
Top 25 AI Career Risk Rankings
Ranked lists across 1592 roles in the index. The most exposed, the most insulated, and the highest projected wage compression in dollar terms. Scores are deterministic and derived from the same structural framework used on every role page.
Four cuts of the same dataset
The most exposed list ranks roles by how much of the day-to-day work AI can already do. A high score does not mean the job disappears. It means a large part of the work is the kind AI handles well, so the production layer of the role faces the steepest pressure on hiring and pay.
The lowest risk of automation list flips that order. These are the roles held safe by professional licences, named accountability, hands-on work, or relationships AI cannot replace. As AI absorbs the work around them, these roles tend to gain value, which is why they are useful for school-leaver decisions, mid-career pivots, or anyone planning a long horizon.
The best-paid durable list takes the safest roles and sorts them by US median pay. Same low-risk careers, ranked by what they actually earn. This is the practical answer when both safety from AI and a strong salary matter together.
The biggest wage compression list shows where the dollar value of AI-replaceable work is largest. We multiply each role's share of routine work by its US median pay. A role with 60% routine work and a $100K salary carries $60K of pay sitting in work AI can already do. A role with the same 60% share and a $40K salary carries only $24K. This list surfaces where the income at risk is concentrated.
Four rankings, four answers
Each leaderboard has its own dedicated page with the full Top 100, methodology, and FAQ. Pick the one that matches the question on your mind.
The 100 jobs where AI can already do the largest share of the day-to-day work.
The 100 careers safest from AI, protected by licences, accountability, or hands-on work.
Where AI safety and a strong salary overlap. The 100 best-paid AI-safe careers.
The 100 jobs where the dollar value of AI-replaceable work is largest.
Prefer the big picture? Every industry ranked by its average AI exposure score, with the number of roles behind each.
Skip the leaderboards. Jump straight to your role.
Type your job title to open the full structural reading: risk band, automation share, durable skills, the six-month plan, and where the role sits in the wider ranking.
Top 25 Most Exposed Roles
The roles where AI can already do the largest share of the day-to-day work. These jobs face the steepest pressure on hiring, pay, and headcount as AI tooling absorbs the production layer.
Reading the columns: Median Wage from BLS OEWS. 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 composite exposure score 0–100.
Top 25 Roles With the Lowest Risk of Automation
The safest jobs in the index. They are protected by professional licences, named accountability, hands-on work, or the kind of judgment AI cannot replace. As AI absorbs the work around them, these roles tend to gain value over time. Useful for school-leaver decisions, mid-career pivots, or anyone planning long term.
Top 25 Best-Paid Durable Roles
Jobs that are both safe from AI and pay well. We start with the safest third of all roles, then sort by US median pay. The practical answer when both automation safety and a strong salary matter together.
Top 25 Biggest Wage Compression
Jobs where the dollar value of AI-replaceable work is largest. We multiply each role's share of routine work by its US median pay, so the list shows where high pay meets high exposure. Only includes roles with US wage data.
Download the complete ranking
All 1592 roles with their exposure scores, task composition, median wage, and compression exposure in CSV or JSON format. Available to Premium members and workforce-licensed teams.
One-time $49 unlock
How these rankings are computed
Two layers feed every row: a deterministic structural score we compute in-house, and three external public datasets that sit alongside it without blending into the score.
Exposure scores derive from role composition inputs in the AI Career Index framework. Same role, same score, every time. No machine learning, no survey data.
Each role is split into routine, strategic, and relational work using category-level priors plus role-specific adjustments. The routine share drives the exposure score.
US median annual wages from BLS OEWS. Wage compression multiplies routine share by median wage to surface where the dollar footprint of compression is largest.
Total US economic value of the role, computed as median wage multiplied by total US employment from BLS OEWS. Reframes individual rankings against macro stakes.
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 domain-specific tooling rather than general-purpose chat assistants.
Rankings update when roles are added or inputs change. See the full Methodology for deterministic scoring inputs, weighting logic, and the full set of derivation rules.
Questions about the rankings
How the scores are computed, how to read the columns, and how often the dataset moves.
Background reading on what the leaderboards show
Curated articles on how to read these rankings, where the compression is hitting hardest, and what to do about your own structural position.
- How to Read the AI Career Risk RankingsThe leaderboards rank jobs by AI exposure, durability, pay, and wage compression. Here is what each column means, how the scores are built, and how to use them for your own move.
- 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.
- What Makes a Job Safe from AI Automation?AI-safe does not mean AI-free. Four structural moats protect careers from AI: licensure, named accountability, physical presence, and adversarial dynamics.
- 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.
- 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 25 Most AI-Insulated Jobs in 2026The roles AI cannot easily touch: protected by licensure, physical presence, judgment, and accountability. Ranked and explained.
- Which Jobs Are Safe from AI in 2026?A structural look at which jobs remain durable against AI compression and the traits they share.
- The 5 Roles Gaining Authority in the AI EconomyWhile compression hollows out the middle, five categories of role are accumulating structural authority. They share traits worth understanding.
- 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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"I hire for a living and the structural reading reframed how I think about candidate positioning. Half of the candidates I was moving forward on were in structurally compressing roles regardless of how the title read. I am now screening for authority density, not title seniority. Quality of placements has noticeably shifted."
"Engineering leadership roles are supposed to appreciate under AI. The framework was more nuanced than that. It distinguished between engineering managers anchored in code review and those anchored in system architecture. The former is compressing. The latter is not. My role needed repositioning and the report named the specific move."
"The bimodal reading of finance roles matched my own observation of our internal career progression patterns. Junior analyst work has been hollowing out for two years. Senior deal work has not. The framework gave me specific language for something our partners had been sensing but not naming."
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