Anthropic’s latest research suggests that while AI is rapidly changing the way work gets done, it hasn’t meaningfully eliminated jobs. At least, not yet. But beneath what Anthropic’s head of economics, Peter McCrory, says is a “still healthy” labor market, early signs are pointing to uneven impacts, especially for younger workers just entering the workforce.
In an interview on the sidelines of the Axios AI Summit in Washington, McCrory said the company’s newest economic impact report finds little evidence of widespread job displacement so far.
“There’s no material difference in unemployment rates” between workers who use Claude for the “most central task of their job in automated ways” — like technical writers, data entry clerks, and software engineers — and workers in jobs less exposed to AI that require “physical interaction and dexterity with the real world.”
But with AI adoption spreading across industries, that could shift — fast. If Anthropic CEO Dario Amodei is to be believed, AI could wipe out half of all entry-level white-collar jobs and push unemployment as high as 20% within the next five years.
“Displacement effects could materialize very quickly, so you want to establish a monitoring framework to understand that before it materializes so that we can catch it as it’s happening and ideally identify the appropriate policy response,” McCrory told TechCrunch.
Staying ahead of those trends is why tracking AI growth, adoption, and diffusion is so important, he said.
“Anything that a computer can do, in principle, Claude and other large language models can [do],” McCrory said. “What we see in practice is that people and businesses are actually bringing a very small subset of tasks to the model.”
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He said Anthropic looked at which roles involve tasks AI is particularly good at, are already being automated, and are tied to real workplace use cases — the areas most likely to signal where displacement could emerge.
Anthropic’s fifth economic impact report, released Tuesday, also found that even where there hasn’t been much displacement yet, there’s a growing skills gap between earlier Claude adopters and newcomers.
Earlier adopters are more likely to get significantly more value from the model, using it for work-related tasks rather than casual or one-off purposes and in more sophisticated ways, like as a “thought partner” for iteration and feedback.
“This points in the direction of [AI] being a skills-biased technology that might potentially reinforce differences and outcomes among those who have higher or more skills at getting value out of these tools, and that could have uneven implications in the labor market,” McCrory said.
He noted that workers who understand how to use AI to augment their work will be in higher demand in the labor market, further widening the gap.
The report also found that “Claude is used more intensely in high-income countries, within the U.S. in places with more knowledge workers, and for a relatively small set of specialized tasks and occupations.”
In other words, despite promises of AI as an equalizer, adoption may already be tilting towards the wealthy, and could amplify those advantages as power users pull further ahead.
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