In a world where every publication and author have their own strong beliefs and everything they publish is in support of those beliefs, Derek Thompson is a breath of fresh air, in pursuit of the truth, often exploring both sides of an argument. He has been writing about the impact of AI on jobs, especially for fresh graduates.
“To be honest with you, I considered this debate well and truly settled. No, I’d come to think, AI is probably not wrecking employment for young people. But now, I’m thinking about changing my mind again.”
What made him change his mind?
“In a new paper, several Stanford economists studied payroll data from the private company ADP, which covers millions of workers, through mid-2025. They found that young workers aged 22–25 in “highly AI-exposed” jobs, such as software developers and customer service agents, experienced a 13 percent decline in employment since the advent of ChatGPT. Notably, the economists found that older workers and less-exposed jobs, such as home health aides, saw steady or rising employment. “There’s a clear, evident change when you specifically look at young workers who are highly exposed to AI,” Stanford economist Erik Brynjolfsson, who wrote the paper with Bharat Chandar and Ruyu Chen, told the Wall Street Journal.
In five months, the question of “Is AI reducing work for young Americans?” has its fourth answer: from possibly, to definitely, to almost certainly no, to plausibly yes. You might find this back-and-forth annoying. I think it’s fantastic. This is a model for what I want from public commentary on social and economic trends: Smart, quantitatively rich, and good-faith debate of issues of seismic consequence to American society.”
The rest of the article is an interview with the authors of the paper with some startling charts on how clear the evidence is. The paper derives its credibility from the data on which it is based – the data comes from America’s largest payroll processing company ADP on real data about millions of workers with rich attributes of age, nature of work, etc.
When asked why does AI impact a certain cohort of employees (age and nature of work) more than others, the authors respond:
“…LLMs learn from what’s written down and codified, like books, articles, Reddit, the internet. There’s overlap between what young workers learn in classrooms, like at Stanford, and what LLMs can replicate. Senior workers rely more on tacit knowledge, which is the tips and tricks of the trade that aren’t written down. It appears what younger workers know overlaps more with what LLMs can replace….One thing I’d add is short-time-horizon tasks vs. long-time-horizon tasks. The strategic thinking that goes into longer-horizon tasks may be something LLMs aren’t as good at, which aligns with why entry-level workers are more affected than experienced workers. Another factor is observable outcomes. Tasks where it’s easy to see whether you did a good job may be more substitutable. The nature of the training process means AI should, in general, be better at those.”
Beyond the specific aspects of AI and jobs, Derek Thompson says the following to help us become better at understanding the world than predicting the future:
“Someone once asked me recently if I had any advice on how to predict the future when I wrote about social and technological trends. Sure, I said. My advice is that predicting the future is impossible, so the best thing you can do is try to describe the present accurately. Since most people live in the past, hanging onto stale narratives and outdated models, people who pay attention to what’s happening as it happens will appear to others like they’re predicting the future when all they’re doing is describing the present.”
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