The harm heading toward AI-displaced workers will not come from a missing training program. It will come from a training system whose contracts pay the trainer for placement, not the worker for income. That is the trap buried inside John Arnold’s Wall Street Journal column, “The U.S. Isn’t Ready to Help Workers Through the AI Disruption”, which argues that federal workforce programs barely improve employment or earnings and that the country should scale sector-based programs with rigorous evaluation and pay-for-performance contracts.
Arnold is right about the failure. The Anthropic-commissioned review he cites examined 56 randomized trials dating back to 1973. Public workforce programs increased long-term employment by 1.7 percentage points and annual earnings by about $800. Most programs produced no measurable benefit. None of the large federally funded programs produced lasting results.
That is not a rounding error. It is a verdict on a system that consumes tens of billions of dollars a year without moving most participants out of low-wage work.
Arnold is also right that sector programs can outperform the generic federal model. They train for specific local employer needs, screen applicants intensely, and graduate people in months rather than years. Per Scholas, his flagship example, trains information-technology workers in 25 cities, and its graduates earn 16 percent more a decade later.
Beautiful result.
Per Scholas serves a few thousand students a year. AI-driven displacement is projected in the tens of millions over the next decade. A program that works for a few thousand does not become a program that works for millions by adding more paperwork. It becomes a program that admits millions of applicants and trains the easiest fraction.
The larger problem is that Arnold treats a distribution crisis as a curriculum crisis. AI lets one worker, equipped with a model, produce what once required three, five, or ten workers. The productivity gain has to go somewhere. Under the current ownership arrangement, it goes to the firm and to capital.
Not to labor.
That is not a training problem. It is a distribution problem.
Consider the worker who is successfully retrained by an ideal sector program. She lands a job at 16 percent above her old wage, the kind of result Per Scholas posts. Then her employer deploys tools that let the firm produce the same output with a smaller workforce, or more output with the same workforce. Her wage gain gets competed away as more workers chase fewer positions and the tools themselves eat into the premium for skilled labor.
The training worked. The economy did not.
No curriculum can teach a worker how to negotiate a fair share of the surplus her AI tools produce for the firm. No performance metric measures whether workers receive that share. Those are choices about ownership, bargaining power, and who gets to keep the productivity gain.
Arnold wants accountability standards, performance-based funding, and rigorous evaluation. Necessary hygiene. Not a cure.
The mechanism inside his preferred contract is simple. When a training contractor is paid for “outcomes,” the contractor picks the metrics. Completion rates are easy to improve by screening out harder cases. Six-month placement rates are easy to improve by counting any job, including the low-wage job the worker already had. Wage gains are easy to improve by funneling applicants into sectors where they would have earned more anyway.
The contractor optimizes for the report.
The worker optimizes for nothing, because the contract is not with the worker. The check leaves the Treasury and arrives at the trainer’s billing address. The worker gets a certificate.
If the old contracts produced 1.7 percentage points, the new contracts will produce 1.7 percentage points with better reports.
The accountability machinery Arnold champions has had twelve years to prove itself. The 2014 Workforce Innovation and Opportunity Act authorized pay-for-performance contracting in principle. Arnold’s own column admits that the provision has become “entangled in regulation” and “may never be used.”
That is not a launch pad. That is a track record.
A regulatory structure that has been captive for a decade will be captive in another decade unless the people receiving the money acquire power over the people writing the rules. The same philanthropic networks that fund the reviews may end up shaping the standards, the contracts, and the definition of success. “Pay for performance” sounds wonderfully stern until one asks who defines performance and who gets paid.
The worker gets asked to perform. The contractor gets paid.
Arnold is right that most publicly funded training programs do not move wages. He is right to demand evidence instead of ceremonial certificates. He is right that a sector program can be better than a generic one-size-fits-all course. But he stops at the point where training meets ownership, because continuing would require asking who captures the gains when AI makes labor more productive.
That question is the whole column.
What would address it? Three structural moves, none of them mysterious.
First, firms should share AI-driven productivity gains with the workers whose labor is being augmented or replaced. That can mean wage floors indexed to firm-level productivity, mandatory profit-sharing when AI deployment reduces headcount, and worker equity stakes in the AI systems they train and operate. If the worker helps create the gain, “the market” should not be allowed to treat her as scenery while capital collects the applause.
Second, AI deployment should count as a labor event. When a firm rolls out AI that displaces more than a set share of its workforce, it should owe a transition fund — not merely training dollars, but income support and paid retraining time — along with a hiring commitment for displaced workers where feasible. The money should follow the person whose income collapsed, not the institution that prepared a glossy outcome report.
That is wage insurance: pay the worker the gap between the old wage and the new one for a fixed period. Add a refundable wage subsidy through the payroll system, and expand the earned-income tax credit for workers displaced mid-career. Existing tax infrastructure can deliver money in weeks. A sector program may take months just to admit someone.
Contractor accountability can remain in the toolkit. It should simply be secondary. The primary payment should go to the worker.
When the check is made out to the person whose income just collapsed, that person has leverage. When the check is made out to the trainer, the trainer has leverage.
Third, break the concentration around AI platforms. A handful of firms control the models, the compute, and the deployment pipelines. That concentration lets them capture the gains instead of sharing them. Antitrust and compute-access policy are now worker policy. A worker cannot bargain effectively with a company that controls the technology, the labor market, the data, and the terms of entry into the next job.
None of this requires inventing a new political species. The wage councils of the 1940s, the advance-notice rules of the WARN Act, and the antitrust push of the early twentieth century already point toward the machinery. The hard part is not designing the tools. The hard part is choosing to use them on capital’s largest players.
Arnold wants a better training system. He is right that the current one is broken. But a better training system, on its own, will train workers to enter an economy that is still arranged to take the gains from their work and hand them upward.
Build the income floor first. Make the productivity gain shareable. Give workers time, money, bargaining power, and a claim on the tools they operate.
That is the difference that scales.