The career ladder isn’t just getting harder to climb. AI is pulling away the bottom rungs — and handing a boost to everyone already at the top.
New research from the Federal Reserve Bank of Dallas finds that artificial intelligence is simultaneously shrinking entry-level employment and raising wages for experienced workers in the same industries. It’s a combination economists rarely see: fewer jobs and higher pay, happening at the same time, in the same sectors.
The Data Behind the Divide
Scott Davis, an assistant vice president in the Dallas Fed’s Research Department, analyzed employment and wage data across more than 200 occupations since ChatGPT’s launch in late 2022. His findings were published in February 2026.
The numbers are stark. Total U.S. employment has grown about 2.5% since fall 2022. Employment in AI-exposed sectors has not kept pace. Computer systems design — one of the most AI-exposed industries — has shed 5% of its workforce. Across the top 10% of AI-exposed industries, employment is down 1%.
But here’s the twist: wages in those same AI-exposed sectors are rising faster than the national average. Nominal average weekly wages across the economy grew 7.5% since fall 2022. In computer systems design, they grew 16.7%. Across the top 10% of AI-exposed industries, wage growth was 8.5%.
Fewer workers. Higher pay per worker. That’s not how technology disruptions usually play out.
The Experience Premium Explains Everything
Davis found no meaningful relationship between AI exposure and wage growth — until he added one variable: the experience premium.
The explanation hinges on a distinction between two kinds of knowledge. Codified knowledge is what you learn from textbooks, manuals, and training programs. Tacit knowledge is what you accumulate through years of practice — the judgment, relationships, and pattern recognition that only experience builds.
Davis’s hypothesis: AI can replicate codified knowledge but not tacit knowledge. That means AI substitutes for workers whose primary value comes from book learning, and complements workers whose value comes from hard-won experience.
Using Bureau of Labor Statistics data that separates entry-level and experienced worker pay, Davis calculated an experience premium for each occupation. Then he tested how AI exposure affected wages differently depending on that premium.
The results were clear. In occupations with a low experience premium — where experienced workers don’t earn much more than beginners, like fast-food cooks or ticket agents — AI exposure was associated with lower wage growth. AI substitutes for everyone in those roles.
In occupations with a high experience premium — lawyers, insurance underwriters, credit analysts, marketing specialists — AI exposure was associated with higher wage growth. AI handles the entry-level work while making expert-level judgment more valuable.
Young Workers Are Being Left Behind
The employment decline in AI-exposed sectors is concentrated among workers under 25. Research from Stanford’s Erik Brynjolfsson and colleagues confirms the pattern: employment totals for older workers have not declined.
Dallas Fed economist Tyler Atkinson notes the issue isn’t layoffs. It’s that young workers aren’t finding jobs in the first place. The entry-level market in AI-exposed fields is getting much harder to break into — a trend consistent with AI accounting for 25% of U.S. layoffs in March 2026, according to Challenger, Gray & Christmas.
This isn’t just about individual hardship. If young workers can’t get their first jobs, they can’t build the tacit knowledge that makes them valuable later. The pipeline that creates experienced experts is breaking at the input.
The Unsustainable Equilibrium
Davis doesn’t pretend this is sustainable. “Leaving new employees off the job ladder is not sustainable in the long run,” he writes.
The current equilibrium works for companies in the short term — AI handles the routine tasks, experienced workers command premium wages, and the payroll shrinks at the bottom. But it’s storing up problems. Organizations need a pipeline of talent. Industries need people who are learning the craft. The economy needs workers who can eventually replace the experienced professionals who will retire.
The rethinking hasn’t happened yet. No one has figured out how to develop tacit knowledge without the years of entry-level practice that used to provide it. Apprenticeships, rotational programs, and structured mentorships are being discussed, but at nowhere near the scale the problem demands.
What This Means for Your Career
If you’re early in your career, the message is blunt: the traditional path of “take an entry-level job, learn the ropes, move up” is fraying in AI-exposed industries. You need to find ways to build tacit knowledge and demonstrate judgment that AI can’t replicate — and you may need to do it outside traditional employment structures.
If you’re an experienced professional, the news is unexpectedly positive. Your accumulated knowledge is becoming more valuable, not less. But that comes with a responsibility: organizations that lose their ability to train the next generation will eventually have no one to pass knowledge to.
The Dallas Fed study doesn’t predict how this resolves. It simply shows that the divergence is real, it’s measurable, and it’s accelerating. The bottom of the career ladder is disappearing while the top gets more lucrative. That’s not a future most people signed up for.
SOURCES
- Federal Reserve Bank of Dallas — AI and the experience premium
- Stanford University (Brynjolfsson et al.) — Employment effects of AI on young workers
- Bureau of Labor Statistics — Occupational wage data
- Challenger, Gray & Christmas — March 2026 layoff report
