Worker categories combine the occupations shown in the simulator list, not every occupation in the economy. Unlisted wage jobs remain in “All other wage jobs.” Values use the selected year and assumptions; the full area shows the modeled path, not a forecast.
Each category below lists its source rows and their current-year counts. For Small Business Owners, the model deducts an assumed overlap with payroll employment; the ranking shows the full owner count. This deduction is an existing model assumption, not a verified count of overlapping small business owners. New LLM-era jobs include an illustrative 2025 workforce estimate, reclassified from other wage jobs to avoid double-counting. Figures are rounded for display.
Human value added is earnings divided by a 55% labor share. AI and robot 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 small business owners 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 group gets an AI task share, a robot task share, a baseline demand trend, and a women’s share from the selected country’s baseline data where available. Automation and growth paths are model assumptions informed by published research. 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 informal-work pathways. Country-specific limits and source definitions are stated beside the starting data.
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 the US occupations come from BLS OEWS, May 2025. This measured baseline is separate from the model’s uncertain projections.
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.
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%. The default Fast adoption case reaches 50% humanoid robot adoption in 2040. The realization slider turns McKinsey's 57% "technically automatable hours" ceiling into job losses. Task concentration raises the loss rate for jobs built around one task, such as driving or checkout. New LLM-era employment is now modeled independently of displacement: Slow uses 3% annual net growth, Moderate 7%, Fast 12%, and The claim, tested 7%. These are illustrative sensitivity assumptions, not likely growth rates established by research. The 30,000 US starting estimate is also very low confidence: about 82,000 generative-AI skill postings in 2023–2024 × an assumed 50% genuinely new roles × 80% distinct filled jobs × 90% retention gives about 30,000. Those conversion percentages are not measured. Older AI/robotics jobs and existing jobs merely using AI tools are excluded from this row. Other re-employment is not separately estimated here. Neither Moderate nor Fast is a validated forecast.
Evidence: Lightcast's 2023–2024 US generative-AI posting figures. Job advertisements are not employed people; the model estimate should not be presented as a measured workforce count. The gender share and wage for these new roles are also illustrative assumptions.
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.
Four of the five fastest-growing roles in LinkedIn's US ranking are AI-related. Published January 7, 2026, the report identifies growth in both technical AI work and the work of implementing, training and evaluating AI systems.
| Rank | Job type | Connection to AI |
|---|---|---|
| 1 | AI engineers | Develop and implement AI models; also called machine learning engineers. |
| 2 | AI consultants and strategists | Help organizations plan and implement AI technologies. |
| 3 | New home sales specialists | Not classified as an AI-related role in this comparison. |
| 4 | Data annotators | Label and review training data for AI and machine learning, often on a project basis. |
| 5 | AI/ML researchers | Design and test AI and machine learning models and algorithms. |
What LinkedIn measured: Researchers examined millions of jobs started by LinkedIn members from January 1, 2023 through July 31, 2025. Titles needed positive growth, sufficient recent job postings and a meaningful size by 2025. Seniority variants were grouped. Internships, volunteer, interim and student roles, and roles dominated by a small handful of employers, were excluded. This is a US platform-based ranking, not a representative national employment census or a Canadian ranking.
What this means for Jobshift: There is observed growth in AI-related work, supporting the inclusion of an AI-created jobs category alongside displacement. However, AI engineering, machine learning research and data annotation existed before ChatGPT. Only genuinely new LLM-era duties belong in our narrower “New Jobs Due to AI/Robotics” baseline; the entire LinkedIn categories cannot simply be added to it. Existing workers moving between occupations are not automatically net new jobs for the economy.
What it does not establish: Four of five is a share of ranked job titles, not 80% of employment or new jobs. The article does not publish the headcounts or numerical growth rates needed to calibrate this category. It cannot validate our illustrative 30,000 US starting estimate, 3% / 7% / 12% annual growth scenarios, a 20-year projection, or a permanent human advantage over AI. Those assumptions remain low confidence and unchanged.
Read LinkedIn's original US report and methodology · Download Jobshift's findings summary (HTML). The public report was retrieved as a web article; the summary is not a LinkedIn-issued PDF. Reviewed October 21, 2026.
The same model equations and scenario assumptions are restarted from BLS May 2022 employment counts, with adoption reset to zero in 2022 and the original adoption timing retained. No parameters are fitted to the observed changes. Observed values are BLS OEWS estimates; rideshare is excluded because it is self-employed and outside OEWS. The assembler row follows the comparable “Miscellaneous Assemblers and Fabricators” SOC category.
“Error” is model change minus observed change, in percentage points. Large mismatches are flagged at an absolute error of 10 points or a wrong direction when the observed change is at least 5%. Values are employment counts, not unique people. May 2022 and May 2025 are survey estimates, not censuses; this two-endpoint check does not show when changes happened. BLS May 2025 uses six panels collected from November 2022 through May 2025, so intermediate annual estimates overlap.
Repeat the automated scenario checks with pnpm --filter @workspace/jobshift test:backtest.
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.
The 2022–2025 check is an initial hindcast, not proof that the model predicts future employment accurately. The BLS May 2025 estimates use six survey panels from November 2022 through May 2025; annual releases in between share panels, so they are not independent. This two-endpoint comparison does not identify when changes happened. The large mismatches below are visible, not calibrated away. No occupation-level May 2026 OEWS release is available yet; this comparison cannot score 2026.
Next steps for stronger evidence are to replace judgment-based task shares and demand trends with published occupation-level exposure and BLS employment projections, and to report ranges across the assumption sliders rather than single scenario paths.