Meet the world’s top AI-pilled economists

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

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