Governance

---

Memorandum for President Trump on AI Governance

---

Background

To maintain United States leadership in AI amid growing Chinese competition, we must promote and protect our unmatched private sector dynamism and entrepreneurial spirit by securing a minimalist regulatory approach that encourages experimentation and advancement.

Domestically, proposed federal regulatory frameworks risk stifling America’s innovation engine and impeding our advancement in the technology race. Bloated bureaucracies, fragmented oversight, and restrictive proposals create the kind of red tape that drives innovation—and jobs—overseas. In contrast, our federalist system provides a dynamic advantage as states serve as laboratories for targeted AI governance, and overreach can be thwarted with preemption.

The United States has a historic opportunity to chart an AI path that serves America and it can achieve that with the following objectives:

• Streamline Regulations and Remove Bureaucratic Obstacles for AI and Emerging Technologies: Undertake a comprehensive review and reform of existing regulations that impact the development, deployment, and use of AI and other emerging technologies. The goal is to remove unnecessary bureaucratic obstacles that hinder innovation, slow down progress, and put U.S. companies at a disadvantage compared to international competitors.
  
• Advance AI Development and Deployment by Focusing on High-Impact Applications and Critical National Interests:  Promote the development and deployment of AI by prioritizing high-impact applications that pose significant opportunities or risks to society, the economy, and national security, while also ensuring the protection of critical datasets and the safety of children online. This involves creating a streamlined regulatory approach, modernizing governance, and establishing specialized roles to address these specific challenges.
  
• Orchestrate a Unified National Data Strategy and Unlock the Power of Data as a Strategic National Asset: The purpose of this strategy and organizational changes would ensure the treatment of data as a critical national asset, driving innovation, economic growth, and national security by fostering data sharing, modernizing data infrastructure, and promoting collaboration across government agencies and with the private sector.

Recommendations

A critical AI governance principle is that U.S. regulators cannot and should not regulate every AI use case. Rather, the U.S. regulatory approach should commit its regulatory resources only to highly consequential AI use cases, whether beneficial or harmful.

Objective 1: Tech Forward Initiative: Unblocking Regulatory Bottlenecks Hindering AI Development

The convergence of AI with critical technologies like biotech, quantum computing, and advanced manufacturing presents unprecedented opportunities, but outdated regulatory frameworks threaten to stifle innovation. For instance, next-generation AI data centers require massive energy inputs—1-5 gigawatts, comparable to multiple power plants^[1]—yet our grid is unprepared,^[2] and environmental reviews for necessary upgrades can take over three years.^[3] This places the United States at risk of falling behind adversaries, particularly China, in the race to define the next era of technological innovation.^[4]

• Reduce Regulatory Burden Limiting Key AI Inputs: The United States should monitor and forecast trends in compute resources, energy usage, data center infrastructure, network capabilities, and data requirements to proactively address constraints. Mechanisms should be put in place to expedite permitting and environmental reviews for grid upgrades, nuclear facilities, transmission lines, and semiconductor fabs to support AI’s growing needs. Interagency processes should be reformed to reduce project delays, aiming to match the pace of international competitors.
  
• Protect U.S. Interests Abroad: The United States should monitor international AI regulations and push back against measures that place asymmetrical burdens on U.S. companies. United States officials should engage with foreign policymakers and international standards-setting bodies through trade representatives to challenge discriminatory regulations that hinder U.S. AI innovation.^[5]
  
• Launch a Rapid Review Program: Relevant agencies should establish expedited approval pathways for high-consequence horizon solutions, similar to the Food and Drug Administration’s Predetermined Change Control Plans (PCCPs). These agencies should emphasize areas where AI can unlock immediate scientific gains, such as drug discovery and clean energy. They should prioritize projects that strengthen domestic research capabilities and reduce dependence on foreign technology. Federal agencies must coordinate to eliminate duplicative reviews and accelerate the deployment of high-priority AI innovations.
  
• Integrate AI into Regulatory Processes: Relevant agencies should leverage AI tools, such as natural language processing systems, to enhance regulatory efficiency (e.g., by automating initial screening of submissions). To strengthen enforcement, these agencies should use AI to monitor sanctions compliance, export controls, and supply chain integrity, flagging risks in real-time. They should apply AI to analyze large datasets to uncover patterns of noncompliance and enforce intellectual property laws, including monitoring and identifying counterfeits. They also should implement AI-powered regulatory monitoring with real-time dashboards to evaluate effectiveness and identify emerging challenges. Integrating AI tools will require training federal employees in AI tools to enhance oversight capabilities and foster innovation-friendly governance.

Objective 2: Focus Efforts on Highly Consequential AI Use Cases

The United States should enact a streamlined regulatory approach that prioritizes high-impact AI applications over broad, stifling regulation.

• Modernize Governance: Agencies should equip relevant  federal regulators with a framework,^[6] AI tools and specialized training to identify and prioritize AI applications that pose significant risks or opportunities in societal, economic, or national security contexts.
  
• Protect National Security Critical Datasets: Federal agency chief data officers (CDOs) should identify both private and public datasets that are critical national security assets and treat them as such. For example, the CDOs should protect these valuable datasets from foreign exploitation (e.g., via elevated cybersecurity protections) while promoting their use in domestic AI research and development.^[7]

Objective 3: Safeguarding Our Future by Protecting Our Children Online

The rise of AI-driven harmful content has exposed children to serious risks, including illicit drug sales, sexual exploitation, and cyberbullying. By adopting a focused approach to protect children, the United States can accelerate its leadership in AI innovation while cutting red tape by leveraging existing authorities and industry-led efforts. 

• Establish a National Kids’ Protection Officer Within the Federal Trade Commission (FTC): Existing FTC staff should be reassigned into a cohesive unit under a National Kids’ Protection officer to consolidate child protection efforts, coordinate policies, and provide clear guidance to parents, educators, and the tech industry. The officer will work with private platforms to develop voluntary compliance frameworks and safe harbors, reducing regulatory burdens while enhancing protections. This streamlined approach ensures consistent safety standards for families, clear compliance guidance for businesses, and accelerated innovation in family-friendly technologies with reinforced safeguards against exploitation.

Objective 4: Establish a National Chief Data Officer (NCDO)

The White House should establish a NCDO remit to treat data as a strategic national asset. The establishment of a NCDO mission is a critical step in organizing America’s vast data assets to grow our innovation ecosystem as a response to Beijing’s global data grab of procuring stockpiles of data for its national advantage.^[8] The mission should include the following:

• National Data Strategy: The White House should develop and implement a comprehensive strategy to harmonize federal data practices across government agencies and in emerging technology sectors. The strategy should:
  • Require interagency data standardization, open-data initiatives, and alignment with national innovation and scientific needs;
  • Modernize federal data IT infrastructure with AI capabilities to identify cost-saving measures and reduce redundancy in collaboration with the federal Chief Information Officer (CIO);
  • Standardize data governance policies to ensure interoperability and seamless data exchange between agencies and the private sector to accelerate data sharing related to scientific research; and
  • Create public-private partnerships^[9] with industry to share data in mission critical domains to unleash new breakthroughs to outpace China’s data efforts. 
    
• Federal Data Utility Hub: A shared data hub can increase Americans’ access to high-value government datasets for research and everyday tasks. The data hub should:
  • Empower individuals, startups, and small businesses, to drive scientific advancements without redundant data-collection efforts;
  • Create rotational programs to embed private-sector data experts in federal agencies and vice versa, fostering knowledge transfer and innovation; and
  • Partner with universities and private institutions to train the next generation of data scientists and AI researchers to fill the void for AI jobs in government. 

Conclusion

As our strategic competitors make rapid advancements in AI development, the United States must ensure our AI governance ecosystem is an accelerator and not a barrier to innovation that serves the American people. These four recommendations streamline regulatory frameworks by removing unnecessary obstacles and increasing regulatory efficiency, protecting our children online, and maximizing our data assets.

---

^[1] Shirin Ghaffary, OpenAI Pitched White House on Unprecedented Data Center Buildout, Bloomberg (2024).

^[2] Eric Schmidt, We Need Energy for AI, and AI for Energy, Project Syndicate (2024).

^[3] Environmental Impact Statement Timelines (2010–2018), Council on Environmental Quality, Executive Office of the President (2020).

^[4] Bits, Bytes, and Bottlenecks: A Story of Two Data Center Corridors, Special Competitive Studies Project (2024).

^[5] The Silent Struggle: How Technical Standards Shape Global Tech Power, Special Competitive Studies Project (2024).

^[6] Framework for Identifying Highly Consequential AI Use Cases, Special Competitive Studies Project (2023).

^[7] From Lab to Leadership: How Data Can Keep America Ahead, Special Competitive Studies Project (2024).

^[8] How the People’s Republic of China Seeks to Reshape the Global Information Environment, U.S. Department of State (2023); Sylvie Zhuang, China Sets Out Ambitious Timeline for Comprehensive Data-Sharing System by 2030, South China Morning Post (2024).   

^[9] Data’s Role in Unlocking Scientific Potential, Special Competitive Studies Project (2024).