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<\/figure>\nWhat the Benchmark Map Actually Measured<\/h2>\n
The method is simple enough to explain in a sentence. Mercor assembled 29 benchmarks into a single index, then mapped each individual benchmark prompt onto the tasks that O*NET uses to describe American work: 18,797 tasks across 923 occupations, sorted into 22 job families.<\/p>\n
One prompt can map to several tasks, so the count is generous rather than strict. Even so, 924 tasks out of 18,797 showed up at all. Coverage was then scored at two thresholds, one prompt and five prompts, which is where the picture sharpens.<\/p>\n
Why ‘covered’ means ‘measured’, not ‘automated’<\/h3>\n
This is the part most readers get backwards. A covered task means someone wrote a test for it. It says nothing about whether a model passes that test, and nothing about whether the work can be automated in your plant.<\/p>\n
Uncovered means the opposite of what people assume too. It does not mean AI can’t do the work. It means nobody has produced evidence either way, which is a worse position to buy from than a low score.<\/p>\n
What the 5-prompt threshold tells you about evidence quality<\/h3>\n
A single prompt is an anecdote. If one question in one benchmark happens to resemble part of a task, that task counts as covered, even though you’d learn almost nothing from the result.<\/p>\n
Five prompts is still a thin sample, but it is the point where a score starts carrying signal rather than noise. Hold your own vendor evaluations to the same bar. If a capability claim rests on one demo against one document, you have an anecdote, not evidence, and the gap between those two is where pilot budgets disappear.<\/p>\n
Where the Coverage Gap Hits Hardest: Production, Transportation, and Healthcare<\/h2>\nThe industrial job families with near-zero benchmark coverage<\/h3>\n
Put the job families side by side and the split is hard to miss. The work that keeps a plant running is barely measured at all, while desk work is almost fully mapped.<\/p>\n
| Job family<\/th>\n | Occupations covered<\/th>\n<\/tr>\n<\/thead>\n |
|---|---|
| Computer & Math<\/td>\n | 29 \/ 36<\/td>\n<\/tr>\n |
| Business & Financial<\/td>\n | 33 \/ 48<\/td>\n<\/tr>\n |
| Management<\/td>\n | 21 \/ 56<\/td>\n<\/tr>\n |
| Architecture & Engineering<\/td>\n | 19 \/ 56<\/td>\n<\/tr>\n |
| Installation & Repair<\/td>\n | 7 \/ 50<\/td>\n<\/tr>\n |
| Healthcare Practitioners<\/td>\n | 12 \/ 89<\/td>\n<\/tr>\n |
| Personal Care<\/td>\n | 0 \/ 31<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Installation and Repair, the maintenance technicians who decide whether a line runs tomorrow, sit at 7 of 50. Healthcare Practitioners, 12 of 89. Personal Care, zero. Management, the people signing off on AI spend, come in at 21 of 56.<\/p>\n So when a vendor says their model “outperforms experts,” ask which experts. The claim is almost certainly anchored in Computer & Math or Business & Financial territory, where benchmarks are dense. It says nothing about a maintenance planner or a sterilization technician.<\/p>\n Why white-collar roles got mapped first<\/h3>\nThis was not an accident of research taste. Mercor’s own analysis shows coverage tracks the annualized wage bill of an occupation: the bigger the labor market and the higher the AI-exposure tag, the more likely a task gets benchmark prompts. Lawyers get heavy coverage. Similarly AI-exposed roles outside that bracket get close to none.<\/p>\n Benchmark builders are following the money, and the money sits in software and professional services. Manufacturing tasks are harder to encode, harder to score, and spread across hundreds of narrow occupations.<\/p>\n That leaves industrial buyers evaluating tools on evidence collected from somebody else’s job. Useful for a procurement deck. Useless for a shift.<\/p>\n Mercor ran a task-level regression and found something worth sitting with: the probability a task is covered by at least five prompts climbs with the occupation’s annualized wage bill. That wage bill comes from BLS mean wage and headcount figures, assuming a 40-hour week. Bigger payroll, better coverage. The curve runs from near zero at the $500m mark up toward 20% for occupations with tens of billions in annual wages.<\/p>\n That is a rational allocation of research effort. If you are building an evaluation, you build it where the market is. The practical consequence for manufacturers is that nobody is in a hurry to measure the work your plant does, because your occupation’s wage bill does not justify the dataset. Waiting for the research community to catch up means making decisions in the dark for years.<\/p>\n Mercor tagged each task with a binary AI-exposure flag from Eloundou et al. (2023), meaning a model could plausibly do some or all of it. You would expect exposure to drive coverage. It does, but far less than money does. Two tasks can carry the same exposure tag and sit at opposite ends of the coverage distribution purely because one belongs to a high-wage-bill occupation.<\/p>\n The source puts it plainly: benchmarks heavily cover AI-exposed lawyers’ tasks but barely touch similarly exposed tasks elsewhere. So exposure tells you a task is technically within reach of current models. It tells you nothing about whether anyone has checked. Treat exposure as a hypothesis, not evidence, and go verify it on your own tasks.<\/p>\n The method is replicable at company scale. List the tasks your quality and operations people actually perform, score each one by hours consumed per month and how repeatable it is, then build an evaluation set from the top handful. You are doing what the researchers did across 18,797 tasks, except you only care about forty of them.<\/p>\n Two weeks of internal evaluation work costs less than one quarter of a deployment that quietly fails. It also produces evidence no public leaderboard will hand you.<\/p>\n Pull real documents, not synthetic examples. Twenty to fifty cases per task is enough: closed deviation reports, supplier audit findings, CAPA write-ups where the outcome is already known and signed off. Historical cases are the point, because you can compare the model’s answer to what your team actually decided.<\/p>\n Mix in the hard ones. If every case is a clean, well-written input, you are testing nothing. Include the ambiguous complaint, the audit finding with missing evidence, the deviation written badly at 3am on night shift.<\/p>\n The people who do the work score the output. Not procurement, not IT. A quality engineer knows within ten seconds whether a root cause statement is defensible in front of an auditor, and that judgement is the only signal worth collecting.<\/p>\n Score on things that carry consequences: Did it identify the correct root cause category? Would this pass a notified body review? How much editing before it goes in the QMS? Rate each case pass, fail, or needs rework, then count. A tool that needs rework on half your cases is not saving anyone time.<\/p>\n |
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