Human value added is earnings divided by a 55% labor share. Machine output is the wage bill of displaced work times a productivity multiplier that rises with adoption.
Sources: US Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics (OEWS), May 2025, annual mean wages. * Pay estimated from the published median. Self-employed count from the BLS Current Population Survey (CPS), June 2026. The sex work baselines (0.6 million in person, 0.4 million online creators) split a midpoint of published estimates that range from 70,000 to 2 million people, so treat those rows as highly uncertain. Online creator pay is the OnlyFans average payout of about $2,520 per active creator per year, which hides a very skewed spread. All dollars are real 2025 dollars.
McKinsey Global Institute (MGI) estimates today's AI agents could in theory automate about 57% of US work hours, and robots another 13%. MGI stresses that this is a ceiling on tasks, not a forecast of lost jobs. The "Automatable tasks that become lost jobs" slider is how this model turns that ceiling into job counts.
Goldman Sachs expects AI to displace 6 to 7% of US workers over the adoption period, with unemployment rising about half a point before new work absorbs people. Morgan Stanley's humanoid model sees 40,000 US robots in 2030, 8 million in 2040 and 63 million by 2050. The robot curve here follows that shape.
What is happening now? Stanford's payroll study finds workers aged 22 to 25 in the most AI-exposed jobs are about 19% below where they would otherwise be, while older workers in the same jobs hold steady. The Yale Budget Lab finds no economy-wide disruption yet. Both can be true. Entry-level knowledge work bends first.
Which pattern does the model show? Knowledge jobs built on codified, repeatable tasks fall early. Physical jobs keep growing until robots get cheap in the late 2030s, then fall fast. Hands-on care keeps growing because an aging country needs more of it.
The brief for this model included a claim that most men and women will become entrepreneurs and a significant share of women will become sex workers. Is there evidence for it? Choose "The claim, tested" and watch what happens to income.
There is real evidence for a shift. Census business applications hit a record 3.23 million in the first half of 2026, and one-person filings are up more than 20% since early 2025, with almost no growth in firms that plan to hire. But customers are finite.
No credible study forecasts a large share of women moving into sex work, but two forces pull in opposite directions. Displacement pushes some women in: a 2025 study found a recession in a female-heavy British industry raised local sex work venues by about 20%. AI pushes the online side out: AI-generated creators already earn about 15% of revenue on Fanvue, and they work around the clock at almost no cost. So the model splits the two. Online creator work collapses, and some of those creators move to in-person work to keep their income. In-person work rises.
So which story fits better? A world where displaced people crowd into self-employment and informal work, or one where new kinds of jobs absorb them? The answer depends mostly on one assumption: how many displaced workers find new jobs. That is a policy and business choice, not a law of nature.
Each occupation gets an AI task share and a robot task share (model assumptions informed by MGI, Morgan Stanley and Anthropic usage research), a baseline demand trend, and a female share rounded from CPS data. Jobs lost each year equal the realization rate times (AI share × AI adoption + robot share × robot adoption). Surviving workers in AI-exposed roles gain a wage premium; heavy displacement pushes wages down. "Not earning" counts displaced people not absorbed by new jobs, new businesses or sex work. Wage counts are jobs, so people holding two jobs appear twice. The 6.9 million incorporated owners already sit inside the wage count and are only counted once.
BLS OEWS May 2025, national table BLS: heavy truck drivers, May 2025 McKinsey Global Institute: Agents, robots, and us (Nov 2025) Goldman Sachs: How will AI affect the global workforce? Morgan Stanley: humanoid robot forecast Stanford Digital Economy Lab: Canaries in the Coal Mine, Aug 2026 update (summary) Yale Budget Lab: Tracking the impact of AI on the labor market Axios: Nasdaq sees AI behind solo founder boom Self-employment statistics 2026 (BLS CPS Table A-9) ILO and NASK: AI and women's jobsThis is a scenario model, not a forecast. Its starting point is measured data. Its growth and decline rates are assumptions built from published research, and they carry very different levels of confidence. Each layer is shown separately below so anyone can challenge it.
Jobs and average pay for all 20 occupations come from the BLS Occupational Employment and Wage Statistics survey, May 2025, a survey of about 1.1 million establishments. Self-employment comes from the BLS Current Population Survey, June 2026. Five pay figures are estimated from published medians and marked with an asterisk. Women's share of each job is rounded from the CPS and is approximate; men and women in the same job are assumed to be displaced at the same rate. Unemployment starts from the 7.1 million people BLS counted as unemployed in September 2026. Security guards and computer programmers sit outside the top 20 and are tracked separately.
Which jobs face pressure first is consistent across independent studies. Codified office work is most exposed to AI agents (McKinsey, Stanford, ILO). Routine physical work is exposed later, through robots (McKinsey, Morgan Stanley). Hands-on care is least exposed and has rising demand (McKinsey).
The adoption curves, the share of automatable tasks that become lost jobs, each occupation's exact task shares, and how displaced people move into new jobs, businesses or sex work are judgment calls. Small changes here move the 2056 results a lot, which is why they are sliders. The sex work baseline is the least certain number in the model.
Two pieces are anchored to published figures. The robot S-curve follows the shape of Morgan Stanley's US path of about 40,000 humanoids in 2030, 8 million in 2040 and 63 million in 2050: slow before 2035, steep in the 2040s. It runs ahead of that path in 2040, at about 27% of full adoption versus Morgan Stanley's 13%, so even the Moderate case is ahead of Morgan Stanley on robots in the late 2030s. The default Fast adoption case reaches 50% robot adoption in 2040. The realization slider turns McKinsey's 57% "technically automatable hours" ceiling into job losses. Task concentration then raises the loss rate for jobs built around one task, such as driving or checkout, toward 100%, because automating that one task leaves little job behind. In the Fast adoption case about 41% of all wage jobs are displaced by 2056, and 45% of those people find new work. Fast adoption is deliberately at the high end of the research, built for keynotes; Moderate is the central estimate.
Pareto check: the top 20 occupations hold about 50.1 million of 155.5 million wage jobs, or 32%. That is not an 80/20 split, because 20 occupations are only 2.4% of the roughly 830 that BLS tracks. The 80/20 rule would compare the top 20% of occupations, about 166 of them, with all jobs.
AI task share and robot task share are the model's estimates of how much of each job's work agents and robots can do once adoption is complete. Demand trend is the yearly change in demand before any automation.
Three upgrades would move the low-confidence layer toward medium:
Backtest it. Run the model from 2022 and check whether it reproduces the actual 2022 to 2026 changes by occupation in BLS data and the Stanford payroll results. A model that cannot replicate the recent past should not be trusted about the future.
Replace judgment with published indices. Swap the AI task shares for an occupation-level exposure index built from actual AI usage or task analysis, and swap the demand trends for the BLS 2024 to 2034 employment projections.
Report ranges, not points. Run thousands of draws across the slider ranges and show the 10th to 90th percentile band for each job.