AI is used in 68% of occupations representing 88% of US employment, from software developers to farmers, industrial engineers, and foresters. It’s not automating away those jobs but is helping workers do their jobs.

By Wolf Richter for WOLF STREET.

Some of the AI company founders and insiders, and AI promoters, were for a while spreading a lot of doom-and-gloom about AI resulting in total-job-destruction Armageddon as nearly all office jobs would soon be done by AI.

But they have recently backed off. And the employment data are showing that actual layoffs in the US have been small, and many people who got laid off found a new job while still on severance pay, collecting two salaries for the remainder of the severance period, and never qualified for unemployment insurance. Layoff announcements by global companies included layoffs in other countries, not impacting the US job market. Some “layoff” announcements included the elimination of vacant job openings. And other layoff announcements were made by companies that were still hiring in other departments, and laid-off workers could apply for those jobs.

So initial unemployment insurance claims, which track the freshly laid-off people who submitted the initial application for unemployment insurance compensation, have been at historic lows and fell further in recent weeks, including the current reporting week released today by the Labor Department, despite the much larger labor force today than years and decades ago:

But there have been a lot of shifts and job changes brought on by the use of AI, and there has been the issue of fresh-out-of-college workers running into a job market where AI has allowed experienced people to automate some grunt work, thereby making it harder for young workers that would initially do that grunt work to enter the job market. All these changes are new and not well tracked yet.

So now Google came out today with an analysis (100-page PDF) of how its AI services are actually being used, how humans interact with AI, both on the job and at home. This includes the use of Google AI mode in browsers, Google’s Gemini app, and Google’s Gemini API (which allows a company to integrate Google’s AI models into its own website and services for content generation, conversational agents, long-document summarization, and custom AI agents).

A one-sentence summary of the analysis might go like this: AI is being widely used on the job, at all kinds of jobs, from coding to auto technician, but is being mostly used by workers to get their jobs done, and to get the job done better, and not to automate away those jobs.

Google charges companies for its Gemini app and Gemini API in several ways: By bundling Gemini with its other software offerings and raising the monthly subscription fees (for example, it integrated Gemini into Workspace and jacked up the monthly subscription by 16.7%, as per Wolf Street Corp.’s experience); via pay-as-go input and output tokens, caching and storage, and add-ons; via its Cloud tools; and in other ways.

The analysis that Google released today was the first in a series of papers – the AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study) – on the usage data of Google AI.

Google’s analysis covers AI use at work, AI use outside of work, and AI use internationally. Given the massive nature of the report, I’m focusing here only on six summaries of work-related portions. The below excerpts are directly quoted from the analysis.

1. AI has diffused very broadly in both work and life. Adoption in the workplace spans all major sectors (e.g., from Professional and Business Services, to Construction, Leisure and Hospitality, and more). It also spans over 68% of all occupations that collectively represent just above 88% of total US employment, including both much-discussed occupations such as software developers and market researchers, and those less-discussed such as farmers, industrial engineers, and foresters.

2. Though broadly used at work, the overall depth of AI’s use is shallow. AI is used for only 21% of total tasks in the median occupation with any AI use. Only 3% of occupations showed AI usage for over 75% of their tasks; these occupations include software quality assurance analysts and testers, human resources specialists, and document management specialists.

3. While there is some task automation, the vast majority of use so far is collaborative and assistive to tasks and work. Non-routine cognitive tasks (e.g. hypothesis testing and creative design) make up only about 35% of the professional tasks in the economy as a whole, yet they make up almost 65% of work-related AI interactions in our data. In our initial attempt to taxonomize intent, only a small amount of this usage appears to be focused on automation based on our classification. Instead, usage of AI for non-routine cognitive work is centered on Partial Drafting and Generation, Review and Refinement, Ideation and Strategy, and Information Retrieval and Learning. Attempts to automate tasks end-to-end represent less than 10% of AI conversations in non-routine cognitive work appearing in our data.

4. AI use is not only a white-collar phenomenon; it is also assisting in physical and manual work. While nearly a third of heavily physical occupations show no observed AI usage, workers in many manual and technical trades are using AI on the job. In these roles, AI frequently acts as a hands-on collaborator for diagnostics, troubleshooting, and real-time learning. We also observe disproportionate multimodal use of AI (i.e., uses that involve images and video) in these contexts: for example, automotive technicians and industrial mechanics using AI to interpret complex test results, debug electrical wiring, and inspect machinery for wear, where the usage rate of multimodal AI is more than 2 times higher than the overall work baseline.

5. Work-related AI usage correlates strongly with higher wages and education. In the US workforce, a 1% increase in an occupation’s median earnings is associated with a more than 2.5% increase in AI usage intensity. Weighted by Gemini conversations, the median salary across observed occupations is around $83,000, roughly $20,000 higher than the true employment-weighted national median. This relationship persists even after controlling for the occupation’s educational attainment (which itself is positively correlated with AI usage).

6. While wages and expertise are typically correlated in the economy, ATLAS points to a complex relationship between AI usage and expertise. When classified according to expertise levels, tasks requiring lower-to-middle levels of expertise tend to see relatively higher AI usage than the highest expertise level tasks, despite ATLAS data also suggesting high-earning workers—and therefore those with more scarce skills and expertise—adopt AI at the highest rates.

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