The AI wage premium hit 62%, and pay growth in AI-exposed jobs is falling behind

Two numbers arrived within a week of each other, and they point in opposite directions.

PwC's 2026 Global AI Jobs Barometer puts the AI wage premium at 62%, which is what employers pay extra for roles that ask for AI skills by name. Then Apollo Global Management ran the numbers on 321 US occupations and found that people working in jobs where AI is genuinely being used saw real wage growth run about 6.7% slower than everyone else after 2023, with no measurable fall in the number of those jobs.

Same technology, and the AI wage premium lands on one group while another group's pay growth stalls. Nobody gets made redundant in either version.

What Apollo measured, and why it isn't the AI wage premium

The study is worth understanding before anyone quotes it in a pay review. Sania Edlich and Torsten Slok used the Anthropic Economic Index, which scores occupations by what share of their tasks have actually been performed using Claude, and compared that against Bureau of Labor Statistics wage data from 2015 to 2025. So it measures observed use rather than the theoretical "could a machine do this" scores that most earlier work relied on. That's a real improvement, and it's why the paper got picked up quickly.

Their conclusion is blunt. Firms are "capturing AI productivity gains through wage compression rather than workforce reduction", to the tune of a conservative $28bn a year across 5.8 million American workers, which is about 3.7% of the labour force.

The distribution is where it gets uncomfortable. The bottom quarter of earners saw wage growth run 10.7% slower. The top quarter showed no statistically significant effect whatsoever. Service occupations, meaning childcare workers, waiters, concierges, social workers, came in at 24.3%, though the authors flag that figure themselves as resting on 239 observations and ask readers to treat it with caution. Blue-collar work showed nothing measurable, which fits the idea that AI hasn't reached far into physical jobs.

Note what this isn't measuring. Apollo says nothing about the AI wage premium itself, because it tracks what happens inside occupations where the tools are in use rather than what happens to people who list AI skills on their CV. The two datasets describe different populations, so they can both be right.

Worth remembering who published this. Apollo is an asset manager and this is a whitepaper rather than a peer-reviewed paper, so it hasn't been through the wringer that academic labour economics usually applies. The exposure measure also comes from a single AI provider, because Anthropic is the only one to have released usage data for public research, and the authors are upfront that this understates real adoption. They also note the post-2023 window may be tangled up with post-pandemic labour dynamics.

The UK data tells a different story

That last caveat turns out to matter more than the paper allows for.

The Centre for British Progress ran a similar exercise on UK data and published a detailed review of the evidence so far. They find the same wage compression in AI-exposed occupations using ONS payroll data. They also find that it starts around 2019, two to three years before ChatGPT reached the public.

Their verdict is direct: because the divergence predates the supposed cause, it can't be treated as evidence of an AI effect. Their alternative explanation is weak productivity growth combined with rising statutory minimum pay, which would produce roughly the pattern we're seeing without any large language model involved.

They found something else that cuts against the redundancy panic. Hours worked in AI-exposed occupations went modestly up, not down, which is what you'd expect if the tools are making those workers more useful rather than surplus. Employment across 412 UK occupations showed no difference at all between the most and least exposed.

And on distribution, the UK evidence lands almost exactly opposite to Apollo. Compression in Britain shows up across the pay range but runs strongest at the top, with upper-quartile pay eroding faster than lower-quartile pay. Apollo found the top quarter untouched and the bottom quarter hit hardest.

Two serious pieces of work, the same question, opposite answers on who's carrying the cost. That's usually a signal to stop quoting either one as settled and look at what both agree on.

Where they do agree

Both find no employment effect. Both find pay growth in exposed occupations running behind. Neither can prove AI caused it.

Meanwhile OpenAI published its own analysis of more than 800,000 work-related ChatGPT messages, and it points at something the wage papers can't see. Across occupation-specific messages, 43.5% concerned tasks historically belonging to a different occupation. HR came third out of eight groups at 69%, behind customer experience at 77% and design at 75%.

The specifics are more telling than the headline. Calculating financial data ranks in the top three finance-related tasks for every single non-finance occupation they looked at. Troubleshooting software does the same for every non-engineering occupation. Sales people are running their own numbers. Marketers are handling customer conversations. Designers spend 35.2% of their messages on other people's work, while design tasks make up only 1.7% of everyone else's, so designers absorb constantly and almost nothing flows back.

Treat this carefully too. OpenAI is measuring its own product, the sample is US-only and self-reported, the unit is a message rather than an hour of work, and the researchers say plainly that they can't tell whether the output was any good, whether a specialist checked it, or how the person would have handled the task without the tool. It shows what people ask a chatbot, which isn't the same as what they do all day.

The bit that lands on your desk

Put the three together and the useful finding has nothing to do with redundancy forecasts, and quite a lot to do with who the AI wage premium actually finds.

People are taking on a wider range of work. Hours are edging up. And the 62% AI wage premium attaches to job titles that name AI explicitly, so it rewards the label rather than the activity. Your analytics lead who put "AI" in their job title is priced differently from your HR advisor who now builds their own dashboards and hasn't told anyone. This is the same dynamic behind 42% of employees using AI in secret, and the same question raised by what AI productivity gains actually cost.

OpenAI's researchers made the point themselves, and it's the most quotable line in any of these papers: if AI changes which workers perform particular tasks, measures built on existing job descriptions "will gradually diverge from how work is actually organized".

Which leaves a practical problem. Job evaluation, pay banding and market benchmarking all rest on job descriptions. If the descriptions are drifting away from the work, then benchmarking data is pricing a role that a fair number of your people stopped doing about eighteen months ago. That distortion compounds every cycle, and it shows up in resignations long before it shows up in a pay review.

Worth a look next time you're refreshing job architecture, and probably worth asking your reward team how recently the descriptions underneath the benchmarks were checked against what people actually do, particularly in the roles where an AI wage premium may already be moving your market data. The wider automation risk picture suggests there's time to do this properly, and of course that window narrows as adoption spreads.

The AI wage premium isn't going anywhere. The open question is whether it keeps flowing to the people who can describe their work well, or eventually reaches the ones simply getting on with it.

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