The United States has a once-in-a-generation opportunity to translate the promise of artificial intelligence (AI) into faster productivity growth and a lighter fiscal burden. Yet that promise is jeopardized if 50 different state capitals experiment with onerous, inconsistent, and sometimes openly contradictory AI rules that do not apply equally to other tools and technologies. A federal proposal now before Congress would pre-empt state regulations that single out AI for special restrictions unless those rules are nondiscriminatory and narrowly tailored. The debate so far has focused on innovation and national competitiveness. Less discussed—but just as important—is how federal pre-emption of state-level AI regulation would benefit the federal balance sheet by up to $269 billion over the next decade.
Below I lay out two major channels: (1) lower procurement costs for the federal government itself, and (2) higher tax receipts flowing from an AI-enabled jump in labor productivity.
Cheaper Federal Procurement Through Scale Economies
A $1.3 trillion opportunity space: In FY 2023 the federal government obligated roughly $759 billion on contracts for everything from aircraft to accounting services, according to the Government Accountability Office. Roughly $478 billion of that total was for services. A large share of those service dollars flow to professional-services contractors—consultants, systems integrators, accountants, lawyers—whose own cost structure is overwhelmingly labor. In fiscal year 2024, the federal government spent about $126 billion on professional services contractors. Over a decade, this suggests that the federal government would spend about $1.3 trillion on professional services contracts alone.
Patchwork compliance costs get passed on to taxpayers, including through reduced AI adoption: In the absence of a federal prohibition on state-level discriminatory regulation against AI, a firm that offers AI-enhanced professional services could have to certify one model for California, another for New York, and in some cases avoid entire markets because the compliance burden is prohibitive, especially when state-level rules contradict one another. This prevents economies of scale, not only in compliance, but in the scale of offerings overall: If your California offices cannot work in coordination with your New York, Texas, and Florida offices due to state-level AI regulations, the scale of your offerings shrinks correspondingly, and your AI adoption rates likely shrink as well. The cost of building fifty bespoke compliance programmes–or putting up “firewalls” between teams working in different states and using different tools subject to different regulations–does not stay in Sacramento or Albany; it is embedded in the cost rates that show up on GSA schedules and ultimately on federal invoices. By pre-empting discriminatory state rules, Congress would let professional services vendors achieve economies of scale and high levels of productivity-enhancing AI adoption across the entire U.S. market, pushing their average cost—and therefore their bid prices—down.
Magnitude of the procurement cost savings: Recent empirical economics research papers suggest a 25% productivity improvement for tasks using AI assistance–using just the already achieved capabilities of AI. If harmonisation of AI rules nationwide increases AI adoption by even just 20% for professional services firms across a 10-year fiscal window, that alone would amount to a 5% productivity improvement. Across $1.3 trillion in baseline federal contracting of professional services across a 10-year window, a 5% productivity improvement allows $65 billion in federal procurement cost savings over a decade.
This estimate is very conservative, as it ignores the many procurement cost savings likely to be achieved by federal services contractors outside of professional services and the likelihood of ongoing dramatic increases in AI capabilities over the next decade. Keep in mind that the modern era of publicly-rolled out generative AI tools began just two and a half years ago in November 2022, and the models and tools available today are vastly improved over the ones available in 2022.
Larger Federal Revenues From AI-Enabled Productivity Improvements
Macroeconomic upside: AI is widely viewed as a general-purpose technology akin to electricity or the internet. Goldman Sachs analysts project that broad AI adoption could raise U.S. GDP growth by roughly 0.4 percentage points a year through the 2030s. Consistent with these findings, McKinsey estimates that AI could increase labor productivity growth by up to 0.6% per year through 2040 with widespread adoption. At 2025 output levels, 0.4 p.p. translates to about $120 billion in additional real GDP annually. Even under a conservative assumption that all other dynamic sources of GDP growth disappeared, broad AI adoption would amount to an extra $1.2 trillion in total output for the U.S. economy over the next decade.
What that means for the Treasury: Federal receipts historically average 17 percent of GDP according to the Congressional Budget Office. Apply that ratio to the incremental GDP of $1.2 trillion over the next decade, and the Treasury would collect roughly $204 billion in extra revenue over the next ten years.
Why state pre-emption is essential to capture those gains: Productivity growth materialises only when technologies diffuse beyond pilot projects and into day-to-day workflows. Compliance fragmentation slows that diffusion in at least three ways:
- Delayed roll-outs: Vendors spend time red-lining state-specific contract riders instead of shipping features.
- Reduced market size: Complying with 50 different regulatory frameworks shrinks each state into its own smaller market, reducing competitive pressures and the incentive to invest in AI tools and training for workers, reducing adoption of AI into workflows.
- Higher legal risk premiums: A single state-level private right of action can freeze an entire category of AI tools statewide–or even nationwide–if insurers refuse to underwrite the risk, and can significantly increase insurance premiums even in less extreme cases, decreasing incentives to adopt AI-enhanced workflows.
Pre-emption of state-level discriminatory regulation removes those frictions, letting AI scale across all 50 states simultaneously. The faster the diffusion, the sooner the productivity dividend reaches federal tax receipts. In the absence of a policy pre-empting state-level discriminatory regulation of AI, AI adoption would likely slow massively.
Policy Implications
The fiscal math is clear: $65 billion in procurement cost savings and up to $204 billion in increased federal tax receipts leave up to $269 billion in fiscal benefits over the next decade that should be counted in fiscal impact scores.
Conclusion
Fiscal sustainability requires both spending discipline and faster growth. A federal pre-emption of discriminatory state AI rules advances both goals at once—cutting procurement costs today while planting the seeds for higher revenue tomorrow. Congress should seize this rare policy lever that aligns innovation, competitiveness, and fiscal responsibility. The numbers add up; now it is time for the statute to follow.
