How to help knowledge workers who lose their jobs to AI
Platformer Casey Newton
A Brookings researcher says AI will hit white-collar workers first, not blue-collar ones. That flips decades of automation history — and she thinks UBI won't fix the fallout.
Based on reporting by Platformer, Casey Newton — read the original for the full story.
Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error
Molly Kinder spent three years at the Brookings Institution studying how generative AI is reshaping work, and her conclusion isn't the tidy binary that dominates most AI-jobs debates. It's not doom, and it's not "nothing to see here." She calls it the messy middle: a long stretch where most jobs survive but losses cluster hard in specific, coveted careers, long before anyone reaches the AGI utopia Silicon Valley keeps promising.
The twist, Kinder argues, is who gets hurt. During the pandemic, the people who could work from a laptop were the lucky ones. Now those same workers — lawyers, consultants, analysts, anyone who can do their job "locked in a closet with a computer" — are the most exposed to language models chewing through their tasks. OpenAI's own usage data backs her up: the heaviest ChatGPT use skews toward knowledge-sector jobs, not the physical, service-sector work that still requires a body in a room.
Kinder frames this against 150 years of labor history. Agriculture mechanized, then manufacturing hollowed out, and knowledge work became the safe harbor — computers made lawyers and analysts more productive without replacing their judgment. What's different now, she says, is that AI might not just assist cognition, it might substitute for it. That would break a pattern that's held since the 1980s, and it would do so in the exact jobs that millions of Americans took on debt to reach, on the promise that a degree still buys a stable, upper-middle-class life.
That's why Kinder thinks the political fallout could be sharper than raw job-loss numbers suggest. Even a narrow disruption, concentrated in visible, well-paid professions, reads as a betrayal of the meritocratic deal — study hard, get the degree, get the secure job. She's already hearing that anxiety directly from students and young professionals worried AI will yank away the exact path they were told to follow.
And her prescription skips the easy Silicon Valley answer. Universal basic income, she argues, would gut the incentive to do essential work like nursing or policing if a check could replace a laptop-class salary. Instead she wants slower, targeted tools: a reinvestment fund that taxes companies for junior layoffs to fund apprenticeships, wage insurance for displaced older workers, and if good jobs really do vanish, deliberate public job creation — an industrial policy built for knowledge workers instead of factory towns. She's now leaving Brookings to build an organization focused on exactly that problem.
My take — AI-written commentary, not fact-checked reporting
I've always been suspicious of the UBI-as-cure-all reflex that shows up whenever tech people talk about AI wiping out jobs — it's a policy designed by people who've never had to convince someone to still show up and drive an ambulance. Kinder's framing is refreshing precisely because it treats displacement as a policy design problem, not an inevitability we just cash-transfer our way through. The bigger pattern here is that the laptop class spent forty years being told automation was something that happened to other people, and now it's their turn to build the safety net they never had to think about.
Read more about this at: Platformer
