Other | Current estimate
US Workforce AI Exposure
Weighted average across 13 sources. Observed so far: ~41% (3 measurements from Yale Budget Lab, Brookings, Dallas Fed, BLS). Projections range 23–93% (median ~40%).
An estimated 67% of US jobs have significant task overlap with current AI capabilities. This is an exposure measure, not a displacement count. It describes what AI could theoretically do, not what has actually happened. The gap between exposure and actual displacement has been wide: while exposure estimates have risen from 25% to nearly 50%, observed macro job losses attributable to AI remain near zero. Exposure is a precondition for displacement, not a guarantee of it.
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
This prediction has two fundamentally different types of evidence: observed employment data (what has actually happened) and forward-looking projections (what researchers estimate will happen). They are shown separately below because they answer different questions.
Filter by evidence tiers
Note: Exposure measures which tasks could be affected by AI, not which jobs will be lost. Sources use fundamentally different definitions of 'exposure' — from individual task overlap to full occupation-level capability mapping. The 23–93% range reflects these definitional differences, not measurement uncertainty. The weighted average is a mathematical summary, not a consensus estimate. Explore task-level detail in the [Task Visualizer](/task-visualizer).
What has happened
Measured employment data from government statistics, large-scale surveys, and administrative records. This is ground truth: what has actually occurred in the labor market.
Each dot is a different measurement source. Click any dot to jump to its source below.
What researchers project
Forward-looking estimates from structural models, institutional surveys, and expert forecasts. The wide range (23–93%) reflects different model assumptions about reinstatement effects, demand elasticity, and adoption speed, not just parameter uncertainty.
Each dot is a different projection source. The x-axis shows when the projection was published. Click any dot to jump to its source. Overlay bars show directional signals from related studies.
Task Visualizer
What parts of your job will be cheaper to do with AI?
See which of your tasks face cost pressure from AI first.
Full Economy Picture
AI and the US Economy
Automation impact by occupation and income tier.
Sources (47)
NYT: Economists shifting from dismissive to 'it's coming'; policy unprepared
Most still do not see much evidence that A.I. is disrupting the job market. But they are starting to take seriously the possibility that it could someday soon.
Fed SBU: 78% of US labor force works at an AI-adopting firm (employment-weighted)
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.
Brookings: 23M non-degree workers have low AI adaptive capacity
Some 15.6 million—or one-fifth of the nation's 70 million STARs—work in roles highly exposed to AI, meaning they reside in the top quartile of observed occupational task exposure among occupations.
Hosseini/Lichtinger: 21.3% of O*NET tasks >80% automatable; avg 10pp labor pool expansion
We define a binary automation exposure indicator that equals one for tasks in categories 4–5, i.e., more than 80 percent automatable. Under this definition, 4,107 tasks (21.3 percent) are classified as exposed. The mean PSS is about 0.11, implying that for the average occupation, GenAI expands the qualified labor pool by 10 percentage points of the workforce.
FRI: 69 economists assign 61% prob to moderate/rapid AI capabilities by 2030
69 economists assign 61.4% probability to moderate or rapid AI capability progress by 2030. In the rapid scenario, AI surpasses humans on most cognitive and physical tasks.
MIT/CCI: 92% of AI apps map to only 6.8% of 39,603 work activities; exposure deep but narrow
92% of AI applications map to only 6.8% of 39,603 classified work activities. AI apps grew 6x from 2022-2024 but activity coverage expanded only 1.2x. 75% of AI market value concentrated in software/information tasks. Based on O*NET 29.1 and TAAFT dataset.
Otgonsuren: structured-task industries face sharpest AI employment cuts
Industries with high volumes of structured, repetitive tasks face the sharpest near-term employment contractions.
Tufts Digital Planet: 4.9M 'tipping point' workers in 33 occupations swing to >40% displacement
There are 4.9 million 'tipping point' workers -- spanning 33 occupations that swing from <10% to >40% displacement in the next 2-5 years across the US.
Anthropic: 49% of jobs have 25%+ of tasks performed using Claude
About 49% of jobs have seen at least a quarter of their tasks performed using Claude.
Imas/Shukla: Exposure != displacement; O-ring complementarities can raise wages under partial automation
Two jobs with identical exposure scores can have completely opposite displacement risks depending on whether their tasks are complements, whether demand for their output is elastic or inelastic, and the incentives of the firm to invest in automation.
Fed: >98% of coder employment in top AI-exposure quintile
The coding occupations are overwhelmingly in the high exposure group according to both metrics, with more than 98 percent of coder employment in the highest quintiles.
Yale Budget Lab: OpenAI exposure quintiles stable; no shift in worker distribution since ChatGPT
The share of workers in the lowest, middle, and highest occupational exposure groups stay stable. Even when specifically examining the unemployed population, there is no clear growth in exposure to generative AI.
GS Research (Briggs): AI can automate tasks accounting for 25% of all US work hours
In the US, AI can potentially automate tasks that account for 25% of all work hours, Briggs' team finds.
Fed/Duke: Large firms expect AI workforce cuts; small firms expect modest gains
Employment effects heterogeneous by firm size: large companies expect AI-driven workforce reductions while smaller firms anticipate modest employment growth. Compositional reallocation of labor both within and across firms, with routine clerical roles declining and skilled-technical roles increasing.
Anthropic: ~70% of workers have some observed AI task coverage; theoretical far exceeds actual
At the bottom end, 30% of workers have zero coverage, as their tasks appeared too infrequently in our data to meet the minimum threshold.
CMU/Stanford: Agent benchmarks target Computer/Math domain — only 7.6% of US employment
Across all benchmarks, examples collectively cover a limited 56.5% of the domain taxonomy but a substantially broader 85.4% of the skill taxonomy.
KPMG: 57% expect humans to manage/direct AI agents
87% Upskilling/reskilling current workforce
MIT FutureTech: 80-95% AI task success projected by 2029 (N=17,205 evals)
AI models successfully complete tasks that take humans approximately 3-4 hours with about a 50% success rate, increasing to about 65% by 2025-Q3. Most text-based tasks projected to reach 80-95% AI success by 2029. 62.6% of O*NET tasks classified as having at least 10% LLM time-savings potential. Based on 17,205 expert evaluations across 3,000+ O*NET tasks and 40+ LLMs.
Yale Budget Lab: 6 exposure metrics agree on who's exposed, disagree on magnitude (778 occupations)
AI exposure metrics broadly agree with each other, but that they disagree with each other more on highly exposed occupations. The key point of disagreement between different AI exposure metrics is in the magnitude of exposure, not whether an occupation is exposed.
Lightcast: Most-exposed roles 70-78% (office/editorial); least 6-14% (healthcare)
Many kinds of writing and editing jobs are among those most exposed, with over 70% of their core skills potentially affected. On the other end of the spectrum, healthcare and first responder jobs dominate the low end of the rankings.
Indeed: 45% of data/analytics postings and 20%+ of dev/IT/R&D now mention AI
45% of data & analytics job postings mentioned AI, the highest among all sectors analyzed...software development, IT systems & solutions, and scientific research & development each mentioned AI 20% or more of the time
Brookings: Admin support has highest exposure (52.5%) + lowest adaptability
Administrative support occupations have lower adaptive capacity (0.360) combined with the highest AI exposure of any major occupation group (0.525).
the overall Generative AI exposure of the tasks in all white-collar occupations is 40% versus 9% for blue-collar and service occupations
NBER: AI exposure and adaptive capacity positively correlated (r=0.502); most exposed workers best positioned
We find a positive correlation (r = 0.502) between AI exposure and a novel measure of worker adaptive capacity to displacement. Higher-income, highly skilled workers in professional occupations typically possess characteristics that enable successful navigation of job transitions.
Computer/mathematical tasks account for ~33% of all Claude.ai conversations and ~50% of API traffic, indicating concentrated AI impact on tech-adjacent roles.
Cognizant: exposure growing 9%/yr (up from 2%); 30% above 2032 projections
93% of all occupations analyzed have at least one task with significant AI exposure. Education task exposure jumped from 11% to 49% — a 4.5x increase. Based on reassessment of 18,000 tasks across 1,000 O*NET occupations.
In recent years, retail trade has the lowest share of tasks that have been automated — around 50% — whereas computers/electronics has the highest share — more than 85%.
Deloitte: 84% of firms have not redesigned jobs around AI (n=3,235)
Despite high expectations for automation, 84% of companies have not redesigned jobs or the nature of work itself around AI capabilities.
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.
NBER (Gans/Goldfarb): Linear exposure indices overstate displacement when tasks are quality complements
Widely-used exposure indices, which aggregate task-level automation risk using linear formulas, will overstate displacement when tasks are complements. The relevant object is not average task exposure but the structure of bottlenecks and how automation reshapes worker time around them.
OECD: ~25% of OECD workers exposed to generative AI
Around a quarter of workers in OECD Member countries are exposed to generative AI, meaning 20% of their job tasks could be done at least 50% faster with the help of generative AI.
MIT Iceberg Index: 11.7% of US wage value exposed to AI across admin, finance, professional services
Analysis of Bureau of Labor Statistics skill taxonomies reveals that current AI systems can technically perform approximately 16 percent of classified labor tasks. The Iceberg Index for digital AI shows values averaging 11.7%—five times larger than the 2.2% Surface Index.
ILO review: exposure converges on high-wage jobs
Productivity gains 20-60% in controlled RCTs, 15-30% in field experiments; AI exposure measures converge toward high-wage jobs being most exposed.
In about 40% of employment in exposed occupations, at least 50% of tasks will be replaceable. Average labor-cost savings ~25% from current tools, potentially 40% as systems improve.
NBER (Brynjolfsson/Hitzig): AI expanding codifiable knowledge frontier, increasing exposure
Transformative AI sharply expands what counts as codifiable -- and therefore transferable -- 'local knowledge,' in three main ways: it makes explicit knowledge more accessible to decision-makers, it increasingly extracts tacit know-how once embedded in human perception and practice, and it generates machine-native knowledge.
678 occupations having on average 23% of their tasks exposed to Generative AI
OECD: One in three OECD job vacancies have high AI exposure (2025)
With one in three job vacancies having high AI exposure, a significant share of jobs in OECD economies are influenced by the rise of AI. Only a small percentage of training courses currently deliver AI content.
PNAS Nexus: AI-exposed occupations face higher unemployment
Workers in AI-exposed occupations face significantly higher unemployment risk.
Brynjolfsson/Shao: Workers want 46% of tasks automated; preferences vary by task type
Workers want 46% of their tasks automated; preferences vary by task type and worker characteristics.
Oxford/OII: Complementary effects 1.7x larger than substitution; AI demand doubling → 5% rise in complementary skill demand
When AI demand doubles in a job ad, demand for complementary skills rises by 5%. Complementary effects are 1.7x larger than substitution effects. Based on 12 million US job vacancies 2018-2023.
17-36% of worker skills exposed at moderate-to-high AI capability level.
JEMS/Census ABS: 18.2% of workers at AI-using firms by 2017 (n=850K firms)
Employment-weighted adoption was just over 18%. AI use in production was found in every sector of the economy. Manufacturing and information led at roughly 12% each. Based on the 2018 Annual Business Survey of 850,000 firms.
OpenAI/UPenn: 80% of workers have ≥10% tasks affected
~80% of the US workforce could have at least 10% of their tasks affected by GPTs. Legal, accounting, and financial analysis are among the highest-exposure occupations.
Almost 40% of global employment is exposed to AI, with advanced economies more affected.
LinkedIn: 55% of members hold jobs impacted by generative AI
55% of LinkedIn members hold jobs that stand to be impacted by generative AI. By 2030, the skills required for jobs will change by up to 65%.
27% of jobs are in occupations at high risk of automation across OECD countries.
Roughly two-thirds of current jobs are exposed to some degree of AI automation; generative AI could substitute up to one-fourth of current work.
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