AI use by pay level: what the best-paid jobs actually hand over
In the five lowest-paid US occupational groups, 97.8% of tasks brought to Claude were classified as work the person could have finished without it. In the five best-paid, 87.9%. The first PayScope Signals report joins Anthropic's May 2026 usage data to BLS wages.
Michael Vavilov
Product leader with a track record of launching AI-driven HR and talent platforms that scale rapidly, boost user acquisition, and create measurable operational efficiencies.
In the five lowest-paid occupational groups in the United States, 97.8% of the tasks people brought to Claude in May 2026 were classified as work they could have completed without it. In the five best-paid groups, 87.9%. Across all 22 groups the share falls as the group's median wage rises, and the rank correlation between pay and that share is strong: Spearman -0.78. Both figures come from Anthropic's own usage data set against Bureau of Labor Statistics median wages.
That points the opposite way from the usual reassurance. The comforting version says AI takes the routine parts and leaves the judgment to the well-paid. What the usage data shows is that the well-paid are the ones handing over work classified as beyond what they could finish alone, while in low-paid work AI is mostly doing things the person was already able to do. Our earlier report on AI job exposure by occupation asked what AI can do. This one asks what it is actually being given, and the two answers are not the same.
Method
Two public sources, joined on the SOC occupation code.
The usage side is the Anthropic Economic Index, release of 26 June 2026, which reports how conversations with Claude were classified by occupation, geography and task attribute. We used the United States rows for May 2026 at SOC major-group level, 22 groups. The accompanying June 2026 report documents the sampling.
The pay side is the Bureau of Labor Statistics Occupational Employment and Wage Statistics programme, May 2025 release, national cross-industry annual median wage for the same 22 groups. That is the current OEWS release, so the wage figures sit twelve months behind the usage figures.
Three terms do the work in this report, and each is quoted from the dataset documentation rather than paraphrased.
Human-only ability is the share of tasks where the classification was "yes, a human could complete the task without AI assistance". A high number means most of what that group brings to Claude is work it could have done alone. Automation and augmentation are the two collaboration buckets a task can be assigned to. Usage share is the percentage of the country's total conversations falling in the group, so the 22 shares sum to the United States total. It is a share of usage and not a rate of adoption.
Every attribute above is assigned by a classifier reading the conversation. Nobody asked the workers.
The 22 groups
| Occupational group | BLS annual median | Share of US Claude usage | Automation | Could have done it alone |
|---|---|---|---|---|
| Management | $126,520 | 5.85% | 45.8% | 92.0% |
| Computer and Mathematical | $109,280 | 21.13% | 66.4% | 85.3% |
| Legal | $102,500 | 1.04% | 32.3% | 78.9% |
| Architecture and Engineering | $99,520 | 2.91% | 47.9% | 88.4% |
| Healthcare Practitioners and Technical | $86,530 | 4.33% | 43.1% | 95.1% |
| Business and Financial Operations | $82,660 | 6.27% | 43.3% | 91.2% |
| Life, Physical, and Social Science | $82,530 | 4.23% | 39.9% | 90.1% |
| Arts, Design, Entertainment, Sports, and Media | $62,750 | 12.91% | 32.6% | 93.6% |
| Educational Instruction and Library | $60,570 | 11.92% | 45.3% | 95.6% |
| Installation, Maintenance, and Repair | $59,620 | 0.81% | 76.4% | 95.5% |
| Construction and Extraction | $59,540 | 0.14% | 70.5% | 97.1% |
| Community and Social Service | $58,300 | 3.20% | 40.0% | 95.5% |
| Protective Service | $50,080 | 0.41% | 77.9% | 97.7% |
| Office and Administrative Support | $47,450 | 7.64% | 59.3% | 92.3% |
| Production | $46,990 | 1.08% | 65.1% | 87.7% |
| Transportation and Material Moving | $44,350 | 0.39% | 55.4% | 96.8% |
| Sales and Related | $38,530 | 11.60% | 46.5% | 98.0% |
| Healthcare Support | $38,340 | 0.68% | 42.0% | 96.5% |
| Building and Grounds Cleaning and Maintenance | $37,680 | 0.17% | 50.5% | 96.5% |
| Farming, Fishing, and Forestry | $36,630 | 0.05% | 71.8% | 99.6% |
| Personal Care and Service | $36,410 | 1.27% | 57.2% | 97.3% |
| Food Preparation and Serving Related | $35,050 | 0.64% | 52.4% | 99.2% |
Claude usage: May 2026, Anthropic Economic Index release of 26 June 2026. Wages: BLS OEWS, May 2025, US national cross-industry.
As pay rises, the share of work people could have done alone falls
This is the strongest relationship in the data and the reason the report exists. Legal sits lowest in the country at 78.9%, on a median of $102,500. Computer and Mathematical is next at 85.3%, on $109,280. At the other end, Farming, Fishing and Forestry is at 99.6% on $36,630, and Food Preparation at 99.2% on $35,050.
Read the extremes plainly. Roughly one in five legal tasks brought to Claude was classified as something the person could not have completed without AI assistance. In food preparation, fewer than one in a hundred.
The gradient is not tidy. Healthcare Practitioners sits at 95.1%, high for a group on an $86,530 median, and Arts and Media at 93.6% is high for a group that supplies 12.91% of all usage. Neither breaks the direction, and with 22 groups it is the ranking that carries the signal rather than any single position.
The automation split points the same way, more weakly
Sort the groups by pay and the five lowest average 54.8% automation against 47.1% for the five highest. Same direction, much weaker signal: across all 22 groups the rank correlation with pay is only -0.35, against -0.78 for the measure above.
One group is responsible for most of that weakness. Computer and Mathematical runs 66.4% automation on a $109,280 median, the highest of any well-paid group and fifth highest of the 22. Software work is the exception to the pattern inside the automation measure, and that is worth stating rather than smoothing away.
The clean end of the finding is Legal against Computer and Mathematical. Both are professional groups on six-figure medians. Legal is 32.3% automation, the lowest of all 22 groups. Computer and Mathematical is double that. Same pay bracket, opposite relationship with the tool. That the distinction matters more than any exposure ranking is the argument of the AI salary band split, made there without data behind it. This is the measured version. The two most automated groups on the list, Protective Service at 77.9% and Installation, Maintenance and Repair at 76.4%, both sit on medians under $60,000 and both supply under 1% of usage.
Where the usage actually sits
Usage is concentrated much more tightly than the pay gradient suggests. Computer and Mathematical alone is 21.13% of all United States Claude conversations. Arts and Media is 12.91%, Educational Instruction 11.92%, Sales 11.60%, Office and Administrative Support 7.64%. Farming is 0.05% and Construction is 0.14%.
So the two findings above describe very different amounts of real activity at each end. The low-paid groups where nearly everything could have been done alone are also the groups sending almost no work. That is a genuine limit on how far the pattern can be pushed, and it belongs next to the finding rather than in a footnote.
What this does not mean
The measurement is Claude, not AI in general. One vendor's traffic, one classifier's labels, and no way to tell from this data what people are doing in other tools.
It describes 22 occupational groups, not individual people. A pattern across groups says nothing about any one lawyer, cook or engineer, and a reader who finds their group in the table has learned something about the group and nothing about their own job.
Nothing here is causal. The data does not say AI raised or lowered anyone's pay. It says what kind of work is being handed over at each pay level, which is a different and narrower claim.
The documentation does not explain how an occupation is assigned to a conversation, and the assignment looks coarse at finer levels of detail, which is why this report stops at the 22 major groups and does not use the 627 individual occupations the same release publishes for the United States. Cells below Anthropic's aggregation and sample thresholds are not published at all, so a missing figure deeper in the data means suppressed rather than zero. And the wage side is a year older than the usage side.
The question this leaves you with
The data cannot tell you which pattern your own week looks like. It has no view of you. What it does is make the distinction worth checking, because the two patterns carry different negotiating positions.
If most of what you hand to AI is work you could have done yourself, the tool is buying you time, and time is the thing an employer captures rather than pays for. If a real share of it is work you could not have finished alone, your output has changed, and that is a compensation conversation with evidence behind it. Our guide to asking for a market adjustment raise covers how to make that case.
Either way, the number that decides it is not in this report. It is what the market currently pays for your role, your skills and your location. See where you sit in the market.
This is the first PayScope Signals report. The series joins public data nobody has put side by side and publishes the result on a date, so the next edition can be compared against this one.
Frequently Asked Questions
What does "could have done it alone" mean in this report?
It is the share of tasks that a classifier labelled as work a human could have completed without AI assistance, taken from the Anthropic Economic Index release of 26 June 2026. A high figure means most of what people in that occupational group bring to Claude is work they were already able to do. A low figure means a larger share of it was classified as work they could not have finished on their own. The label is applied by a classifier reading the conversation, not reported by the worker.
Does AI mean less pay for workers?
This data cannot answer that, and it is worth being clear about why. It measures what kind of task is handed to Claude at each pay level, not what happened to anyone's wages afterwards. What it does show is that the pattern differs sharply by pay: in the five lowest-paid US occupational groups, 97.8% of tasks brought to Claude were classified as work the person could have done alone, against 87.9% in the five best-paid.
Which occupations send the most work to AI?
Computer and Mathematical occupations account for 21.13% of all United States Claude conversations in May 2026, more than any other group by a wide margin. Arts, Design and Media follow at 12.91%, Educational Instruction and Library at 11.92%, and Sales at 11.60%. Farming, Fishing and Forestry is the smallest at 0.05%.
Is software work an exception to the pattern?
Yes, on the automation measure. Computer and Mathematical occupations run 66.4% automation on a $109,280 median wage, the highest of any well-paid group and fifth highest of all 22, which cuts against the general direction that better-paid work sees more augmentation. On the other measure it fits the pattern: at 85.3%, it has the second lowest share of tasks people could have done alone.
How current is this data?
The usage figures are for May 2026, from the Anthropic Economic Index release published on 26 June 2026. The wage figures are from the Bureau of Labor Statistics Occupational Employment and Wage Statistics programme, May 2025 release, which is the most recent available. The two sides are therefore twelve months apart.
