Democrats pivot on AI: Less regulation, more redistribution

Sen. Mark Kelly (D-Ariz.) has released a new artificial intelligence (AI) policy roadmap. Notably, the focus of Kelly’s “AI for America” plan departs from other federal AI policy proposals introduced by Democrats, which emphasized strong regulation of AI model development and deployment. Instead, it calls for an “AI Horizon Fund” funded by taxes on large companies involved in the development and use of AI.

The proposal envisions channeling these dollars into various labor market interventions, such as union apprenticeship programs and a safety net for displaced workers, and infrastructure upgrades, especially for energy and water systems. This suggests Democrats may be shifting their rhetoric on AI, although the scant details so far make it hard to know how much difference this will make in terms of actual policy. Kelly’s proposal also comes at a time when Democrats control neither Congress nor the White House, so their priorities could shift whenever they regain congressional majorities and the presidency.

Setting aside these uncertainties, there appears to be support for Kelly’s approach. President Barack Obama tweeted on X that “We need more ideas like the ones @SenMarkKelly has outlined on how we can shape the future being created by artificial intelligence.” Several other major Democratic Party figures and labor leaders have also declared support.

Sen. Kelly’s AI for America proposal can be contrasted with President Joe Biden’s 2023 Executive Order (EO) 14110, which envisioned a strong role for government intervention in the development of AI technologies. EO 14110 emphasized safety and “responsible innovation” as AI policy cornerstones. President Donald Trump rescinded EO 14110, then issued EO 14179, which is aimed at “removing barriers to American leadership in artificial intelligence.” In contrast to the safety-focused Biden era AI policy statement, Kelly’s AI roadmap stresses “strengthening the foundation of our success” in achieving “an early and commanding lead in AI thanks to our culture of innovation, world-class infrastructure, and unmatched ability to train and attract top talent.” Safety and equity considerations are present in Kelly’s proposal but receive much less attention.

Democrats have faced a series of high-profile setbacks when attempting to impose other strong regulations on how artificial intelligence is developed and used. In Colorado, Democratic Gov. Jared Polis convened a special session in August 2025 to amend Senate Bill 24-205, the Colorado Artificial Intelligence Act. That law would impose significant obligations on developers and users of “high-risk artificial intelligence systems” in sectors like healthcare. Lawmakers ultimately voted instead to delay implementation until June 30, 2026, rather than reopen the framework for amendment as Polis had sought. And in California, a federal judge blocked Assembly Bill 2839, which sought to restrict election-related deepfakes, as unconstitutionally overbroad, writing, “Most of AB 2839 acts as a hammer instead of a scalpel, serving as a blunt tool that hinders humorous expression.”

These defeats may be inspiring a new Democratic strategy that avoids direct restrictions on AI models and instead focuses on taxing industry to fund workforce and infrastructure programs. Kelly’s AI for America lays out a framework for managing the economic and social impacts of artificial intelligence without directly restricting innovation. The proposed AI Horizon Fund is the plan’s central feature. Kelly frames this fund as a way to ensure that the large technology companies benefiting most from AI’s growth also bear responsibility for addressing the broader costs that their expansion places on society. The fund is presented as a mechanism to channel private gains into public priorities.

Kelly describes these taxes as “common sense” because the firms generating “enormous profits” from AI should be required to offset the costs imposed on workers, communities, and public infrastructure. Of course, AI companies already do pay taxes just like any other business, so in practice, this funding mechanism transforms the proposal into a form of targeted redistribution, singling out private earnings from a particular type of technological innovation to be redirected into public spending priorities.

One of the key areas identified for this reinvestment is education and workforce development. The roadmap calls for expanding union apprenticeship programs and channeling resources into community colleges so that workers can gain the skills needed in an AI-driven economy. It also supports the creation of an “AI economic adjustment program” to supplement the incomes of displaced workers. AI for America also encourages increased labor union involvement in the design and deployment of AI to benefit workers, which raises questions about how much pivoting Democrats actually plan to do on their previous calls for AI model regulation.  Each of these interventions are cast as a way to ensure that technological change creates upward mobility for a broad base of workers, rather than widening inequality.

The second area of focus is infrastructure, where the plan highlights the strain that data center growth will place on water and electric systems. By directing AI company contributions into these public utilities, Kelly argues that firms can “offset these impacts” and “strengthen the systems and infrastructure on which they depend.” In practice, this would mean redistributing private sector gains into federally directed local or regional projects in energy, water, and other essential services.

This approach not only reinforces the idea that AI profits should be harnessed for broad social benefits rather than remaining in the hands of the companies that generate them, but it also raises practical questions about utility regulation and the roles of various levels of government.

Both public and private electric utilities typically rely on user revenue from their “ratepayers” to finance infrastructure improvements, which is subject to regulation by state and local utility regulators. AI companies are among those ratepayers, so if regulated rates are insufficient to generate revenue to finance improvements or if costs are poorly allocated, policymakers should direct their attention to state and local utility regulation.

Where federal involvement in utility infrastructure finance exists, it is principally in the form of loans and loan guarantees, such as the Environmental Protection Agency’s Clean Water State Revolving Fund and the Department of Energy’s Title 17 Energy Financing Program. These subsidized credit assistance programs play a relatively small role in U.S. utility networks and—importantly—require that a substantial amount of project risk be retained by utilities and their ratepayers.

Kelly’s proposal recommends new “financing mechanisms” to supplement the traditional utility ratepayer model, but it says nothing about how existing regulation is denying utilities the ability to, in the words of his roadmap, “raise capital quickly and recover their investments fairly without disproportionately impacting the communities that host new AI infrastructure.” As energy economist Lynne Kiesling noted in a Reason Foundation commentary:

By temporarily scaling down operations or shifting workloads to off-peak periods, data centers can help balance supply and demand, stabilize prices, and reduce the need for expensive and emissions-heavy peaking power plants.

However, the regulatory and market institutions have to enable such markets and price signals to reduce frictions that maintain the timing mismatch between demand growth and increasing supply. They do not. While some demand response integration exists in wholesale power markets, it’s limited and heavily constrained.

Thus, the problem is not a relatively simple one of limited access to capital, which Kelly’s proposal aims to address in an equitable manner. Instead, the heavily regulated market design in utilities limits the ability of providers to match supply with customer demand efficiently. The upshot is that, absent market-oriented reforms, federal financing assistance will merely perpetuate and likely worsen the underlying problems that constrain utilities’ responses to the growth of data centers, and result in project risk being increasingly shifted to taxpayers.

There are limited instances in the United States where governments have asked specific technology firms to help offset the societal impacts of their operations beyond ordinary taxation.

One instructive precedent is the Universal Service Fund, which requires U.S. telecommunications providers to contribute to a pool that subsidizes broadband and telephone service in rural and underserved areas. These programs suggest that targeted levies or partnerships aimed at offsetting industry impacts are not without precedent, even if they remain relatively rare in the technology sector.

Ideally, a light regulatory touch would be the ideal path. But Democrats are likely to heavily involve the government at some level in their proposals. Ultimately, whether this shift from regulation to redistribution benefits or harms innovation will depend on the scale of the required contributions, as well as how those revenues are directed. Heavy-handed regulation that restricts the design or deployment of AI models could stifle startups and slow the development of foundational technologies that underpin the broader ecosystem.

Yet if the new approach functions as an industry-specific tax that grows too large and funds programs of dubious value, it could limit the ability of U.S. companies to reinvest profits in research, infrastructure, and global competitiveness that would deliver real value to consumers. The balance between these two risks will determine whether policies like Kelly’s proposal strengthen the AI sector or instead constrain its long-term growth.

The post Democrats pivot on AI: Less regulation, more redistribution appeared first on Reason Foundation.

Source: https://reason.org/commentary/democrats-pivot-on-ai-less-regulation-more-redistribution/