. So these, these instruments exist looking forward a lot. And so that's useful because it's useful for forecasting instead of just contemporaneous economic conditions. Yeah. Additionally, interest rates are useful going back to the discussion about stocks because the effect of aligned AI and unaligned AI goes in the same direction on interest rates, unlike equities or other asset prices. So within the class of economic indicators you can look at that are forward looking, interest rates are nice because they both go in the same direction for aligned and unaligned AI. And I @ least do really want to take seriously these risks from unaligned AI. And then third, as you say, in general financial market prices in particular, Even more so than other prices in the economy are useful to look at because financial market prices update quickly, are liquid, unlike wages again for example, those are sort of sticky, only maybe update every year if you're lucky. So we have lots of empirical evidence that financial markets are good at behaving in a forward looking way. There's various sort of amusing historical anecdotes you can point to providing some evidence, I won't say demonstrating but providing some evidence that financial markets are forward looking in a useful way. One that I like and I think is relatively robust is Armin Alchian who was a great price theorist, economist, worked at rand in the 1950s. The the sort of defense think tank and the hydrogen bomb had super bomb had just been tested for the first time and it was not known publicly what sort of material was used to develop the bomb, just like uranium was used to develop the the atomic bomb. And so he went and looked at the stock performance of various metal producers and saw that I'm going to get the element incorrect. Something like the lithium producer had outperformed other ore producing companies in the period around the hydrogen bomb test. And he writes this report internal to Rand saying oh this is evidence, I think it's Lithium was the key ingredient in the hydrogen bomb and famously his superiors who knew what went into the development of that technology forced him to burn the draft of the paper. So that's a very cutesy anecdote. We shouldn't read too much into cutesy anecdotes versus systematic analyses. But that's one vivid example of why financial markets are good at incorporating forward looking information.

[Gus Docker]

Yeah, it's actually an interesting point because there was a bunch of secrecy surrounding the development of nuclear weapons and you might imagine that there would be secrecy surrounding the development of AGI also. Maybe this is even a nationalized project that's completely locked down. And how would that affect whether the market would know anything or incorporate anything about AI progress into the interest rate or into the price of public companies?

[Basil Halperin]

Yeah, great question. So there's definitely a world where it's like 10 people in a basement working on AGI. None of that information leaves the basement. They don't leak to anyone. No one really knows or everyone keeps their lips shut. No one secretly trades to make a bunch of money and no financial market prices show up until the AI in a box is unleashed upon the world. That's logically consistent if AI occurs more gradually as I think one should compared to the 2000s era. Discussion Nick Bostrom Super Intelligence Year discussion of AI seems much more plausible today than it did 10 or 15 years ago. There's just a lot of public information. The information will get incorporated or the information will get leaked. So I don't know how much people in these labs are doing trading on the side. If, if you know, you're an ML researcher and anthropic and you're up to that, I'd love to hear from you. There are other cutesy examples from history. So Suresh Nidu and co authors have a sort of hilarious example of. They look at 20 different CIA orchestrated coups during the Cold War and they go and look at. It's something like United Fruit Company in Costa Rica, I believe I could be getting that wrong. Was the monopolist fruit producer in Costa Rica. There was some sort of revolution in Costa Rica. Forgive my ignorance of Latin American history here, where United Fruit Co. Was pushed out, CIA for reasons of fighting communism, goes and tries to orchestrate a coup in Costa Rica. And in this paper they show that the stock of United Fruit Co. And in parallel in these other 20 incidents that prior to the coup attempt there was excess returns to these companies. And they had this anecdotal evidence, again, sort of incredible, that there were just a lot of leaks from the government initiatives and that insiders were trading on these expectations.

[Gus Docker]

Yeah. So this would mean that we would have to expect AI development to be unrealistically contained and secret secret for it not to affect public markets even. You know, especially if we have stories of, of leaks like that that affect prices. That, that's actually, that's surprising to me that, that you would see a consistent pattern like that. Is, is it. Would it just be a kind of insiders seeing an opportunity to make, to make money and then acting on that?

[Basil Halperin]

Yeah. Or government regulators eventually get permission to go into these labs and keep track of what they're doing and it leaks out via that. Any sort of story like that. Obviously this is all speculation and again, it's totally logically consistent that the information remains private, either through social pressure or legal force, et cetera. And it'd be interesting. I haven't done this. If we went and looked on Polymarket and Kalshi or something about GPT5 release dates. How much does it look like there's leaks going on with that? That. That would be some interesting evidence. But I think the overriding, the more important point plausibly is the fact that takeoff is slower than the AI in a box in the basement.

[Gus Docker]

Yeah, there's the slower takeoff part. There's also a question of how we're developing AI. So in the era of scaling, you need massive data centers. That is something that gets out almost immediately. And perhaps if we're moving to a paradigm of trying to automate AI research and development, that's something that can perhaps be done more internally and more in secret. And yeah, of course we're speculating here, but this is, this is something that could push us in direction. Push in the direction of less public information. I think.

[Basil Halperin]

Think that sounds totally right to me. Big picture, what I would say is that there are definitely scenarios you can tell where interest rates will not move before some sort of transform of AI. I think it's like the best guess and high probability that they will go up quite a bit beforehand and maybe even already have. And then another thing to say is that if you have AI in the basement where it's developed in secret, no one leaks out about it, perhaps via the story you tell. If that leads to nanobots terraforming the Sahara Desert with solar panels and then colonizing the stars and so on, and that is not leading to richer humans for one reason or another, or richer, vast, vast majority of humans say even that would not show up in traits because there wouldn't be this consumption smoothing mechanism of people expecting to be rich in the future lowering savings rates today. That said, if Sam Altman's getting super rich in the future, then maybe he has enough money to push around markets, even if he's the only one to move interest rates. So the mechanism or the story here really is focused on this particular transformative AI scenario of either unaligned AI human extinction or aligned AI leading to consumption growth.

[Gus Docker]

Yeah.

[Basil Halperin]

Yeah.

[Gus Docker]

How would you settle an argument between you and a person like Daniel Cocatello, who's been on this podcast. And just for listeners to remember here, Daniel has very short timelines. He expects us to get to AGI by 2027, perhaps 2028. Now. And this is not a story like developing AGI in a basement. This is more like developing AGI in a desert of like robots quickly building up the. The facilities you need. And this is all happening extremely quickly. So in a sense, if you have very short timelines, how do you argue? How would you argue with such a person? Or how would you settle differences and find out who's right beyond just waiting and seeing? Just because if there are information that can't be incorporated into interest rates, it seems like the mechanism you would use for predicting what's going to happen is not really active.

[Basil Halperin]

Yeah. So I Think there's a bunch of points one can make here. One is that I'll note that personally I find the market based perspective most useful for being less worried about short timelines worlds because as discussed earlier, you might expect markets to be more efficient at these shorter horizons. So this perspective gives me tangibly substantially more confidence that AI 2027 is less likely. That's one point to make. Another point to make is perhaps markets are wrong, perhaps AI 2027 is correct, and indeed perhaps this is a good time for me to say I personally am substantially more bullish on prospects for transformative AI than markets are. And in fact I've reallocated my most valuable asset, plausibly my most valuable asset, at least for now, my human capital, away from traditional macroeconomic issues that I studied for a decade. I was obsessed with monetary policy for almost a decade before trying GPT3 for the first time in summer 2020, freaking out a little bit, and now sort of spending at least half of my time working on economics of AI. So to some extent I won't fight the AI 2027 argument fully. That's the second point. A third point is again, this market based perspective can only speak to, I think this particular scenario of advanced AI leading to rapid economic growth or human extinction. It can't speak to if this is all happening in the desert, not affecting the broad based economy. If there's sort of a separate AI robot economy, this market perspective won't speak to that. And then finally, yeah, we can debate inside views about is AI 2027 likely or not and set aside the market base for effective. I think that would be the most productive thing to do on that. Which is maybe another point to make here, which is inside views on AI timelines like Ajay Kotra's BioInkras report, like AI 2027, like the work done by many others I think is extremely useful, extremely useful. Complement to this market space view, which does not, for example, take a stance on the compute centric worldview, which I think the vast majority of recent AI work, AI forecasting work, is leaning pretty heavily, if not entirely relying on the idea that scaling up compute is sort of all you need at least eventually to have transformative AI. The marketplace perspective. Just saying, will we see rapid economic growth or human extinction? That's all you need.

[Gus Docker]

Yeah, yeah, makes sense. Yeah, let's see. Oh, you mentioned perhaps a lot of profits from, from AI development will flow to someone like Sam Altman or say a broader group of investors and leading AI figures. What does it mean? If wealth is extremely concentrated, what does that mean? You know, specifically the question is something like they might save and invest differently from the average person. And so if the wealth is much more concentrated, what does that mean for interest rates?

[Basil Halperin]

Yeah. So what I can say is that the idea that AI could lead to rapid economic inequality or even like immiseration of large swaths of humanity while making others really rich. Mm. There are elements of that which I would put in, I mentioned before this list of two or three best critiques of this whole framework. So it could be up there where like if you talk to people in San Francisco about their savings behavior, I think you do sort of get one of two responses. But it's two very polarized responses. One is, yeah, I'm not, I'm not saving for my kids college education. Which is a real life example from your former, your former guest and my colleague at uva, Anton Korenek. He was interviewed by npr and him and his wife talk about putting less into their kids college savings accounts. So that's one possibility consistent with our story. The other possibility is people talking about wanting to save a lot because of uncertainty about the future. Yeah, there's more that could be said there. But one important point is that that, that would indeed push down interest rates and is a potential counterargument. The way I think about that is.

[Gus Docker]

Wait a minute, what would, what exactly would push down interest rates in this scenario?

[Basil Halperin]

So if I'm really worried about not having a job in five years because of AI.

[Gus Docker]

Yeah.

[Basil Halperin]

Even if there's 30% GDP growth, if that's all captured by Sam Altman and co and I'm unemployed, then I don't want to draw down my savings today so that I have those savings in five years when I'm unemployed and that higher savings rate will push down interest rates, so to speak, again, again, interest rates clearing the market and supply and demand for savings. So I guess number one, historically, again, we don't see that higher economic growth is associated with this higher idiosyncratic risk that would lead to higher savings and lower interest rates. That's one point. Again, AI could be different and there's good stories and mechanisms to think that maybe it could be. So another thing I think about here is that asset prices in general are sort of like a wealth weighted risk tolerance weighted average of people in the economy. This came up in the discussion earlier about heterogeneous beliefs in the marginal trader. But it's also particularly relevant here where if Sam Altman in the future is capturing all this wealth and he's just saving it all. That's depressing interest rates. So if from 2025 to 2030 we have sort of the economy as exists today in 2030, 99% of humanities unemployed, 1% is super rich, and there's 30% consumption growth within that 1% from 2030 to 2035. That would mean that from 2030 to 2035, interest rates are reflected by or are reflecting the consumption growth of that 1%. And so the 10 year interest rate today reflects like the average of the first five year period and the second five year period. So the ten year interest rate today would still reflect at least in half the rapid consumption growth in the latter half of the period. Although the first five years might have depressed interest rates due to precautionary savings due to fear of unemployment by me and the masses.

[Gus Docker]

Yeah. So you think this is plausibly a good counter argument, but how plausible is the scenario itself? Do you think that AI will lead to kind of extreme wealth inequality?

[Basil Halperin]

Yeah, so to a large extent, I think this is a question of political economy and depends on what the political response is. So economists for the last decade have spent a lot of time thinking about the China shock in the US where China entered the World Trade Organization. This led to a lot more trade between US and China, and arguably this led to losses of manufacturing jobs in the US and the standard econ argument would be more free trade between China and the US is good for the economy as a whole. It might impact some people negatively, but those people can be made better off by, for example, taxing consumers who get cheap goods from China, transferring some of that tax income to those who lose their jobs, or helping them retrain to Newark. There was limited amount. There was a limited amount of such policies in the US in response to the China shock. And that might make you pessimistic that in the future there could be similar amounts of redistribution and that you could see skyrocketing inequality. I think it's hard to get away from the idea that there will be skyrocketing inequality in a truly transformative AI scenario. But skyrocketing inequality might still be consistent with everyone being better off. Just because 30% GDP growth, as discussed earlier, is such a massive growth rate. It's hard to have 30% growth in the economy overall and leave the vast majority of people, or even a large chunk of people worse off.

[Gus Docker]

Yeah. So something like perhaps Sam Altman is a multi trillionaire, but the average person is a millionaire. And so even though we have extreme inequality what we actually care about is more how people, you know, the welfare of the population at large.

[Basil Halperin]

Yeah. And I think the China experience here is a good example where if I'm recalling my numbers correctly, like inequality has certainly skyrocketed in China over the last 40, 45 years but poverty has been reduced so much the average person, their life has been improved so substantially that concerns about inequality are less of an issue in China than in the West.

[Gus Docker]

Yeah, yeah. Before we move on to other topics, we've talked about interest rates as a great indicator of what's going to happen with AI. Are there any other indicators that might be interesting to look at? So I wrote someone down or some of them down that I could think of. Maybe capex capital expenditure from the large corporations, maybe patent filings, perhaps papers published by the AI corporations. What do you think of other indicators? We've discussed stock prices as perhaps not, I mean somewhat useful but, but perhaps not as useful as, as interest rates is there. If you were to write another paper on, on, on any other indicator, what would you choose?

[Basil Halperin]

So if I to choose an indicator for forecasting it, it would be equities because stock prices are forward looking.

[Gus Docker]

Yeah.

[Basil Halperin]

Capital expender is also forward looking to some extent but is less likely to be made on third year horizons. Wages again are contemporaneous. So I think there's a lot of good work that still could be done with stock prices and understanding why is Nvidia's market cap so high? How much of that is future profits because of high markups versus actual quantities are going to be high? Why has Nvidia increased so much more than TSMC has? Like there's a lot that could be done with stock prices that hasn't been done. I would love for someone to write that paper more more broadly about economic indicators for interpreting AI. The big ones I think are wages, interest rates, the labor share of the economy. So how much of national income is going to workers versus going to capital owners and other and something like unemployment or labor force participation. So how many people can find jobs and want jobs or how many people just are working in the economy as a whole? Those prices and quantities I think would really reflect the different scenarios that people talk about for the possible implications of AI. And you could also look at those by sector in the economy in particular to see if AI is affecting the economy in a heterogeneous way. For example automating all of white collar work whereas while making blue collar work more relatively valuable. So you might see skyrocketing employment in factory workers and rapidly declining employment in software engineers. So those four or five big indicators for the economy as a whole and by sector.

[Gus Docker]

Yeah. When you think of the labor share. Yeah. Of the economy. So how do you define that? Because I'm guessing most people, most individuals are both kind of capital owners and workers. And so maybe people own some assets. They own, they have some retirement funds, they own a house maybe and they also go to a job. How do you define the labor share versus the kind of capital share of the economy? Me?

[Basil Halperin]

Yeah. This terminology that I and economists and others use, I think it's actually really bad of workers versus capitalists, this Marxian framing when exactly as you say, most people have some mix of those certainly at any point in time and then even more so over the life cycle where I won't belabor that pun intended. So what is the labor share of Technical definition is the total wage income earned by workers divided by gdp. So that reflects in the world as we know it today, at least that reflects. How to put this. It reflects what share of output is attributable to labor in a specific sense, under specific assumptions that apply pretty well, we think, to today's world.

[Gus Docker]

Yeah. Okay, interesting. So. So it. You can't really look at papers directly or look at patent filings directly or something like that. I'm just wondering whether there's some like intellectual output from the companies that would be worth looking into a measuring. Is it the case that whenever there's a new paper or whenever there's a new patent filing that is immediately incorporated into the price of, of. Of the stock or how should I think about this?

[Basil Halperin]

Yeah, so I should have said all your suggestions were also great. Someone should like make a dashboard of all these things, capital expenditures in particular. I'll note this gets a lot of discussion, but I think it's still underrated. That hyperscaler capex that's like Google meta, et cetera, their capital expenditure is like 1% of GDP. That's comparable to the height of the dot com boom. Not clear that we're at the height of anything. The numbers I've seen for the railway boom in the UK in mid 19th century is like 2% sustained for 25 years of GDP on railway investment. So 1% is getting up there. Of course I should say in the railway era that 2% wasn't constant. Over time it was higher and lower. So there's a lot of capital expenditure. I just moved to Virginia. Basically the ground's covered in wires from all the data centers. Obviously that's an exaggeration but a lot of data centers here. You bring up the impact of patents on stock prices. There's very interesting work that's been done by folks looking at historically when patents were granted. If you look at the stock price of firms who were granted that patent around the day the patent was was granted, you can see a large impact of patents on stock prices and you can cumulate that. Add that up over all companies in the country or the world over the course of a year and you can sort of see a measure of innovations value captured by company profits as reflected in stock prices. And you can see how that varies over time. Lina Cogan and many co authors have done this very cool work. So something like that could be done today as well. AI, it's a bit trickier because there's less gets patented. Again, many of these companies aren't directly listed though. Again they often have publicly listed affiliates like Microsoft, et cetera, Google. The moonshot economic indicator that I think of is benchmarks are this huge thing in the AI world. We want to look at how good is AI or new AI models at well defined tasks that are scorable in an automatic way. Or like the Math Olympiad recently in economics, there's this database produced by the government in the US, the ONA database that has this list of 19,000 tasks in the economy performed by American workers. And it would be amazing if we could have some very expensive program because it would be very expensive to do measuring what fraction of tasks can models perform themselves or how much do models improve human productivity on each task and keep track of that over time by task. Then you could do things like what share of tasks has gone up over time or what share of tasks is automatable and how has that changed over time? You could do the standard thing in AI and take a trend and just extrapolate it. You could try and see when will we have 100% automation of the economy of 2025. Er, tasks. New tasks are constantly being invented that I think would be the moonshot for if the government decided it wanted to throw a lot of money at understanding the economic implications of AI. That would be what I would suggest.

[Gus Docker]

Yeah, that would be extremely valuable data to have that would be super interesting but also quite complex as you mentioned. Yeah. So how would these tasks change over time? So I would imagine as AI gets better, some tasks are now automatable. Those tasks now make up a smaller percentage of what people actually do. So the task list from 2024 is not as relevant in 2028 anymore. So I guess what you're tracking is also which tasks people are spending their time doing.

[Basil Halperin]

Yes. So the way I put this is that the second moonshot I wish the government would find would be keeping track of exactly this. Where this ONET database of tasks is static, over decades it gets very occasional updates. So number one, like we could really do with an update in the year 2025. That'd be great. Or potentially just sort of a new way of thinking about this. Like should we be using technology to keep track of what people are doing minute by minute of the workday and even using AI to classify what people are working on or something more dynamically. So like maybe this whole static database structure needs a rethink. I don't think that sounds like it'd be really hard for one individual researcher to try and do on their own, but the government might be able to do it. So a one time big update would be nice. Even better would be real time updating every year or whatever. This list of tasks. How is people's time allocated across these tasks changing over time? What new tasks are added versus deleted that humans do versus machines? That would all be great. We, to my knowledge at least, don't have that information.

[Gus Docker]

Yeah, we would have a much more granular view of what's happening. I think when you're looking at the unemployment rate or the labor participation rate, it's all condensed into one number and you don't really know what's causing what. And of course you can see that there's not a massive effect of AI on unemployment yet. But certainly it would be great to see whether some industries are being affected. This could be happening right now. And maybe we don't have great insight just because it's not moving the needle on the one number that we might be looking at.

[Basil Halperin]

Yeah. And of course these numbers are certainly the US I'm sure elsewhere broken down by industry, there's surveys that are pretty frequent that get you richer demographic information. So we can see things like our younger coders losing jobs. That'd be super interesting. But those surveys, while, you know, in the tens of thousands, provide very nice data for lots of purposes. The data is pretty noisy, is pretty small when you kind of want to go down to. For new graduates working in cs, how is their employment looking? Expanding those or using private sector data, which I think there's, there's work that should come out soon on this topic that would be really useful to look at.

[Gus Docker]

Yeah. Here's a question I've asked a bunch of Guests. It's about the difference in economic effects, the economic effects of AI and then what we see on benchmarks. It is surprising to me and I think to other people also that we are seeing this very strong performance on a bunch of benchmarks. So AIs are now incredible at passing college exams. They can pass the bar exam, they can do all. They can score highly on medical kind of examinations. They can, they can do especially well on, on coding and math and so on. How is it, how is it the case that I am in daily dialogue with a chatbot that is better at me at math and coding, but it is not yet. It is not yet. It doesn't have massive economic effects yet.

[Basil Halperin]

Yeah. So like this is just one of the greatest questions of our time. So I can offer some speculations or a couple different answers. One is like maybe capabilities really are this good and it just hasn't. The technology has not diffused through the economy. So firm managers are old crusty. They don't want to adopt AI to replace workers or workers don't want to adopt AI because they don't know about it. Things like that. That's one possibility. Slow diffusion in general is a major lesson of economic history that technology takes time to diffuse for various reasons. I don't think that's sort of the main reason, just that people in San Francisco want to fantasize or something that old fusty people don't want to adopt new technologies. I don't even think many people believe that because it's so a priority. Hard to believe.

[Gus Docker]

Yeah. It doesn't seem super plausible to me either just because, I mean it is quite easy to incorporate these models into your workflow and you can. I mean it's something that's available to individual workers that they can use for themselves and they have incentives to try to use these models to make their lives easier and so on. It is something that is, yeah, much, much more easy to implement in the economy than say if you had to spread physical hardware to do something better.

[Basil Halperin]

That's definitely true, but I think will lead to, or I want to disambiguate two things where one is pure diffusion. Just have people learned about this technology or something like that? Another is incorporation into workflows. Let me drill in on that. Where workflows involving other people can be hard to change around. There's a Microsoft study where they experimentally rolled out Copilot to some workers versus others and they found that workers who started using Copilot really reduced the amount of time they spent on Email a lot because Copilot was helping write emails faster. But the time spent in meetings didn't change at all. Because the coordination with other people is not something that AIs as they exist today in helpful, harmless chatbots can really help with and work by. People like Eric Marjolssen and others has emphasized that to fully gain the benefits of a new technology, firms will often typically, historically have to reorganize their internal processes to take advantage of these new technologies. The most famous historical example being the adoption of electricity initial factories in the 19th century. Sort of just taking existing workflows, sort of plugging electricity in, not changing anything else, but then over the course of actual decades really changing to the sort of model table setup that completely changed these internal processes to best harness the new technology. And it's very plausible that the sorts of intangibles, these internal processes are slow to figure out and not something directly improved by AI. The counterpoint there is that maybe we'll have drop in remote workers in two years or whatever. And those are drop in remote workers. You don't need to change from internal processes at all.

[Gus Docker]

Yeah. So these drop in remote workers will be able to understand the context they're working in. They would function like remote workers, basically. They would understand the context, they would understand the code base, say, they would read the internal documents, emails and so on. And the first part of that argument is actually quite plausible to me that we are not taking as much advantage of these models as we could. We're not properly integrating them, we're not kind of pushing to get as much out of them as we could because it takes time. It's difficult to integrate AI into all of our processes.

[Basil Halperin]

Yeah, totally. So these two arguments, diffusion and reorganizing internal processes, sort of take the idea that these models are really good. They're just other things that need to change before the effects diffuse through the economy. But it's also possible that benchmarks just aren't representative of economic tasks. As hinted at earlier, benchmarks need to be things where the outputs verifiable. Math Olympiad is something where you can check whether you have the right answer or not. Maybe take some time to verify a proof or something, but it is sort of checkable. Whereas writing an economics paper, how do you verify whether you did a good job on that or not? Much harder. So benchmarks are a certain kind of task to my knowledge. There's not so much work investigating how is progress advancing on tasks by characteristic. So there's this famous meter study meter being model Evaluation and Threat Research, a sort of think tank that studies evaluates new LLM models, new LLMs. They have the same study showing that the horizon of tasks that LLMs can do has been increasing, sort of doubling every seven months or something like that. So GPT2 could do like a one second task. GPT5 can do a two hour and 15 minute task, something like that on a narrow set of tasks that they have these hcast, machine learning or software engineering type of tasks. That's amazing research, a super important data point. And in the paper they look at if you have messy tasks versus non messy tasks, do we see faster slower progress on messy tasks versus non messy tasks? They don't actually have very many very messy tasks or any maximally messy tasks at all. They don't find differential progress in messy versus non messy tasks. They do find a lower level of progress, but the slope is the same. I would still be interested in seeing for ONET as a whole, for example, does that sort of result hold up? Because I think it's very plausible that benchmarks are these narrowly defined tasks that don't really capture the breadth of what a worker does every day. Like work is pretty complicated.

[Gus Docker]

Yeah, yeah. And actual workers tend to carry out plans over weeks or months, perhaps years and so on. But still, I mean, what does it even mean? What does it even mean for a task to take a week or a month or something? That is something that I'm kind of interested in because we are kind of on this curve according to the meter study where the models in two or three years, I think we'll be able to do a month's work. A month? A task that would, that would take a human a month with 50% success rate. What is a month long task? I can't actually kind of perhaps conceptualize what it would mean to work on one task for a month. Perhaps I just lack focus or something. But do you have like, do you think it's plausible that throughout the economy there are tasks that are very long term and what would be examples there?

[Basil Halperin]

So there definitely are tasks that are very long term and they're very painful. I say that as a researcher where I. Yes, to me having worked in industry before going to grad school, like research just has such a long cycle before you put out a paper or whatever, it's much more painful. Feedback loops are much slower. So those tasks do exist. But exactly as you said, I think that's not a huge share of the economy. The way the framing of this that I find convincing, I'm totally recapitulating an argument from Toby Ord here is that so if a model can do a one minute task, why can't it do two one minute tasks in a row? And that means it can do a two minute task. And the argument he makes is that if you can do a one minute task with 50% probability, as you said, this is what the meter study is looking at, then that means doing a two minute task. Those two tasks are getting chained together at 50% probability each. If those were say, independent probabilities, then it would be a 25% probability of succeeding on the 2 minute task. Hence why models are worse at longer horizon tasks than short horizon tasks. I agree. Without sort of decomposing it that way, it's not obvious what distinguishes a short horizon task versus a long horizon task. It does feel like there is something that maybe we just haven't captured. Otherwise, this Toby Ord framework is what I find most useful for thinking about it.

[Gus Docker]

Yeah, yeah. This is, I think also what you see in coding that if a model is unable to fix its own mistake, those mistakes accumulate over time such that the output is no longer useful beyond a certain kind of time horizon of a task. I do still. I mean, perhaps this is getting too philosophical, but it's just, even if you've written a paper and you're now trying to incorporate feedback on the paper and this is kind of this tedious and slow process that might be necessary, necessary for good research, you are not spending, you know, you're not spending all your time on this one task for six months, say. Right. So yeah, I mean even, even book writing or something very, very concentrated. Okay, my question is, what do you think is the upper limit for how humans can, for how much time humans can spend on a task and could the AI be approaching that limit?

[Basil Halperin]

That's a very interesting question. Upper limit. The sort of rule of thumb I have in my head for when would I be satisfied that we have AGI is when any task that takes a month, AI can do it. That's just a completely made up number. Another thought I can throw out is thinking of decomposing the world into tasks. This task based framework that has in economics become very dominant very quickly in this macro labor area and is also used often in the AI world. Perhaps it's just sort of a conceptual error to decompose the world into individual tasks as opposed to thinking about how tasks fit together into a broader puzzle. And human civilization as a whole or groups within the human civilization certainly spend have initiatives that last many years to achieve a goal. And maybe there's some category error to think of decomposing these into smaller tasks and they're not separable in some way because you have to hold context in your head. And if you don't have a long enough context window, possibly these LLMs don't. You just can't, you can't do it. So you can't decompose it that way or the self correction like you describe can't be separated across these tasks. And so you really need to think in terms of like grand projects, grants, ARCs, grants, initiatives. But this is me just philosophizing though. If someone wants to come up with a replacement for the task model in economics, I think there could be something real there.

[Gus Docker]

Yeah, interesting. I think we should end by chatting a bit about the most interesting kind of open problems as you see it. We've touched upon some of them in this conversation. Right. The things we would like to know about AI that are at the intersection of AI and economics. Are there other that come to mind as like, this is the research paper you would love to see or you would love to write.

[Basil Halperin]

So the big answer is that there's a lot, there's so much low hanging fruit. It's super exciting to work in the economics of AI. I have to advertise it to other economists. You should, you should consider transitioning over. Don't get hung up on the sunk cost of all the years you've spent studying monetary policy or whatever.

[Gus Docker]

Yeah. The question is whether being an economist actually makes you better at avoiding the sunk cost fallacy or whether economists are kind of just as human as the rest of us.

[Basil Halperin]

That would be interesting paper. I would read that paper in terms of most important questions. So some of the most important questions that I think of as like the hamming question. What's the most important question in your field? I'll distinguish between empirical questions or sort of, sort of applied micro empirical questions versus microeconomic theory, questions that might have relevance to AI safety and then grand macro theory or maybe a bit of empirical macro questions and macros where I have the most familiarity. So let me start there and we can talk about the others if there's time or interest. So within macro, I think the big question is will AI lead to a speed up in economic growth or will it get bottlenecked by certain sectors or areas? So those bottlenecks could be things like energy. We just don't have enough fossil fuels. That's a limited quantity that bottlenecks AI. The price of fossil fuels is going to spike land, potentially land, very plausibly, or certain sectors of the economy AI just isn't good at. Clearly we've seen much more progress in the cognitive domain than in the physical domain. Robotics has a lower level of progress. Will we end up all as blue collar workers in 10 years? So where will the bottlenecks be? I think is plausibly the best answer to the Hammond question. Yeah, the second one is this idea of automating AI research that you brought up a bit earlier. How much, to put in economic terms, how much dynamic complementarity is there between AI today and AI tomorrow to spell that out, if we have faster AI progress, Let me put this a different way, going back to IJ Goode, at the very least there's this idea of recursive self improvement, that if you have better AI, it can write better, it can do better AI research, which will lead to better AI, so on and so forth. And the speculation that's often layered on top of this is that this would lead to an intelligence explosion. That's IJ Goode's terminology. A recursive loop like that does not necessarily lead to an explosion, does not necessarily lead to a mathematical singularity. It depends on the strength of that feedback loop. I'm trying to think about how to say this, about drawing grass in the air. If the feedback loop is super strong, then you can indeed have a mathematical singularity. Infinite growth in finite time. If the feedback loop is only moderately strong, you can just have exponential growth, which is sort of what we're used to in the post war era or in the last 200 years. If the reinforcement, the feedback loop is too weak, you can even have things leveling off. So you have self improvement, it leads to faster AI and it's always leading to a little bit more and more AI improvements. But things level off. So what is the strength of that feedback loop? What is the diminishing returns, the intertemporal diminishing returns to AI progress? Or to put another way, are ideas getting harder to find in AI or are they getting easier to find, in which case you would see a singularity? There's a limited amount of work on that. Egg and Tamai Bezirogu, I think have some papers on this looking at progress in AI chess and maybe one other domain. And anyway they have the best work on this, More work on that could be done.

[Gus Docker]

That one is really interesting. I mean it's potentially so consequential for the future we're likely to see, isn't it? The Case that in any domain you will kind of pick the low hanging fruit, find the ideas that are easiest to find first, and then you will face the kind of. It will be more and more difficult to find good ideas for how to improve beyond that. Or is that a misunderstanding of the kind of ideas and growth literature in economics?

[Basil Halperin]

So that's definitely the prior in the literature. I think that's definitely the right priority. Just thinking about reality.

[Gus Docker]

Yeah.

[Basil Halperin]

One could imagine though that it takes a really long time to pick all that low hanging fruit sufficiently. Such a long time that for an extended period, like, you know, maybe even centuries or whatever, there is a period of increasing returns to scale where getting the low hanging fruit allows you to, you know, beef up your muscles and pick fruit even faster. Even though eventually you'll have to reach higher up on the tree and those strong muscles won't help you reach the apples. To really extend the metaphor. And so if we look today, what do we see? Increasing returns or decreasing returns? Yeah. So on the macro side of things, those are sort of the top two things that I think of. On the micro theory side of things, I will give a pitch for. I think there are a lot of lessons one could take from microeconomic theory for AI safety for agent foundations work like the von Neumann Morgenstern axioms that are sort of often discussed just as one example in the AI safety world that is sort of the foundation of modern economics. One would hope that there's further lessons from micro theory there. And there has been work. Dunn, Eric Chen, Alexis Gersingerin and Sami Peterson have very interesting work on the AI alignment problem from a microeconomic theory perspective, as do a few others. And so I can imagine that in a few years, if this problem seems increasingly serious, that there will be more microeconomic theorists working on this question.

[Gus Docker]

Yeah. Do you think there are other areas of economic theory? Perhaps something that's, that was conceived way before AI was even a thing that's relevant to thinking about AI.

[Basil Halperin]

Yeah, great question. So I think there's a lot, I think there are a lot of essays or papers that could be written just applying ideas from econ, broadly, in particular perhaps economic theory to the problem of AI. So for example, after the 2008 financial crisis, regulators developed this idea of stress tests for financial institutions going in and doing a simulated scenario of a financial crisis in banks, given their sort of asset holdings, their loans, et cetera, to see if they would survive the financial crisis. This made up financial crisis. So there's this large econ theory literature for some reason Investigating when is this efficient or something like that. That's not so far from the idea of red teaming in AI where anthropic has a team, others have teams trying to battle test LLMs in the worst case scenario. In fact, there's a paper by Joao Guerrero, Sergio Rebelo and a third coauthor, forgive me, I'm forgetting, taking this exact idea to the AI world. And in fact, in the first draft of the paper, if I'm not mistaken, they didn't even use the term red teaming because they were economists. They hadn't heard this term in newer drafts of the paper, like oh yeah, this is a theory of red teaming, the optimality of red teaming. Or there's a literature on insurance against cyber attacks of firms so firms can take out insurance about getting hacked. And there's been discussion, Gabriel Weil and others have written nicely about should AI companies be forced to take out liability insurance as a mechanism for encouraging them to internalize risks from advanced AI? The cyber insurance risk literature could be adapted there or I'm sure other parts of the insurance literature. And in fact Gabriel's work is of course directly economics related itself. So those are some miscellaneous examples. Yeah, I think there's a bunch of things that could be done.

[Gus Docker]

Yeah, and I think in general, and this is just my impression as a non researcher, but I think there's something about finding an intersection of some area that's been studied deeply and then applying those ideas to a completely different area. So AI and economics would be an example. Perhaps a Jacarto's report on bio anchors is an example of studying machine learning by looking at evolution and kind of finding some intersection that's unexplored is often fruitful, is my impression.

[Basil Halperin]

Yeah, totally. So to circle back the whole conversation, the way this paper on AI and interest rates came about is that again for like a decade I was obsessed with monetary policy. How do we prevent recessions? How do we Prevent it under 2008 and in the monetary policy world, central bankers are very worried about predicting future inflation. And so this gave me this background on forecasting. In particular, central bankers often look at financial market expectations of future inflation over the next 10 or 30 even years because there's instruments that directly forecast inflation expectations. And so that sort of market monetarism or market based perspective is how Trevor Zak and I ported this interest rates perspective to the AI forecasting world.

[Gus Docker]

Oh, perfect. I think that ties up our conversation nicely. Basil, thanks a lot for chatting with me. It's been great.

[Basil Halperin]

Thanks very much, Gus. For inviting me on super fun conversation.