Methodology

The five dimensions in full

• Task resistance — how much of the day-to-day task list current AI already does at usable quality. Scored inverted, so a high number means AI does less of it.
• Embodiment — physical work in unpredictable physical environments. Robotics lags language models badly, so hands-on work scores high here.
• Liability shield — is a licensed human legally required to sign, and personally on the hook if it goes wrong? Software can draft; it cannot be sued.
• Trust premium — do buyers specifically pay for a human relationship, presence, or accountability, rather than just the output?
• Judgment & accountability — does the role own consequential calls made with incomplete information, where someone has to carry the decision?

Verdicts

• SAFE 67–100 — AI changes the tools, not the job.
• EXPOSED 34–66 — the job survives but shrinks or splits; the routine tier is at risk.
• COOKED 0–33 — the core task list is already automatable; what remains is adoption speed.

Does a low score mean you get replaced?

The question the whole site exists to answer, and it deserves a direct answer rather than a rubric. Three things are true at once, and only the first one is what most people expect.

At the level of an occupation, exposure has already translated into fewer jobs. Between 2019 and 2025 the median COOKED occupation lost 13.4% of its workers and 75% of them shrank, against +8.3% for the median SAFE occupation. That is BLS counting people, not a forecast.

But pay did not fall with it. Real wages moved by +0.7% in the median COOKED occupation and -0.3% in the median SAFE one. If AI were degrading exposed work in place — same job, weaker position, worse pay — exposed wages would fall relative to safe ones. They do not. So the effect is not "your job gets worse". It is that there are fewer of the job.

And the shrinking is mostly hiring that stops, not people who are dismissed. A double-digit decline spread over six years is overwhelmingly posts not backfilled and slower recruitment, not layoffs. Which means most people already doing an exposed job are not replaced at all — the person who loses is the one who never gets hired. That harm is real, falls almost entirely on new entrants, and is invisible in both the headcount and the wage of everyone already inside.

One more distinction worth holding: the score and the BLS projection on each page run on different clocks. The score is what current AI could do to the work today — the rubric says "already performs at usable quality" and "score today's robotics, not sci-fi". The projection is how many jobs exist a decade out. Adoption speed and demand growth sit in between, which is why exposed occupations can still be projected to grow.

What the verdicts are not

• Not a prediction with a date — we score capability exposure, not adoption timelines.
• Not a judgment about worth — "cooked" describes task exposure, not people.
• Not static — verdicts are re-scored as capabilities change; every page shows a review date, and disputes ship with credit.

How a verdict is actually produced

Every number on this site comes out of the same pipeline. It is written down here so you can find the step you disagree with.

1. Population. All 830 detailed occupations from the BLS Occupational Employment and Wage Statistics, May 2025 — the 6-digit SOC level. Broader groupings are excluded because they double-count employment. No occupation is chosen or dropped for being interesting.
2. Blind scoring. Each occupation is scored on its own, against the fixed rubric above, by claude-opus-5. The model sees the occupation title, SOC code, category and employment count — and nothing else. It does not see other occupations' scores, so it cannot anchor on them or drift as a run progresses.
3. Fixed calibration anchors. The prompt carries five reference points with published score bands (data entry keyers, paralegals, software developers, registered nurses, electricians). They hold the scale steady across 830 independent calls. We re-score those anchors to check the scale has not moved: the last check landed 4 of 5 in band at medium reasoning effort and 5 of 5 at high.
4. The score is computed, not asserted. The model supplies five dimension scores; the total is calculated from them here, and the verdict follows from the total by fixed thresholds. A dimension outside 0–20 is rejected and the occupation re-scored. The model's own opinion of the total is discarded.
5. Justification, separately. A second pass takes those scores as fixed and explains each one. It is permitted to refuse — if a score cannot be defended it is recorded as disputed rather than dressed in a plausible sentence. That makes the justification an independent check, not a rationalisation.
6. Reliability testing. A random sample is re-scored from scratch in a fresh context and the two runs compared. The agreement rate, the drift, and the direction of the drift are published above. We test a random sample precisely because testing only the wobbly ones would flatter the result.
7. Adjudication, by a rule fixed in advance. 143 occupations have been scored twice. Publishing whichever run happened to come first is a choice made by arrival order, so we apply one rule to all of them: average the runs, but only act on the result if it survives the arithmetic. We recompute the merge five ways — per dimension and on the total, rounding halves up, down, and to even — and change the verdict only when every one of those agrees. 10 occupations passed that test and were re-scored. 18 did not: their label depends on how a halfway point is rounded, so we publish the original, mark it contested, and show both runs on the page. The remaining 115 had two runs that agreed.
   
   We tried the obvious version first — average the two runs, round, republish — and it moves 14 verdicts, of which only 10 survive being rounded a different way. A verdict that depends on our rounding convention is not a finding about anyone's job, so those changes are not published.
8. Evidence, kept separate from scoring. Reported deployments are collected and linked to occupations, but they are not an input to the score. The rubric measures capability exposure; the evidence shows what is being done about it. Keeping them apart means a wave of press coverage cannot quietly move a verdict.
9. Re-scoring on evidence, not on a clock. Because re-scoring drifts a couple of points on its own, a scheduled refresh would mostly republish our own noise. An occupation is re-examined when new deployments accumulate against it, and a new score inside the measured noise band updates the review date without changing the published number.

The scoring prompt and rubric are at /method/scoring-prompt.txt; every score and rationale is at /reports.json. If you think a verdict is wrong, you can read exactly how it was reached — and say so. The implementation code is not published; it is shared with researchers and journalists on request, and it is not what a verdict is checked against.

Third external check: a competing estimate

Employment trend and real wages test these scores against outcomes. This tests them against a rival method — the best-known one there is. Frey & Osborne (2013) estimated probability of computerisation for 702 occupations, and most automation-risk numbers online still derive from it. Joining on SOC code gives 760 occupations scored by both.

Put on the same axis, the two scales correlate at r = 0.521. Moderate, which is the interesting answer: strong enough that this is measuring a real and related thing rather than generating noise, weak enough that it is not merely restating a 2013 paper.

The distribution of the disagreement is the actual test, and it was predicted before it was run. A robotics-era model should agree about physical work and be wrong about desk work, because generative AI did not exist when it was written.

On physical work the two methods land within 1.6 points of each other out of 100 — near identical across 458 occupations. On desk work they separate by 27.3 points, and this register is the more pessimistic one every time. That is the signature you would want: agreement where the older model is valid, divergence confined to where it is out of scope.

It is a consistency check, not a proof — two methods agreeing can both be wrong, and neither has been tested against what actually happens to these occupations. Source: Frey & Osborne (2013) probabilities as published by willrobotstakemyjob.com, whose data feed states it is free to use in other projects. Aggregates only; their per-occupation figures are not stored or republished here. Reproduce with scripts/validate-against-frey-osborne.mjs. Fetched 2026-08-15.

Known limits of this method

Every number here has a way of being wrong. These are the ones we know about, stated before anyone has to find them.

• The verdict is a step function over a continuous score. An occupation at 33 is COOKED and one at 34 is EXPOSED, off a one-point difference. Re-scoring the same occupation moves results by a point or two, so near a boundary the label is less stable than the number behind it. 79 of 830 occupations sit within one point of a boundary; each says so on its own page.
• EXPOSED is a wide band. It spans 34–66 and holds 530 of 830 occupations. For most of the register the score and the reasoning carry the information, not the chip.
• Occupations that already survived an automation wave are scored as though they hadn't. The scorer sees a title, a SOC code, a category and a headcount — no history. So an occupation that a previous wave already ran through gets judged on what its name suggests rather than on who is left doing it. Word Processors and Typists scores 8/100, but the people still in it are the ones forty years of computerisation did not remove: headcount is down 46% since 2017 while the median wage held roughly flat in real terms, which is the signature of a surviving specialist core rather than a job about to be finished off. 59 of 830 occupations carry that pattern and each says so on its own page. The scores are left as published — adjusting them toward a hypothesis without re-running the scorer would manufacture agreement rather than earn it. The fix is to give the scorer this history at the next re-score. Raised by a reader on r/dataisbeautiful, and correct.
• Scores are assigned by a language model, not measured. Every occupation was scored by claude-opus-5 against the rubric above, with the same prompt and fixed calibration anchors. That makes it reproducible and auditable, not authoritative.
• We measured how reproducible it is, and publish the result. Scored independently a second time, a random sample of 60 occupations produced the same verdict 97% of the time, with the score moving a median of 2 points and no directional bias — the average change was +0.35 points against a spread of 3.6. In plain terms: run it twice and you get a slightly different number, about as likely to be higher as lower. Imprecise, not optimistic or pessimistic.
  
  We then went looking for the weak spot on purpose. Every one of the 79 occupations sitting within a point of a threshold was re-scored — not a random sample, deliberately the hardest cases — and 18 of them came back with a different verdict. That is a 23% flip rate at the edges, against 0% among the 64 re-scored occupations that sit in the middle of a band.
  
  So the instrument is steady in the middle and genuinely shaky at the edges. That is why every near-boundary page says so on its own page rather than leaving it in this footnote.
  
  Re-running does not fix the edges, and nothing else does either. A continuous score has to become one of three words, and near a threshold that conversion is genuinely arbitrary. Averaging the two runs settles only 10 cases robustly; 18 stay marked contested on their own pages because the answer changes depending on how you round a halfway point.
• The five dimensions are not independent, and we double-count physical work. Across all 830 occupations, task resistance and embodiment correlate at 0.86 — physical work isn't screen work, so it scores high on both for the same underlying reason. The other three cluster too. A factor analysis of our own output finds two real dimensions explaining 83% of the variance, not five: roughly "can a machine physically do this" and "will law and custom permit it". Because physicality occupies two of five slots, it carries about half the score — an accident of how the rubric was divided, not a judgment anyone made. The practical effect is that manual work scores somewhat higher, and desk work somewhat lower, than a non-duplicating instrument would produce. We keep all five because they explain why a job is protected, which is what you'd want to argue with; but the weighting is an artefact and you should know it.
• The verdict boundaries are arbitrary, and they fall where the data is densest. Scores are roughly normal — mean 50, standard deviation 17 — so the cutoffs at 33 and 66 sit almost exactly one standard deviation either side of the middle, which is where most occupations are. Nothing in the data clusters at those points; they are thirds of a scale. That is why 79 occupations sit within a single point of a different headline.
• The scorer knows where the thresholds are, and it aims at them. The rubric states that 67 is the bottom of SAFE, and the model has read it. The result is a pile-up exactly on the line: 35 occupations score precisely 67, while only 3 score 66. Given how dense the distribution is around there, roughly 12 would be expected at each — so the point just above the line is about 3.0× as crowded as it should be, and the point just below is at 0.26×.
  
  The same thing happens at the other line, in the same direction: 6 occupations score 33 against 18 at 34, where about 12 would be expected at each. Both cutoffs show a deficit immediately below and a surplus immediately above. Chance does not pick the same direction twice.
  
  In practice the model appears to decide which label a job deserves and then assemble dimensions that clear the bar — the reverse of the process described above. Recomputing the total from the dimensions does not prevent this, because the dimensions were chosen to hit the total. The artefact reproduces in independent re-runs, so it is a property of the instrument rather than of one bad batch.
  
  What this means when you read a page: for occupations at or near 67, the number is doing the work of a category label and its apparent precision is not real. Re-scoring that group returned a mean of 66.5 — the level is about right, so we have not adjusted it. It is the granularity at the cutoff that is manufactured, and you should not read "67 vs 66" as a measured difference.
• Checked against what actually happened to employment. Every score here is a judgement about what AI can do. Employment is not — it is counted by the BLS. Comparing the two across 758 occupations with data for both 2019 and 2025 gives the register its only external test, and it passes in the right direction:
  
  COOKED: median -13.4%, 75% shrank · EXPOSED: median -2.2%, 53% shrank · SAFE: median +8.3%, 31% shrank
  
  The gradient is clean across all three bands. But the correlation between the raw score and the raw change is only 0.31 — the score explains about a tenth of the variation. Employment moves on demand, offshoring, demographics and the business cycle, and automation is one term among several.
  
  This is a consistency check, not a prediction test. The scores were written in 2026 by a model that already knows desktop publishing collapsed. It was not asked to forecast 2019, so some of this agreement is hindsight rather than insight, and we will not know how much until the register is tested against years it could not have seen. The honest claim is that the verdicts are consistent with the recent past, not that they predicted it.
• A second external check, on pay, which the register fails. Employment is one outside test. Wages are another, and independent of it — a job can hold its headcount while the pay quietly erodes, which is the less visible half of what automation does to an occupation. We had the series for 755 occupations from the first BLS import and had never looked at it.
  
  Median real pay change, 2019–2025 (nominal, deflated by roughly 25% US CPI):
  COOKED +0.7% · EXPOSED -1.1% · SAFE -0.3%
  
  There is no gradient. The three bands are within about a point and a half of each other and they are not even in the right order — the most exposed band did marginally better than the safest. Real pay fell for 52% of occupations regardless of verdict, which says a great deal about the period and nothing whatever about our scoring.
  
  So the register predicts headcount weakly and pay not at all. We publish it because a page that names its own worst calls cannot sit on a null result for being inconvenient. It also sharpens what the score is for: it describes what the work exposes to automation, and exposure turns out to be a poor guide to what anyone gets paid. If you came here to find out whether your wages will hold, this register cannot tell you.
• Where the score and the job count most disagree. The gradient above holds on average, so the places it breaks are what tell you how much to trust this about one specific job — yours. These are the widest gaps in each direction, picked by the data rather than by us.
  
  Read them carefully, because "the score was wrong" is usually the wrong conclusion. Terrazzo Workers scored 68 and SAFE and then lost 60% of its workforce, and we called that our worst miss for a while. But terrazzo is hand-troweled stone laid on site by a crew: the rubric was right that AI can't do it. The trade shrank because demand for terrazzo floors shrank. Filing that as a scoring failure confused an occupation getting smaller with an occupation being automated, which is the exact error this whole page exists to warn against — committed by us, on the page warning about it.
  
  Scored SAFE, shrank anyway: Hairdressers, Hairstylists, and Cosmetologists 75/100, -20.8% (305,710 workers) · Brickmasons and Blockmasons 68/100, -13.4% (52,550 workers) · Kindergarten Teachers, Except Special Education 75/100, -12.4% (108,870 workers)
  
  Scored COOKED, grew anyway: Medical Secretaries and Administrative Assistants 28/100, +59.0% (961,610 workers) · Web and Digital Interface Designers 31/100, +37.6% (113,330 workers) · Market Research Analysts and Marketing Specialists 28/100, +32.6% (899,580 workers)
  
  Both lists carry a 50,000-worker floor, so a small occupation where a few hundred jobs swing the percentage cannot headline them. Worker counts are shown because a 20% fall across 300,000 people and a 60% fall across 1,200 are not the same kind of number, and only one of them is stable.
  
  The important thing about these misses is that none of them is an automation story. Pandemic closures, building cycles, demographics and public policy all move headcount, and over a window containing 2020 they move it hard. We are not going to attribute a cause to each one, because this data cannot support that — an employment series says how many people held a job, never why. What it can show is the limit, stated twice from both directions: exposure is not employment. The rubric can be right about what the work involves and still tell you nothing about whether the job count goes up or down, because automation is one term among several and rarely the loudest.
• The score is not a one-way ratchet, and reading it as one is the most common mistake. Because AI capability only improves, it is natural to assume every score can only fall. Two of the five dimensions do track capability and will tend to decline: task resistance and embodiment. The other three — liability shield, trust premium, and judgment & accountability — track law, what buyers will pay for, and who is answerable. Those are institutional, they respond to exactly the pressure AI creates, and they move in both directions. Task resistance can also rise without any new law: when the routine tier of a job is automated away, what remains is the judgment tier, so the occupation shrinks while the surviving work becomes harder to automate.
  
  Each occupation page names the specific changes that would raise its score, marked by whether they are already in motion. Those are conditions, not forecasts. They are also capped honestly: for many occupations every available lever still leaves the verdict where it is, and the page says so rather than offering comfort the rubric does not support.
• There is no cost-to-automate term, and that matters. Automation is an investment decision, not a capability threshold. A job can be technically automatable for years and never automated because the wage is low, the sites are small, or the work varies too much to be worth the capital. We score what AI can do, not what anyone will pay to deploy — so treat these as exposure, not as a schedule.
• We score the modal worker, at the national median. Not the elite tier, not the worst case, and not your specific employer. A BLS title often bundles jobs that feel unrelated from the inside.
• Transfer routes are computed, not curated. The "where this experience transfers" list on each page comes from overlap in O*NET skill and knowledge profiles — how you work and what you work on — filtered so a suggestion has to clear every one of: at least 10 points more resistant, no large jump in required training, no licence you'd have to start a new pipeline to get, no meaningful pay cut, and not itself losing headcount.
  
  Those last three exist because similarity alone produces nonsense a skill vector cannot see. Without them the engine recommends restaurant cooks retrain as manicurists (a licensed trade, and a pay cut) and cement masons move into terrazzo work — an occupation this page names above as having lost 60% of its workforce. Even with the filters, this is a starting point for research rather than careers advice, and some of it will still be wrong. Where nothing clears the bar, the page says so instead of lowering it.