Recent AI and Privacy Work in Congress

Three testimonies (one today!) and a comment

Comments to Energy and Commerce Privacy Working Group

On Monday, Neil and I submitted comments to the Energy and Commerce Committee’s Privacy Working Group’s “Data Privacy and Security Framework.” Our policy work is at both the state and federal level so we were glad to provide our unique perspective to the Committee as it sees an urgency in bringing federal and state law into sync. It’s getting a little complicated out there in the states: 19 states have consumer privacy laws and every state is considering some form of AI regulatory proposal. For years members of Congress have recognized how one federal privacy standard could protect consumers and provide clarity for businesses large and small. However, an answer has remained elusive. Now, AI regulatory proposals in the states have skyrocketed and Congress is looking at a double whammy: 1) modern AI systems rely on data thus raising the stakes and complexity of privacy rules and 2) there’s a growing patchwork of AI laws in the states lending urgency to explore what a federal standard could look like. In our comment, our work in the states is why we offer up case studies from California on privacy and Colorado on AI and suggest that Congress preempt onerous and divergent state standards with clear rules that prioritize innovation.

Neil’s Three Testimonies

Tune in here at 2:30 PM ET today for Neil’s third congressional testimony in two weeks. He will be providing his remarks before the Joint Economic Committee, discussing the ways the government can utilize AI.

On April 1st, Neil Chilson provided testimony before the House Committee on Oversight and Government Reform Subcommittee on Economic Growth, Energy Policy, and Regulatory Affairs. If one testimony wasn’t enough, he then testified the next day before the House Judiciary Subcommittee on the Administrative State, Regulatory Reform, and Antitrust of the Committee on the Judiciary. These were both important topics and I’m glad Neil was invited to provide his perspective because the U.S. finds itself in a pivotal moment in AI development and leadership. On one hand, AI promises unprecedented economic growth and societal benefits. On the other, regulatory and infrastructure challenges threaten to hamper America’s leading edge. Neil gave recommendations on how the U.S. can sustain global leadership in AI while ensuring the technology benefits everyone.

I recommend reading both in full and listening to his remarks and the Q&A. But if you’re looking for some quick hits, read Logan’s summaries of his two testimonies below.

Summary of Last Week’s Testimonies

AI’s Transformative Promise

AI represents perhaps the most general-purpose technology ever created, with potential applications across every industry. In healthcare, for example, new AI-powered tools can streamline administrative tasks, spot diseases earlier, and guide drug discovery. Similarly, self-driving vehicles could reduce accidents by up to 90%, saving billions in lost productivity and lives.

Beyond efficiency, AI can empower smaller teams and entrepreneurs. By handling repetitive tasks and analyzing huge data sets, AI unleashes human creativity to solve complex problems and launch new products. Larger firms already benefit from advanced AI—now, smaller companies can tap into the same power through cloud-based services and open-source software. It’s an economic game-changer, one that could lead to new industries, better wages, and broader prosperity.

Laying the Launchpad for U.S. AI Dominance

The Trump administration under Executive Order 14179—“Removing Barriers to American Leadership in Artificial Intelligence,” is advancing a light-touch, pro-growth strategy. Vice President J.D. Vance echoed this at the Paris AI Summit, saying that “excessive regulation” would stifle a transformative industry. Such a stance frames the challenge neatly: How do we preserve AI’s explosive potential to innovate while ensuring enough regulatory structure exists to protect consumers and encourage competition?

Software Regulation

Historically, the United States took a “permissionless” approach to software innovation—there’s no single Federal Computing Agency. Instead, general-purpose laws and sector-specific regulations address safety, civil rights, finance, and other key issues. This flexible framework has propelled rapid AI advancement, preventing a single regulatory bottleneck.

However, the surge of AI-related bills—over 900 introduced in statehouses—threatens a messy patchwork of conflicting rules. Some of these efforts are thoughtful, such as Utah’s AI Act, which integrates AI into existing consumer protection laws and provides a testing environment for innovators. Others are overly broad, imposing heavy compliance burdens that favor only large incumbents. Maintaining a streamlined, uniform approach is critical to ensuring smaller innovators can thrive.

The Energy Bottleneck

For AI to reach its fullest potential, we need abundant, affordable power—and fast. The primary roadblock is permitting: the current National Environmental Policy Act (NEPA) and related litigation often create years-long delays for new energy projects. Repeatedly revising environmental impact statements and defending against lawsuits balloon costs and timelines, discouraging both public and private investment in energy.

Texas’s ERCOT grid shows a different approach, adding unprecedented amounts of generation capacity via simpler, “connect and manage” interconnection rules. This strategy, alongside fewer layers of federal oversight, helps new power sources come online quickly. Such speed is essential for the rising wave of AI-driven data centers, which draw enormous power and need a robust grid to function reliably.

The Current Landscape Suggests AI Competition is Vibrant

Despite concerns about consolidation, the AI industry is remarkably competitive. AI is a layered ecosystem involving hardware (GPUs, specialized chips), cloud infrastructure, foundational models (GPT-style systems), and the end-user applications that transform these tools into products. At each layer, a range of players aggressively compete for market share.

For instance, while Nvidia may currently be leading in hardware, AMD, Intel, and Google (TPUs) are pushing improvements and innovation forward. In the cloud, Amazon Web Services, Microsoft Azure, and Google Cloud vie for enterprise clients while smaller cloud providers gain traction. AI startups are exemplifying economic dynamism and competition, attracting millions in venture capital and churning out new models and consumer-facing applications. Additionally, open-source efforts, like Stable Diffusion in image generation, accelerate widespread adoption and undercut the argument that a few big players dominate everything.

The Real Threat is Overregulation

Heavy-handed oversight often advantages large incumbents who can absorb compliance costs. Decades of economic research show that regulations sometimes become tools to box out smaller rivals, ironically hurting the very competition they aim to protect.

Conflicting laws across states, along with international frameworks that often target American tech, risk strangling the AI ecosystem by adding unnecessary complexity. Such fragmentation slows product launches, dissuades investors, and can prevent innovative solutions from ever reaching the market. Overregulation risks extinguishing the very competition that delivers cutting-edge AI tools and widespread societal benefits.

Recommendations for an AI Moonshot

• Replace NEPA with Substantive Protections

  • Remove the current National Environmental Policy Act (NEPA) and install focused environmental safeguards to streamline project approvals.

• Shorten Legal Challenge Windows

  • Cut the statute of limitations for judicial review of NEPA decisions to 180 days, down from six years, to reduce delays.

• Limit Scope of Environmental Reviews

  • Require agencies to consider only direct, proximate, and reasonably foreseeable impacts, preventing overly broad analyses.

• Expand Categorical Exemptions

  • Exclude routine actions from exhaustive reviews, speeding up the permitting process.

• Restrict Injunctive Relief

  • Permit injunctions only when claims are prompt, brought by directly affected parties, and involve imminent, substantial harm without other legal remedies.

• Simplify Federal Land Leasing

  • Direct agencies to adopt procedures that expedite leasing and resource development on federal lands.

• Accelerate Grid Interconnection

  • Mandate swift Federal Energy Regulatory Commission action to connect new energy sources, ensuring adequate power for AI needs.

Recommendations to Keep a Competitive AI Ecosystem

• Preempt Restrictive State AI Regulations

  • Enact federal rules overriding patchwork state AI laws, thereby lowering compliance barriers and encouraging innovation nationwide.

• Establish Negative Liability Protections

  • Shield general-purpose AI model developers from disproportionate litigation over third-party misuse, enabling more market entrants.

• Create Safe Harbor Provisions

  • Offer minimal, clear compliance standards that foster innovation while leveling the playing field for smaller players.

• Codify the Right to Compute

  • Ensure no undue government restrictions on access to computational resources, removing a key barrier for startups and smaller businesses.

• Clarify Liability Frameworks

  • Differentiate between foundational model developers and deployers, reducing uncertainty and promoting broader AI adoption.

• Accelerate Federal Data Access

  • Require the release and digitization of government data sets, giving innovators the raw materials needed to train advanced AI systems.

Conclusion

The U.S. stands on the brink of an AI revolution that promises sweeping economic and societal gains, yet also faces risks from outdated energy-permitting processes and fragmented regulation. By reforming NEPA, modernizing the grid, and introducing policies that preempt restrictive state laws while clarifying liability frameworks for AI innovators, Congress can foster a dynamic ecosystem that balances innovation with accountability. Doing so ensures that new entrants continue to challenge incumbents, open-source projects flourish, and the transformative benefits of AI reach every corner of the American economy.