We can't retrain our way out of AI's economic disruption

Molly Kinder

Jun 24, 2026

I work on the topic of AI and jobs for a living, which means I am often asked one particular question. Someone at a dinner, in a hallway or at a conference, leans in, hopeful, and asks: Can’t we just retrain people for the new jobs?

I understand the pull of that question and why people gravitate to it. The mental model is straightforward: over here is a big group of jobs that AI is threatening to take, and over there are the jobs that will survive and the new ones that will be created. Can’t we just ferry people from the risky shore to the safe one? Retrain them, match them, help move them across? It’s intuitive, it’s tidy, and it seems doable.

Believe me, I wish I could say yes, that we could do this at the scale required. It would make me a lot less worried about the “Messy Middle” if we could. But my honest answer is that we can’t. Put simply: we are not going to retrain our way out of the economic disruption ahead.

Here is the heart of what I've come to believe, and the argument of this essay. What we face is not a transition to a stable new shore. It is an economic transformation of historic scale, one that threatens the best jobs we have and the very notion of work as we know it. For decades, technology hollowed out the middle and lifted the top, so "retrain people for the jobs on the rise" made intuitive sense. AI threatens to flip that logic, coming first for the jobs we have always told people to climb toward, and it won't stop there. Once the disruption reaches the safe harbor itself, there is no higher ground left to send people to, and no map for a shore that keeps moving. That is not a problem retraining was built to solve.

To be clear, I am not arguing against retraining. We absolutely should improve our workforce system and our adjustment policies. I am excited about several promising new efforts that are trying to do just that. Plenty of people will need help moving into jobs, and we need better ways to help them get there. Any serious strategy for AI’s economic impacts will have training somewhere in it.

What I am arguing is a matter of emphasis. If we convince ourselves that training is the answer, the main lever, the thing we can all agree on and fund and point to, we risk giving ourselves false comfort and avoiding the bolder, harder, messier work that this moment demands.

It is easy to see how this could happen. The promise of retraining is seductive. It offers a practical, employer-friendly solution that appeals across the aisle in large part because it asks nothing of anyone with power. We could let the technology rip, let the companies and shareholders reap the gains, benefit from the growth and the geopolitical edge that follow, and then, after the fact, move the people on the losing end into something new, and presumably good. No one has to slow down, or wrestle with hard tradeoffs, or challenge the status quo.

We have been seduced by this promise before. A few generations ago we made a grand bargain about trade. Political leaders pledged that retraining would carry the displaced safely to the other side of a changing economy. We are still dealing with the fallout.

The cautionary tale of deindustrialization

Three decades ago, America was gripped by a fierce debate over an earlier economic disruption, much as we are now over AI. The fight over trade and NAFTA dominated the 1992 presidential election, with third-party candidate Ross Perot warning of a “giant sucking sound” of good manufacturing jobs draining south to Mexico. On one side was the fear of job loss. On the other were the arguments for expanded trade: competitiveness, growth, new jobs, cheaper goods. Those arguments won.

In September 1993, four presidents stood together on a single White House stage to vouch for NAFTA, two Republicans and two Democrats.

In his remarks that day (video below), President Clinton met the jobs fear head on. He named the wound plainly, noting that economic change "has often been cruel to the middle class," and then made the turn: "we have to make change their friend." Then came the grand bargain to displaced workers, centered on the promise of retraining:

“We no longer need an unemployment system, we need a reemployment system. And we have to create that. And that’s our job.”

The centerpiece of that reemployment strategy was Trade Adjustment Assistance, or TAA. Created under Kennedy in 1962 and expanded for the NAFTA era, it was the government’s designated bridge for workers displaced by trade: enhanced income support while you looked for work, and funding to retrain you for something new.

The real test came a few years later, when Clinton signed the legislation that opened the door to China’s entry into the WTO. That decision, far more than NAFTA itself, is where the employment shock landed. And the bridge did not hold.

The consequences were devastating. Rising Chinese imports erased an estimated two to 2.4 million jobs. According to the definitive paper on the “China shock”, economists David Autor, David Dorn and Gordon Hanson found that in the hardest-hit towns of the Midwest and Southeast, the job losses were never replaced: employment fell one-for-one (as jobs did not rise to replace them), wages and labor-force participation stayed depressed for over a decade, and the displaced mostly did not move or recover. The promised “reemployment” response never came. At its peak, TAA reached only about 130,000 people on average per year, a tiny fraction of those who were displaced.

The social toll that followed was just as severe. As I noted in my recent Substack, a generation was lost to deaths of despair, opioid addiction, family dissolution, declining male labor force participation, a crisis of meaning that Case and Deaton and others have documented exhaustively. A widespread sense of abandonment fueled a powerful political backlash and a right wing populist rise that endure today.

Studies of TAA's effects conflict, but the broad consensus three decades later is that the country's trade adjustment and retraining programs failed to live up to their promise.

While noting this failure, some experts have pointed to execution flaws, not the fundamental premise of retraining, as the culprit. In a recent essay for Equitable Growth, Jacob Leibenluft noted that the program reached only a sliver of the workers it was meant for, moved at the speed of paperwork, and spent more energy keeping the wrong people out than getting the right ones in. By learning from these past mistakes, perhaps a future iteration can do better.

Retraining has a mixed record at best

But step back from TAA to the wider evidence on retraining and the picture does not brighten much. Across decades of careful evaluation, the record is mixed, and modest, at best.

To see why, start with one town that did everything right. When General Motors padlocked its Janesville, Wisconsin plant two days before Christmas in 2008, thousands of laid-off workers went back to school, just as we tell people to. The local technical college steered people toward the fields the labor data called hot, won a federal earmark, and brought in local employers to say what they needed. If retraining works anywhere, it should have worked here.

Amy Goldstein, then a journalist at the Washington Post, spent years digging into what happened next. In addition to telling the stories of displaced workers in her outstanding book Janesville, she also teamed up with two labor economists and tracked the numbers. They found that the workers who retrained ended up no better off than the neighbors who didn’t, and by most measures worse: less likely to be working, earning less, with nearly four in ten earning nothing at all. And the part that should stop the policy world cold: the people who trained for the promising fields, the jobs the data pointed straight at, were no more likely to be employed than anyone else. They had trained for the future, and in a town that had just lost its anchor, the future wasn’t hiring.

That is the trap retraining walks into. Not bad teaching, but the plain reality that you cannot train your way into a job that isn’t there. As the economist Anthony Carnevale put it to Goldstein, “if there’s no demand for magic, there’s no demand for magicians.”

And yet retraining is the answer nearly everyone reaches for, at every turn. Janesville was not a backwater no one was watching. It was the model everyone pointed to. Barack Obama had stood inside that very plant in 2008 telling workers that with the right support it could run for another hundred years. By 2012, with the plant cold, the cure had become bipartisan gospel. Obama, Mitt Romney, and Paul Ryan, Janesville’s own congressman and that fall’s Republican vice-presidential nominee, were all promising from their podiums to retrain the displaced and ready them for the jobs of the future. Even as left and right agreed on almost nothing that year, they agreed on this.

And they still do. This month, Ryan co-launched a high-profile bipartisan commission on AI and work with Gina Raimondo, Biden’s commerce secretary. The new version is sharper, more grounded in evidence, more honest about how little we can predict, and it deserves credit for that. But at its center sits the same promise Clinton made in 1993: that we can build a bridge across the disruption and walk people over to safe ground on the far side.

Retraining endures, but not because the evidence keeps vindicating it. The most comprehensive synthesis we have, David Card and colleagues’ meta-analysis of more than 200 studies, illustrates a wider finding in the data: the average effect is small. They find that training does close to nothing in the first year, and that its longer-run payoff, two to three years out, lands somewhere around 5 to 10 percentage points on the odds of being employed. That is real, and I do not want to wave it away. But to put it in perspective, the authors themselves size it at roughly the effect of finishing a community college degree. A useful nudge at the margin, but not a bridge across a chasm.

There is one consistent bright spot, and it is strong enough that I want to give it its full weight rather than a passing nod. Sector-based training, bound tightly to real, identified demand from employers in fields like health care, IT, or the skilled trades, can genuinely work. This is backed by real evidence from randomized trials of programs like Year Up, Project QUEST, and Per Scholas, which have found earnings gains that are large, that persist, and in several cases that grow over time, the rare social program that clears the bar economists set highest. The evidence shows that training does work under a specific condition: the jobs are concrete, someone is waiting to hire, and the curriculum is built backward from that demand. Sectoral training succeeds precisely because the demand is already there and already known.

Retraining fixes the worker, not the job, and ignores preference

The thing that makes retraining succeed is the very thing shocks like trade, and next AI, can destroy. Sector-based training works when there is strong demand for jobs people can, and are willing to, step into. The moment a shock destroys the demand, or scrambles which jobs are safe, or creates a clash with the jobs people truly want, the very mechanism that makes sectoral training work is the mechanism that breaks.

In the communities hollowed out by trade, the strong demand was for the jobs that just died: a specific type of middle class, unionized, family sustaining job that provided identity and dignity. Millions of jobs in this middle rung were wiped out. What rose in their place was something else – something employers may have demanded, but the workers who lost jobs didn’t. I worry this is exactly the pattern that AI will replicate.

These new jobs failed the displaced on three counts at once.

Crucially, most of it did

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