Policymakers responding to the potential threats AI poses to the economy should thus consider ways of strengthening the labor movement and rebuilding our support for unemployed workers. An idea I’ve been developing—“Solidarity Insurance”—offers one way of achieving both goals at once. Solidarity Insurance would involve deputizing labor unions to run a revamped system of jobless benefits and retraining services, in the process creating opportunities for workers to connect with unions and organize collectively across the country.

The threat of tech displacing workers isn’t new

75 years ago, a leading labor federation sought to help its members understand the threats new technology posed to their jobs, including machines automating assembly lines. Their leadership argued that businesses “tell us that some of the newest gadgets are simply marvelous…and some of them closely resemble ‘mechanical brains.’ That isn’t any comfort, however, to the man who’s been tossed out of a job by a machine…he’ll probably say, ‘Yeah! But what am I supposed to use for money?’”

So the mounting anxiety about what AI might do to the American economy reflects a long history of technology threatening labor power.

Facing that earlier wave of technological disruption, the Congress of Industrial Organizations (CIO) stressed that it had “never been known as an organization that sought to halt technical developments.” What the CIO wanted instead was power: power to shape “the manner in which technological change is introduced,” to ensure that the gains from new technology would be shared with American workers, and, above all, to protect “the welfare of those who lose jobs through no fault of their own.”

The CIO’s approach—fighting for labor’s voice to shape the design and deployment of new technologies as well as for the welfare of displaced Americans—worked in its time, delivering a generation of broadly-shared growth that swelled the ranks of the middle class. It holds the same promise in our AI age.

Two big obstacles to shared prosperity in the AI economy

But two key obstacles stand in the way of delivering shared prosperity in an economy reshaped by AI. The ability of Americans to bargain collectively over the terms of their jobs is far weaker today than in the 1950s: union membership has fallen from roughly a third of the workforce to about one in ten workers. This means that technological change is now something that just happens to workers, rather than something workers have the power to shape. In addition, the safety net for displaced workers—fought for and built by organized labor—has eroded over the decades, offering ever-stingier support to out-of-work Americans. Perhaps most concerningly, the protections offered by unemployment insurance (UI) have weakened considerably since the mid-century, with a smaller and smaller proportion of unemployed Americans claiming benefits.

The good news is that policymakers can rebuild worker voice and strengthen the safety net at the same time; indeed, each can reinforce the other. I call this approach “Solidarity Insurance.”

We need Solidarity Insurance

Solidarity Insurance would reform our existing UI system to create new freestanding “worker choice” funds operated by labor unions or worker centers to pay benefits to unemployed workers, match workers with new jobs, and connect them with retraining opportunities. Participation in the program would require government certification as a bona fide labor organization. When a worker loses their job, they would have a choice to enroll in a state-run UI fund, or to enroll instead in a union-run fund, directing their payroll contributions there, and turning to that fund for benefits, job search help, or retraining if they lose their jobs.

These worker choice funds would be run by labor unions or similar worker organizations—such as worker centers and other worker advocacy groups—building on these groups’ extensive experience delivering these services and their deep connections with workers across the country. Worker choice funds would operate alongside traditional UI funds run by state governments, and would be financed through payroll contributions from employers and workers as well as federal subsidies. (Worker choice funds could charge additional contributions above and beyond the standard UI tax rates for additional services, too.)

Payroll contributions and payments would follow workers. If a worker opted to enroll in a union-run worker choice fund as opposed to a traditional state program, the state would transfer sufficient funds to cover benefit payments in that worker choice fund. In this way, Solidarity Insurance would layer onto the existing UI system, offering workers a different choice for managing their benefits and reemployment support. (As I discuss below, Solidarity Insurance should also be part of a broader set of reforms to bolster UI finances, access, and benefits).

Putting worker organizations in charge of supporting displaced workers (or workers at risk of displacement who want retraining) would ensure that labor market benefits and services actually respond to workers’ needs, and that displaced workers land in good-paying, high-quality, union-covered jobs. Just as important, it would seed new opportunities for labor organizing, growing the labor movement itself. Both American history and the experience of peer democracies with stronger labor movements point to the power of worker organizations connecting workers to the safety net—unemployment benefits, training programs, and job search assistance alike.

Americans support putting worker organizations at the center of the response to AI displacement. Polling conducted by the Center for Shared AI Prosperity (CSAIP) with Blue Rose Research found that at least half of all voters supported the idea of Solidarity Insurance after hearing about pros and cons of the policy.

How can policymakers move to Solidarity Insurance?

Ideally, Congress would pass new legislation explicitly creating a legal framework for union- or worker-organization-run funds to pay out benefits and provide training and job search assistance to workers.

Any legislation must also reform the UI system to safeguard workers in the AI era, by closing the current UI system’s gaps in eligibility (especially for the growing number of gig workers or independent contractors excluded from the UI system), generosity, and duration and setting benefits at levels adequate for American workers.

Solidarity Insurance also belongs in a bigger UI reform package that creates a greater role for labor in managing the transition to an AI economy. Polling by CSAIP and Blue Rose Research found that while Americans expressed moderate support for Solidarity Insurance, they were even more supportive of proposals that would create union-run training options for workers (at 61% support), union-run assistance for applying for UI benefits and finding new work (64% support), and building unions into a new governance structure together with businesses and states to ensure that UI is responsive to changes in the labor market (73% support). Voters were also very supportive of taxing AI companies to improve UI system funding (75% support). These are all changes that could be bundled with Solidarity Insurance.

Part of that broader reform package should include bolstering UI finances. Policymakers should consider additional payroll taxes by employers and workers (which would maintain the sense of an earned benefit, key to the program’ political durability) alongside other revenue sources, including from companies that are developing or implementing AI tools at work. Polling from CSAIP finds that voters are very strongly supportive of AI company contributions to bolstering UI finances, much more so than worker or broader employer contributions. At the same time, policymakers should carefully weigh the trade-offs between the popularity of outside revenue from AI companies versus the political durability of worker and employer contributions (as the case of Social Security has made so powerfully).

Other important policy design questions deserve careful analysis, too, including which worker organizations would be eligible to administer worker choice funds (and how the government would certify them), what fiduciary duties those funds would owe the workers who enroll in them, and how the federal government would support funds’ administrative costs.

We should also create stronger oversight mechanisms for the UI system to ensure that it remains responsive to workers’ changing needs. Right now the UI system is overseen by the U.S. Department of Labor (DOL). Like many federal agencies responsible for managing state-by-state social programs, DOL views states, not workers, as the primary constituency to which it is accountable. To ensure that workers are front and center in program oversight, Congress should create a Solidarity Insurance governance board that includes representatives from labor, business, the states, and DOL. The board should be charged with monitoring labor market conditions, understanding trends and forecasts, and then recommending and implementing changes to the system to keep the program responsive to workers’ needs.

Both states and labor organizations could begin moving towards Solidarity Insurance now to prepare for future Congressional legislation. Without waiting for Congress, states could launch navigator programs, partnering with worker organizations to help unemployed Americans better access UI benefits and other reemployment support. Past research I have conducted suggests that these partnerships can help more workers access UI benefits with less delay and administrative burden, and also help foster greater interest in workplace collective actions. These programs could also help states and worker organizations build the organizational “muscle” needed to eventually operate worker choice funds, when authorized by Congress and the Department of Labor.

A final consideration involves the branding of the system. I have opted here to describe the proposal as Solidarity Insurance to capture the idea that the program would build a stronger working class identity in response to AI. CSAIP polling suggests that other options could resonate even more strongly with voters, especially focusing on the idea of “worker choice” or “worker funds”.

Don’t ditch UI

Some analysts argue that AI’s displacement risks demand abandoning existing social programs in favor of entirely new universal benefits that would guarantee an income floor for all Americans. But decades of research on American social policy suggest that this path would be extraordinarily difficult. Study after study confirms that Americans hold deeply ingrained preferences for social programs grounded in “deservedness”—above all, the perception that recipients have worked for their benefits. History also points to the political power of building on existing programs wherever possible; Medicare’s durability, for example, owes much to its association with Social Security.

With these political lessons in mind, the UI system offers an existing social insurance program that most Americans view as an “earned” benefit and that already delivers financial support and services to jobless Americans. The payroll contributions that fund the system, as with Social Security, give workers a material stake in their program. The system has longstanding and growing gaps—low and declining recipiency, inadequate benefit generosity, and burdensome applications—but it has nonetheless proven remarkably effective at supporting millions of out-of-work Americans during downturns. During the COVID-19 pandemic, the regular UI system plus temporary federal expansions kept more than 5 million Americans out of poverty in 2020 alone.

UI is thus a very promising platform for expanding support to out-of-work Americans, including workers displaced by AI—so long as such reforms are coupled with efforts to improve benefit access and generosity. Indeed, to the extent that it will be white-collar workers who are most exposed to potential disruption from generative AI, tying Solidarity Insurance to the response to AI risk could help broaden the political coalition of workers in support of expanding UI benefits.

To be sure, Solidarity Insurance is a response to a world where paid work remains an important part of society for most Americans and what is needed is stronger support to help more workers navigate transitions between jobs or occupations. I view that as the most plausible scenario for generative AI’s impact on the American economy, at least in the medium run. If generative AI eliminates paid work altogether for large portions of the workforce, we would need other policy responses.

Union-run insurance already works in other countries

While this proposal might sound far-fetched, several other countries–primarily Belgium, Finland, Sweden, and Denmark–administer UI benefits directly through union-linked funds that pay benefits to workers and help connect workers with other services to navigate unemployment (dubbed the “Ghent system” after the Belgian city where it originated). And a broader set of countries across Northern and Western Europe build worker organizations and employer representatives into the administration and oversight of UI benefits and employment services, even if unions do not administer UI funds as in Ghent system countries. Together, these countries’ experiences make clear the power of having unions, rather than the state alone, administering an unemployment system for workers. The United States has its own precedent: during the New Deal era, unions in some cases helped state and local safety net agencies enroll their members in benefits and administer those programs.

Most importantly, worker organizations that engage with workers day in and day out can design and target benefits and services to be maximally effective. One of the current UI system’s central failures is precisely its unresponsiveness to workers’ needs—in applications, benefit structure, and connections to training and reemployment support. Comparative research consistently finds that empowering worker organizations to manage UI benefits and labor market supports produces services that are better targeted and administered, especially during economic shocks. Unions and other worker organizations can draw on their deep, on-the-ground knowledge of working conditions and the economy as a whole to keep benefits and retraining responsive to the changing realities of work. Research in the United States likewise finds that workforce training and job placement programs work best when unions play a strong role—so building labor organizations more centrally into retraining could help more workers match into high-paying, good-quality jobs. Workers could still benefit from union-provided retraining even if they have not yet lost their jobs, helping smooth the potential future disruptions presented by AI.

Beyond making benefits and services more responsive and effective for displaced workers, worker-managed funds would also build collective worker power. Administering UI funds and labor market services gives unions a way to connect with unorganized workers. It does so by creating more opportunities for workers to regularly interact with unions, which can share information on how to organize new workplaces, including teaching workers about their labor rights, and encourage workers to join unions as dues-paying members. The system would also help workers stay connected to unions even after they lost their jobs. Currently, if a worker leaves a unionized job, they typically lose their union membership. But union-run insurance would permit workers to maintain ties to unions throughout unemployment. Because of these mechanisms, past research consistently finds that building worker organizations into the delivery of social programs builds and sustains strong union membership. Ghent-system countries have maintained markedly higher union density even while facing the same pressures of global trade, deindustrialization, and employer opposition as their peers. Perhaps the strongest testament to these systems’ power is that conservatives seeking to weaken unions have made rolling back the Ghent model a priority.

By helping more workers to organize and strengthening unions’ clout in the economy, Solidarity Insurance would mean that more workers have the opportunity to bargain collectively with their employers, including over AI itself. Solidarity Insurance should thus be seen as a complement to broader changes in American labor law that would expand workers’ ability to form and join unions, and for those unions to bargain on behalf of their members.

The urgency of worker voice in the AI transition

Regardless of the specific paths to new legislation, policymakers and labor organizers should move quickly. Americans are deeply anxious about the impacts of AI on the economy, and their lack of voice in particular. In a recent large-scale Gallup survey on job quality and working conditions, I found that 55% of workers did not have the say they wanted on the use of AI in the workplace (among other technologies)—no surprise given that the vast majority of workers lack access to a union.

Solidarity Insurance is not the only step policymakers should take to protect American workers from the risks AI poses to the workforce. But it represents a major stride toward realizing the vision the CIO articulated in the 1950s—still urgent today—that “workers should not be forced to carry all of the sacrifice or burden of adjustment [to new technology],” and that policymakers must introduce measures “to aid affected workers…as…a part of the cost of introduction of new machines.”

Americans are pessimistic about what AI will do to the economy

Only 18% of Americans think that AI will make their own household’s finances better, while half of Americans are uncertain about how the technology will impact their personal financial future. And 39% of Americans think AI will make the economy weaker in the next five years.

Americans think wealthy investors and CEOs will capture the gains if AI does lead to increasing economic growth. As CSAIP has noted before, Americans see AI as the manifestation of a rigged economy that is built to protect the elite.

Americans see an AI-driven recession coming soon

While Trump touts the AI-driven stock market boom, Americans see trouble on the horizon. Over half of Americans have heard a lot or a little about concerns that the amount of money being invested into AI is creating a financial bubble. 41% of Americans say this investment is a bubble, and 38% of voters say that if that bubble bursts, it’s going to happen in the next 1 to 3 years.

While Americans aren’t certain who will be harmed by an AI-fueled recession, they’re more likely to think ordinary families will suffer the consequences, rather than wealthy investors and tech companies.

Here’s what’s most interesting: when Americans hear the term “AI-caused financial crisis” they’re pretty evenly split on whether they think that means a traditional recession caused by an investment bubble bursting, or an AI cyberattack against the financial system.

Build brakes, ban bailouts

Americans overwhelmingly reject the idea of bailouts for AI companies. More than two thirds of Americans think that taxpayers should never bail out AI companies if their investment bets do not pay off.

Even when we ask Americans if the government should step in during an AI economic crash to protect the jobs and retirement savings of regular people, they are still more likely to think the government should stay out of it.

One caveat: young Americans have a slightly different view. More than half of Americans aged 18 to 34 call the AI boom a bubble, compared to 41% of Americans overall. If that bubble bursts, 45% of young Americans say the government should step in to limit the damage, compared to just 32% of Americans overall. One explanation is that young voters are more anxious about AI-driven job loss and may be more supportive of aggressive government action to protect employment. Another explanation is memory. These voters (like me) were kids during the 2008 financial crisis, and they may not remember the backlash to Wall Street bailouts.

But in the aggregate, instead of bailing out failed AI investments, Americans think the government should put guardrails in place now before bailouts are potentially necessary. I see an opening in this polling for three common-sense safeguards:

First, stop AI companies from becoming too big — or too embedded — to fail. Americans are anxious about the growing dependence of banks, hospitals, and utilities on a small number of AI models. They want the government to set limits on how much any one AI company controls. Importantly, they embrace this view even over opposing arguments that setting limits could give Chinese AI companies a competitive edge.

Second, limit our financial system’s exposure to AI decision-making. Nearly two thirds of Americans think it’s dangerous for AI to make trading and lending decisions, believing that the risk of triggering a financial crisis outweighs any benefits of increasing efficiency or growth.

Finally, put AI companies on the hook for cyber attacks. Two thirds of Americans think AI companies should bear the costs if their products enable hackers to gain access to the financial system, the power grid, or the health care system. That suggests a political opening to put cost-sharing rules in place before taxpayers have to pick up the tab for sophisticated hacks.

Pain down both paths

Here’s what I think this polling tells us: Americans are bracing for pain down both economic paths. If AI delivers on the promises of its investors, Americans expect to be left behind as the economy is upended in favor of the wealthy and powerful. Remember, nearly half of Americans under 30 are already worried about AI taking their job in the next ten years. But if AI doesn’t deliver on these disruptive promises, then Americans expect the bubble to burst, and they’re worried that their families will be caught in the economic fallout.

Is that a contradiction in terms? Can AI be so powerful it will upend the job market, while also being so overhyped that investments in the technology will fail? I’d argue that Americans aren’t really making a prediction about the technology itself here. They’re reflecting the truth about our rigged economy: there is no structure in place today to protect ordinary Americans from AI-driven economic displacement under either scenario. Whether AI works and automates away jobs, or it fails and triggers a more “traditional” recession, Americans believe that wealthy investors will keep the winnings, while everyone else is forced to cover the losses. That’s why we need brakes before bailouts.

The path to surviving the AI transition isn’t techno-utopian policy experiments like a Universal Basic Income proposed by Elon Musk and others. Instead, it is stopping immediate safety net cuts, taxing tech capital more than human labor, and ensuring the long-standing safety net programs Americans already trust can match the transitions our economy faces.

CSAIP’s national survey of 56,000 Americans shows that this approach is essential to allaying Americans’ fears about AI and building trust in government leaders to lessen the shock.

Washington is slashing the safety net just as the AI shock arrives

Americans need more economic security as automation threatens job security. But Washington is cutting the safety net right as the AI shock arrives. The extreme One Big Beautiful Bill Act has already forced about 5 million Americans off food assistance and 2.2 million off Medicaid, with the worst yet to come. New rules taking effect in January will push millions more off SNAP and Medicaid and put rural hospitals at risk. The law also left the Child Tax Credit’s refundable portion capped at $1,700, far short of the pandemic-era credit that drove child poverty to a record low of 5.2 percent in 2021. It has stayed above 13 percent ever since that credit lapsed.

The enhanced Affordable Care Act subsidies expired at the end of last year, after a shutdown fight failed to save them. Average premium payments jumped 58 percent and average deductibles rose 37 percent. CBO projects 3.7 million more people uninsured in 2027, and 3.8 million more a year on average through 2034. Now congressional Republicans are talking openly about cutting Medicare and Social Security.

Americans oppose these cuts. And they are happening as real wages are squeezed. They contribute to the feeling bipartisan majorities of voters already share that the economy is leaving families behind. A National Bureau of Economic Research study found a clear relation between cuts and Americans’ views about the economy. For every $1,000 in lost child tax credit benefits, consumer sentiment declined by about 1.7 points (2.4%).

CSAIP’s research finds Americans want to build on the very safety net under threat

CSAIP’s poll of 56,000 Americans finds they want an active government response to protect people from AI disruption. They are not interested in being paid not to work, which is what proposals like a universal basic income sometimes promise to do. They want to work, and support policies that transfer resources from corporations and the wealthiest Americans to support workers and families.

When asked about specific policies, they favor retraining and compensating workers who are impacted by AI automation – and they favor strengthening the existing social safety net.

The survey shows that proposals to expand on existing programs score among the highest of any AI response we tested. “Using AI profits and tech-billionaire taxes to strengthen Social Security” scores in the 97th percentile. That same survey shows deep net support for expanding current programs, measured after voters hear arguments on both sides: “SS bridge for older workers” +39, “expand the EITC” +26, “expand CTC” +18.

Support for existing programs probably reflects familiarity. Plus, with trust in government low, many likely doubt Washington could design something new and get it right.

But big disruption demands big changes

Of course, the threat AI poses to workers and families is exposing deep cracks in our existing safety net in three big ways:

First, much of our existing safety net spending is funded primarily by a capped tax on labor. That no longer makes sense. Wages are growing more unequal, so more income sits above payroll tax caps and escapes capture, and labor’s share of US wealth has fallen to its lowest level ever recorded – a trend AI will accelerate without a policy response. Repeated cuts to taxes on capital gains but not on labor income has resulted in a perverse incentive to replace people with machines.

Second, our current programs were designed for an earlier era when it was presumed a breadwinner worked for a single paternalistic employer for 40 years at a union wage while a homemaker was available to provide caregiving. That system was never truly inclusive, and it has grown less so as automation, globalization, and pressure on unionization reduced worker leverage. Workers are now more likely to be in insecure “gig” jobs without health or retirement benefits. And, as they work longer hours in an effort to attain economic security, they face new costs of caring for a child or parent. AI will exacerbate these trends if, as industry executives promise, it replaces or deskills additional jobs – including in professional services where workers have largely retained security from previous technology and trade shocks.

And third, our skimpy unemployment insurance was designed for a factory retooling. It leaves behind today’s gig workers, new entrants, low-paid workers, intermittent workers, those who needed to quit, or those who were fired for cause. Our current training programs are underfunded and, by the evidence, not ready for an AI transition which could upend entire sectors.

The current support system designed in the middle of the last century for a different economy is divided into a maze of programs administered in wildly divergent ways often with extra administrative burdens imposed on means-tested programs.

AI requires a redesign and expansion of these programs if Americans are to continue contributing to the new AI economy and share in the enormous wealth AI may produce.

Stop the cuts first. Then build on what people already trust.

That ambitious task cannot begin by making Americans less secure while promising to create an entirely new system that undermines the dignity of work. Intentional or not, this is what Washington is offering Americans today and it will only increase their cynicism about government and big tech.

Instead, the first thing political leaders should do is stop the cutting. Then, build on top of the scaffolding that Americans already trust.

The CSAIP survey shows support for this approach. Proposals to “modernize” current programs win net support: “modernize disability” +44, “modernize UI” +24. Specific proposals for how to expand existing programs also score high: “public option” for health care +26, “paid Family and Medical Leave” +23, “portable benefits” +20, “strengthen SNAP” +34.

The survey also shows support for new efforts to fill in major gaps that AI will exacerbate. They back a subsidized public jobs program along New Deal lines, by +32. Asked how government should respond to AI job losses, 62% choose creating public jobs vs. 17% for direct income support without work. And, despite elite skepticism that “pink collar” care jobs could ever be a popular part of the solution, “guaranteed jobs caring for family” scores +44 and “expand care economy” +46.

Americans want action now

Economically anxious Americans are demanding action from their political leaders. If those leaders instead stand by as they did during past disruptions from NAFTA and the China Shock to the internet and the Great Financial Crisis – or make things worse - populist anger against both them and AI is likely to increase.

But underneath those headlines, something more important is happening in how everyday Americans actually view AI.

As an opinion researcher, I’d argue that the most important fact about public attitudes toward AI has nothing to do with the technology itself. Instead, it’s that AI is emerging at the most populist moment in recent American memory — and everything about AI is being viewed through Americans’ anti-elite lens.

That means AI in practice is seen as the manifestation of an economy that Americans see as not built for them, as part of a system that is built to protect the elite, with no regard for the harm to regular people.

The Backdrop: An Economy Americans Don’t Trust

To say Americans are unhappy with the status quo today would be an understatement. Americans have an extremely pessimistic view of our economy, with nearly two-thirds (65%) saying their life has gotten less affordable over the past year. Less than a third say they have a secure job, and only one in four say they’re confident about their financial future.

When we ask people why they’re struggling, they see an economy organized around somebody else’s interests, at their expense. Nearly two thirds of Americans — 62% — say the system is rigged for the wealthy and elite.

So when tech companies promise that AI will benefit everybody, people hear that in the context of everything that they already believe about corporate power. 76% of Americans today say large tech companies mostly make decisions that benefit only their own bottom lines, and just 8% of Americans say they benefit the public. Notably, something we find consistently throughout our research is that these attitudes are remarkably bipartisan. Among Trump voters, 75% say large tech companies mostly make decisions that benefit only their own bottom lines.

Sentiment is Degrading, Fast

It’s hard to overstate how much and how quickly sentiment toward AI is deteriorating. Since June, the share of Americans who say they are pessimistic about AI has grown from 40% to 49%, while optimism around AI has slipped from 36% to 33%. In other words, the gap between pessimism and optimism has grown from 4 points to 16 points in just three months. Swing voters, who were evenly split in June, are now pessimistic by a 17-point margin.

A big piece of that pessimism is over jobs. We asked Americans how worried they are that AI will significantly change or replace their job over the next year and over the next ten years. Concern is highest among the youngest Americans: nearly half of Americans under 30 are worried about AI taking their job in the next ten years, and over 40% of 18-year-olds are worried about the next year alone. These numbers are climbing quickly, and it’s the youngest Americans who are the most freaked out. Of course they feel this way! After all, it’s the people building this technology who have spent the last few years making stark predictions that AI will wipe out jobs.

This is why Americans don't believe reassurances from leaders in tech or government that everything will be OK. We asked how much people trust leaders in government and tech when some say AI won't cause widespread job losses. In March, distrust outweighed trust by 41 points. By September, that gap had grown to 56 points.

So, let me say this bluntly: “AI isn’t going to threaten your job” is not a tenable political message anymore. Whatever you think the real scale of job displacement will be, Americans don’t believe these reassurances. And they don’t just think you’re wrong, they think you’re lying to them. That creates an obvious opening for anyone willing to say what they’ll actually do to protect people.

OK, What Do People Actually Want Around AI Then?

A big part of what we’re doing at CSAIP is trying to bring public opinion research directly into policy development by testing what people want from an AI economy, and building proposals that can withstand opposition arguments and counter-messaging. As we’ve written up previously, CSAIP and Blue Rose Research tested 79 policy ideas for protecting workers and families from AI. Respondents heard arguments against each proposal before we measured support. Even after that, 61 of 79 ideas still had more support than opposition. Three themes about this research really stood out to me:

First, there is an enormous openness amongst Americans towards making those profiting from AI pay their fair share. Support for higher corporate taxes on AI companies beat opposition by 44 points, even after people heard the case against it. Americans see these companies capturing the upside of AI and tech advancements and feel zero loyalty to them.

Second, any benefits related to AI have to be tangible. For example, severance when a job gets automated away is very popular with Americans, as is getting paid when a company uses your personal data. By contrast, in our testing we saw that more abstract proposals like sovereign wealth funds struggled, likely because people need to understand how a policy will actually change their lives.

Finally, people want meaningful work in their future, not just a check. Americans want a credible vision of their role in the future economy: how they will make a living, what they will contribute, and whether they will have some control over their lives. Apprenticeships and training tied to real jobs were among the most popular ideas we tested — and though training alone probably can’t match the scale of disruption people are worried about, these types of policies will be critical in any type of policy intervention.

Where This Leaves Us

AI is arriving at a moment when Americans already believe the economy is rigged for the wealthy and powerful. That’s the lens through which they see every part of this story — jobs, AI, safety, all of it.

That’s an opening for anyone willing to take it. People are looking for leaders who can name who they’re fighting for, and what they’re fighting to win for them. AI is moving fast, and public opinion is moving right alongside it, which is why Blue Rose Research is proud to partner with CSAIP to track how Americans’ views about AI evolve. AI is both one of our biggest threats and one of the biggest opportunities of our lifetimes — now we just have to meet the moment.

Fitting that first criterion is why public opinion research from Blue Rose Research is so crucial. They recently tested 79 different possible policy responses with American. Each time, they used a best-practice common to Blue Rose reports: giving arguments for and against a policy, and measuring how exposure to these arguments (meant to represent the debate likely to occur once a policy is actually proposed) changes their minds.

Generally speaking, exposure to the debate made public support for policies fall. This makes sense; lots of things seem like a good idea at first glance but less promising when you weigh the details a bit. But even after that, the vast majority of the ideas (61) still were supported on net after exposure to arguments, and twelve ideas were supported by a margin of 40 or more points, suggesting a deep consensus.

So we have some ideas that the public starts out very receptive towards. Great! The question then becomes: are these ideas sound, and sized to the scale of the problem? Here the news is a little less encouraging.

The full list of policies earning a margin of 40 or more is below; you can click to see a more detailed description.

Expand Apprenticeships (+66)

Some policymakers are proposing to scale up “earn-while-you-learn” apprenticeships into new fields — including AI infrastructure and roles that work alongside AI. Under this policy, more workers could train for skilled jobs by working a paid position under experienced mentors.

Require Severance for Automated-Away Jobs (+63)

Some policymakers are proposing to require that companies provide severance pay or transition support to workers they replace with automation. Under this policy, a company that eliminates a job through AI or automation would have to give the affected worker a defined payout or help finding new work.

Sector-Based Job Training (+60)

Some policymakers are proposing training programs built around specific high-demand industries, run with employers who agree to hire graduates, plus support like childcare and transportation to help people finish. Under this policy, workers would train as a group for openings in growing fields.

Employee Ownership (ESOPs) (+50)

Some policymakers are proposing to expand employee stock ownership plans, which give workers an ownership stake in the companies they work for through tax-advantaged trusts. Under this policy, more workers would own a piece of their employer and share in its profits — so that as AI boosts company value, employees benefit alongside investors.

Data Dividend (+48)

Some policymakers are proposing that tech and AI companies pay people for the personal data they collect. Under this policy, the data companies use to build and train AI would be treated as something you own, and firms would have to compensate you when they use it.

No Billionaire Should Pay a Lower Rate Than a Nurse (+47)

Some policymakers are proposing a rule ensuring the very wealthiest pay at least as high a tax rate as middle-class workers. Under this policy, billionaires and the ultra-rich could no longer use investment income and loopholes to pay a lower effective tax rate than teachers, nurses, and other working people.

Invest in the Care Economy (+46)

Some policymakers are proposing major public investment in care work — home care, childcare, and eldercare — fields that are labor-intensive and hard to automate. Under this policy, the government would pay for higher wages in care jobs, expanding a part of the economy where human workers remain essential even as AI advances.

Modernize Disability Benefits (+44)

Some policymakers are proposing to update the rules for Social Security Disability Insurance and Supplemental Security Income so people can build modest savings and work where they’re able. Currently, these programs limit how much money recipients can have in savings, with limits set decades ago, and recipients can lose benefits if they save a few thousand dollars or take part-time work.

Guaranteed Jobs Caring for Family (+44)

Some policymakers are proposing a jobs program that guarantees paid work caring for people — looking after young children, aging parents, and sick or disabled relatives. Under this policy, the government would pay people to do caregiving that families now do unpaid or can’t afford to hire out.

Make Big Corporations Pay a Minimum Tax (+43)

Some policymakers are proposing to require large, highly profitable corporations to pay a minimum amount of tax. Under this policy, big companies reporting large profits would owe at least a set minimum rate, even if deductions and credits would otherwise lower their tax bill.

Fund the IRS to Crack Down on Wealthy Tax Cheats (+43)

Some policymakers are proposing to fund the IRS so it can pursue wealthy individuals and corporations that don’t pay the taxes they legally owe. Under this policy, the agency would have the resources to audit complex high-end returns and collect unpaid taxes.

Universal Retirement Accounts (+42)

Some policymakers are proposing universal, portable retirement accounts that follow workers from job to job, with employer and government contributions, paying guaranteed income in retirement. Under this policy, everyone — including gig and part-time workers without an employer plan — would build retirement savings automatically.

A response scaled to the problem

If you go by raw popularity, the message you get from this list is that job retraining should be at the center of our response to AI disruption. Larger apprenticeship programs are the most popular single policy, with sector-based retraining programs meant to help people pivot into newly growing fields not far behind.

I have a few reservations, though. Alongside the policy polling, Blue Rose made short 20-60 second video ads, each focused on a different aspect of AI’s economic disruption, and tested them against three advocacy outcomes: whether voters see AI development as economically harmful, whether they want Congress to regulate AI, and whether they name AI as the more important issue facing the country.

The first two hardly need help. 70 percent already say AI development hurts the economy and 68 percent already want Congress to act. Only 13 percent rank AI as the more pressing issue. The ads moved concern about AI’s economic effects by 3 to 6 points and support for congressional action by 0.4 to 2.2 points. On salience they moved nothing: the best result in the field was 1.8 points, from the longest and most maximalist ad tested, and the apprenticeships ad moved it 0.3 points.

To be clear: job retraining, if it worked, and if there really are sectors where demand for workers is growing, is a fine policy to promote. But those are big “ifs.”

The federal government has funded workforce retraining programs for a very long time. The Wagner-Peyser Act in 1933, a key part of the New Deal, kicked efforts off; then there was the Manpower Development Training Act of 1962, the Comprehensive Employment and Training Act of 1973, the Job Training Partnership Act of 1982, the Workforce Investment Act of 1998, and finally the Workforce Innovation and Opportunity Act of 2014. A new bill in the litany, A Stronger Workforce for America Act of 2026, was marked up by the House Education and Labor Committee this past April; a version almost passed in late 2024 until Elon Musk and president-elect Trump killed the funding bill it was a part of. The programs these bills authorize are funded per the appropriations process, but generally get between $3-5 billion a year.

There is, correspondingly, a large literature on how effective this spending has been. David Roodman and Maxim Massenkoff at Anthropic just released an excellent evidence review summarizing what we’ve learned. The results are mixed. Overall, they conclude that job retraining programs subjected to randomized trials increased employment rates by 1.7 percentage points, against a baseline of 63 percent in the control group, and increase earnings by $800 per year on average

But when small programs are scaled up, they tend to lose effectiveness. The intensive Adult and Dislocated Worker programs first authorized under the 1998 law were subject to a large national randomized trial, which found no positive impacts after 30 months. A similar randomized evaluation began in the 1990s for Jobs Corps, a program more specifically targeting youth. Follow-ups after four years found gains to participants’ employment and earnings levels, but a longer-run review of study participants twenty years on found null results, with earnings gains fading away rapidly.

The best results have come from sectoral programs (backed by a margin of 60 points in our poll), which a recent evidence review led by Harvard’s Larry Katz estimates as leading to persistent 12-34 percent boosts to earnings. But as Roodman and Massenkoff note, these sectoral programs aren’t an across-the-board answer. They generally screen out the overwhelming majority, over 80 percent, of applicants, and last months rather than years. They aren’t capable of imparting skills that require longer to master. They also have a lot of variability in their effectiveness, and attempts to copy them have often ended in failure.

They also require an ability to identify and target jobs for which demand remains. We don’t know what, if any, such jobs will be left after the AI shock. “Sector-based training works when there is strong demand for jobs people can, and are willing to, step into,” the economist and AI analyst Molly Kinder noted recently. “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.”

I’d have no objection to job training programs being part of the government’s AI response — but there are major landmines here that we’ll need to avoid. We’ve learned it’s very easy to develop training programs with weak to no effects. My fear is that a hastily deployed expansion of these programs developed in the next couple of years might look more like the programs with dismal evaluation results than the carefully designed ones Katz evaluated.

Deploying ineffective or unpopular programs could be worse than doing nothing at all. The Comprehensive Employment and Training Act of 1973 (CETA) led to a wave of hundreds of thousands of CETA jobs that often went to ex-welfare recipients, which led CETA jobs to become demonized and unpopular much as welfare had been, and set the stage for the Reagan administration to wind the program down. That had a long-lasting, damaging effect on the US’s ability to do job training at scale. We can’t let a failure like that happen again.

More to the point, the size of the AI shock could be large enough that no realistically sized job retraining program is adequate to handle it. Our current job training infrastructure is sized to deal with hundreds of thousands of people, not millions, and certainly not tens of millions. This is fine if AI reduces employment by, say, 1 percent. But what if it reduces employment by 10 percent? What if there is a broad collapse in the return to cognitive labor, and workers from accountants to lawyers to engineers have to pivot into care work or other manual labor? There is no realistic job training system that could handle that kind of shock.

Then again, plenty of other popular policies in the above list seem inadequate to the moment too. Universal retirement accounts are a perennial proposal in the retirement policy world, but beyond serving as one form of capital redistribution, how exactly are they a specific response to AI? Why would broader employee ownership, regardless of its other virtues, help people whose entire companies are struggling due to AI?

The next stage of AI policy development should focus on identifying popular options that actually do show promise at dealing with sweeping job loss (things like required severance for AI job loss, or a jobs guarantee targeting care work) and developing them into viable plans capable of becoming law. For instance, how do we determine which job losses are due to AI? Should we require severance for all job loss at certain companies? How will we finance it?

These are answerable questions. But we have to keep our focus on ideas scaled to the problem, not familiar options from the past.

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CSAIP is working to close that gap. We sit at the intersection of public opinion research and policy design. Policy should respond to the concerns (and hopes!) Americans are expressing every day about how the economy should work in a potential era of major disruption. We partner with Blue Rose Research to continuously test how Americans are thinking about AI. And we help incubate responsive economic policies to ensure that the gains from AI flow to all Americans.

Importantly, while CSAIP publishes public opinion research, we do not simply endorse policies with the highest levels of support. Of course, the most popular policies may not be the best policies for responding to and redistributing the benefits of AI. Some policies that match the scale of the challenge face an uphill battle to earn support from the American public. Policymakers shouldn’t abandon these policies, but they should be honest about the political headwinds. We plan to bring together polling and rigorous policy analysis to identify solutions that are both popular and effective. That starts with knowing where people are today.

How do we measure public support for AI economic policies?

We’ve tested 79 policy ideas that could address the impact of AI on the economy. Our national survey of 56,000 respondents measured public support on 79 policies that range from AI-specific taxation to the creation of a U.S. sovereign wealth fund, from “data dividends” to wage insurance. Our survey results are available here. We went broad because all popularity is relative, so we need to understand where people are on a diverse battery of economic policies before we can say much about any specific intervention. We will update the rank order (relative popularity) of these policies over time and expand the economic policies we test as the field evolves.

Our surveys include pro and con arguments. Blue Rose Research measures how support changes after respondents read very short arguments for and against the policy. Net support reported in this post, and elsewhere in CSAIP research, reflects net support after argumentation. After reading the arguments, respondents are asked to choose whether they support or oppose the policy without an undecided option. Net support expresses the difference between support and opposition in percentage points. This helps us understand how fixed opinions already are on any given policy idea, and how vulnerable a policy might be to basic opposition. More information about the polling methodology at Blue Rose Research can be found here.

We test AI policies against generic economic proposals. To understand what the public thinks about AI specific economic policies, we’ve also included other economic policies in this battery as useful sign posts for baseline support. These baseline policies do not include specific language related to AI disruption.

Americans support most progressive economic policies addressing AI disruption

Our polling indicates that Americans are broadly supportive of economic policies addressing AI disruption. Of the 79 economic policies we tested, 61 hold net approval even after strong opposition arguments. The dozen policy ideas that top the chart all maintain an approval margin of +40pp in the face of opposition arguments. And 48 policies maintain a net approval of +15pp or more.

In particular, we see significant support for policies that are straightforwardly redistributive (meaning they transfer resources from corporations and the wealthiest Americans to support workers and families), and that put AI companies on the hook (meaning they specifically ascribe responsibility to AI companies for disruption they may cause). When we look at policies that directly mention AI, 31 of 48 economic policies had majority support, and 19 of 27 of the policies specifically mentioning AI economic disruption had majority support.

Our bottom line finding is this: Americans want an active government response to protect people from AI disruption, and they are open to a big range of tools to do this. Americans strongly support job retraining and compensating workers who are impacted by AI automation, strengthening the existing social safety net, and funding training, apprenticeship, and care work through progressive taxation schemes.

For this initial polling instrument, CSAIP focused on four major policy areas:

• Taxation and revenue

• Income support and safety nets

• Labor market and workforce development

• Ownership, governance, and stakeholder models

Labor market and workforce development policies had significantly more support after argumentation than the other three categories tested. Taxation and revenue policies and ownership governments and stakeholder models also provide the least familiar mechanisms for addressing AI economic disruption.

What this polling doesn’t tell us

We’re clear-eyed about what this polling cannot tell us:

Will this policy match the scale of the disruption we may face? A popular policy is not the same as an effective policy. This polling helps us understand where the public is already open to a policy idea, and where bold policy ideas will need strong organizing to gain traction.

What message will break through? How will public opinion shift over time? A one-sentence, pro-con argumentation in our polling provides an initial read of how AI economic policies stack up against each other, rather than messaging guidance for any specific policy. Think of this polling instrument as a jumping-off point for future polling and message testing which will give us a better sense of what kinds of arguments make these policies more or less compelling to Americans.

What’s next?

Policy should be guided by listening to the American public. CSAIP is committed to sharing public polling that helps us build economic solutions that meet the scale of the challenges we face, and we’re committed to sparking new policy development that harnesses public opinion to meet this moment. Explore our policy polling here, and join us by submitting your ideas.

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The safety net we have today won’t bail us out of widespread AI disruption

Even before AI began to raise the prospect of widescale economic displacement, our social safety net was failing to effectively deliver economic security.

Let’s look at unemployment insurance (UI), the stalwart New Deal program that forms an essential part of the social safety net. Even before AI, UI was in crisis. It had failed workers in the transition to a gig economy. The share of unemployed workers who actually receive UI has fallen to just one in four, driven in large part by the growing share of workers who are classified by their employers as independent contractors. Benefits are too low to keep people financially stable, and assistance is time-limited. The program is administered through a miserably complex patchwork of state laws that make the benefits extremely difficult to carry across state lines if you’re trying to relocate to find a new job. And the program basically denies help to any recent college graduate who cannot find work but also doesn’t have a work history.

Put simply, today’s UI is not capable of absorbing even a modest level of AI related job disruption. That’s ominous, because UI is ostensibly the most important safety net program for an era in which automation may swell the ranks of the unemployed, millions of Americans may need to shift careers, and young workers are already graduating into an economy where entry-level opportunities are shrinking.

But UI is not an outlier. Many of the most important safety net programs are built around the assumption that most Americans can find work that will sustain them. Medicaid and food stamps? Locked behind work requirements. Social Security? Tied to your lifetime wages. The social contract between the American welfare state and the American people assumes deregulated labor markets will expand prosperity, and provides inadequate help for the people who can’t cut it in the private market. But a safety net built around the primacy of work is set up to fail when the future of work itself is shifting under our feet. Our social safety net is profoundly ill-equipped to provide economic security if automation upends our working lives.

So we have a social safety net that is structurally misaligned with the direction our economy may be heading. The question for policymakers now is how do we harness a moment of rupture and outrage to finally transform the safety net for a 21st century economy?

AI economic policy should start with listening

This is where real-time, iterative public opinion testing becomes so valuable. Practically speaking, we need to be clear-eyed about which policies are popular enough to have a viable political pathway. But there’s a principled reason, too. As AI continues to tip the balance of power in our economy and democracy towards an emerging tech oligarchy, we should pay extra care to ground any AI policy agenda in the aspirations, fears, and opinions of the American public instead of the oligarchs’ vision of a post-AGI utopia.

CSAIP recently polled 56,000 Americans on their views on potential AI economic policies. You can access the data and learn more about our polling methods here. This poll is a treasure trove for policymakers: it tests baseline public opinion on 79 economic policy ideas for managing the AI transition. And it shows that public opinion is, once again, way ahead of the policy process.

What we’re hearing: Americans broadly support bold progressive policies to protect people from AI displacement

CSAIP’s data shows that the public is demanding a new economic vision for a world fundamentally transformed by AI. Americans overwhelmingly support dozens of policies to redistribute the gains of AI and to protect workers and families from economic displacement. The good news for policymakers is that Americans seem open to a wide range of policy tools: progressive taxation of AI profits and wealth, a stronger safety net to help workers impacted by automation, expanded investments in workforce development, and new ownership and governance models.

People want to be workers in the AI economy

Here’s what jumps out to me in this poll: The policies that appear to resonate most deeply with Americans speak to their aspirations to be workers and owners in an economy that AI is transforming, generally outperforming policies that are untethered to work.

The most popular policies in CSAIP’s poll center around protecting employment and ownership: expanding apprenticeships in new fields shaped by AI (+66pp), providing retraining or severance pay for automated-away jobs (+63pp), delivering sector-based job training in high-demand fields (+60pp), and creating employee stock ownership plans to give workers an ownership stake in the companies they work for (+50pp). In fact, the most popular policy in this survey expands apprenticeships in new fields that work alongside AI. So while Americans are anxious about the AI transition, they also want policies that deal them into a changing economy and ensure they’re not left behind.

By contrast, policies that expand access to benefits but don’t emphasize work were repeatedly underwater in this poll. A universal basic income policy ranks second to last at -33pp. Even a proposal to provide guaranteed income only to workers who have specifically lost a job to AI underperforms at -20pp. For those of us who believe expanding access to cash assistance is an important way to modernize the social safety net, these findings reflect a public skepticism we haven’t overcome yet.

The poll also raises big policy questions. We have been told a federal jobs program is politically impossible. That just doesn’t appear to be true. A proposal to create a modern version of a Works Progress Administration jobs program to employ workers who lost their job to AI scores an approval margin of +32pp. How can we design policy to match the public appetite for a program like this? At the same time, a proposal to expand paid apprenticeship programs scores an even higher approval margin of +66pp. These programs offer something similar to workers who are worried about being left behind in a changing economy. So why does one policy design command even higher public support?

An uphill battle for the boldest policies

Our poll found that strengthening programs that Americans are already familiar with sometimes outperforms novel policies that would establish new systems of redistribution. Support for policies that strengthen familiar programs like food stamps and disability insurance were more resilient against the opposition’s argument than proposals that ask Americans to trust something new, like a social wealth fund or AI profit windfall taxes.

One explanation for this dynamic is that trust in government is very low, and Americans may not believe that bold policies can actually be delivered well. Another explanation is that because the AI transition sparks deep anxieties about rapid change, programs that feel familiar and predictable may seem preferable to policies that themselves promise radical change.

But this dynamic should raise alarms for those of us who worry that the safety net we have today, even if improved, is not structurally up to the task of delivering security and prosperity. What this poll shows us is that there may be a mismatch between the baseline comfort that Americans feel with status quo programs (even programs that already underperform!) and the need for policies that are proportionate to the scale of the disruption we may face. How we avoid falling into a familiarity trap will be a key test for safety net redesign in an AI economy.

Sparking a new social contract

What an era of disruption calls for is not just pumping up the existing programs we have; AI may well demand that we reimagine the foundations of our social contract. In the most prosperous nation on earth, economic security should be a birthright, not conditional on the decisions corporations make about the future of work. And if the AI revolution does in fact usher in the wealth and productivity that its boosters promise, that should catalyze deep reinvestments in our social safety net so that this prosperity is shared. If this moment of transition is to become a moment of transformation, we’ll need an ongoing practice of listening and testing our ideas with the public.

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At CSAIP, we believe there is uncertainty around how precisely AI will impact the economy in terms of both timing and magnitude. We also concur with David Autor, the MIT economist quoted in the piece, who says CEOs “may have realized it was simply bad business to say that your great new product will destroy the economy.” Has anything in the last six months fundamentally altered the economics of AI-driven jobs displacement or is there simply a new narrative emerging?

Blue Rose Research, which advises CSAIP, has been tracking that public sentiment on AI, and its trackers point to a worsening scenario in how voters perceive and experience AI. Over the last six months the share of voters who personally know someone who lost a job or had work replaced by AI rose from 7 percent in mid-January to 9 percent by early July, a jump of roughly a third and the clearest single movement in the series. Concern about one's own job climbed in the same window, from 30 to 33 percent for the next ten years and from 21 to 23 percent for the next year, with the increase concentrated among the most concerned.

We also see shifts in public trust in AI. Optimism about AI’s future impact fell from 47 to 43 percent since late November. Belief that AI data centers benefit one’s community dropped from 30 to 25 percent. Topline trust in AI companies barely moved, but its composition changed. The share who trust these companies “not at all” rose from 27 to 30 percent as softer skeptics hardened, and only 3 percent now say they trust AI companies a lot. Just 13 percent say the benefits of AI outweigh the risks, against 39 percent who say the reverse.

Other polling shows that the public isn’t looking for doom narratives either. What the last six months show is a public whose direct experience and stated trust are both moving against the industry. The approaches that succeed will be those focused on concrete accountability over reassurance and abstract fear. Anyone proposing policy or trying to reset the story about AI and the economy will be working against that current rather than with it.

I became a CSAIP advisor because Americans seem so far ahead of the political system at this moment of technological change: they are demanding real civic imagination from leaders and policymakers. Americans have a long history of fighting for economic rights, social citizenship, and just entitlements. Together we created highly flawed yet remarkable government programs from Social Security to Obamacare, tax-and-spend programs that can create a temporary sense of security.

Still, I was most suspicious that Americans were genuinely open to new taxes to make these visions real. Would we be able to think about taxation differently in light of AI? The past decades of fights over the tax code have been downright depressing. I have been worrying at night about the feasibility of any effort to expand the necessary taxation to support the best parts of the welfare state. Just last summer, Congress locked in $3.8 trillion in extended tax cuts — and then turned around and used the resulting deficit as justification to cut Medicaid and food stamps.

So one of the first CSAIP polls pressure tests a core idea: Does AI create a genuine window of opportunity to organize around new taxation to support plans that address job market disruption?

The initial answer is strong yes, but with some caveats.

At baseline, support for AI-related taxes is robust. An energy tax on AI data centers gets overwhelming endorsement, with 76% of Americans in favor, resulting in a +64 percentage point net margin of support. A wealth tax on AI fortunes above $50M comes in at +53. A higher corporate tax rate on AI companies sits at +44. These are not subtle findings. Taxes are just not a hard sell. People want a fairer economy.

I was intrigued to see how specific AI-related taxes seem to be quite popular. Where Americans are already frustrated (especially around data centers) there is genuine enthusiasm for new taxes targeting the people and companies at the cutting edge of AI. A lot more research is needed to know how much we should extrapolate from the poor performance of the inheritance tax loophole.

Figure 1. Net support margin (support minus oppose) at baseline1

The caution: moveable opinion and many undecided on policy

Our partners at public opinion research firm Blue Rose Research have developed a thoughtful approach to measuring attitudes, which CSAIP will use often. Polling questions measure both a baseline support for a policy with a basic description, and then measure support for a policy after an interviewee has read strong arguments for and against any given proposal. This approach gauges if the opinions expressed are deeply felt, or weakly held and likely to crumble in the face of opposition.

When voters are exposed to generic pro-and-con argumentation for and against a policy, support drops on most proposals, sometimes substantially. Support for an AI data center energy tax falls from +64 to +36, and support for a wealth tax on AI fortunes drops from +53 to +24. Similarly, on nearly every proposal, roughly a quarter of respondents said they didn’t know. That uncertainty is consequential.

These realities are not shocking for nascent policy debates, and it is not necessarily bad news for tax enthusiasts. Americans are still forming their opinions, and taxes are not poison pills. In fact, support for new AI taxes is starting off from a point of strength. Still, proponents cannot assume that such strong baseline support for any new taxes will hold once opponents mount a campaign.

Figure 2. Net support margin at baseline and after generic pro-and-con argumentation2

AI-specific taxes versus broad tax reform: A generational fault line worth watching

There are two buckets of taxes that policymakers could advance in a world disrupted by AI deployment: AI-specific taxes (aimed at addressing the expected accumulation of wealth among the people and corporations that will profit from AI) and more general taxes (aimed at broadly capturing more of the wealth held by the largest corporations and the super-wealthy). There is an early policy debate occurring regarding which bucket of taxes make the most sense when it comes to raising revenue to address AI-driven job loss. CSAIP started by testing a number of AI-related taxes.

Beneath the aggregate support numbers, the data reveals a generational divide. All age groups agree that something needs to change, but start to diverge on what. When asked which tax philosophy best reflects their view, younger voters (18–34) are nearly as likely to favor targeted, AI-specific taxes (30%) as broad tax reform (32%), while older voters (65+) tilt heavily toward broad reform (47%) and are least likely to support AI-specific taxes (21%). Most importantly, the “status quo” camp is small across all ages.

Figure 3. Tax philosophy by age group — "When it comes to taxes and Artificial Intelligence (AI), which of the following comes closest to your view?"3

Younger Americans are clearly worried about a starved state. They are seeing AI wealth expand rapidly. Their openness to AI-specific taxes may reflect greater direct familiarity with the AI capabilities and the companies building it. For them, the question may not be whether the wealthy should pay more in general, but whether AI specifically has created a new kind of wealth that warrants its own form of responsibility to reinvest in society.

A window of opportunity

These results confirm that AI taxation is not a fringe position. It is where public opinion is already pointing. But “already pointing” is not the same as “already arrived.” I am excited to get into the nitty gritty of these policies and combine public opinion research with economic policy research to craft a new vision for progressive taxation in a world drastically changed by AI.

The first step of that work will come as a wide range of folks respond to the CSAIP RFI. We are hoping economists, policymakers, historians, and other creative thinkers will submit ideas for addressing key questions like: How would a token tax actually work? Is a value-added tax (VAT) possible in the United States? How would the IRS administer a robot tax? What would the impact of AI-specific taxes be on the development of AI in the United States and around the world?

The data center energy tax may be the most potent near-term entry point: highest baseline support, a concrete and visible target, easily tied to utility costs voters already feel. The token tax is getting more and more momentum. The robot tax (which would force companies using advanced AI to pay Social Security and Medicare taxes on the expense, the same way they would on a worker) sees its support fall the least after argument. It may be the most durable idea under pressure, grounded in Social Security and Medicare protections that voters across generations trust. But does the economics of a robot tax make just as much sense as the rhetoric of a robot tax? That’s exactly the type of issue CSAIP wants to explore.

The larger opportunity is not any single proposal; it is the chance to shape the terms of a debate that is still being written. CSAIP is committed to tracking this as it evolves, and to making research public. Join us by submitting your ideas.

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1

Figure 1 — Baseline Policy Support (AI tax proposals: support and opposition (N = 6,000)

2

Figure 2 — Policy Support After Pro and Con Argumentation (AI tax proposals: support and opposition (N = 6,000). Same proposals after respondents read generic arguments for and against each policy.)

3

Figure 3 — Tax Philosophy by Age Group (N = 2,753)

“When it comes to taxes and Artificial Intelligence (AI), which of the following comes closest to your view?”

• AI is creating so much new wealth that it needs its own targeted taxes on AI billionaires and AI corporations.

• AI doesn’t change the underlying problem — we should overhaul the entire tax code so everyone pays their fair share.

• AI is just another industry and the tax code should stay as it is.

• Not sure