Could AI create jobs while leaving workers behind? McKinsey models the US transition
A 29 September McKinsey Global Institute report models occupational moves through 2035. Its scenarios are planning assumptions, not observed redundancies or a forecast for an individual worker.
Editorial responsibility: The Impact of AI Editorial Desk · Report a factual concern
At a glance
- 1The scenarios concern US occupational transitions through 2035, not measured layoffs.
- 2Our analysis focuses on whether workers can afford and access the proposed routes.
Living evidence record
Impact record IAI-14096RJ
Evidence stage
Announced
Confidence
Developing
Reporting basis
Source analysis
Independent support
Not yet
Record status
Monitoring
Last checked
30 September 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what McKinsey Global Institute published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
What the new report actually estimates
McKinsey Global Institute published its US workforce study on 29 September 2026. It estimates that about 11 million workers in declining occupations could need to change occupations by 2035, with a scenario range of six million to 16 million. The central estimate represents roughly 7% of current US employees. It is a model output, not a count of people already displaced.
The analysis combines occupational employment data, automation assumptions and demand scenarios. It uses Lightcast job postings to compare skills and advertised pay, alongside credential and training requirements. The report considers four transition dimensions: skills, credentials, training time and wages. A language model helped synthesise qualification requirements. There is no single survey sample that makes these projections representative observations of future employment.[1]
Our analysis: a growing market can still be hard to enter
The practical question is whether a worker can reach the new opportunity. Imagine an administrator offered a route into a growing healthcare occupation. The destination might have vacancies and better pay, yet require years of study, supervised practice and a licence. A household needing this month's income cannot treat that vacancy as an immediately available replacement. This hypothetical example illustrates why a net employment total tells only part of the story.
A useful local response would start with a transition budget, not a course catalogue. How many paid learning hours can the employer provide? Can the worker retain benefits while retraining? Is the destination within commuting distance, and are employers actually hiring entry-level applicants? A pathway that looks close on a skills map may remain inaccessible because of childcare, transport or the cost of an unpaid placement. These questions are our interpretation of what a credible transition programme should test.
What workers and employers can measure
For a worker, the most informative exercise is to separate changes within a job from a move to another occupation. Keep a short record of tasks that disappear, tasks that expand and new checking responsibilities. Ask an employer which changes are already funded and which are exploratory. That provides a basis for discussing training without treating a national scenario as a personal probability of redundancy. It also avoids assuming that familiarity with a chatbot is equivalent to a recognised professional qualification.
Employers evaluating a transition programme should follow the whole group offered support, including people who cannot enrol or who leave early. Count sustained placements, earnings after transition and the time taken to find work. Record the denominator for each measure. Reporting only successful graduates would conceal whether the programme works for staff with the greatest barriers. In a pilot, comparison with a similar group receiving existing support would make the findings more useful than a collection of selected success stories.
Limits, global relevance and the next evidence
The report is US-specific and produced by a consultancy with commercial interests in organisational change. Its assumptions can be examined, but its publication is not independent proof that those outcomes will occur. Our assessment would change with observed transitions, earnings and unemployment durations that either support or contradict the model, particularly if results were reported separately by occupation, age and region.
Readers elsewhere should use the questions rather than import the numbers. Qualification routes, employment protections and access to education differ between countries. A UK employer would need UK labour-market and licensing evidence before applying a US pathway. For policymakers, the most useful next step would be to publish the cost and distribution of transition support alongside any headline jobs estimate. The public benefit should be judged by whether people can secure sustainable work, not simply by the size of a projected market.[1]
What this means for people
- Workers need accessible pathways and income support, not just predictions about expanding occupations.
Global context
US modelling requires local validation before use in other countries.
What the evidence does not yet show
- Model assumptions are uncertain; job postings do not measure completed worker transitions.
- Results cannot be transferred directly to other national labour markets.
What to watch next
- Observed job moves, earnings and training completion, including people who do not complete programmes.
Evidence trail
Sources used for this report
Links checked 30 September 2026
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
Continue the story
Related reporting
Work & Skills
Who is workplace AI leaving behind? PwC finds an access gap—but not its cause
PwC surveyed 49,364 workers across 48 countries and regions. AI use rose, while reported access to learning fell; the cross-sectional responses show an association, not proof that AI caused the divide.
5 min · 2 sources
Work & Skills
Will AI take my job? What the evidence says about exposure, hiring and skills
The ILO estimates task exposure, the OECD examines skills and IMF staff study hiring. Together they show uneven risks and practical questions, not a prediction for one worker.
6 min · 4 sources
Work & Skills
If robots can do much physical work, why are so few tasks economical to automate?
Anthropic researchers rate 7,594 physical job tasks and estimate that robots can perform 74% in some setting, yet are currently cheaper than people for only 0.3% of all work. The gap between technical exposure and economic adoption is the result that matters for workers.
6 min · 1 source
Reader discussion
Add evidence, experience or a question
No account is required. Reader notes are published after a brief civility, relevance and safety check; disagreement is welcome.
Published reader notes
0No published reader notes yet. You can start the evidence-led discussion above.
Prefer a private correction or response? Contact the newsroom.