Introduction

Mark Fisher’s concept of "capitalist realism" once described a world where the dominance of economic organization made alternatives seem unimaginable. Today, that sentiment feels different as two competing narratives about artificial intelligence and labor compete for attention. One scenario casts AI as a labor substitute, automating routine tasks and compressing hiring needs. The other frames AI as a complement, amplifying human judgment where it is hardest to codify. A fresh analysis of quits-rate data reveals a growing divide between these two economies—and Gen Z is at the center of the shift.

What Happened

Gad Levanon of the Burning Glass Institute ran a sector-specific quits-rate analysis, ranking each industry’s rate against its own 25-year history rather than across all sectors. The result: finance, insurance, information, and professional and business services (FIIPB) saw quits fall to the 13th percentile—the lowest level since 2013—down from 2.5% in 2019, a 28% drop. The rest of the private economy held near its historical norm at the 44th percentile, while government, education, and health care sat at the 71st percentile, largely unchanged from 2019. Levanon noted that FIIPB employment peaked in early 2023 and has been declining since, as workers quit only when they have another destination; in a sector shedding jobs, there is simply nowhere to turn. Stanford’s Digital Economy Lab added another layer using high-frequency ADP payroll data, finding that workers aged 22 to 25 in AI-exposed occupations are now 19% below the hiring level they would have reached had their trajectory kept pace with less-exposed peers. The gap is not the result of mass layoffs—it is a sustained shortfall in new hiring. A Bank of America Institute report from September 9 shows Gen Z’s job-switching rate has outpaced every other generation since 2021, even as broader hiring slows, and those who do switch are securing the largest pay bumps of any cohort.

Why This Matters

The data reveals what Levanon calls a "decline in the labor intensity of white-collar work": output keeps growing while the headcount needed to produce it shrinks, driven by technology and near-term expectations of AI capabilities. For early-career workers, this means fewer entry points. Tyler Cowen of Marginal Revolution draws a sharp line between "intelligence" that can be automated and "Polanyi knowledge"—the tacit, contextual expertise that protects seasoned professionals. Stanford’s payroll data confirms that early-career workers lack that protective buffer. The implications stretch beyond individual careers. A recent working paper by David Autor and colleagues on AI-assisted patent lawyering found that long-term advantages concentrated entirely among senior lawyers, with junior associates showing no average gain—the largest benefits accrued to those who already retained the most foundational expertise. Even the media sector, often quick to sound the alarm on AI, has significant skin in the game, as Semafor’s Reed Albergotti notes that AI safety narratives make for "an incredibly fun story." The broader question is whether the next generation can build the foundational knowledge AI tools cannot easily replicate, especially as high-school bans on AI and shifting workplace norms reshape the learning landscape.

Key Takeaways

  • FIIPB’s quits rate has collapsed to historic lows, reflecting a sector in contraction rather than expansion.
  • Young workers in AI-exposed roles are experiencing a hiring slowdown, not a layoff wave, creating silent entry barriers.
  • Gen Z is switching jobs at the highest rate since 2021, often commanding larger pay increases when they move.
  • The "two capitalisms" framework—where AI substitutes some work while complementing others—explains the uneven impact across sectors.
  • Foundational expertise remains a prerequisite for extracting durable skill from AI; without it, the next generation may struggle to bridge the gap.
  • Education and workplace training systems must evolve to ensure tacit knowledge transfer does not stall as AI adoption accelerates.

Conclusion

The future of work is shaping up as a split screen, where AI both eliminates and elevates depending on the nature of the task and the experience level of the person doing it. For Gen Z, the road ahead depends on recognizing that the traditional apprenticeship ladder is being pulled up even as the rungs above it grow narrower. As Tyler Cowen reflects, the visible cost to current practitioners is real, but the unseen gains from AI may ultimately redefine what work looks like—provided the next generation is given space to learn, adapt, and fill the roles that will define the next economy.