The one piece of data that could actually shed light on your job and AI

Within Silicon Valley's orbit, an AI-fueled jobs apocalypse is spoken about as a given. The mood is so grim that a societal impacts researcher at Anthropic, responding to a call for more optimistic visions of AI's future, said there might be a recession in the near term and a "breakdown of the early-career ladder." Her less-measured colleague Dario Amodei, the company's CEO, has called AI "a general labor substitute for humans" that could do all jobs in less than five years. And those ideas are not just coming from Anthropic, of course. 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 predict which jobs were most at risk of automation. However, Imas argues that while this data is useful, it's not enough to fully understand the implications of AI on employment.

"We need to start thinking about the granularity of tasks within jobs," Imas said. "We need to understand not just what tasks are being automated, but also how they are interconnected and how they contribute to the overall structure of a job." He points out that many jobs are not just a collection of individual tasks, but rather a series of steps that build on one another, requiring a certain level of skill and experience to navigate.

Imas believes that economists need to start collecting data on the specific skills and knowledge required for each task within a job. This would involve interviewing workers, analyzing job descriptions, and looking at the skills required for each task. By doing so, economists could gain a better understanding of how AI might impact different parts of the job market and identify which jobs are most vulnerable to automation.

Moreover, Imas argues that this data could help policymakers develop targeted interventions to support workers who are most at risk of being displaced by AI. For example, if economists discover that a particular skill is in high demand across multiple jobs, policymakers could invest in training programs to help workers acquire those skills.

However, collecting this type of data is not without its challenges. Imas acknowledges that it will require significant resources and time to gather and analyze the necessary information. Additionally, the field of AI is evolving rapidly, which means that the data collected today may become outdated quickly.

Despite these challenges, Imas remains optimistic about the potential benefits of this approach. "We need to start thinking about how we can use data to inform our policies and prepare for the future," he said. "If we don't start now, we might be left without the tools we need to address the challenges that AI will bring."

In the meantime, workers and policymakers alike are left to grapple with the uncertainty surrounding AI's impact on the job market. While some economists argue that AI will create as many jobs as it eliminates, others warn that the disruption could be profound and far-reaching.

As the conversation around AI's impact on employment continues, it's clear that more data is needed to understand the full scope of the challenge. By collecting and analyzing granular data on the tasks and skills within jobs, economists could help policymakers develop more effective strategies to support workers and ensure a smooth transition to an AI-driven economy.