New studies provide an emerging picture of AI use and its labor market impacts.
AI use tracks income and education.
New Census data shows 77 percent of adults in households earning $150K or more used AI in the last two months, versus just 42 percent earning under $25K. Educational attainment is also stark: 75 percent of bachelor’s degree holders versus 33 percent of those without a high school diploma. An FT–Focaldata poll (story / study) of US and UK workers found more than 60 percent of high earners using AI daily, compared with 16 percent of lower earners. An Anthropic analysis shows where the gains concentrate: workers in the most AI-exposed occupations earn 47 percent more on average than those in the least-exposed, and graduate degree holders make up 17 percent of the most-exposed group versus 4.5 percent of the unexposed.
The benefits compound where adoption is already highest.
Among Americans who have used AI, the Census Bureau asked how it’s shaping their work and daily lives. The same income and education divides appear:
- 53 percent of those earning more than $150K say AI makes them more productive, versus 30 percent under $25K.
- 52 percent of bachelor’s degree holders feel more productive, versus 31 percent with only a high school diploma.
- 21 percent of bachelor’s degree holders say AI has already changed their field of work, versus just 5.1 percent of those with a high school diploma.
A new Anthropic survey of 81,000 Claude users adds a wrinkle: the largest self-reported productivity gains show up at both ends of the wage distribution: high earners are the most enthusiastic, but low-wage workers also report large gains, often from AI letting them take on tasks previously out of reach. For high earners, AI amplifies what they already do. For low-wage workers, it expands what they can do at all.
The gender gap is real.
Census data show 62 percent of men have used AI, compared with 53 percent of women. The gap widens on confidence: 27 percent of male AI users feel prepared to use AI at work, versus just 17 percent of women. An FT analysis, citing Google’s chief economist, estimates women are roughly 20 percent less likely to use AI overall. But training closes the gap. Google research cited in the same piece found that a single training session tripled daily AI usage among UK women.
AI is handing incumbents a scale advantage while lowering the cost of starting something new.
A Federal Reserve FEDS Note synthesizing three federal surveys finds that the largest firms lead AI adoption, while pointing to earlier research showing a jump in AI-related business formation after ChatGPT’s launch in 2022.
In other words, AI is doing two things at once: handing incumbents a scale advantage while lowering the cost of starting something new. Anthropic’s survey picks up the same signal, with the largest productivity gains appearing in management – a category disproportionately composed of solopreneurs and founders using Claude to build their businesses.
The through-line is that AI’s benefits are flowing to people who already have the education, income, workplace support, and confidence to use it. Another Census paper helps explain why. Productivity differences across firms aren’t just about who they employ, but how work is organized into tasks and matched with skills and technologies. AI doesn’t replace whole jobs; it improves specific tasks within them. Organizations already optimized around those tasks capture outsized gains, while others see little impact.
This is why “AI adoption” is the wrong frame, and why the productivity gap won’t close on its own. The organizations pulling ahead aren’t buying more licenses; they’ve decomposed work into tasks and matched those tasks to the right tools and people. Without that redesign, adoption becomes a tax on workflows that aren’t ready for it: more tools, same bottlenecks, widening gap.
The bigger question is what happens next. Agentic AI is arriving fast and can perform a greater range of tasks. The skills required to manage a team of agents (clear writing, workflow design, delegation, quality oversight) are the same ones that already correlate with education and income.
One path from this could lead to extreme concentration, where a small group of elite individuals and firms commands swarms of agents while everyone else competes against them. Jensen Huang has already sketched what this would look like within a single company: Nvidia’s 75,000 employees could eventually work alongside 7.5 million agents.
Or the same capability cuts the other way. Agents give a single founder the legal, design, research, and operations leverage that used to require a payroll or a portfolio of consultants.
Which future we get isn’t predetermined. It depends on the choices we make now about training, workflow redesign, and on-ramps that turn access into the ability to actually build something.
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