John Cassidy writes about possible routes by which the development of A.I. can benefit workers rather than replace them.
Later in the week, as other analysts poked holes in the Citrini scenario, the market recovered some of its losses. But the gyrations illustrate the power of two assumptions about A.I. that go largely unquestioned, on Wall Street and elsewhere: that the new technology is so powerful that it will transform the economy utterly; and that, despite being designed by humans, it’s now a force unto itself, whose progress can’t be reshaped or redirected.
In short, we are all slaves to the A.I. algorithms and their inner workings, which remain somewhat mysterious even to their creators. When you think about it, this second assumption is both terrifying and ahistoric. In the paperback edition of their 2023 book, “Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity,” Daron Acemoglu and Simon Johnson, two M.I.T. economists, point out that the lesson of earlier economic transformations is: “you can’t stop technological change, but you can shape it.” In early British textile factories, women and children worked twelve-plus-hour days in unsanitary environments. It took the advent of factory legislation to shorten the workday and improve working conditions. And in many countries, including the United States, the rise of labor unions was a key factor in insuring that technology-driven productivity gains fed through to wage increases and expanded employment benefits as well as higher profits. The Treaty of Detroit, a five-year wage contract agreed upon by General Motors and the United Auto Workers in 1950, made this compact explicit. Acemoglu and Johnson are leaders in their field: they shared the 2024 Nobel Prize in Economics with a University of Chicago economist, James Robinson. In a new report for the Brookings Institution titled “Building pro-worker AI,” which Acemoglu and Johnson wrote with another noted M.I.T. economist, David Autor, they challenge the assumption of societal powerlessness in the face of A.I. They lay out a policy agenda designed to make sure that it acts as “a force magnifier for human expertise” rather than as a job killer. “We have a lot of agency, a lot of choice in shaping the future of technology,” Acemoglu told the MIT Sloan Management Review, “and different futures correspond to different winners and losers, different benefits, different costs, different productivities.” As an example of how A.I. could be used in a pro-worker fashion, the report points to an Electrician’s Assistant , developed by Schneider Electric, a French-based multinational company. When confronted by a tricky problem, the electrician feeds information and pictures into an assistant, which is a large language A.I. model. The assistant conducts a diagnosis and issues recommendations, in an iterative fashion, for how to fix the problem. It also helps the electrician file maintenance reports, and the paper cites evidence that the time spent on this task has been halved. “Tools akin to EA could be readily built to support many additional trade and modern craft workers, such as plumbers, building contractors, and health-care workers,” the report says. This is an encouraging story, but how representative is it? For every example like the Electrician’s Assistant, there is one in which A.I. is already displacing jobs, or, at least, it’s being used as an excuse for big layoffs. Last week, Block, a financial-services platform, announced that it was getting rid of four thousand workers, out of ten thousand in total, on the ground that A.I. could do their jobs. Even in cases where companies have employed A.I. programs without engaging in mass layoffs, they have often been used to surveil and coerce workers rather than empower them. Amazon has an Associate Development and Performance Tracker program that it employs in its warehouses and always-on cameras that it deploys in its delivery vehicles, which are two notorious instances. Last week, Burger King said that it’s testing new A.I.-powered headsets, which can be used, among other things, to check whether its customer-service employees say “please” and “thank you.” The three M.I.T. economists don’t underestimate the scale of the challenge. “None of the big companies are pouring even a small fraction of their investment into developing A.I. as a pro-human, pro-worker tool,” Acemoglu said in his interview. To reorient things, he and his colleagues make a series of policy recommendations, including changing the tax laws, fostering competition in the A.I. sector, and giving workers a direct stake in A.I. One key proposal is for the government to use its financial power—both as a provider of research grants, and as a buyer and user of technology systems—to push the development of A.I. in a pro-worker direction. In the health and education sectors, for example, which together make up about twenty-five per cent of the nation’s G.D.P., government is a major purchaser of tech products—a position it could use to demand the development of A.I. assistants that enhance workers’ capabilities. When I called up Autor last week, to ask him about the report, he cited the opportunity for A.I. assistants to help nurses carry out more demanding medical tasks, and to help teachers offer their students personalized support. “We pay for this stuff, we use it, the welfare of our children and grandchildren depends on it,” Autor said, referring to taxpayers. “I’m not saying the government should take over A.I., but it should use its power to shape its development.” In theory, the tax code could also be used to reshape the incentives of A.I. developers and users. When firms make investment decisions, they often have a choice of buying new labor-saving equipment, such as a chatbot, or hiring new workers and retraining existing ones. The current tax code, with its low rates on capital income and accelerated depreciation schedules, pushes businesses in the first direction. It “favors capital to an enormous extent, while it is very burdensome toward workers,” Autor pointed out. One way to change this would be to raise taxes on capital and reduce taxes on labor: that would make the code more neutral. A more drastic and more politically challenging option, which Autor said is worth considering, would be to tax consumption rather than working. Part of the report that particularly caught my eye is a section titled “Discouraging expertise theft.” Right now, A.I. companies “freely scrape content from websites, social media, YouTube, newspapers, Wikipedia, and blogs, then statistically recombine this material and sell access to the results,” the report notes. “Authors, journalists, visual artists, musicians, translators, and countless other creators find their work appropriated as training data, with no compensation or control.” A recently published book, “The Means of Prediction,” by an Oxford economist, Maximilian Kasy, likens this grab to the enclosure of common land by landlords during medieval times—a development which greatly benefitted the landlords but destroyed the livelihoods of many small farmers. “A lot of the internet is being enclosed and resold to us as private property,” Autor said. “This is a huge reallocation of property rights.” With some firms using the performance of their own employees as data to train A.I. models, the report argues that the appropriation issue goes well beyond the internet: “Few employees would willingly train an apprentice designed to replace them, and yet this is precisely what happens when companies use worker expertise to build automation systems.” To address this issue, the report calls for new “legal frameworks that support workers’ ownership of their capabilities and creative output.” That sounds good, but what would it mean in practice? One model could be the book-publishing industry, where Anthropic has agreed to pay $1.5 billion to resolve a class-action lawsuit filed by authors and publishers for copyright infringement. But lawsuits are, at best, a very limited solution. The expertise of most cognitive workers—doctors, teachers, lawyers, consultants, accountants, software programmers, and so on—isn’t copyrighted. Neither are the physical skills that packers, drivers, builders, and other blue-collar workers possess, and which robotics companies are busy trying to replicate. This broader threat of appropriation and immiseration demands broader policy responses, and Autor suggested two of them: wage insurance for workers displaced by A.I. and a universal basic capital endowment. Under the first proposal, which Autor said is based on a trade-adjustment policy implemented by the Obama Administration, workers who are made redundant by A.I. and are forced to take lower-paying jobs, would receive a temporary federal wage subsidy. The proposed universal basic capital endowment would be made available to everyone. At birth, they would receive a government-funded investment account—the idea being that it could eventually grow large enough to provide them with a supplementary income source that wasn’t dependent on them supplying labor. Proposals of this nature have long been popular on parts of the left, where they are sometimes referred to as asset-based redistribution. More recently, in the One Big Beautiful Bill Act of 2025, Donald Trump appropriated the appeal, if not the substance, of this approach by establishing tax-advantaged investment accounts for Americans born between January 1, 2025, and December 31, 2028, which the federal government will “seed” with a thousand-dollar contribution. To have a real impact on people’s lives and incomes, a universal basic capital endowment would have to be much larger than the Trump accounts, and it would likely have to be financed by raising taxes on large agglomerations of wealth. Autor didn’t get into financing details, but toward the end of our talk he pointed out two facts about the modern U.S. economy that were evident even before the onset of A.I.: wealth ownership is highly concentrated and the share of national income accruing to workers rather than owners of capital is falling sharply . On its current course, A.I. seems likely to accentuate both these trends, with alarming implications for inequality, welfare, and democracy. It’s easy to question whether the proposals that Autor and his colleagues have put forward are adequate to address this challenge—he freely admitted that they don’t have an “omnibus solution”—but at least they are tackling the right questions. ♦
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