These conversations have unsurprisingly left many workers in a panic (and are probably contributing to support for efforts to entirely pause the construction of data centers, some of which gained steam last week). The panic isn’t being helped by lawmakers, none of whom have articulated a coherent plan for what comes next.
Even economists who have cautioned that AI has not yet cut jobs and may not result in a cliff ahead are coming around to the idea that it could have a unique and unprecedented impact on how we work.
Alex Imas, based at the University of Chicago, is one of those economists. He shared two things with me when we spoke on Friday morning: a blunt assessment that our tools for predicting what this will look like are pretty abysmal, and a “call to arms” for economists to start collecting the one type of data that could make a plan to address AI in the workforce possible at all.
On our abysmal tools: consider the fact that any job is made up of individual tasks. One part of a real estate agent’s job, for example, is to ask clients what sort of property they want to buy. The US government chronicled thousands of these tasks in a massive catalogue first launched in 1998 and updated regularly since then. This was the data that researchers at OpenAI used in December to judge how “exposed” a job is to AI (they found a real estate agent to be 28% exposed, for example). Then in February, Anthropic used this data in its analysis of millions of Claude conversations to see which tasks people are actually using its AI to complete and where the two lists overlapped.
But knowing the AI exposure of tasks leads to an illusory understanding of how much a given job is at risk, Imas says. “Exposure alone is a completely meaningless tool for predicting displacement,” he told me.
Sure, it is illustrative in the gloomiest case—for a job in which literally every task could be done by AI with no human direction. If it costs less for an AI model to do all those tasks than what you’re paid—which is not a given, since reasoning models and agentic AI can rack up quite a bill—and it can do them well, the job likely disappears, Imas says. This is the oft-mentioned case of the elevator operator from decades ago; maybe today’s parallel is a customer service agent solely doing phone call triage.
But for the vast majority of jobs, the case is not so simple. And the specifics matter, too: Some jobs are likely to have dark days ahead, but knowing how and when this will play out is hard to answer when only looking at exposure.
Take writing code, for example. Someone who builds premium dating apps, let’s say, might use AI coding tools to create in one day what used to take three days. That means the worker is more productive. The worker’s employer, spending the same amount of money, can now get more output. So then will the employer want more employees or fewer?
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