Other | Current estimate | Data through Mar 2026
Observed AI Use at Work
An estimated 46.3% of US workers have AI observed doing some part of their actual work. This is the measured counterpart to exposure: platform telemetry and nationally representative worker surveys, rather than capability mapping. The number is sensitive to where the threshold sits. Requiring AI to touch at least a quarter of a job's tasks yields 49%; counting any observed coverage at all yields about 70%. Reach has run well ahead of depth: AI shows up somewhere in occupations covering 88% of US employment, but the median occupation with any use runs it on only 21% of its tasks.
This is observed data from real-world surveys and measurements, not a prediction. See the full methodology for details on weighting, source validity, and recency bias.
Indicators Over Time
The chart below tracks how this estimate has shifted over time as new research and data emerge. Every source is color-coded by evidence quality; use the tiers below to filter what appears on the chart and in the weighted average above.
Filter by evidence tiers
Note: Sources set different thresholds for what counts as “using AI,” so the spread here is definitional as much as empirical. Anthropic's 49% counts jobs where at least a quarter of tasks show up in Claude traffic; its 70% counts any observed coverage at all; Hartley et al. ask workers directly. Breadth is not depth: Google's ATLAS finds Gemini use somewhere in occupations covering 88% of US employment, but the median occupation with any use runs AI on only 21% of its tasks. That 88% figure measures how far AI has reached, not how many workers use it, which is why it sits below the chart rather than on it. For what AI could do rather than what it is doing, see US Workforce AI Exposure.
Directional research signals
Studies with a clear directional finding but no single plottable value — e.g. “entry-level hiring fell” or “no measurable displacement detected.” Stacked blocks show net evidence per month; positive and negative signals cancel. Hover any column to see the studies.
Each data point is from a different source. Dots are color-coded by evidence tier. Click any dot to jump to its source.
Task Visualizer
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Full Economy Picture
AI and the US Economy
Automation impact by occupation and income tier.
Sources (6)
Google ATLAS: median occupation uses AI for only 21% of tasks — broad but shallow
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Google's AI & Economy ATLAS v1.0 (Iscenko, Strand, Chen, Imas, Manyika et al., Google/Google DeepMind, Jul 23 2026; reviewed by Diane Coyle and David Autor). 15M de-identified interactions across Gemini App, AI Mode, and Gemini API (Apr 6-19, 2026), mapped to 800+ occupations, 4,000 O*NET tasks, 300 ATUS activities, 150 countries, 140 languages. Work: 'AI adoption spans occupations covering just above 88% of US employment' (68% of detailed occupations) but 'penetration remains shallow' — 'AI is used for only 21% of total tasks in the median occupation with any AI use'; only 3% of occupations show usage for >75% of tasks. 'Attempts to automate tasks end-to-end represent less than 10% of AI conversations in non-routine cognitive work'; >25% for routine cognitive work. Non-routine cognitive tasks = 35% of O*NET universe but 65% of work interactions. Wages: 'a 1% increase in an occupation's median earnings is associated with a more than 2.5% increase in AI usage intensity'; Gemini-weighted median salary $82,919 vs $62,252 employment-weighted national median. Home: 86% of conversational use is non-work; government/civic queries over-represented ~20x vs time spent; household time-savings valued at $14.9B-$149B/yr (0.5-5% scenarios). Global: 1% GDP/capita ↑ → 0.9% usage ↑; English only ~1/3 of conversations across 143 languages.
OpenAI: 66pp capability overhang — 90% theoretical vs 23.8% realized exposure in high-risk jobs
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Jobs at high automation risk: gap 66.2 pp, 23.8% realized, 90.0% theoretical. Jobs that grow with AI: gap 49.7 pp, 22.7% realized, 72.4% theoretical. Jobs that will reorganize: gap 52.3 pp, 14.9% realized, 67.1% theoretical. Jobs with less immediate change: gap 21.0 pp, 6.4% realized, 27.4% theoretical. Across every job category, current usage lags behind the possible. Exposure alone is a weak predictor of immediate labor market pressure.
Fed SBU: 78% of US labor force works at an AI-adopting firm (employment-weighted)
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The SBU estimates an employment-weighted firm AI adoption rate of around 78 percent and an LLM adoption rate of about 54 percent. In this context, employment weighting approximates the share of the labor force working at firms that have adopted AI.
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At the bottom end, 30% of workers have zero coverage, as their tasks appeared too infrequently in our data to meet the minimum threshold.
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Computer/mathematical tasks account for ~33% of all Claude.ai conversations and ~50% of API traffic, indicating concentrated AI impact on tech-adjacent roles.
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LLM adoption among U.S. workers increased from 30.1% to 38.3% between December 2024 and December 2025. Small effects on wages in exposed occupations; no significant effects on job openings or total jobs.
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