Leading AI Economist Links Robots to Minimum Wage Hikes After Identifying Impact on Entry-Level Jobs

Erik Brynjolfsson has spent the past few years constructing one of the most detailed empirical depictions of how technology is reshaping the American workforce—and for workers at the lower end of the corporate hierarchy, the picture grows increasingly grim.

Last August, the Stanford economist—who has been a leading thinker on artificial intelligence (AI) for years—made news when he and his team released a that showed the AI revolution was already exerting a ‘substantial and uneven impact on entry-level workers in the U.S. labor market,’ especially young individuals aged 22 to 25 in white-collar domains such as software engineering and customer service.

Now, in a released via the National Bureau of Economic Research this February, Brynjolfsson and a group of co-authors turned their focus to blue-collar America—and discovered that minimum wage hikes are speeding up the adoption of industrial robots in factories.​

Taken together, the two papers trace the outlines of a labor market transformation that is squeezing workers from both ends: AI encroaching from the top, automation moving in from the bottom.

The white-collar warning shot

The August 2025 study was based on an exceptionally robust dataset—high-frequency payroll records from millions of American workers generated by ADP, the largest payroll software company in the nation. What Brynjolfsson and his co-authors uncovered was remarkable: Since the widespread adoption of generative AI tools in late 2022, the employment of early-career workers in the most AI-vulnerable occupations dropped by 13% relative to the baseline, even after accounting for broader firm-level disruptions. In contrast, older, more seasoned workers in the same fields experienced stable or growing employment.​

The new study, co-written with J. Frank Li from the University of British Columbia, Javier Miranda from Germany’s Halle Institute for Economic Research, Robert Seamans from NYU’s Stern School of Business, and Andrew J. Wang from Stanford, shifts focus from algorithms to assembly lines. Leveraging confidential U.S. Census Bureau microdata linked with customs import records, the team monitored industrial robot adoption among approximately 240,000 single-unit U.S. manufacturing firms from 1992 to 2021—identifying robot adopters at the point they started importing machines from overseas suppliers in Japan, Germany, and Switzerland.​

The core finding is clear and consistent: A 10% rise in the minimum wage is linked to an approximately 8% increase in the probability that a manufacturing firm will adopt industrial robots, compared to the average adoption rate in the sample. ‘Firms facing higher minimum wages are more prone to adopt robots,’ the authors stated, ‘even after factoring in observable firm and local economic characteristics.’

The logic echoes the white-collar narrative, though the mechanism differs, with the authors asserting these effects are ‘economically significant.’ Similar to how AI becomes economically viable when it can substitute the structured work of a junior software engineer or customer service representative, an industrial robot becomes more appealing when the cost of a human performing repetitive assembly or welding increases. In both instances, a rise in labor costs at the lower end of the skill spectrum tips the balance in favor of machines.​

“While robots may enhance productivity,” Brynjolfsson and his authors wrote, “they may also alter the structure of employment, especially in low-wage sectors as typically found in manufacturing.”

A rigorous test

The manufacturing study’s most compelling evidence comes from a geographic quasi-experiment. Rather than simply comparing firms in high-wage states to those in low-wage states—an approach vulnerable to the objection that those states differ in countless other ways—the researchers focused specifically on companies located in counties that sit directly on state borders, comparing businesses on opposite sides of the same line. These firms face nearly identical local economies, labor markets, and industries. The only meaningful difference is which state’s minimum wage law applies to them.​

Under this stringent border-pair test, a 10% minimum wage increase was still associated with an 8.4% rise in robot adoption—a figure that held up across multiple regression specifications and closely matched the broader aggregate analysis the team conducted at the state level. The effect was robust to controls for firm size, age, industry, and whether a state had right-to-work laws on the books.​

A pattern across borders

The finding is not unique to the U.S. A of Turkey found a sharp 33.5% minimum wage hike in 2016 drove medium and large firms to increase robot use, particularly in industries heavy with blue-collar, routine-task workers.

in China found similar dynamics from 2008 to 2012, with a 10% minimum wage increase raising the probability of robot adoption, with stronger effects at high-productivity and private-sector firms.

German researchers the country’s minimum wage introduction in 2015 found plants with high shares of simple manual workers in routine tasks were the most likely to respond by adopting robots.​

The policy tension

Brynjolfsson and his co-authors were cautious in their conclusions, fitting for a non-peer-reviewed working paper. The manufacturing paper does not seek to measure downstream employment effects—whether workers displaced by robots secure new jobs or at what wages—and the authors recognize that robot adoption can sometimes correlate with higher firm-level productivity and even employment growth, as some international firm-level research has shown. But on the key policy issue—whether minimum wage hikes drive automation—the evidence is now difficult to ignore. And given Brynjolfsson’s August discovery that AI is concurrently eroding the entry-level white-collar labor market, policymakers face a compounded challenge: two distinct technologies, encroaching on two separate workforce segments, through two distinct mechanisms, at the same time.

“Policymakers might consider complementary strategies to mitigate potential displacement effects,” the authors wrote, “such as retraining programs or targeted support for small firms”—a recommendation that, in light of the parallel AI findings, may be arriving just in time.​

For this story,  journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.