Top 100 Most Exposed Jobs to AI
These are the 100 roles where AI can already do the largest share of the day-to-day work. The list comes from the AI Career Index exposure score, a 0 to 100 measure worked out the same way every time. It blends the mix of work in the role (routine, strategic, relational), what AI can already do on each kind of task, and things like personal responsibility, licensing, and physical presence.
Why these jobs are most exposed to AI
Exposure is a read on the work, not a verdict. A role here means a large part of the typical work is now something a machine can do, which shows up as slower hiring, fewer junior roles, and pressure on pay for the routine part of the job. It does not mean the title disappears. It means the value moves up: the people who come through the squeeze are the ones who own the judgment, the system design, or the personal responsibility that AI cannot legally or practically carry.
If your role is on this list, the practical step is to move toward the work AI cannot do. The role pages linked from the table below show, for each role, what exactly is being taken over (the tasks, tools, and sub-tasks) and what the durable work looks like. The Top 100 Best-Paid Durable Roles ranking shows the highest-paying alternatives for someone planning a move.
The ranking spans 1592 roles across 31 industry hubs. Wages are sourced from BLS OEWS May 2025. AI Adoption is sourced from the Anthropic Economic Index (CC BY 4.0). Economic impact multiplies median wage by total US employment to show where the dollar value of compression sits.
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Top 100 Most Exposed Roles
Reading the columns: Median Wage from BLS OEWS, May 2025. AI Adoption is the share of work in the role already showing real-world AI usage today, from the Anthropic Economic Index; a 0% reading means no measurable usage in the AEI sample, not that AI cannot apply. Econ. Impact is the role's total US economic value (median wage × total US employment). Score is the combined AI exposure score, from 0 to 100.
How these rankings are computed
Two things feed every row: a score we work out ourselves the same way every time, and three outside public datasets that sit alongside it without changing the score.
Exposure scores come from the mix of work in each role. Same role, same score, every time. No machine learning, no survey data.
Each role is split into routine, strategic, and relational work, using a category baseline plus role-specific tweaks. The routine share drives the exposure score.
US median annual wages from BLS OEWS. Wage compression multiplies the share of routine work by median wage to show where the dollar value of compression is largest.
Total US economic value of the role, computed as median wage multiplied by total US employment from BLS OEWS. Puts each role's ranking against the bigger economic picture.
Adoption is the share of work in a role that already shows real-world AI usage. It separates roles where AI could apply from roles where it already does. A 0.0% reading means the AEI sample observed no measurable usage for that occupation, common for specialised medical, legal, and trade roles whose practitioners use their own specialist software rather than general-purpose chat assistants.
Rankings update when roles are added or inputs change. See the full Methodology for the scoring inputs, the weighting, and the full set of rules.
Download the complete ranking
All 1592 roles with their exposure scores, work mix, median wage, and wage compression in CSV or JSON format. Available to Premium members and workforce-licensed teams.
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Common questions about this ranking
Other rankings in this set
Three other views of the same data, each answering a different career-planning question.
What the most-exposed roles share
Curated articles on which roles compress fastest, how to read the signals, and how to prepare before the wage premium leaks.
- How to Tell If Your Job Is at Risk from AIMost advice on AI job risk is anecdotal. Five structural factors decide whether your role gets compressed first or holds value as automation accelerates.
- The Mid-Career Compression TrapMid-career professionals are the most structurally exposed cohort in the AI economy. They produce too much currency, hold too little authority, and are the easiest layer to compress.
- The Hidden Cost of Career Drift in the AI EconomyDrift is not neutral in the AI economy. Each quarter without deliberate structural action is measurable lost ground against a market changing pay in real time.
- How to Read a Job Description for AI Compression RiskA job description is a structural map. Five signals that tell you whether a role is on the compression side or the appreciation side of the AI economy.
- The Bimodal Job Market: Why the Middle is Hollowing OutThe 2026 labour market is bifurcating: top-tier roles are pricing upward, bottom-tier physical work is durable, and the middle is compressing fastest.
- Entry-Level Careers in the AI EconomyThe entry-level career ladder is being rewired by AI. A structural guide to which junior roles still exist, which disappear, and what graduates should build now.
- The 25 Most AI-Exposed Jobs in 2026A ranked look at the roles under the most direct AI compression pressure, what they have in common, and why exposure doesn't mean extinction.
- The 5 Roles Most Exposed to AI Compression in 2026Compression is not random. Five categories of professional role are absorbing the heaviest structural pressure right now, and they share specific structural traits.
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