Meet the world’s top AI-pilled economists

Artificial intelligence has already created trillions of dollars of market value and turned a handful of tech nerds into celebrities. The public is excited and terrified in equal measure. Many brainy people, from Bill Gates to Elon Musk, say that the technology is just getting started. Talk to academic economists, though, and most seem oddly uninterested in studying the impact of the potentially world-changing technology. The centre of gravity in ai economics is instead shifting away from universities—and a gang of “ai-pilled economists” is leading the charge (see chart 1).

University researchers can move fast when they want to. After Lehman Brothers, an investment bank, collapsed in 2008 and sparked the global financial crisis, economists turned the study of bank runs and credit crunches from a niche pursuit into a mainstream interest. Two months into covid-19, in 2020, close to a third of working papers on economics published by America’s National Bureau of Economic Research (nber), a prestigious repository of economic thought, focused on the pandemic’s effects in some way. Some of this work burst into the mainstream, including by Nick Bloom of Stanford University, an expert in working from home, and Emily Oster of Brown University, who studied school closures.

Chart 1

Three and a half years after the launch of Chatgpt ushered in the ai age, by contrast, economic analysis of the technology remains comparatively scarce. The proportion of nber papers which focus on ai is rising, but not especially fast (see chart 2). Even in 2024, after the covid emergency was over and the ai era had truly begun, the number of covid-related papers exceeded the number of ai-related ones. This year the nber is likely to host more conferences on health care than on ai.

Some academic economists have seized the ai opportunity. Susan Athey of Stanford is exploring what happens if ai puts people out of work. Basil Halperin of the University of Virginia has written lucidly about how financial markets price ai developments. Yet none is nearly as recognisable as Mr Bloom or Ms Oster. And few economists seem to recognise the research potential. “I’ve been shocked by how few of my colleagues have even tried to speak with Anthropic or Openai”, says one superstar academic economist who insists he talks to ai labs constantly.

A lot of the research that exists is highly abstract. According to ideas/Repec, a bibliographic database dedicated to economics, Daron Acemoglu of mit is the highest-ranked wonk in ai economics. A paper by Mr Acemoglu published early in 2024 features a complex model of economic growth under ai, and implies modest aggregate productivity gains. It has already received more than 1,000 citations. But the model underestimates the potentially transformative effect of new ai products coming to market, argues Tyler Cowen of George Mason University. “The gains from ai measure as small because it is assumed ai will not be doing new things.”

Chart 2

Many ai-linked empirical studies also appear to rest on flawed assumptions. A paper by Erik Brynjolfsson of Stanford University (ranked fifth by ideas/Repec) and colleagues suggests that young people’s employment in ai-exposed occupations has sharply dropped, implying that the technology is already transforming the labour market. Yet attributing the trend to ai means believing that firms started shedding young workers upon the very first release of Chatgpt—a product that was nowhere close to being good enough to replace humans.

Academic economists may be slow and sloppy for two reasons. The first relates to the type of shock that ai represents. In 2020 covid changed the world almost overnight, and the effects were visible almost instantly in the data. By contrast, ai is changing the economy under its bonnet. The average unemployment rate across the oecd, a club of rich countries, is about the same as when Chatgpt was first released (see chart 3). What is more, gdp numbers contain practically no ai-specific data—investment in ai data centres, for instance, can only be guessed at. With no clear macroeconomic impact and no microeconomic data, there is little for wonks to analyse.

The second factor is that economists, as a rule, are a fairly techno-sceptical bunch. Historical research shows that technology raises incomes, but only slowly, with all sorts of non-technological factors (including financial frictions and cultural resistance) holding it back. In Britain it was decades before the technological breakthroughs of the industrial revolution translated into faster growth.

A recent paper by Mr Halperin and colleagues, reporting on the results of a survey, captures this scepticism. Even under a scenario where ai progress is “rapid” by 2030—meaning that ai can compete with or surpass the brainiest humans—the median academic economist expects American gdp growth of just 3.5% in 2050 (compared with 5.3% for ai researchers). Only 11% of leading economists agree that the use of ai over the next decade “will lead to a substantial increase in the unemployment rates in advanced countries”, according to a survey by the University of Chicago. If most academic economists do not think that ai will be transformational, they may prefer to stick with other research areas they consider weightier.

ai-curious economists are finding a cosier home in two places away from the academy. The first is government, and in particular statistical offices and central banks. Surveys from America’s Census Bureau and Statistics Canada track ai adoption across the economy. The Bank of England’s monthly “decision maker panel” has explored businesspeople’s perceptions of ai, while the British government recently created an “ai economics institute” to improve research on the topic. At a recent conference at the oecd, government beancounters puzzled over how to update measures of productivity for the ai age. Much of this work will not set the world on fire, but it performs a crucial public service: building the data infrastructure on which future economists will rely.

The second, more significant place is on the front lines of the technology. In the 2010s ai labs hoovered up many brilliant computer scientists to design their models. Ufuk Akcigit of the University of Chicago and colleagues find that by 2019 more than two-thirds of ai researchers worked in industry, up from less than half in 2001. Now something similar is happening to economists.

Anthropic has appointed Anton Korinek of the University of Virginia (who comes in second on the ideas ranking) to its economics-research team. Openai hired Ronnie Chatterji of Duke University as its chief economist. Google DeepMind, the tech giant’s in-house frontier lab, recently hired Alex Imas of the University of Chicago as its “director of agi economics” (referring to the “artificial general intelligence” that would match or best humans at most intellectual tasks). According to The Economist’s rough tally, a few dozen ai-pilled dismal scientists have accepted jobs at the big labs.

Chart 3

The attraction of an ai lab is clear. They have access to the best data, as well as the ear of policymakers. Accept a position there and before long Dwarkesh Patel, Silicon Valley’s favourite podcaster, will ask you on his show. Tech firms also have deeper pockets than universities do. Even relatively junior economist positions at an ai lab can pay $300,000 a year or more: not high relative to an ai programmer, perhaps, but well above what an early-career professor teaching Econ 101 would earn. (Some lucky ones may get their hands on stock options in the world’s hottest firms.)

The quality of extramural ai research is rising. In work at the Peterson Institute for International Economics, a think-tank, Mr Korinek and Patrick McKelvey of the Bank of Canada have built what they call “ai gdp” for America. The paper shows that, properly measured, it grew by more than 2,000% in both 2024 and 2025. Mr Imas publishes a useful tracker of the effect of ai on productivity. According to his judgment, there is encouraging evidence of small-scale productivity gains, but little evidence of large macro effects.

All very exciting (at least to ai-pilled economic journalists). But for every clever study by Mr Korinek or Mr Imas the labs still produce a dud. Anthropic’s “economic index”, released to great fanfare, is not really an index but a random collection of data about usage of its chatbot, Claude. In March Anthropic published a report concluding that “people get better at using Claude through experience”. No duh. Last year Openai published descriptive work showing that 20-25% of messages on Chatgpt involved “seeking information”. Riveting stuff.

No doubt the quality will improve over time. Still, if frontier ai research migrates inside firms, economists may follow the path already taken by the “tech economists” at Microsoft, Google and elsewhere. These wonks typically spend less time on big questions of social import (asking, say, whether social media is good for children) and more on narrow questions, such as how best to design auctions for selling ads. Mr Akcigit’s study notes that after making a permanent transition from academia to industry, ai researchers produce fewer papers but more patents, in effect a “reorientation from open science towards proprietary innovation”.

Then there are conflicts of interest. Lab researchers are likely to face pressure to publish work that makes ai look useful and safe. Last year Tom Cunningham, an economics researcher, left Openai after reportedly growing frustrated about what he could and could not publish. He ended up at metr, a research institute dedicated to evaluating ai models and the threats they pose. In a world with great possibilities but also great dangers, society needs disinterested researchers to say what they really think. Academic economists have ground to make up.

The world’s top “AI-pilled economists” are a select group of elite academics who have left traditional universities to join the front lines of frontier artificial intelligence laboratories. According to an analysis by The Economist, these “dismal scientists” are being actively headhunted by big tech labs to study the macroeconomic effects of Artificial General Intelligence (AGI), labor disruption, and capital allocation. [1, 2]

The Leading AI Labs and Their Elite Economists

The transition mimics the tech talent poaching wave of the 2010s, but this time, the target is economic expertise rather than computer science. [1]

Ronnie Chatterji (OpenAI): Formerly a professor at Duke University and a senior economic advisor in the U.S. government, Chatterji serves as the Chief Economist at OpenAI. His research focuses on how AI impacts economic growth, productivity, and future job creation. [1, 3, 4, 5, 6]
Anton Korinek (Anthropic): A professor from the University of Virginia, Korinek joined the economics research team at Anthropic. Ranked near the very top of global economic ideas indices for AI impact, his work deeply covers the risks of labor obsolescence and the mechanics of an AGI-driven economy. [1]
Alex Imas (Google DeepMind): Formerly a professor at the University of Chicago, Imas was hired as the Director of AGI Economics at Google DeepMind. He actively researches complex market dynamics, including how the “labor share” of global income might shift toward capital as automation dominates entire supply chains. [1, 7]

Why Economists Are Leaving the Ivory Tower

The migration of top academic minds into private tech labs is driven by three distinct competitive advantages that universities simply cannot match: [1]

Unparalleled Data Access: Tech giants hold proprietary, real-time data on enterprise software spend, user interactions, and labor automation trends. [1, 8]
Direct Policy Influence: Economists operating inside these labs hold the ears of global policymakers who are scrambling to build regulatory frameworks for fast-evolving models. [1]
Vast Financial Resources: AI labs possess significantly deeper pockets than traditional academic institutions, allowing them to fund massive, high-speed research initiatives. [1]

The Critical Debates They Are Tackling

These economists are tasked with answering fundamental questions that will define the next decade of global fiscal policy: [9]

The Productivity Paradox: While most economists agree AI will trigger a near-term spike in enterprise productivity, there is massive divergence on whether it will ultimately net-destroy or net-create employment opportunities. [10, 11]
The “Wealth Pump” Effect: AI-pilled researchers study the structural risks of extreme wealth concentration—specifically, whether AI will disproportionately enrich the tech elite who own the infrastructure while leaving displaced knowledge workers behind. [12]
The Rise of Agentic Markets: Economists at these firms are modeling a future “economy of AI agents” where software tokens autonomously negotiate, buy, and sell goods, drastically driving down transaction costs but introducing unpredictable market volatility. [13]

. Inside the Race to Build AI Data Centers in Space

. I tested AI glasses in Paris. Here’s what they got wrong

. Elon Musk and co may relish march of the robots but there must be AI boundaries in the workplace

. Microsoft CEO Satya Nadella warns AI could leave entire industries struggling if value stays with few companies

. India, France Aim To Expand AI, Data & Academic Partnerships By 2030

. AI buys robot and car, does exactly what experts warned.

. How Unilever is building an AI-first enterprise at scale. Introducing the OpenAI Partner Network

. Helping students and parents prepare for the final exams period

. Read Sundar Pichai’s 2026 Commencement Address at Stanford University-2

. Run Gemma on the edge with the Coral Board

. SwiftUI previews and hot reload in Codex

. Google Fitbit Air is now HSA/FSA eligible in the U.S.