McKinsey Modeled the U.S. Job Market Through 2035. 11 Million People Will Have to Start Over.

Last Tuesday, the McKinsey Global Institute published an 82-page report modeling how AI and automation could reshape American employment between 2025 and 2035. The authors mapped roughly 1,800 occupations, the skills each one requires, and what it would take for a worker to move from one to another.

The headline finding is more optimistic than most of what gets written about AI and work. McKinsey estimates automation could reduce labor demand by the equivalent of about 36 million jobs over the decade. Over the same period, an aging population, rising incomes, the infrastructure build-out and the AI industry itself could create demand for about 41 million. The economy comes out ahead by roughly 5 million jobs.

Most of the disruption also stays inside existing occupations. About 25 million affected workers would keep the same type of job, though the work itself would change. The remaining 11 million, roughly 7% of the current U.S. workforce, may need to leave their occupation and move into a different one entirely.

The report puts its own conclusion in one line: "The next decade's challenge is mobility, not scarcity."

The number that matters is one in seven

McKinsey didn't stop at counting jobs. It tried to work out whether each of those 11 million workers could realistically get from a shrinking occupation to a growing one, scoring every possible move on skill overlap, pay, training time and credential requirements.

Almost every worker has some route to a growing job. Only about 14% have what McKinsey calls a direct pathway, meaning strong skill overlap, no pay cut and less than six months of training. Another 41% face a winding pathway that involves meaningful retraining of up to two years. The remaining 45% are on what the report calls an unpaved pathway, with large skill gaps, long credentialing timelines or lower pay.

The report's own examples show how far apart those categories are. A dishwasher could move into home health aide work, but the two jobs share only about 20% of their required skills, and the move involves certification. An office assistant could become a project manager and see a large raise, but many would need a bachelor's degree and a project management certification first.

The destination jobs are generally good ones. McKinsey found that 57% of employment in growing occupations falls in the top two wage brackets, and only about 3% of transitioning workers would take a pay cut if they complete the move. Getting there is the difficult part, and for most of these workers the report describes a long road.

A decade of pandemic-level job switching

In a typical year outside the pandemic, about 215,000 American workers move into a different occupational group. McKinsey's base case requires about 770,000 a year for ten straight years, which is 3.6 times the historical rate.

The U.S. has hit that pace once before. From 2019 to 2022, roughly 788,000 workers a year made the same kind of move, and the labor market absorbed it without the long-term dislocation many economists expected. McKinsey points to that period as evidence the system can handle the volume.

The pandemic shift lasted about three years, though, and this one is projected to run for ten. The report describes it as potentially the largest and most sustained workforce transformation in U.S. history. It also notes that Americans change employers less often today than they did in the late 1990s and early 2000s, so the starting point for this kind of mobility is weaker than it was a generation ago.

The 11 million figure is a base case. If companies adopt automation faster than expected or use it to replace more labor, McKinsey says the number could climb past 16 million. Slower adoption could bring it down to about 6 million.

Who is being asked to move

Much of the public conversation about AI and jobs has centered on software engineers, analysts and other knowledge workers. McKinsey's projections point somewhere else.

More than 75% of the workers who may need to change occupations come from three groups: office and administrative support, retail and sales, and transportation and logistics. About a third come from just five jobs, which are customer service representatives, retail sales associates, office assistants, cashiers and warehouse workers. McKinsey estimates automation could eventually take on around 80% of current work hours in office and administrative roles.

The risk is also uneven across the workforce. According to the report, lower-wage workers are 7.6 times as likely as higher-wage workers to need a new occupation. Workers without a college degree are about 1.8 times as likely as those with a bachelor's. Younger workers and women are each about 1.6 times as likely as their counterparts, and Black and Hispanic workers about 1.2 times as likely as white workers, largely because of which occupations they are concentrated in.

Those same groups also have the fewest easy exits. Only about 10% of workers in the lowest wage bracket, earning under $38,000 a year, have a direct pathway to a growing job. In the highest bracket, close to 40% do. The people most likely to be displaced are, on McKinsey's numbers, the least likely to land somewhere quickly.

The gate is often the job posting

The finding in this report that deserves the most attention from employers is in its fifth chapter, and it has gotten very little coverage.

McKinsey found that in many cases, a worker already has the skills a growing job requires and could move into it without losing pay. What blocks the move is a qualification listed on the job posting. About 85% of growing jobs call for a credential or certification of some kind. Among growing occupations, 84% require some postsecondary education, compared with 45% of the occupations in decline.

Some of those requirements are set by law. Nursing licenses and commercial driving permits aren't going away, and McKinsey counts about 38% of growing employment as carrying a legally required credential. Many of the rest are employer preferences, and the report points out that companies can change those on their own without waiting for a regulator.

That puts a meaningful share of the barrier facing these 11 million workers inside corporate hiring criteria. McKinsey recommends structured, skills-based hiring, with consistent interview criteria, practical assessments and clearer definitions of what a role actually requires, so that employers can see transferable skills that a degree filter would hide.

A caveat worth taking seriously

These are model outputs, and McKinsey says so plainly. Every figure in the report depends on assumptions about how fast companies adopt automation and how much of that automation turns into reduced headcount. The 6 million to 16 million range exists because those assumptions are uncertain, and nobody knows where AI capabilities will be in 2030, let alone 2035.

It's also fair to note who wrote it. McKinsey advises many of the companies making automation decisions, and some readers will weigh the findings with that in mind.

Some trends could make the transition easier than the base case suggests. The report expects the U.S. to have more jobs than workers in 2035 because the population is aging, and as baby boomers retire, openings in growing fields could pull younger workers in faster. The pandemic period also showed the labor market can absorb a surge of occupational switching better than many forecasts predicted.

Even so, the structure of the problem holds up across the scenarios McKinsey ran. The jobs being created and the jobs being lost sit in different occupations, require different skills and credentials, and are held by different kinds of workers. That mismatch exists whether the final number is 6 million or 16 million.

Where companies come in

One of the more useful sections of the report separates what AI can automate from what companies decide to do with that capacity. McKinsey estimates automation could absorb about 54% of current U.S. work hours by 2035. It projects that only about 21% of work hours actually translate into reduced labor demand, because roughly 60% of the impact is offset in other ways.

Some of that offset comes from relieving chronic overwork in understaffed fields like nursing. Some comes from cheaper work creating more demand, as with auditing, where one study found employment rose 4.3% after firms adopted AI. Some comes from new tasks such as reviewing AI output, handling exceptions and supervising automated systems. McKinsey's view is that the technology sets the ceiling and management sets the pace.

That lines up with what Gartner reported in May. About 80% of large enterprises deploying AI had cut staff, and those cuts showed no correlation with better returns. Gartner's Helen Poitevin said at the time that workforce reductions "may create budget room, but they do not create return." Gartner also predicts that half of companies that cut customer service staff because of AI will rehire for similar roles by 2027.

Some employers are already building the internal pathways McKinsey describes. IKEA retrained 8,500 workers instead of letting them go. Unilever lets employees spend up to 20% of their time working in a different function, and Bank of America publishes free curricula on adaptability and critical thinking. Demand for these skills is rising quickly. McKinsey found that demand for AI fluency in job postings has grown about elevenfold since 2022, adaptability about fivefold, and curiosity, resilience and willingness to learn roughly threefold.