Google Data Shows Automation Intent Above 25% in Routine Cognitive Work

Google published its first AI & Economy ATLAS report on Thursday, finding that more than a quarter of AI conversations involving routine cognitive work targeted end-to-end task automation. The company's announcement highlighted the report's finding that attempts to automate tasks end to end represented less than 10% of conversations involving non-routine cognitive work and did not include the routine-work figure. ATLAS classifies what users asked AI to do; its authors state that the data cannot show whether the intended output was achieved or how effective the exchange was.

Automation intent by task

The report maps tasks using five categories from Autor and Thompson: Routine Cognitive, Routine Manual, Non-Routine Cognitive Analytic, Non-Routine Manual and Non-Routine Interpersonal. Under that categorization, "routine tasks are ones that are codifiable, meaning they can be fully specified through a set of structured instructions or rules." The report defines "Task Automation" as asking AI to execute a core task or major sub-task end to end.

The routine category recorded automation intent in more than a quarter of conversations. For non-routine cognitive work, including hypothesis testing and creative design, automation attempts accounted for less than 10% of conversations.

Google built a classifier that assigns conversation clusters to five categories: Task Automation, Partial Drafting and Generation, Review and Refinement, Ideation and Strategy, and Information Retrieval and Learning. The report states that de-identified conversations are summarized and grouped into anonymized clusters, and that researchers "instruct Gemini 3.1 Flash Lite to select a label that best fits the role the AI is asked to perform in the cluster summary." The report calls the method "a preliminary attempt to deduce the relationship between AI usage and human work within a task" and notes that clusters "could nest multiple user intents."

ATLAS says these findings "should not be considered definitive" and describes the difference as potentially meaningful. Interpersonal and manual tasks showed even less automation intent under the same classification.

"Just because you're using AI doesn't mean it's going to automate your job," Scott Strand, a Google economist and one of the report's researchers, told the Journal.

The ATLAS sample

Roughly 14.7 million de-identified interactions across the Gemini App, Google AI Mode and the Gemini API, sampled between April 6 and April 19, 2026, make up the report's sample. The announcement gives no collection window. Work-related activity accounted for 14% of conversational, non-API usage.

The report describes the breadth and task depth as "shallow" diffusion. AI activity appeared in more than 68% of all occupations, and those occupations represent 88.4% of employed U.S. civilian workers. The median occupation with any recorded AI use showed activity in 21% of its constituent tasks. Three percent of occupations showed use across more than three-quarters of their tasks.

Fabien Curto Millet, Google's chief economist, told the Journal, "It's the start of a whole research pipeline from us." Version 1.0 describes the results as "early observations."

Earnings and physical work

For U.S. civilian workers, median annual earnings were $62,252 when weighted by employment and $82,919 when weighted by Gemini conversations, the report calculated. A 1% increase in an occupation's median earnings was associated with more than 2.5% higher AI-use intensity. Nearly a third of heavily physical occupations showed no observed AI use. Among those with use, automotive technicians and industrial mechanics had multimodal activity more than twice the overall work baseline.

ATLAS states that higher-wage workers may automate routine cognitive tasks while collaborating with AI on non-routine work, a pattern that "could lead to a deepening of wage inequality" between that group and workers less able to use AI.

Payroll and hiring records

ATLAS cites Massenkoff and McCrory's suggestion that hiring may have slowed for younger workers in exposed occupations. The Google study does not test whether AI use by experienced workers reduces entry-level hiring.

Built by Stanford economist Erik Brynjolfsson with ADP Research, the Canaries dashboard tracks payroll records. As of April 2026, employment among workers ages 22 to 25 in the most AI-exposed roles contracted 3.8% from a year earlier; employment for the same ages in the least-exposed occupations grew 2%. "In the aggregate, AI's impact on jobs remains modest. But when AI's impact is measured by career stage, dramatic differences emerge," said Nela Richardson, ADP's chief economist.

A Swedish study based on register data and job advertisements by Magnus Lodefalk, Lydia Löthman, Erik Engberg and Michael Koch found a 5.5% employment decline among workers ages 22 to 25 in high-exposure occupations relative to less-exposed roles; workers ages 31 to 49 showed little change. The authors reported that the hiring decline was roughly four times the change in separations. "The adjustment occurs almost entirely through reduced hiring," they wrote in ProMarket on June 17.

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Marcus Schuler

San Francisco

Editor-in-Chief and founder of Implicator.ai. Former ARD correspondent and senior broadcast journalist with 10+ years covering tech. Writes daily briefings on policy and market developments. Based in San Francisco. E-mail: editor@implicator.ai