Despite AI hype, Google's data shows workers aren't automating themselves away

Anyone following the AI space is by now familiar with lofty claims that AI models will soon be better than humans at everything and capable of replacing vast swaths of the human workforce. In a new study from Google Research, though, a team that looked at how workers are actually using Gemini “[did] not find evidence… to support the claims that AI is about to cause massive automation and displacement of white-collar work…” The paper, released last week, introduces the “AI & Economy ATLAS,” an Activity, Task, Landscape, and Adoption Study of 15 million anonymized AI interactions across the Gemini App, Google’s AI Mode, and the Gemini API. Their initial review of the data finds that, while AI sees some significant use across a wide variety of occupations, that use “remains shallow and overwhelmingly collaborative in nature, with end-to-end task automation limited in scope.” To come to these conclusions, Google researchers used an automated classifier to sort work-based AI interactions using the Bureau of Labor Statistics’ Standard Occupational Classifications and O*NET’s more detailed database of specific work interactions. While this method required some probabilistic classification of “inherently uncertain” interactions, verification by human reviewers found it to be a reliable gauge of how Gemini prompts were being used for work. Unsurprisingly, white-collar jobs in fields like computers, finance, and arts and entertainment were some of the ones where the volume of Gemini use was overrepresented (when compared to their prevalence across the US economy). Financial/market analysts, software developers, and systems administrators were some of the relatively heaviest users of AI for job-related tasks, while salespeople, transportation workers, and food preparation/service workers were heavily underrepresented in the AI use data. That doesn’t mean models like Gemini have been useless for more manual blue-collar jobs, though. The researchers found thousands of examples of industrial machinery mechanics using Gemini for “analyzing test results and machine error messages,” for instance, on top of tens of thousands of conversations where auto mechanics used Gemini to help with “testing vehicle components and systems, rewiring systems, and inspecting parts for wear.” These workers were much more likely than others to feed Gemini a photo for reference, rather than text.