Pretty much everyone agrees that artificial intelligence has the potential to reshape the economy in the coming decades. But no one is sure what effect the technology is having right now.<\/p>\n
According to some measures, A.I. is contributing to high unemployment rates among new graduates<\/a> and might already have destroyed tens of thousands of jobs. Other sources suggest companies might actually be adding workers as a result of the technology.<\/p>\n A.I. might be contributing to the U.S. inflation problem, or part of the solution to it<\/a>. It might be responsible for a recent pickup in productivity growth, or might be playing virtually no role \u2014 or the productivity boom itself might be a mirage.<\/p>\n Researchers can\u2019t even agree on basic questions like how many companies are using A.I. or which workers are most vulnerable to the disruptions it could cause.<\/p>\n The conflicting signals partly reflect the challenge of detecting economic shifts in real time. Government statistics are inherently backward looking, and they are better at measuring broad trends than developments in specific sectors or regions. New technologies that might lead to the emergence of new products, jobs or entire industries can be particularly difficult to measure.<\/p>\n What makes A.I. different is the speed of its spread through the economy<\/a>. It has taken less than four years for generative A.I. to go from a novelty useful mostly for writing limericks to a powerful tool adopted by the world\u2019s largest corporations. Economists have become convinced<\/a> that the technology will have profound implications for workers and the economy, even as they disagree about what those implications will be. By the time the data is clear, they warn, it could be too late for policymakers to figure out how to respond.<\/p>\n \u201cThe stakes are super high,\u201d said Nathan Goldschlag, director of research at the Economic Innovation Group, a think tank. \u201cGetting the policy right is going to depend on getting the measurement right. You can\u2019t get the policy right if you don\u2019t know what\u2019s happening.\u201d<\/p>\n Mr. Goldschlag on Thursday published a report<\/a> documenting the challenge of A.I. measurement and proposing steps to improve it. He and other experts argue that the government and the private sector should be devoting more resources to the problem.<\/p>\n They are getting at least a hearing in Washington. In June, a bipartisan group of senators introduced a bill<\/a> that would expand data collection and require the federal government to produce an annual report on A.I.\u2019s effect on the labor force.<\/p>\n \u201cThe government\u2019s got to make some big decisions about A.I. and about the economy, and if you\u2019re doing that in a vacuum, you\u2019re going to make mistakes,\u201d said Senator Mark Kelly, an Arizona Democrat and one of the bill\u2019s sponsors. \u201cThis affects millions of Americans\u2019 lives and millions of businesses. And you can\u2019t do this smartly without reliable data.\u201d<\/p>\n Mixed Signals<\/p>\n Policymakers aren\u2019t flying completely blind. Since 2023, the Census Bureau has asked companies about their A.I. use in a biweekly survey. It has also included questions about the technology in an annual business survey, although only intermittently.<\/p>\n Researchers have developed several measures of \u201cA.I. exposure,\u201d many of which use a government database of job descriptions to assess which occupations will be most affected. Economists can use those measures to figure out whether the most exposed occupations are adding jobs more slowly, for example, or experiencing different rates of wage growth.<\/p>\n The trouble is that the sources often tell confusing or contradictory stories. Surveys reach wildly different estimates of companies\u2019 A.I. use based on how questions are asked. A.I. exposure measures tell different stories about which jobs will be most affected. In one study<\/a>, economists at Northwestern University and American University found that when they used different exposure measures, those could influence not just the scale of A.I.\u2019s effect on jobs but the direction. A.I. was hurting employment according to some measures, and helping according to others.<\/p>\n \u201cIt\u2019s like going to the doctor and getting three different diagnoses for the same condition,\u201d said Michelle Yin, a Northwestern University economist who was one of the study\u2019s authors.<\/p>\n Part of the problem is that the best-known measures of the economy were developed for an era before personal computers and the internet, let alone A.I. The monthly jobs report from the Bureau of Labor Statistics, for example, provides breakdowns of job growth in manufacturing, retail and construction, but not in technology, which has adopted A.I. tools most aggressively. Instead, tech is spread across several categories, including information, a broad sector that also includes newspapers and film studios.<\/p>\n The jobs report provides even less information on occupations that might be vulnerable to displacement, such as software developers, accountants and customer service agents. The most recent breakdown<\/a> of detailed occupations is from May 2025, an eternity ago in the fast-evolving world of A.I.<\/p>\n Still, economists say that for all its shortcomings, government data will be crucial for understanding A.I.\u2019s effect over time. Researchers at the Yale Budget Lab, for example, have begun publishing a monthly analysis<\/a> based on government data that tracks \u201coccupational churn,\u201d how quickly the types of jobs that make up a given industry are changing. The measure is designed to be something of an early warning system for A.I.\u2019s effects. As companies begin adopting the technology, the researchers theorize, they are likely to begin hiring for different roles even if their total number of employees doesn\u2019t change right away.<\/p>\n \u201cIt\u2019s easy to pick up case studies in retrospect,\u201d said Martha Gimbel, executive director of the lab. \u201cWhat makes this time different is we are actually trying to measure this and figure this out in real time.\u201d<\/p>\n But those efforts could be hampered by a federal statistical system that has been plagued by falling response rates to government surveys. Shrinking budgets have made it hard for statistical agencies to fill the gaps. Erika McEntarfer, who led the Bureau of Labor Statistics until President Trump fired her last year, said an additional $10 million a year in funding would allow the agency to expand the sample size of its monthly labor market survey so that it could do a better job of capturing economic shifts.<\/p>\n \u201cThe data we\u2019re currently using to understand A.I.\u2019s impact on the labor market is in jeopardy because of funding shortfalls,\u201d she said. \u201cIt would take only some very modest investments to shore them up.\u201d<\/p>\n Private Data<\/p>\n Many economists aren\u2019t waiting for the government to catch up. Several research teams have released A.I. measures based on private-sector data that is more detailed and more timely, albeit less comprehensive, than what is available from the government.<\/p>\n