As the United States overhauls nearly every element of its strategic nuclear forces, artificial intelligence is set to play a larger role—initially in early‑warning sensors and decision‑support tools, and likely in other mission areas. Improved detection could strengthen deterrence, but only if accompanying hazards—automation bias, model hallucinations, exploitable software vulnerabilities, and the risk of eroding assured second‑strike capability—are well managed. 

To ensure responsible AI integration, the Office of the Assistant Secretary of Defense for Nuclear Deterrence, Chemical, and Biological Defense Policy and Programs (OASD (ND-CBD)), the U.S. Strategic Command (STRATCOM), the Defense Advanced Research Projects Agency (DARPA), the Office of the Undersecretary of Defense for Policy (OUSD(P)), and the National Nuclear Security Administration (NNSA), should jointly develop a standardized AI risk-assessment framework guidance document, with implementation led by the Department of Defense’s Chief Digital and Artificial Intelligence Office (CDAO) and STRATCOM. Furthermore, DARPA and CDAO should join the Nuclear Weapons Council to ensure AI-related risks are systematically evaluated alongside traditional nuclear modernization decisions. 

Challenge and Opportunity 

The United States is replacing or modernizing nearly every component of its strategic nuclear forces, estimated to cost at least $1.7 trillion over the next 30 years. This includes its:

• Intercontinental ballistic missiles (ICBMs)
• Ballistic missile submarines and their submarine-launched ballistic missiles (SLBMs) 
• Strategic bombers, cruise missiles, and gravity bombs
• Nuclear warhead production and plutonium pit fabrication facilities 

Simultaneously, artificial intelligence (AI) capabilities are rapidly advancing and being applied across the national security enterprise, including nuclear weapons stockpile stewardship and some components of command, control, and communications (NC3) systems, which encompass early warning, decision-making, and force deployment components.

The NNSA, responsible for stockpile stewardship, is increasingly integrating AI into its work. This includes using AI for advanced modeling and simulation of nuclear warheads. For example, by creating a digital twin of existing weapons systems to analyze aging and performance issues, as well as using AI to accelerate the lifecycle of nuclear weapons development. Furthermore, NNSA is leading some aspects of the safety testing and systematic evaluations of frontier AI models on behalf of the U.S. government, with a specific focus on assessing nuclear and radiological risk.

Within the NC3 architecture, a complex “system of systems” with over 200 components, simpler forms of AI are already being used in areas including early‑warning sensors, and may be applied to  decision‑support tools and other subsystems as confidence and capability grow. General Anthony J. Cotton—who leads STRATCOM, the combatant command that directs America’s global nuclear forces and their command‑and‑control network—told a 2024 conference that STRATCOM is “exploring all possible technologies, techniques, and methods” to modernize NC3. Advanced AI and data‑analytics tools, he said, can sharpen decision‑making, fuse nuclear and conventional operations, speed data‑sharing with allies, and thus strengthen deterrence. General Cotton added that research must also map the cascading risks, emergent behaviors, and unintended pathways that AI could introduce into nuclear decision processes.

Thus, from stockpile stewardship to NC3 systems, AI is likely to be integrated across multiple nuclear capabilities, some potentially stabilizing, others potentially highly destabilizing. For example, on the stabilizing effects, AI could enhance early warning systems by processing large volumes of satellite, radar, and other signals intelligence, thus providing more time to decision-makers. On the destabilizing side, the ability for AI to detect or track other countries’ nuclear forces could be destabilizing, triggering an expansionary arms race if countries doubt the credibility of their second-strike capability. Furthermore, countries may misinterpret each other’s nuclear deterrence doctrines or have no means of verification of human control of their nuclear weapons.

While several public research reports have been conducted on how AI integration into NC3 could upset the balance of strategic stability, less research has focused on the fundamental challenges with AI systems themselves that must be accounted for in any risk framework. Per the National Institute of Standards and Technology’s (NIST) AI Risk Management Framework, several fundamental AI challenges at a technical level must be accounted for in the integration of AI into stockpile stewardship and NC3.

Not all AI applications within the nuclear enterprise carry the same level of risk. For example, using AI to model warhead aging in stockpile stewardship is largely internal to the Department of Energy (DOE) and involves less operational risk. Despite lower risk, there is still potential for an insufficiently secure model to lead to leaked technical data about nuclear weapons. 

However, integrating AI into decision support systems or early warning functions within NC3 introduces significantly higher stakes. These systems require time-sensitive, high-consequence judgments, and AI integration in this context raises serious concerns about issues including confabulations, human-AI interactions, and information security: 

• Confabulations: A phenomenon in which generative AI systems (GAI) systems generate and confidently present erroneous or false content in response to user inputs, or
  prompts. These phenomena are colloquially also referred to as “hallucinations” or “fabrications”, and could have particularly dangerous consequences in high-stakes settings. 

• Human-AI Interactions: Due to the complexity and human-like nature of GAI technology, humans may over-rely on GAI systems or may unjustifiably perceive GAI content to be of higher quality than that produced by other sources. This phenomenon is an example of automation bias or excessive deference to automated systems. This deference can lead to a shift from a human making the final decision (“human in the loop”), to a human merely observing AI generated decisions (“human on the loop”). Automation bias therefore risks exacerbating other risks of GAI systems as it can lead to humans maintaining insufficient oversight.

• Information Security: AI expands the cyberattack surface of NC3. Poisoned AI training data and tampered code can embed backdoors, and, once deployed, prompt‑injection or adversarial examples can hijack AI decision tools, distort early‑warning analytics, or leak secret data. The opacity of large AI models can let these exploits spread unnoticed, and as models become more complex, they will be harder to debug.

This is not an exhaustive list of issues with AI systems, however it highlights several key areas that must be managed. A risk framework must account for these distinctions and apply stricter oversight where system failure could have direct consequences for escalation or deterrence credibility. Without such a framework, it will be challenging to harness the benefits AI has to offer.

Plan of Action 

Recommendation 1. OASD (ND-CBD),  STRATCOM, DARPA, OUSD(P), and NNSA, should develop a standardized risk assessment framework guidance document to evaluate the integration of artificial intelligence into nuclear stockpile stewardship and NC3 systems. 

This framework would enable systematic evaluation of risks, including confabulations, human-AI configuration, and information security, across modernization efforts. The framework could assess the extent to which an AI model is prone to confabulations, involving performance evaluations (or “benchmarking”) under a wide range of realistic conditions. While there are public measurements for confabulations, it is essential to evaluate AI systems on data relevant to the deployment circumstances, which could involve highly sensitive military information.  

Additionally, the framework could assess human-AI configuration with specific focus on risks from automation bias and the degree of human oversight. For these tests, it is important to put the AI systems in contact with human operators in situations that are as close to real deployment as possible, for example when operators are tired, distracted, or under pressure. 

Finally, the framework could include assessments of information security under extreme conditions. This should include simulating comprehensive adversarial attacks (or “red-teaming”) to understand how the AI system and its human operators behave when subject to a range of known attacks on AI systems.

NNSA should be included in this development due to their mission ownership of stockpile stewardship and nuclear safety, and leadership in advanced modeling and simulation capabilities. DARPA should be included due to its role as the cutting edge research and development agency, extensive experience in AI red-teaming, and understanding of the AI vulnerabilities landscape. STRATCOM must be included as the operational commander of NC3 systems, to ensure the framework accounts for real-word needs and escalation risks. OASD (ND-CBD) should be involved given the office’s responsibilities to oversee nuclear modernization and coordinate across the interagency. The OUSD (P) should be included to provide strategic oversight and ensure the risk assessment aligns with broader defense policy objectives and international commitments.

Recommendation 2. CDAO should implement the Risk Assessment Framework with STRATCOM

While NNSA, DARPA, OASD (ND-CBD) and STRATCOM can jointly create the risk assessment framework, CDAO and STRATCOM should serve as the implementation leads for utilizing the framework. Given that the CDAO is already responsible for AI assurance, testing and evaluation, and algorithmic oversight, they would be well-positioned to work with relevant stakeholders to support implementation of the technical assessment. STRATCOM would have the strongest understanding of operational contexts with which to apply the framework. NNSA and DARPA therefore could advise on technical underpinnings with regards to AI of the framework, while the CDAO would prioritize operational governance and compliance, ensuring that there are clear risk assessments completed and understood when considering integration of AI into nuclear-related defense systems.

Recommendation 3. DARPA and CDAO should join the Nuclear Weapons Council 

Given their roles in the creation and implementation of the AI risk assessment framework, stakeholders from both DARPA and the CDAO should be incorporated into the Nuclear Weapons Council (NWC), either as full members or attendees to a subcommittee. As the NWC is the interagency body the DOE and the DoD responsible for sustaining and modernizing the U.S. nuclear deterrent, the NWC is responsible for endorsing military requirements, approving trade-offs, and ensuring alignment between DoD delivery systems and NNSA weapons. 

As AI capabilities become increasingly embedded in nuclear weapons stewardship, NC3 systems, and broader force modernization, the NWC must be equipped to evaluate associated risks and technological implications. Currently, the NWC is composed of senior officials from the Department of Defense, the Joint Chiefs of Staff, and the Department of Energy, including the NNSA. While these entities bring deep domain expertise in nuclear policy, military operations, and weapons production, the Council lacks additional representation focused on AI.

DARPA’s inclusion would ensure that early-stage technology developments and red-teaming insights are considered upstream in decision-making. Likewise, CDAO’s presence would provide continuity in AI assurance, testing, and digital system governance across operational defense components. Their participation would enhance the Council’s ability to address new categories of risk, such as model confabulation, automation bias, and adversarial manipulation of AI systems, that are not traditionally covered by existing nuclear stakeholders. By incorporating DARPA and CDAO, the NWC would be better positioned to make informed decisions that reflect both traditional nuclear considerations and the rapidly evolving technological landscape that increasingly shapes them.

Conclusion 

While AI is likely to be integrated into components of the U.S. nuclear enterprise, without a standardized initial approach to assessing and managing AI-specific risk, including confabulations, automation bias, and novel cybersecurity threats, this integration could undermine an effective deterrent. A risk assessment framework coordinated by OASD (ND-CBD), with STRATCOM, NNSA and DARPA, and implemented with support of the CDAO, could provide a starting point for NWC decisions and assessments of the alignment between DoD delivery system needs, the NNSA stockpile, and NC3 systems.

This memo was written by an AI Safety Policy Entrepreneurship Fellow over the course of a six-month, part-time program that supports individuals in advancing their policy ideas into practice. You can read more policy memos and learn about Policy Entrepreneurship Fellows here.

While AI’s transformative advances have enormous positive potential, leading scientists and industry executives are also sounding the alarm about catastrophic risks on a global scale. If left unmanaged, these risks could undermine our ability to reap the benefits of AI progress. While the U.S. government has made some progress, including by establishing the Center for AI Standards and Innovation (CAISI)—formerly the US AI Safety Institute—current government capacity is insufficient to respond to these extreme frontier AI threats. To address this problem, this memo proposes scaling up a significantly enhanced “CAISI+” within the Department of Commerce. CAISI+ would require dedicated high-security compute facilities, specialized talent, and an estimated annual operating budget of $67-155 million, with a setup cost of $155-275 million. CAISI+ would have expanded capacity for conducting advanced model evaluations for catastrophic risks, provide direct emergency assessments to the President and National Security Council (NSC), and drive critical AI reliability and security research, ensuring America is prepared to lead on AI and safeguard its national interests.

Challenge and Opportunity 

Frontier AI is advancing rapidly toward powerful general-purpose capabilities. While this progress has produced widely useful products, it is also generating significant security risks. Recent evaluations on Anthropic’s Claude Opus 4 model were unable to rule out the risk that the model could be used to advise novice actors to produce bioweapons, triggering additional safeguards. Meanwhile, the FBI warns that AI “increases cyber-attack speed, scale, and automation”, with a 442% increase in AI-enhanced voice phishing attacks in 2024, and recent evaluations showing AI models rapidly gaining offensive cyber capabilities. 

AI company CEOs and leading researchers have predicted that this progress will continue, with potentially transformative AI capabilities arriving in the next few years–and fast progress in AI capabilities will continue to generate novel threats greater than those from existing models. As AI systems are predicted to become increasingly capable of performing complex tasks and taking extended autonomous actions, researchers warn of these additional risks, such as loss of human control, AI-enabled WMD proliferation, and strategic surprise with severe national security implications. While timelines to AI systems surpassing dangerous capability thresholds are uncertain, this proposal attempts to lay out a US government response that is robust to a range of possible timelines, while taking the above trends seriously.

Current U.S. Government capabilities, including the existing Center for AI Standards and Innovation (CAISI), are not adequately resourced or empowered to independently evaluate, monitor, or respond to the most advanced AI threats. For example, current CAISI funding is precarious, its home institution (NIST)’s offices are reportedly “crumbling”, and its budget is roughly one-tenth of its counterpart in the UK. Despite previous underinvestment, CAISI has consistently produced rigorous model evaluations, and in doing so, has earned strong credibility with industry and government stakeholders. This also includes support from legislators: bipartisan legislation has been introduced in both chambers of Congress to authorize CAISI in statute, while just last month, the House China Committee released a letter noting that CAISI has a role to play in “understanding, predicting, and preparing for” national security risks from AI development in the PRC.

A dedicated and properly resourced national entity is essential for supporting the development of safe, secure, and trustworthy AI to drive widespread adoption, by providing sustained, independent technical assessments and emergency coordination—roles that ad-hoc industry consultations or self-reporting cannot fulfill for paramount matters of national security and public safety.

Establishing CAISI+ now is a critical opportunity to proactively manage these profound risks, ensure American leadership in AI, and prevent strategic disadvantage as global AI capabilities advance. While full operational capacity may not be needed immediately, certain infrastructure, such as highly secure computing, has significant lead times, demanding foresight and preparatory action. This blueprint offers a scalable framework to build these essential national capabilities, safeguarding our future against AI-related catastrophic events and enabling the U.S. to shape the trajectory of this transformative technology.

Plan of Action 

To effectively address extreme AI risks, develop more trustworthy AI systems, and secure U.S. interests, the Administration and Congress should collaborate to establish and resource a world-class national entity to inform the federal response to the above trendlines. 

Recommendation 1. Establish CAISI+ to Lead National AI Safety and Coordinate Crisis Response. 

CAISI+, evolving from the current CAISI within the National Institute of Standards and Technology, under the Department of Commerce, must have a clear mandate focused on large-scale AI risks. Core functions include: 

1. Advanced Model Evaluation: Developing and operating state-of-the-art platforms to test frontier AI models for dangerous capabilities, adversarial behavior or goals (such as deception or power-seeking), and potential weaponization. While the level of risk presented by current models is very uncertain, even those who are skeptical of particular risk models are often supportive of developing better evaluations. 
2. Emergency Assessment & Response: Providing rapid, expert risk assessments and warnings directly to the President and the National Security Council (NSC) in the event of severe AI-driven national security threats. The CAISI+ Director should be statutorily designated as the Principal Advisor on AI Risks to the President and NSC, with authority to:
   1. Submit AI threat assessments to the President’s Daily Brief (PDB) when intelligence indicates imminent or critical risks
   2. Convene emergency sessions of the NSC Deputies Committee or Principals Committee for time-sensitive AI security threats
   3. Maintain direct communication channels to the National Security Advisor for immediate threat notification
   4. Issue “Critical AI Threat Warnings” through established NSC emergency communication protocols, similar to those used for terrorism or WMD threats 
3. Foundational AI Reliability and Security Research: Driving and funding research into core AI alignment, control, and security challenges to maintain U.S. technological leadership while developing trustworthy AI systems. This research will yield dual benefits to both the public and industry, by enabling broader adoption of reliable AI tools and preventing catastrophic incidents that could devastate the AI sector, similar to how the Three Mile Island disaster impacted nuclear energy development. Following the model of NIST’s successful encryption standards, establishing rigorous AI safety benchmarks and protocols will create industry-wide confidence while ensuring American competitiveness.

Governance will feature clear interagency coordination (e.g., with the Department of Defense, Department of Energy, Department of Homeland Security, and other relevant bodies in the intelligence community) and an internal structure with distinct directorates for evaluations, emergency response, and research, coordinated by CAISI+ leadership.

Recommendation 2. Equip CAISI+ with Elite American Talent and Sustained Funding

CAISI+’s efficacy hinges on world-class personnel and reliable funding to execute its mission. This necessitates: 

1. Exceptional American Talent: Special hiring authorities (e.g., direct hire, excepted service) and competitive compensation are paramount to attract and retain leading U.S. AI researchers, evaluators, and security experts, ensuring our AI standards reflect American values.
2. Significant, Sustained Funding: Initial mainline estimates (see “Funding estimates for CAISI+” below) suggest $155-$275 million for setup and an annual operating budget of $67-$155 million for the recommended implementation level, sourced via new appropriations, to ensure America develops strong domestic capacity for defending against AI-powered threats. If funding is not appropriated, or if appropriations fall short, additional support may be able to be sourced via a NIST Foundation.

Recommendation 3. Equip CAISI+ with Essential Secure Compute Infrastructure. 

CAISI+ must be able to access secure compute in order to run certain evaluations involving proprietary models and national security data. This cluster can remain relatively modest in scale. Other researchers have hypothesized that a “Trusted AI Verification and Evaluation Cluster” for verifying and evaluating frontier AI development would need only 128 to 512 state-of-the-art graphical processing units (GPU)s–orders of magnitude smaller than the scale of training compute, such as the recent Llama 3.1 405 B model’s training run use of a 16,000 H100 GPU cluster, or xAI’s 200,000 GPU Colossus cluster. 

However, the cluster will need to be highly secure–in other words, able to defend against attacks from nation-state adversaries. Certain evaluations will require full access to the internal “weights” of AI models, which requires hosting the model. Model hosting introduces the risk of model theft and proliferation of dangerous capabilities. Some evaluations will also involve the use of very sensitive data, such as nuclear weapons design evals–introducing additional incentive for cyberattacks. Researchers at Gladstone AI, a national security-focused AI policy consulting firm, write that in several years, powerful AI systems may confer significant strategic advantages to nation-states, and will therefore be top-priority targets for theft or sabotage by adversary nation-states. They also note that neither existing datacenters nor AI labs are secure enough to prevent this theft–thereby necessitating novel research and buildout to reach the necessary security level, outlined as “Security Level-5” (SL-5) in RAND’s Playbook for Securing AI Model Weights.

Therefore, we suggest a hybrid strategy for specialized secure compute, featuring a highly secure SL-5 air-gapped core facility for sensitive model analysis (a long-lead item requiring immediate planning), with access to a secondary pool of compute for additional capacity to run less sensitive evaluations via a formal partnership with DOE to access national lab resources. CAISI+ may also want to coordinate with the NITRD National Strategic Computing Reserve Pilot Program to explore needs for AI-crisis-related surge computing capability.

If a sufficiently secure compute cluster is infeasible or not developed in time, CAISI+ will ultimately be unable to host model internals without introducing unacceptable risks of model theft, severely limiting its ability to evaluate frontier AI systems.

Recommendation 4. Explore Granting Critical Authorities 

While current legal authorities may suffice for CAISI+’s core missions, evolving AI threats could require additional tools. The White House (specifically the Office of Science and Technology Policy [OSTP], in collaboration with the Office of Management and Budget [OMB]) should analyze existing federal powers (such as the Defense Production Act or the International Emergency Economic Powers Act) to identify gaps in AI threat response capabilities–including potential needs for an incident reporting system and related subpoena authorities (similar to the function of the National Transportation Safety Board), or for model access for safety evaluations, or compute oversight authorities. Based on this analysis, the executive branch should report to Congress where new statutory authorities may be necessary, with defined risk criteria and appropriate safeguards.

Recommendation 5. Implement CAISI+ Enhancements Through Urgent, Phased Approach

Building on CAISI’s existing foundation within NIST/DoC, the Administration should enhance its capabilities to address AI risks that extend beyond current voluntary evaluation frameworks. Given expert warnings that transformative AI could emerge within the current Administration’s term, immediate action is essential to augment CAISI’s capacity to handle extreme scenarios. To achieve full operational capacity by early 2027, initial-phase activities must begin now  due to long infrastructure lead times:

Immediate Enhancements (0-6 months):

1. Leverage NIST’s existing relationships with DOE labs to secure interim access to classified computing facilities for sensitive evaluations
2. Initiate the security research and procurement process for the SL-5 compute facility outlined in Recommendation 3
3. Work with OMB and Department of Commerce leadership to secure initial funding through reprogramming or supplemental appropriations
4. Build on CAISI’s current voluntary agreements to develop protocols for emergency model access and crisis response
5. Begin the OSTP-led analysis of existing federal authorities (per Recommendation 4) to identify potential gaps in AI threat response capabilities

Subsequent phases will extend CAISI’s current work through:

• Foundation-building activities (6-12 months): Implementing the special hiring authorities described in Recommendation 2, formalizing enhanced interagency MOUs to support coordination described in Recommendation 1, and establishing the direct NSC reporting channels for the CAISI+ Director as Principal Advisor on AI Risks.
• Capability expansion (12-18 months): Beginning construction of the SL-5 facility, operationalizing the three core functions (Advanced Model Evaluation, Emergency Assessment & Response, and Foundational AI Reliability Research), and recruiting the 80-150 technical staff outlined in the funding breakdown.
• Full enhanced capacity (18+ months): Achieving the operational capabilities described in Recommendation 1, including mature evaluation platforms, direct Presidential/NSC threat warning protocols, and comprehensive research programs.

Conclusion

Enhancing and empowering CAISI+ is a strategic investment in U.S. national security, far outweighed by the potential costs of inaction on this front. With an estimated annual operating budget of $67-155 million, CAISI+ will provide essential technical capabilities to evaluate and respond to the most serious AI risks, ensuring the U.S. leads in developing and governing AI safely and securely, irrespective of where advanced capabilities emerge. While timelines to AI systems surpassing dangerous capability thresholds are uncertain, by acting now to establish the necessary infrastructure, expertise, and authorities, the Administration can safeguard American interests and our technological future through a broad range of possible scenarios.

This memo was written by an AI Safety Policy Entrepreneurship Fellow over the course of a six-month, part-time program that supports individuals in advancing their policy ideas into practice. You can read more policy memos and learn about Policy Entrepreneurship Fellows here.

States and localities are eager to leverage artificial intelligence (AI) to optimize service delivery and infrastructure management, but they face significant resource gaps. Without sufficient personnel and capital, these jurisdictions cannot properly identify and mitigate the risks associated with AI adoption, including cyber threats, surging power demands, and data privacy issues. Congress should establish a new grant program, coordinated by the Cybersecurity and Infrastructure Security Agency (CISA), to assist state and local governments in addressing these challenges. Such funding will allow the federal government to instill best security and operating practices nationwide, while identifying effective strategies from the grassroots that can inform federal rulemaking. Ultimately, federal, state, and local capacity are interrelated; federal investments in state and local government will help the entire country harness AI’s potential and reduce the risk of catastrophic events such as a large, AI-powered cyberattack.

Challenge and Opportunity 

In 2025, 45 state legislatures have introduced more than 550 bills focused on the regulation of artificial intelligence, covering everything from procurement guidelines to acceptable AI uses in K-12 education to liability standards for AI misuse and error. Major cities have followed suit with sweeping guidance of their own, identifying specific AI risks related to bias and hallucination and directives to reduce their impact on government functions. The influx of regulatory action reflects burgeoning enthusiasm about AI’s ability to streamline public services and increase government efficiency.

Yet two key roadblocks stand in the way: inconsistent rules and uneven capacity. AI regulations vary widely across jurisdictions — sometimes offering contradictory guidance — and public agencies often lack the staff and skills needed to implement them. In a 2024 survey, six in ten public sector professionals cited the AI skills gap as their biggest obstacle in implementing AI tools. This reflects a broader IT staffing crisis, with over 450,000 unfilled cybersecurity roles nationwide, which is particularly acute in the public sector given lower salaries and smaller budgets.

These roadblocks at the state and local level pose a major risk to the entire country. In the cyber space, ransomware attacks on state and local targets have demonstrated that hackers can exploit small vulnerabilities in legacy systems to gain broad access and cause major disruption, extending far beyond their initial targets. The same threat trajectory is conceivable with AI. States and cities, lacking the necessary workforce and adhering to a patchwork of different regulations, will find themselves unable to safely adopt AI tools and mount a uniform response in an AI-related crisis. 

In 2021, Congress established the State and Local Cybersecurity Grant Program (SLCGP) at CISA, which focused on resourcing states, localities, and tribal territories to better respond to cyber threats. States have received almost $1 billion in funding to implement CISA’s security best practices like multifactor authentication and establish cybersecurity planning committees, which effectively coordinate strategic planning and cyber governance among state, municipal, and private sector information technology leaders. 

Federal investment in state and local AI capacity-building can help standardize the existing, disparate guidance and bridge resource gaps, just as it has in the cybersecurity space. AI coordination is less mature today than the cybersecurity space was when the SLCGP was established in 2021. The updated Federal Information Security Modernization Act, which enabled the Department of Homeland Security to set information security standards across government, had been in effect for seven years by 2021, and some of its best practices had already trickled down to states and localities. 

Thus, the need for clear AI state capacity, guardrails, and information-sharing across all levels of government is even greater. A small federal investment now can unlock large returns by enabling safe, effective AI adoption and avoiding costly failures. Local governments are eager to deploy AI but lack the resources to do so securely. Modest funding can align fragmented rules, train high-impact personnel, and surface replicable models—lowering the cost of responsible AI use nationwide. Each successful pilot creates a multiplier effect, accelerating progress while reducing risk.

Plan of Action 

Recommendation 1. Congress should authorize a three-year pilot grant program focused on state and local AI capacity-building.

SLCGP’s authorization expires on August 31, 2025, which provides two unique pathways for a pilot grant program. The Homeland Security Committees in the House and Senate could amend and renew the existing SLCGP provision to make room for an AI-focused pilot. Alternatively, Congress could pass a new authorization, which would likely set the stage for a sustained grant program, upon successful completion of the pilot. A separate authorization would also allow Congress to consider other federal agencies as program facilitators or co-facilitators, in case they want to cover AI integrations that do not directly touch critical infrastructure, which is CISA’s primary focus. 

Alternatively, the House Energy and Commerce and Senate Commerce, Science, and Transportation Committees could authorize a program coordinated by the National Institute of Standards and Technology, which produced the AI Risk Management Framework and has strong expertise in a range of vulnerabilities embedded within AI models. Congress might also consider mandating an interagency advisory committee to oversee the program, including, for example, experts from the Department of Energy to provide technical assistance and guidance on projects related to energy infrastructure.

In either case, the authorization should be coupled with a starting appropriation of $55 million over three years, which would fund ten statewide pilot projects totaling up to $5 million plus administrative costs. The structure of the program will broadly parallel SLCGP’s goals. First, it would align state and local AI approaches with existing federal guidance, such as the NIST AI Risk Management Framework and the Trump Administration’s OMB guidance on the regulation and procurement of artificial intelligence applications. Second, the program would establish better coordination between local and state authorities on AI rules. A new authorization for AI, however, allows Congress and the agency tasked with managing the program the opportunity to improve upon SLCGP’s existing provisions. This new program should permit states to coordinate their AI activities through existing leadership structures rather than setting up a new planning committee. The legislative language should also prioritize skills training and allocate a portion of grant funding to be spent on recruiting and retaining AI professionals within state and local government who can oversee projects.

Recommendation 2. Pilot projects should be implementation-focused and rooted in one of three significant risks: cybersecurity, energy usage, or data privacy.

Similar to SLCGP, this pilot grant program should be focused on implementation. The target product for a grant is a functional local or state AI application that has undergone risk mitigation, rather than a report that identifies issues in the abstract. For example, under this program, a state would receive federal funding to integrate AI into the maintenance of its cities’ wastewater treatment plants without compromising cybersecurity. Funding would support AI skills training for the relevant municipal employees and scaling of certain cybersecurity best practices like data encryption that minimize the project’s risk. States will submit reports to the federal government at each phase of their project: first documenting the risks they identified, then explaining their prioritization of risks to mitigate, then walking through their specific mitigation actions, and later, retrospectively reporting on the outcomes of those mitigations after the project has gone into operational use.

This approach would maximize the pilot’s return on investment. States will be able to complete high-impact AI projects without taking on the associated security costs. The frameworks generated from the project can be reused many times over for later projects, as can the staff who are hired or trained with federal support. 

Given the inconsistency of priorities surfaced in state and local AI directives, the federal government should set the agenda of risks to focus on. The clearest set of risks for the pilot are cybersecurity, energy usage, and data privacy, all of which are highlighted in NIST’s Risk Management Framework. 

• Cybersecurity. Cybersecurity projects should focus on detecting AI-assisted social engineering tactics, used to gain access into secure systems, and adversarial attacks like “poisoning” or “jailbreaking”, which manipulate AI models to produce undesirable outputs. Consider emergency response systems: the transition to IP-based, interconnected 911 systems increases the cyberattack surface, making it easier for an attack targeting one response center to spread across other jurisdictions. A municipality  could seek funding to trial an AI dispatcher with necessary guardrails. As part of their project, they could ensure they have the appropriate cyber hygiene protocols in place to prevent cyberattacks from rendering the dispatcher useless or exploiting vulnerabilities in the dispatcher to gain access to underlying 911 systems that multiple localities rely on. 

• Energy Usage. Energy usage projects should calculate power needs associated with AI development and implementation and the additional energy resources available to prevent outages. Much of the country faces a heightened risk of power outages due to antiquated grids, under-resourced providers, and a dearth of new electricity generation. AI integrations and supportive infrastructure that require significant power will place a heavy burden on states and potentially impact the operation of other critical infrastructure. A sample project might examine the energy demands of a new data center, powering an AI integration into traffic monitoring, and determine where that data center can best be constructed to accommodate available grid capacity.

• Data Privacy. Finally, data privacy projects should focus on bringing AI systems into compliance with existing data laws like the Health Insurance Portability and Accountability Act (HIPAA) and the Children’s Online Privacy Protection Act (COPPA) for AI interventions in healthcare and education, respectively. Because the U.S. lacks a comprehensive data privacy law, states might also experiment with additional best practices, such as training models to detect and reject prompts that contain personally identifiable information. A sample project in this domain might integrate a chatbot into the state Medicaid system to more efficiently triage patients and identify the steps the state can take to prevent the chatbot from handling PII in a manner that does not comply with HIPAA.

If successful, the pilot could expand to address additional risks or support broader, multi-risk, multi-state interventions.

Recommendation 3. The pilot program must include opportunities for grantees to share their ideas with other states and localities.

Arguably the most important facet of this new AI program will be forums where grantees share their learnings. Administrative costs for this program should go toward funding a twice-yearly (bi-annual) in-person forum, where grantees can publicly share updates on their projects. An in-person forum would also provide states with the space to coordinate further projects on the margins. CISA is particularly well positioned to host a forum like this given its track record of convening critical infrastructure operators. Grantees should be required to publish guidance, tools, and templates in a public, digital repository. Ideally, states that did not secure grants can adopt successful strategies from their peers and save taxpayers the cost of duplicate planning work. 

Conclusion 

Congress should establish a new grant program to assist state and local governments in addressing AI risks, including cybersecurity, energy usage, and data privacy. Such federal investments will give structure to the dynamic yet disparate national AI regulatory conversation. The grant program, which will cost $55 million to pilot over three years, will yield a high return on investment for both the ten grantee states and the peers that learn from its findings. By making these investments now, Congress can keep states moving fast toward AI without opening the door to critical, costly vulnerabilities.

This memo was written by an AI Safety Policy Entrepreneurship Fellow over the course of a six-month, part-time program that supports individuals in advancing their policy ideas into practice. You can read more policy memos and learn about Policy Entrepreneurship Fellows here.

The most advanced AI systems remain ‘black boxes’ whose inner workings even their developers cannot fully understand, leading to issues with reliability and trustworthiness. However, as AI systems become more capable, there is a growing desire to deploy them in high-stakes scenarios. The bipartisan National Security Commission on AI cautioned that AI systems perceived as unreliable or unpredictable will ‘stall out’: leaders will not adopt them, operators will mistrust them, Congress will not fund them, and the public will not support them (NSCAI, Final Report, 2021). AI interpretability research—the science of opening these black boxes and attempting to comprehend why they do what they do—could turn opacity into understanding and enable wider AI adoption.

With AI capabilities racing ahead, the United States should accelerate interpretability research now to keep its technological edge and field high-stakes AI deployment with justified confidence. This memorandum describes three policy recommendations that could help the United States seize the moment and maintain a lead on AI interpretability: (1) creatively investing in interpretability research, (2) entering into research and development agreements between interpretability experts and government agencies and laboratories, and (3) prioritizing interpretable AI in federal procurement. 

Challenge and Opportunity

AI capabilities are progressing rapidly. According to many frontier AI companies’ CEOs and independent researchers, AI systems could reach general-purpose capabilities that equal or even surpass humans within the next decade. As capabilities progress, there is a growing desire to incorporate these systems into high-stakes use cases, from military and intelligence uses (DARPA, 2025; Ewbank, 2024) to key sectors of the economy (AI for American Industry, 2025). 

However, the most advanced AI systems are still ‘black boxes’ (Sharkey et al., 2024) that we observe from the outside and that we ‘grow,’ more than we ‘build’ (Olah, 2024). Our limited comprehension of the inner workings of neural networks means that we still really do not understand what happens within these black boxes, leaving uncertainty regarding their safety and reliability. This could have resounding consequences. As the 2021 final report of the National Security Commission on AI (NSCAI) highlighted, “[i]f AI systems routinely do not work as designed or are unpredictable in ways that can have significant negative consequences, then leaders will not adopt them, operators will not use them, Congress will not fund them, and the American people will not support them” (NSCAI, Final Report, 2021). In other words, if AI systems are not always reliable and secure, this could inhibit or limit their adoption, especially in high-stakes scenarios, potentially compromising the AI leadership and national security goals outlined in the Trump administration’s agenda (Executive Order, 2025). 

AI interpretability is a subfield of AI safety that is specifically concerned with opening and peeking inside the black box to comprehend “why AI systems do what they do, and … put this into human-understandable terms” (Nanda, 2024; Sharkey et al., 2025). In other words, interpretability is the AI equivalent of an MRI (Amodei, 2025) because it attempts to provide observers with an understandable image of the hidden internal processes of AI systems.

The Challenge of Understanding AI Systems Before They Reach or Even Surpass Human-Level Capabilities

Recent years have brought breakthroughs across several research areas focused on making AI more trustworthy and reliable, including in AI interpretability. Among other efforts, the same companies developing the most advanced AI systems have designed systems that are easier to understand and have reached new research milestones (Marks et al., 2025; Lindsey et al., 2025; Lieberum et al. 2024; Kramar et al., 2024; Gao et al., 2024; Tillman & Mossing, 2025). 

AI interpretability, however, is still trailing behind raw AI capabilities. AI companies project that it could take 5–10 years to reliably understand model internals (Amodei, 2025), while experts expect systems exhibiting human‑level general-purpose capabilities by as early as 2027 (Kokotajlo et al., 2025). That gap will force policymakers into a difficult corner once AI systems reach similar capabilities: deploy unprecedentedly powerful yet opaque systems, or slow deployment and fall behind. Unless interpretability accelerates, the United States could risk both competitive and security advantages. 

The Challenge of Trusting Today’s Systems for High-Stakes Applications

We must understand the inner workings of highly advanced AI systems before they reach human or above-human general-purpose capabilities, especially if we want to trust them in high-stakes scenarios. There are several reasons why current AI systems might not always be reliable and secure. For instance, AI systems could exhibit the following vulnerabilities. First, AI systems inherit the blind spots of their training data. When the world changes—alliances shift, governments fall, regulations update—systems still reason from outdated facts, undermining reliability in high-stakes diplomatic or military settings (Jensen et al., 2025).

Second, AI systems are unusually easy to strip‑mine for memorized secrets, especially if these secrets come as uncommon word combinations (e.g., proprietary blueprints). Data‑extraction attacks are now “practical and highly realistic” and will grow even more effective as system size increases (Carlini et al., 2021; Nasr et al., 2023; Li et al., 2025). The result could be wholesale leakage of classified or proprietary information (DON, 2023).

Third, cleverly crafted prompts can still jailbreak cutting‑edge systems, bypassing safety rails and exposing embedded hazardous knowledge (Hughes et al., 2024; Ramesh et al., 2024). With attack success rates remaining uncomfortably high across even the leading systems, adversaries could manipulate AI systems with these vulnerabilities in real‑time national security scenarios (Caballero & Jenkins, 2024).

This is not a comprehensive list. Systems could exhibit vulnerabilities in high-stakes applications for many other reasons. For instance, AI systems could be misaligned and engage in scheming behavior (Meinke et al., 2024; Phuong et al., 2025) or have baked-in backdoors that an attacker could exploit (Hubinger et al., 2024; Davidson et al., 2025). 

The Opportunity to Promote AI Leadership Through Interpretability

Interpretability offers an opportunity to address these described challenges and reduce barriers to the safe adoption of the most advanced AI systems, thereby further promoting innovation and increasing the existing advantages those systems present over adversaries’ systems. In this sense, accelerating interpretability could help promote and secure U.S. AI leadership (Bau et al., 2025; IFP, 2025). For example, by helping ensure that highly advanced AI systems are deployed safely in high-stakes scenarios, interpretability could improve national security and help mitigate the risk of state and non-state adversaries using AI capabilities against the United States (NSCAI, Final Report, 2021). Interpretability could therefore serve as a front‑line defense against vulnerabilities in today’s most advanced AI systems.

Making future AI systems safe and trustworthy could become easier the more we understand how they work (Shah et al., 2025). Anthropic’s CEO recently endorsed the importance and urgency of interpretability, noting that “every advance in interpretability quantitatively increases our ability to look inside models and diagnose their problems” (Amodei, 2025). This means that interpretability not only enhances reliability in the deployment of today’s AI systems, but understanding AI systems could also lead to breakthroughs in designing more targeted systems or attaining more robust monitoring of deployed systems. This could then enable the United States to deploy tomorrow’s human-level or above-human general-purpose AI systems with increased confidence, thus securing strategic advantages when engaging  geopolitically. The following uses the vulnerabilities discussed above to demonstrate three ways in which interpretability could improve the reliability of today’s AI systems when deployed in high-stakes scenarios.

First, interpretability could help systems selectively update outdated information through model editing, without risking a reduction in performance. Model editing allows us to selectively inject new facts or fix mistakes (Cohen et al., 2023; Hase et al., 2024) by editing activations without updating the entire model. However, this ‘surgical tool’ has shown ‘side effects’ causing performance degradation (Gu et al., 2024; Gupta et al., 2024). Interpretability could help us understand how stored knowledge alters parameters as well as develop stronger memorization measures (Yao et al., 2023; Carlini et al., 2019), enabling us to ‘incise and excise’ AI models with fewer side effects.

Second, interpretability could help systems selectively forget training data through machine unlearning, once again without losing performance. Machine unlearning allows systems to forget specific data classes (such as memorized secrets or hazardous knowledge) while remembering the rest (Tarun et al., 2023). Like model editing, this ‘surgical tool’ suffers from performance degradation. Interpretability could help develop new unlearning techniques that preserve performance (Guo et al., 2024; Belrose et al., 2023; Zou et al., 2024).

Third, interpretability could help effectively block jailbreak attempts, which can only currently be discovered empirically (Amodei, 2025). Interpretability could lead to a breakthrough in understanding models’ persistent vulnerability to jailbreaking by allowing us to characterize dangerous knowledge. Existing interpretability research has already analyzed how AI models process harmful prompts (He et al., 2024; Ball et al., 2024; Lin et al., 2024; Zhou et al., 2024), and additional research could build on these initial findings

The conditions are ripe to promote technological leadership and national security through interpretability. Many of the same problems that were highlighted in the 2019 National AI R&D Strategic Plan remained the same in its 2023 update, echoing those included in NSCAI’s 2021 final report. We have made relatively little progress addressing these challenges. AI systems are still vulnerable to attacks (NSCAI, Final Report, 2021) and can still “be made do the wrong thing, reveal the wrong thing” and “be easily fooled, evaded, and misled in ways that can have profound security implications” (National AI R&D Strategic Plan, 2019). The field of interpretability is gaining some momentum among AI companies (Amodei, 2025; Shah et al., 2025; Goodfire, 2025) and AI researchers (IFP, 2025; Bau et al., 2025; FAS, 2025). 

To be sure, despite recent progress, interpretability remains challenging and has attracted some skepticism (Hendrycks & Hiscott, 2025). Accordingly, a strong AI safety strategy must include many components beyond interpretability, including robust AI evaluations (Apollo Research, 2025) and control measures (Redwood Research, 2025).

Plan of Action 

The United States has an opportunity to seize the moment and lead an acceleration of AI interpretability. The following three recommendations establish a strategy for how the United States could promptly incentivize AI interpretability research. 

Recommendation 1. The federal government should prioritize and invest in foundational AI interpretability research, which would include identifying interpretability as a ‘strategic priority’ in the 2025 update of the National AI R&D Strategic Plan. 

The National Science and Technology Council (NSTC) should identify AI interpretability as a ‘strategic priority’ in the upcoming National AI R&D Strategic Plan. Congress should then appropriate federal R&D funding for federal agencies (including DARPA and the NSF) to catalyze and support AI interpretability acceleration through various mechanisms, including grants and prizes, R&D credits, tax credits, advanced market commitments, and buyer-of-first-resort mechanisms. 

This first recommendation echoes not only the 2019 update of the National AI R&D Strategic Plan and NSCAI’s 2021 final report––which recommended allocating more federal R&D investments to advance the interpretability of Al systems (NSCAI, Final Report, 2021; National AI R&D Strategic Plan, 2019),, but also the more recent remarks by the Director of the Office of Science and Technology Policy (OSTP), according to whom we need creative R&D funding approaches to enable scientists and engineers to create new theories and put them into practice (OSTP Director’s Remarks, 2025). This recommendation is also in line with calls from AI companies, asserting that “we still need significant investment in ‘basic science’” (Shah et al., 2025).

The United States could incentivize and support AI interpretability work through various approaches. In addition to prize competitions, advanced market commitments, fast and flexible grants (OSTP Director’s Remarks, 2025; Institute for Progress, 2025), and challenge-based acquisition programs (Institute for Progress, 2025), funding mechanisms could include R&D tax credits for AI companies undertaking or investing in interpretability research, and tax credits to adopters of interpretable AI, such as downstream deployers. If the federal government acts as “an early adopter and avid promoter of American technology” (OSTP Director’s Remarks, 2025), federal agencies could also rely on buyer-of-first-resort mechanisms for interpretability platforms. 

These strategies may require developing a clearer understanding of which frontier AI companies undertake sufficient interpretability efforts when developing their most advanced systems, and which companies currently do not. Requiring AI companies to disclose how they use interpretability to test models before release (Amodei, 2025) could be helpful, but might not be enough to devise a ‘ranking’ of interpretability efforts. While potentially premature given the state of the art in interpretability, an option could be to start developing standardized metrics and benchmarks to evaluate interpretability (Mueller et al., 2025; Stephenson et al., 2025). This task could be carried out by the National Institute of Standards and Technology (NIST), within which some AI researchers have recommended creating an AI Interpretability and Control Standards Working Group (Bau et al., 2025). 

A great way to operationalize this first recommendation would be for the National Science and Technology Council (NSTC) to include interpretability as a “strategic priority” in the 2025 update of the National AI R&D Strategic Plan (RFI, 2025). These “strategic priorities” seek to target and focus AI innovation for the next 3–5 years, paying particular attention to areas of “high-risk, high-reward AI research” that the industry is unlikely to address because it may not provide immediate commercial returns (RFI, 2025). If interpretability were included as a “strategic priority,” then the Office of Management and Budget (OMB) could instruct agencies to align their budgets with the 2025 National AI R&D Strategic Plan priorities in its memorandum addressed to executive department heads. Relevant agencies, including DARPA and the National Science Foundation (NSF), would then develop their budget requests for Congress, aligning them with the 2025 National AI R&D Strategic Plan and the OMB memorandum. After Congress reviews these proposals and appropriates funding, agencies could launch initiatives that incentivize interpretability work, including grants and prizes, R&D credits, tax credits, advanced market commitments, and buyer-of-first-resort mechanisms. 

Recommendation 2. The federal government should enter into research and development agreements with AI companies and interpretability research organizations to red team AI systems applied in high-stakes scenarios and conduct targeted interpretability research. 

AI companies, interpretability organizations, and federal agencies and laboratories (such as DARPA, the NSF, and the U.S. Center for AI Standards and Innovation) should enter into research and development agreements to pursue targeted AI interpretability research to solve national security vulnerabilities identified through security-focused red teaming. 

This second recommendation takes into account the fact that the federal government possesses unique expertise and knowledge in national security issues to support national security testing and evaluation (FMF, 2025). Federal agencies and laboratories (such as DARPA, the NSF, and the U.S. Center for AI Standards and Innovation), frontier AI companies, and interpretability organizations could enter into research and development agreements to undertake red teaming of national security vulnerabilities (as, for instance, SABER which aims to assess AI-enabled battlefield systems for the DoD; SABER, 2025) and provide state-of-the-art interpretability platforms to patch the revealed vulnerabilities. In the future, AI companies could also apply the most advanced AI systems to support interpretability research. 

Recommendation 3. The federal government should prioritize interpretable AI in federal procurement, especially for high-stakes applications. 

If federal agencies are procuring highly advanced AI for high-stakes scenarios and national security missions, they should preferentially procure interpretable AI systems. This preference could be accounted for by weighing the lack of understanding of an AI system’s inner workings when calculating cost. 

This third and final recommendation provides for the interim and assumes interpretable AI systems will coexist in a ‘gradient of interpretability’ with other AI systems that are less interpretable. In that scenario, agencies procuring AI systems should give preference to AI systems that are more interpretable. One way to account for this preference would be by weighing the potential vulnerabilities of uninterpretable AI systems within calculating costs during federal acquisition analyses. This recommendation also requires establishing a defined ‘ranking’ of interpretability efforts. While defining this ranking is currently challenging, the research outlined in recommendations 1 and 2 could better position the government to measure and rank the interpretability of different AI systems. 

Conclusion 

Now is the time for the United States to take action and lead the charge on AI interpretability research. While research is never guaranteed to lead to desired outcomes or to solve persistent problems, the potential high reward—understanding and trusting future AI systems and making today’s systems more robust to adversarial attacks—justifies this investment. Not only could AI interpretability make AI safer and more secure, but it could also establish justified confidence in the prompt adoption of future systems that are as capable as or even more capable than humans, and enable the deployment of today’s most advanced AI systems to high-stakes scenarios, thus promoting AI leadership and national security. With this goal in mind, this policy memorandum recommends that the United States, through the relevant federal agencies and laboratories (including DARPA, the NSF, and the U.S. Center for AI Standards and Innovation), invest in interpretability research, form research and development agreements to red team high-stakes AI systems and undertake targeted interpretability research, and prioritize interpretable AI systems in federal acquisitions.

Acknowledgments 

I wish to thank Oliver Stephenson, Dan Braun, Lee Sharkey, and Lucius Bushnaq for their ideas, comments, and feedback on this memorandum.

This memo was written by an AI Safety Policy Entrepreneurship Fellow over the course of a six-month, part-time program that supports individuals in advancing their policy ideas into practice. You can read more policy memos and learn about Policy Entrepreneurship Fellows here.

The opportunities presented by advanced artificial intelligence are immense, from accelerating cutting-edge scientific research to improving key government services. However, for these benefits to be realized, both the private and public sectors need confidence that AI tools are reliable and secure. This will require R&D effort to solve urgent technical challenges related to understanding and evaluating emergent AI behaviors and capabilities, securing AI hardware and infrastructure, and preparing for a world with many advanced AI agents.

To secure global adoption of U.S. AI technology and ensure America’s workforce can fully leverage advanced AI, the federal government should take a strategic and coordinated approach to support AI assurance and security R&D by: clearly defining AI assurance and security R&D priorities; establishing an AI R&D consortium and deploying agile funding mechanisms for critical R&D areas; and establishing an AI Frontier Science Fellowship to ensure a pipeline of technical AI talent. 

Challenge and Opportunity

AI systems have progressed rapidly in the past few years, demonstrating human-level and even superhuman performance across diverse tasks. Yet, they remain plagued by flaws that produce unpredictable and potentially dangerous failures. Frontier systems are vulnerable to attacks that can manipulate them into executing unintended actions, hallucinate convincing but incorrect information, and exhibit other behaviors that researchers struggle to predict or control.

As AI capabilities rapidly advance toward more consequential applications—from medical diagnosis to financial decision-making to military systems—these reliability issues could pose increasingly severe risks to public safety and national security, while reducing beneficial uses. Recent polling shows that just 32% of Americans trust AI, and this limited trust will slow the uptake of impactful AI use-cases that could drive economic growth and enhance national competitiveness.

The federal government has an opportunity to secure America’s technological lead and promote global adoption of U.S. AI by catalyzing research to address urgent AI reliability and security challenges—challenges that align with broader policy consensus reflected in the National Security Commission on AI’s recommendations and bipartisan legislative efforts like the VET AI Act. Recent research has surfaced substantial expert consensus around priority research areas that address the following three challenges.

The first challenge involves understanding emergent AI capabilities and behaviors. As AI systems get larger, also referred to as “scaling”, they develop unexpected capabilities and reasoning patterns that researchers cannot predict, making it difficult to anticipate risks or ensure reliable performance. Addressing this means advancing the science of AI scaling and evaluations.

The second challenge is securing AI hardware and infrastructure. AI systems require robust protection of model weights, secure deployment environments, and resilient supply chains to prevent theft, manipulation, or compromise by malicious actors seeking to exploit these powerful technologies. Addressing this means advancing hardware and infrastructure security for AI.

The third challenge involves preparing for a world with many AI agents—AI models that can act autonomously. Alongside their potentially immense benefits, the increasing deployment of AI agents creates critical blind spots, as agents could coordinate covertly beyond human oversight, amplify failures into system-wide cascades, and combine capabilities in ways that circumvent existing safeguards. Addressing this means advancing agent metrology, infrastructure, and security.

While academic and industry researchers have made progress on these problems, this progress is not keeping pace with AI development and deployment. The market is likely to underinvest in research that is more experimental or with no immediate commercial applications. The U.S. government, as the R&D lab of the world, has an opportunity to unlock AI’s transformative potential through accelerating assurance and security research.

Plan of Action

The rapid pace of AI advancement demands a new strategic, coordinated approach to federal R&D for AI assurance and security.  Given financial constraints, it is more important than ever to make sure that the impact of every dollar invested in R&D is maximized.

Much of the critical technical expertise now resides in universities, startups, and leading AI companies rather than traditional government labs. To harness this distributed talent, we need R&D mechanisms that move at the pace of innovation, leverage academic research excellence, engage early-career scientists who drive breakthroughs, and partner with industry leaders who can share access to essential compute resources and frontier models. Traditional bureaucratic processes risk leaving federal efforts perpetually behind the curve.

The U.S. government should implement a three-pronged plan to advance the above R&D priorities.

Recommendation 1. Clearly define AI assurance and security R&D priorities

The Office of Science and Technology Policy (OSTP) and the National Science Foundation (NSF) should highlight critical areas of AI assurance and security as R&D priorities by including these in the 2025 update of the National AI R&D Strategic Plan and the forthcoming AI Action Plan. All federal agencies conducting AI R&D should engage with the construction of these plans to explain how their expertise could best contribute to these goals. For example, the Defense Advanced Research Projects Agency (DARPA)’s Information Innovation Office could leverage its expertise in AI security to investigate ways to design secure interaction protocols and environments for AI agents that eliminate risks from rogue agents.

The priorities would help coordinate government R&D activities by providing funding agencies with a common set of priorities, public research institutes such as the National Labs to conduct fundamental R&D activities, Congress with information to support relevant legislative decisions, and industry to serve as a guide to R&D.

Additionally, given the dynamic nature of frontier AI research, OSTP and NSF should publish an annual survey of progress in critical AI assurance and security areas and identify which challenges are the highest priority.

Recommendation 2. Establish an AI R&D consortium and deploy agile funding mechanisms for critical R&D

As noted by OSTP Director Michael Kratsios, “prizes, challenges, public-private partnerships, and other novel funding mechanisms, can multiply the impact of targeted federal dollars. We must tie grants to clear strategic targets, while still allowing for the openness of scientific exploration.” Federal funding agencies should develop and implement agile funding mechanisms for AI assurance and security R&D in line with established priorities. Congress should include reporting language in its Commerce, Justice, Science (CJS) appropriations bill that supports accelerated R&D disbursements for investment into prioritized areas.

A central mechanism should be the creation of an AI Assurance and Security R&D Consortium, jointly led by DARPA and NSF, bringing together government, AI companies, and universities. In this model:

• Government provides funding for personnel, administrative support, and manages the consortium’s strategic direction
• AI companies contribute model access, compute credits, and engineering expertise
• Universities provide researchers and facilities for conducting fundamental research

This consortium structure would enable rapid resource sharing, collaborative research projects, and accelerated translation of research into practice. It would operate under flexible contracting mechanisms using Other Transaction Authority (OTA) to reduce administrative barriers.

Beyond the consortium, funding agencies should leverage Other Transaction Authority (OTA) and Prize Competition Authority to flexibly contract and fund research projects related to priority areas. New public-private grant vehicles focused on funding fundamental research in priority areas should be set up via existing foundations linked to funding agencies such as the NSF Foundation, DOE’s Foundation for Energy Security and Innovation, or the proposed NIST Foundation.

Specific funding mechanisms should be chosen based on the target technology’s maturity level. For example, the NSF can support more fundamental research through fast grants via its EAGER and RAPID programs. Previous fast-grant programs, such as SGER, were found to be wildly effective, with “transformative research results tied to more than 10% of projects.”

For research areas where clear, well-defined technical milestones are achievable, such as developing secure cluster-scale environments for large AI training workloads, the government can support the creation of focused research organizations (FROs) and implement advanced market commitments (AMCs) to take technologies across the ‘valley of death’. DARPA and IARPA can administer higher-risk, more ambitious R&D programs with national security applications.

Recommendation 3. Establish an AI Frontier Science Fellowship to ensure a pipeline of technical AI talent that can contribute directly to R&D and support fast-grant program management

It is critical to ensure that America has a growing pool of talented researchers entering the field of AI assurance and security, given its strategic importance to American competitiveness and national security. 

The NSF should launch an AI Frontier Science Fellowship targeting early-career researchers in critical AI assurance and security R&D. Drawing from proven models like CyberCorp Scholarship for Service, COVID-19 Fast Grants, and  proposals such as for “micro-ARPAs”, this program operates on two tracks:

1. Frontier Scholars: This track would provide comprehensive research support for PhD students and post-docs conducting relevant research on priority AI security and reliability topics. This includes computational resources, research rotations at government labs and agencies, and financial support.
2. Rapid Grant Program Managers (PM): This track recruits researchers to serve fixed terms as Rapid Grant PMs, responsible for administering EAGER/RAPID grants focused on AI assurance and security. 

This fellowship solves multiple problems at once. It builds the researcher pipeline while creating a nimble, decentralized approach to science funding that is more in line with the dynamic nature of the field. This should improve administrative efficiency and increase the surface area for innovation by allowing for more early-stage high-risk projects to be funded. Also, PMs who perform well in administering these small, fast grants can then become full-fledged program officers and PMs at agencies like the NSF and DARPA. This program (including grant budget) would cost around $40 million per year.

Conclusion

To unlock AI’s immense potential, from research to defense, we must ensure these tools are reliable and secure. This demands R&D breakthroughs to better understand emergent AI capabilities and behaviors, secure AI hardware and infrastructure, and prepare for a multi-agent world. The federal government must lead by setting clear R&D priorities, building foundational research talent, and injecting targeted funding to fast-track innovation.  This unified push is key to securing America’s AI leadership and ensuring that American AI is the global gold standard.

This memo was written by an AI Safety Policy Entrepreneurship Fellow over the course of a six-month, part-time program that supports individuals in advancing their policy ideas into practice. You can read more policy memos and learn about Policy Entrepreneurship Fellows here.

Imperative for a Renewed Economic Paradigm

So far, President Trump’s tariff policies have generated significant turbulence and appear to lack a coherent strategy. His original tariff schedule included punitive tariffs on friends and foes alike on the mistaken basis that trade deficits are necessarily the result of an unhealthy relationship. Although they have been gradually paused or reduced since April 2, the uneven rollout (and subsequent rollback) of tariffs continues to generate tremendous uncertainty for policymakers, consumers, and businesses alike. This process has weakened America’s geopolitical standing by encouraging other countries to seek alternative trade, financial, and defense arrangements. 

However, notwithstanding the uncoordinated approach to date, President Trump’s mistaken instinct for protectionism belies an underlying truth: that American manufacturing communities have not fared well in the last 25 years and that China’s dominance in manufacturing poses an ever-growing threat to national security. After China’s admission to the WTO in 2001, its share of global manufacturing grew from less than 10% to over 35% today. At the same time, America’s share of manufacturing shrank from almost 25% to less than 15%, with employment shrinking from more than 17 million at the turn of the century to under 13 million today. These trends also create a deep geopolitical vulnerability for America, as in the event of a conflict with China, we would be severely outmatched in our ability to build critical physical goods: for example, China produces over 80% of the world’s batteries, over 90% of consumer drones, and has a 200:1 shipbuilding capacity advantage over the U.S. While not all manufacturing is geopolitically valuable, the erosion in strategic industries, which went hand-in-hand with the loss of key manufacturing skills in recent decades, poses potential long-term challenges for America.

In addition to its growing manufacturing dominance, China is now competing with America’s preeminence in technology leadership, having leveraged many of the skills gained in science, engineering, and manufacturing for lower-value add industries to compete in higher-end sectors. DeepSeek demonstrated that China can natively generate high-quality artificial intelligence models, an area in which the U.S. took its lead for granted. Meanwhile, BYD rocketed past Tesla in EV sales and accounted for 22% of global sales in 2024 as compared to Tesla’s 10%. China has also been operating an extensive satellite-enabled secure quantum communications channel since 2016, preventing others from eavesdropping. 

China’s growing leadership in advanced research may give it a sustained edge beyond its initial gains: according to one recent analysis of frontier research publications across 64 critical technologies, global leadership has shifted dramatically to China, which now leads in 57 research domains. These are not recent developments: they have been part of a series of five year plans, the most well known of which is Made in China 2025, giving China an edge in many critical technologies that will continue to grow if not addressed by an equally determined American response.

via US Efforts to Contain Xi’s Push for Tech Supremacy Are Faltering in Bloomberg

An Integrated Innovation, Economic Foreign Policy, and Community Development Approach 

Despite China’s growing challenge and recent self-inflicted damage to America’s economic and geopolitical relationships, America still retains many ingrained advantages. The U.S. still has the largest economy, the deepest public and private capital pools for promising companies and technologies, and the world’s leading universities; it has the most advanced military, continues to count most of the world’s other leading armed forces as formal treaty allies, and remains the global reserve currency. Ordinary Americans have benefited greatly from these advantages in the form of access to cutting edge products and cheaper goods that increase their effective purchasing power and quality of life – notwithstanding Secretary Bessent’s statements to the contrary.

The U.S. would be wise to leverage its privileged position in high-end innovation and in global financial markets to build “industries of the future.” However, the next economic and geopolitical paradigm must be genuinely equitable, especially to domestic communities that have been previously neglected or harmed by globalization. For these communities, policies such as the now-defunct Trade Adjustment Assistance program were too slow and too reactive to help workers displaced by the “China Shock,” which is estimated to have caused up to 2.4 million direct and indirect job losses. 

Although jobs in trade-affected communities were eventually “replaced,” the jobs that came after were disproportionately lower-earning roles, accrued largely to individuals who had college degrees, and were taken by new labor force entrants rather than providing new opportunities for those who had originally been displaced. Moreover, as a result of ineffective policy responses, this replacement took over a decade and has contributed to heinous effects: look no further than the rate at which “deaths of despair” for white individuals without a college degree skyrocketed after 2000.

via NBER Working Paper: Places versus People: the Ins and Outs of Labor Market Adjustment to Globalization 

via Working class white Americans are now dying in middle age at faster rates than minority groups in Brookings

Nonetheless, surrendering America’s hard-won advantages in technology and international commerce, especially in the face of a growing challenge from China, would be an existential error. Rather, our goal is to address the shortcomings of previous policy approaches to the negative externalities caused by globalization. Previous approaches have focused on maximizing growth and redistributing the gains, but in practice, America failed to do either by underinvesting in the foundational policies that enable both. Thus, we are proposing a two-pronged approach that focuses on spurring cutting-edge technologies, growing novel industries, and enhancing production capabilities while investing in communities in a way that provides family-supporting, upwardly mobile jobs as well as critical childcare, education, housing, and healthcare services. By investing in broad-based prosperity and productivity, we can build a more equitable and dynamic economy.

Our agenda is intentionally broad (and correspondingly ambitious) rather than narrow in focus on manufacturing communities, even though current discourse is focused on trade. This is not simply a “political bargain” that provides greater welfare or lip-service concessions to hollowed-out communities in exchange for a return to the prior geoeconomic paradigm. Rather, we genuinely believe that economic dynamism which is led by an empowered middle-class worker, whether they work in manufacturing or in a service industry, is essential to America’s future prosperity and national security – one in which economic outcomes are not determined by parental income and one where black-white disparities are closed in far less than the current pace of 150+ years.

via Peter Levine via Brookings

via Mapping the road to prosperity and parity for Black and Latino residents across America from McKinsey Institute for Economic Mobility

Thus, the ideas and agenda presented here are neither traditionally “liberal” nor “conservative,” “Democrat” nor “Republican.” Instead, we draw upon the intellectual traditions of both segments of the political spectrum. We agree with Ezra Klein’s and Derek Thompson’s vision in Abundance for a technology-enabled future in which America remembers how to build; at the same time, we take seriously Oren Cass’s view in The Once and Future Worker that the dignity of work is paramount and that public policy should empower the middle-class worker. What we offer in the sections below is our vision for a renewed America that crosses traditional policy boundaries to create an economic and political paradigm that works for all.

Policy Recommendations 

Investing in American Innovation

Given recent trends, it is clear that there is no better time to re-invigorate America’s innovation edge by investing in R&D to create and capture “industries of the future,” re-shoring capital and expertise, and working closely with allies to expand our capabilities while safeguarding those technologies that are critical to our security. These investments will enable America to grow its economic potential, providing fertile ground for future shared prosperity. We emphasize five key components to renewing America’s technological edge and manufacturing base:

Invest in R&D. Increase federally funded R&D, which has declined from 1.8% of GDP in the 1960s to 0.6% of GDP today. Of the $200 billion federal R&D budget, just $16 billion is allocated to non-healthcare basic science, an area in which the government is better suited to fund than the private sector due to positive spillover effects from public funding. A good start is fully funding the CHIPS and Science Act, which authorized over $200 billion over 10 years for competitiveness-enhancing R&D investments that Congress has yet to appropriate. Funding these efforts will be critical to developing and winning the race for future-defining technologies, such as next-gen battery chemistries, quantum computing, and robotics, among others.

Capability-Building. Develop a coordinated mechanism for supporting translation and early commercialization of cutting-edge technologies. Otherwise, the U.S. will cede scale-up in “industries of the future” to competitors: for example, Exxon developed the lithium-ion battery, but lost commercialization to China due to the erosion of manufacturing skills in America that are belatedly being rebuilt. However, these investments are not intended to be a top-down approach that selects winners and losers: rather, America should set a coordinated list of priorities (leveraging roadmaps such as the DoD’s Critical Technology Areas), foster competition amongst many players, and then provide targeted, lightweight financial support to industry clusters and companies that bubble to the top.

Financial support could take the form of a federally-funded strategic investment fund (SIF) that partners with private sector actors by providing catalytic funding (e.g., first-loss loans). This fund would focus on bridging the financing gap in the “valley of death” as companies transition from prototype to first-of-a-kind / “nth-of-a-kind” commercial product. In contrast to previous attempts at industrial policy, such as the Inflation Reduction Act (IRA) or CHIPS Act, they should have minimal compliance burdens and focus on rapidly deploying capital to communities and organizations that have proven to possess a durable competitive advantage.

Encourage Foreign Direct Investment (FDI). Provide tax incentives and matching funds (potentially from the SIF) for companies who build manufacturing plants in America. This will bring critical expertise that domestic manufacturers can adopt, especially in industries that require deep technical expertise that America would need to redevelop (e.g., shipbuilding). By striking investment deals with foreign partners, America can “learn from the best” and subsequently improve upon them domestically. In some cases, it may be more efficient to “share” production, with certain components being manufactured or assembled abroad, while America ramps up its own capabilities.

For example, in shipbuilding, the U.S. could focus on developing propulsion, sensor, and weapon systems, while allies such as South Korea and Japan, who together build almost as much tonnage as China, convert some shipyards to defense production and send technical experts to accelerate development of American shipyards. In exchange, they would receive select additional access to cutting-edge systems and financially benefit from investing in American shipbuilding facilities and supply chains.

Immigration. America has long been described as a “nation of immigrants.” Their role in innovation is impossible to deny: 46% of companies in the Fortune 500 were founded by immigrants and accounted for 24% of all founders; they are 19% of the overall STEM workforce but account for nearly 60% of doctorates in computer science, mathematics, and engineering. Rather than spurning them, the U.S. should attract more highly educated immigrants by removing barriers to working in STEM roles and offering accelerated paths to citizenship. At the same time, American policymakers should acknowledge the challenges caused by illegal immigration. One such solution is to pass legislation such as the Border Control Act of 2024, which had bipartisan support and increased border security, supplemented by a “points-based” immigration system such as Canada’s which emphasizes educational credentials and in-country work experience.

Create Targeted Fences. Employ tariffs and export controls to defend nascent, strategically important industries such as advanced chips, fusion energy, or quantum communications. However, rather than employing these indiscriminately, tariffs and export controls should be focused on ensuring that only America and its allies have access to cutting-edge technologies that shape the global economic and security landscape. They are not intended to keep foreign competition out wholesale; rather, they should ensure that burgeoning technology developers gain sufficient scale and traction by accelerating through the “learn curve.”

Building Strong Communities

Strong communities are the foundation of a strong workforce, without which new industries will not thrive beyond a small number of established tech hubs. However, strengthening American communities will require the country to address the core needs of a family-sustaining life. Childcare, education, housing, and healthcare are among the largest budget items for families and have been proven time and again to be critical to economic mobility. Nevertheless, they are precisely the areas in which costs have skyrocketed the most, as has been frequently chronicled by the American Enterprise Institute’s “Chart of the Century.” These essential services have been underinvested in for far too long, creating painful shortages for communities that need them most. As such, addressing these issues form the core pillars of our domestic reinvestment plan. Addressing them means grappling with the underlying drivers of their cost and scarcity. These include issues of state capacity, regulatory and licensing barriers, and low productivity growth in service-heavy care sectors. A new policy agenda that addresses the fundamental supply-side issues is needed to reshape the contours of this debate.

via Visual Capitalist

Expand Childcare. Inadequate childcare costs the U.S. economy $122 billion in lost wages and productivity as otherwise capable workers, especially women, are forced to reduce hours or leave the labor force. Access is further exacerbated by supply shortages: more than half the population lives in a “childcare desert,” where there are more than three times as many children as licensed slots. Addressing these shortages will alleviate the affordability issue, enabling workers to stay in the workforce and allow families to move up the income ladder.

Fund Early Education. Investments in early childhood education have been demonstrated to generate compelling ROI, with high-quality studies such as the Perry preschool study demonstrating up to $7 – $12 of social return for every $1 invested. While these gains are broadly applicable across the country, they would make an even greater difference in helping to rebuild manufacturing communities by making it easier to grow and sustain families. Given the return on investment and impact on social mobility, American policymakers should consider investing in universal pre-K.

Invest in Workforce Training and Community Colleges. The cost of a four-year college education now exceeds $38K per year, indicating a clear need for cheaper BA degrees but also credible alternatives. At the same time, community colleges can be reimagined and better funded to enable them to focus on high-paying jobs in sectors with critical labor shortages, many of which are in or adjacent to “industries of the future.” Some of these roles, such as IT specialists and skilled tradespeople, are essential to manufacturing. Others, such as nursing and allied healthcare roles, will help build and sustain strong communities.

Build Housing Stock. America has a shortage of 3.2 million homes. Simply put, the country needs to build more houses to address the cost of living and enable Americans to work and raise families. While housing policy is generally decided at lower levels of government, the federal government should provide grants and other incentives to states and municipalities to defray the cost of developing affordable housing; in exchange, state and local jurisdictions should relax zoning regulations to enable more multi-family and high-density single-family housing. 

Expand Healthcare Access. American healthcare is plagued with many problems, including uneven access and shortages in primary care. For example, the U.S. has 3.1 primary care physicians (PCPs) per 10,000 people, whereas Germany has 7.1 and France has 9.0. As such, the federal government should focus on expanding the number of healthcare practitioners (especially primary care physicians and nurses), building a physical presence for essential healthcare services in underserved regions, and incentivizing the development of digital care solutions that deliver affordable care.

Allocating Funds to Invest in Tomorrow’s Growth

Investment Requirements

While we view these policies as essential to America’s reinvigoration, they also represent enormous investments that must be paid for at a time when fiscal constraints are likely to tighten. To create a sense of the size of the financial requirements and trade-offs required, we lay out each of the key policy prescriptions above and use bipartisan proposals wherever possible, many of which have been scored by the Congressional Budget Office (CBO) or another reputable institution or agency. Where this is not possible, we created estimates based on key policy goals to be accomplished. Although trade deals and targeted tariffs are likely to have some budget impact, we did not evaluate them given multiple countervailing forces and political uncertainties (e.g., currency impacts).

Potential Pay-Fors

Given the budgetary requirements of these proposals, we looked for opportunities to prune the federal budget. The CBO laid out a set of budgetary options that collectively could save several trillion over the next decade. In laying out the potential pay-fors, we used two approaches that focused on streamlining mandatory spending and optimizing tax revenues in an economically efficient manner. Our first approach is to include budgetary options that eliminate unnecessary spending that are distortionary in nature or are unlikely to have a meaningful direct impact on the population that they are trying to serve (e.g., kickback payments to state health plans). Our second approach is to include budgetary options in which the burden would fall upon higher-earning populations (e.g., raising the cap on payroll and Social Security taxes). 

As the table below shows, there is a menu of options available to policymakers that raise funding well in excess of the required investment amounts above, allowing them to pick and choose which are most economically efficient and politically viable. In addition, they can modify many of these options to reduce the size or magnitude of the effect of the policy (e.g., adjust the point at which Social Security benefits for “high earners” is tapered or raise capital gains by 1% instead of 2%). While some of these proposals are potentially controversial, there is a clear and pressing need to reexamine America’s foundational policy assumptions without expanding the deficit, which is already more than 6% of GDP.

Conclusion

America is in need of a new economic paradigm that renews and refreshes rather than dismantles its hard-won geopolitical and technological advantages. Trump’s tariffs, should they be fully enacted, would be a self-defeating act that would damage America’s economy while leaving it more vulnerable, not less, to rivals and adversaries. However, we also recognize that the previous free trade paradigm was not truly equitable and did not do enough to support manufacturing communities and their core strengths. We believe that our two-pronged approach of investing in American innovation alongside our allies along with critical community investments in childcare, higher education, housing, and healthcare bridges the gap and provides a framework for re-orienting the economy towards a more prosperous, fair, and secure future.

The bioeconomy is a pivotal economic sector driving national growth, technological innovation, and global competitiveness. However, the biotechnology innovation and biomanufacturing sector faces significant challenges, particularly in scaling technologies and overcoming long development timelines that don’t align with short-term return expectations from investors. These extended timelines and the inherent risks involved lead to funding gaps that hinder the successful commercialization of technologies and bio-based products. If obstacles like the ‘Valleys of Death, a lack of capital at crucial development junctures, that companies and technology struggle to overcome are not addressed, this could result in economic stagnation and the U.S. losing its competitive edge in the global bioeconomy.

Government programs like SBIR and STTR lessen the financial gap inherent in the U.S. bioeconomy, but existing financial mechanisms have proven insufficient to fully de-risk the sector and attract the necessary private investment. In FY24, the National Defense Authorization Act established the Office of Strategic Capital within the Department of Defense to provide financial and technical support for its 31 ‘Covered Technology Categories’, which includes biotechnology and biomanufacturing. To address the challenges associated with de-risking biotechnology and biomanufacturing within the U.S. bioeconomy, the Office of Strategic Capital within the Department of Defense should house a Bioeconomy Finance Program. This program would offer tailored financial incentives such as loans, tax credits, and volume guarantees, targeting both short-term and long-term scale-up needs in biomanufacturing and biotechnology. 

By providing these essential funding mechanisms, the Bioeconomy Finance Program will reduce the risks inherent in biotechnology innovation, encouraging more private sector investment. In parallel, states and regions across the country should develop regional specific strategies, like investing in necessary infrastructure, and fostering public-private partnerships, to complement the federal government’s initiatives to de-risk the sector. Together, these coordinated efforts will create a sustainable, competitive bioeconomy that supports economic growth, and strengthens U.S. national security.

Challenge & Opportunity

The U.S. bioeconomy encompasses economic activity derived from the life sciences, particularly in biotechnology and biomanufacturing. The sector plays an important role in driving national growth and innovation. Given its broad reach across industries, impact on  job creation, potential for technological advancements, and requirement for global  competitiveness, the U.S. bioeconomy is a critical sector for U.S. policymakers to support. With continued development and growth, the U.S. bioeconomy promises not only economic benefits, but also strengthens national security, health outcomes, and environmental sustainability for the country.

Ongoing advancements in biotechnology, including artificial intelligence and automation, have accelerated the growth of the bioeconomy, making the sector both globally competitive and an important domestic economic sector. In 2023, the U.S. bioeconomy supported nearly 644,000 domestic jobs, contributed $210 billion to the GDP, and generated $49 billion in wages. Biomanufactured products within the bioeconomy span multiple categories (Figure 1). Growth here will drive future economic development and address societal challenges, making the bioeconomy  a key priority for government investment and strategic focus.

Figure 1. Bioeconomy Valorization Cascade

Biomanufactured products span a wide range of categories, from pharmaceuticals and chemicals, which require small volumes of biomass but yield high-value products, to energy and heat, which require larger volumes of biomass but result in lower-value products. Additionally, there are common infrastructure synergies, bioprocesses, and complementary input-output relationships that facilitate a circular bioeconomy within bioproduct manufacturing. Source: https://edepot.wur.nl/407896

An important driving force for the U.S. bioeconomy is biotechnology and biomanufacturing innovation. However, bringing biotechnologies to market requires substantial investment, capital, and most importantly, time. Unlike other technology sectors which see returns on investment within a short period of time, often, there is a misalignment between scientific and capitalistic expectations. Many biotechnology based companies rely on venture capital, a form of private equity investments, to finance their operations. However, venture capitalists (VCs) typically operate on short return on investment timelines, which may not align with the longer development cycles characteristic of the biotechnology sector (Figure 2). Additionally, the need for large-scale and the high capital expenditures (CAPEX) required for commercially profitable production, along with the low-profit margins in high-volume commodity production, create further barriers to obtaining investment. While this misalignment is not universal, it remains a challenge for many biotech startups.

The U.S. government has implemented several programs to address the financing void that often arises during the biotechnology innovation process. These include the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, which provide phased funding across all Technology Readiness Levels (TRLs); the DOE Loan Program Office, which offers debt financing for energy-related innovations; the DOE Office of Clean Energy Demonstrations which provides funding for demonstration-scale projects that provide proof of concept; and the newly established Office of Strategic Capital (OSC) within the DOD (as outlined in the FY24 National Defense Authorization Act), which is tasked with issuing loans and loan guarantees to stimulate private investment in critical technologies. An example is the office’s new Equipment Loan Financing through OSC’s Credit Program.

Figure 2. Representative Biotechnology & Non-Biotechnology Development Cycle Timeline

Biotechnology development timelines typically take around ~10+ years to complete and reach the market due to longer R&D and Demonstration & Scale-Up phases, while non-biotechnology development timelines are generally much shorter, averaging around ~5+ years.

While these efforts are important, they are insufficient on their own to de-risk the sector to the degree which is needed to realize the full potential of the U.S. bioeconomy. To effectively support the biotechnology innovation pipeline at critical stages, the government must explore and implement additional financial mechanisms that attract more private investment and mitigate the inherent risks associated with biotechnology innovation. Building on existing resources like the Regional Technology and Innovation Hubs, NSF Regional Innovation Engines, and Manufacturing USA Institutes, help stimulate private sector investment and are crucial for strengthening the nation’s economic competitiveness. 

The newly established Office of Strategic Capital (OSC) within the DOD is well-positioned to enhance resilience in critical sectors for national security, including biotechnology and biomanufacturing, through large-scale investments. Biotechnology and biomanufacturing inherently require significant CAPEX, expenses related to the purchase, upgrade, or maintenance of physical assets. This requires substantial amounts of strategic and concessional capital to de-risk and accelerate the biomanufacturing process. By creating, implementing, and leveraging various financial incentives and resources, the Office of Strategic Capital can help build the robust infrastructure necessary for private sector engagement.

To achieve this, the U.S. government should create the Bioeconomy Finance Program (BFP) within the OSC, specifically tasked with enabling and de-risking the biotechnology and biomanufacturing sectors through financial incentives and programs. The BFP should focus on different levels of funding based on the time required to scale, addressing potential ‘Valleys of Death’ that occur during the biomanufacturing and biotechnology innovation process. These funding levels would target short-term (1-2 years) scale-up hurdles to accelerate the biotechnology and biomanufacturing process, as well as long-term (3-5 years) scale-up challenges, providing transformative funding mechanisms that could either make or break entire sectors.

In addition to the federal programs within the BFP to de-risk the sector, states and regions must also make substantial investments and collaborate with federal efforts to accelerate biomanufacturing and biotechnology ecosystems within their own areas. While the federal government can provide a top-down strategy, regional efforts are critical for supporting the sector with bottom-up strategies that complement and align with federal investments and programs, ultimately enabling a sustainable and competitive biotechnology and biomanufacturing industry regionally. To facilitate this, regions should develop and implement state-wide investment initiatives like resource analysis, infrastructure programs, and a cohesive, long-term strategy focused on public-private partnerships. The federal government can encourage these regional efforts by ensuring continued funding for biotechnology hubs and creating additional opportunities for federal investment in the future.

Plan of Action

To strengthen and increase the competitiveness of the U.S. bioeconomy, a coordinated approach is needed that combines federal leadership with state-level action. This includes establishing a dedicated Bioeconomy Finance Program within the Office of Strategic Capital to create targeted financial mechanisms, such as loan programs, tax incentives, and volume guarantees. Additionally, states must be empowered to support commercial-scale biomanufacturing and infrastructure development, leveraging tech hubs, cross-regional partnerships, and building public-private partnerships to build capacity and foster innovation nationwide.

Recommendation 1. Establish and Fund a Bioeconomy Finance Program

Congress, in the next National Defense Authorization Act, should codify the Office of Strategic Capital (OSC) within DOD and authorize the creation of a Bioeconomy Finance Program (BFP) within the OSC to provide centralized federal structure for addressing financial gaps in the bioeconomy, thereby increasing productivity and competitiveness globally. In 2024, Congress expanded the OSCs mission to offer financial and technical support to entities within its 31 ‘Covered Technology Categories,’ including biotechnology and biomanufacturing. Additionally, in order to build resilience in the sector and maintain a competitive advantage globally while also strengthening national security, these substantial expenditures should be housed within the OSC. Establishing the BFP within the OSC at the DOD would allow for a targeted focus on these critical sectors, ensuring long-term stability and resilience against political shifts. 

The DOD and OSC should leverage its own funding as well as its existing partnership with the Small Business Administration to direct $1 billion to set up the BFP to create and implement initiatives aimed at de-risking the U.S. bioeconomy. The Bioeconomy Finance Program should work closely with relevant federal agencies, such as the DOE, Department of Agriculture (USDA), and the Department of Commerce (DOC), to ensure a long-term cohesive strategy for financing bioeconomy innovation and biomanufacturing capacity.

Recommendation 2. Task the Bioeconomy Finance Program with Key Initiatives

A key element of the OSC’s mission and investment strategy is to provide financial incentives and support to entities within its 31 ‘Core Technology Categories’. By having BFP design and manage these financial initiatives for the biotechnology and biomanufacturing sectors, the OSC can leverage lessons from similar programs, such as the DOE’s loan program, to address the unique needs of these critical industries, which are essential for national security and economic growth.

Currently, the OSC has launched a credit program for equipment financing. While this is a necessary first step in fulfilling the office’s mission, the program is open to all 31 ‘Core Technology Categories’, resulting in broad, dilutive funding. To accelerate the bioeconomy and reduce risks in biotechnology and biomanufacturing, it is crucial to allocate resources specifically to these sectors. Therefore, BFP should take the lead in several key financial initiatives to support the growth of the bioeconomy, including:

Loan Programs

The BFP should develop specific biotechnology enabling loan programs, in addition to the new equipment loan financing program run by the OSC. These loan programs should be modeled after those in the DOE LPO, focusing on biomanufacturing scale-up, technology transfer, and overcoming financing gaps that hinder commercialization.

Example loan programs:

• DOE Title 17 Clean Energy Financing Program
• USDA Business & Industry Loan Guarantee
• Solar Foods EU Grant/Loan

Tax Incentives

The BFP office should create tax incentives tailored to the bioeconomy, such as, transferable investment  and production tax credits. For example, the 45V tax credit for production of clean hydrogen could serve as a model for similar incentives aimed at other bioproducts.

Example tax incentives:

• The Inflation Reduction Act’s transferable tax credits are the gold standard for this category.
  • 45X Advanced Manufacturing Production Credit
  • 45U Nuclear Power Production Tax Credit
  • 45Z Clean Fuel Production Credit

Volume Guarantees & Procurement Support

To mitigate risks in biomanufacturing, the office should establish volume guarantees for various bioproducts, offering financial assurance to manufacturers and encouraging private sector investment. An initial assessment should be conducted to identify which bioproducts are best suited for such guarantees. Additionally, the office should explore the possibility of procurement programs to increase government demand for bio-based products, further incentivizing industry growth and innovation. This effort should be undertaken in coordination with the USDA’s BioPreferred Program to minimize redundancy and to create a cohesive procurement strategy. In addition, the BFP should look to the procurement innovations promoted by the Office of Federal Procurement Policy to find solutions for forward funding to create a functioning market.

Example Volume Guarantees & Procurement Support:

• Heavy Forging Press Infrastructure Lease Agreement
• NASA and USAF buying Fairchild semiconductors in advance of needing them, and overbought performance
• Advance Market Commitments
• Joint Venture Partnerships
• Other Transaction Authorities

Recommendation 3. Develop Pipeline Programs to Address Financial and Time Horizon Needs

Utilizing the key initiatives highlighted above, the BFP should create a two-tiered financial mechanisms pipeline and program to address both the short-term and long-term financial needs. The different financial levels could potentially include:

• Level 1 – Short Term Scale-Up (1-2 years) Programs 
  • Subsidized cost of electricity and other utilities (waste, wastewater treatment, natural gas, energy, etc.)
  • Funding for demonstration-scale projects and early-stage engineering development. Similar to the DOEs Office of Clean Energy Demonstrations or the DODs’ Defense Industrial Base Consortium round one $1-2M engineering grants)
  • Tax holidays for corporate taxes and property taxes
  • Allowing accelerated depreciation to reduce tax liabilities
  • Land grants or subsidies for manufacturing assets
  • Fast-track permitting and site preparation to avoid long waits
  • Labor and workforce subsidies
  • Removal of export duties on products created in the U.S. and shipped overseas

• Level 2 – Long Term Scale-Up (3-5 years) Programs
  • Large-scale transferable tax credits (either production or investment tax credits) for manufacturing. Similar to the tax credits seen in the Inflation Reduction Act for clean energy.
  • Large-scale manufacturing grants
  • Large-scale, low-interest manufacturing loans and loan guarantees
  • Government procurement contracts or commitment for offtake, such as partial/full volume guarantees
  • Government direct or indirect equity investments in biomanufacturing and biotechnology innovations

Recommendation 4.  State-Level Initiatives, Infrastructure Development, and Public-Private Partnerships

While federal efforts are crucial, a bottom-up approach is needed to support biomanufacturing and the bioeconomy at the state level. The federal government can support these regional activities by providing targeted funding, policy guidance, and financial incentives that align with regional priorities, ensuring a coordinated effort toward industry growth. States should be encouraged to complement federal initiatives by developing programs that support commercial-scale biomanufacturing. Key actions include:

• State-Level Bioeconomy Resource Analysis: Each state and region should conduct their own analysis to understand the bioeconomy resources at their disposal and determine what relevant resources they would need to establish or strengthen state or regional bioeconomies. Identifying  these resources will help the nation understand its true bioeconomic potential by understanding where certain biomass is contained, what facilities are available and needed to develop an economically sustainable bioeconomy, and create data to better understand the economic return on investment.
  • Once the analysis is completed, States should collaborate with federal agencies like the DOE, DOC, and Economic Development Administration (EDA) to create and apply for specialized grants for commercial-scale biomanufacturing facilities based off of these analyses. Grants should prioritize non-pharmaceutical biomanufacturing to expand the scope of bioeconomy growth beyond traditional sectors. 

• Utility Infrastructure Grants: Another critical area is the creation of utility infrastructure needed to support biomanufacturing, such as wastewater treatment and electricity infrastructure. States should receive targeted funding for these infrastructure projects, which are essential for scaling up production. States should take these targeted funds and establish their own granting mechanism to build necessary, regional infrastructure that is needed long-term to support the U.S. bioeconomy.

• Tech Hub Partnerships: States should leverage existing tech hubs to serve as centers for innovation in bioeconomy technologies. These hubs, which are already positioned in regions with high technological readiness, can be incentivized to partner with other regions that may not yet have robust tech ecosystems. The goal is to create a collaborative, cross-regional network that fosters knowledge-sharing and builds capacity across the country.

• Foster Public-Private Partnerships (PPP): To ensure the success and sustainability of these initiatives, states should actively foster PPPs that bring together government, industry leaders, and academic institutions. These partnerships can help align private sector investment with public goals, enhance resource sharing, and accelerate the commercialization of bioeconomy technologies. By engaging in collaborative R&D, sharing infrastructure costs, and co-developing new biotechnologies, PPPs will play a crucial role in driving innovation and economic growth in the bioeconomy sector. In addition to fostering PPPs, regions should proactively work on creating models that enable these partnerships to become self-sustaining, helping to mitigate potential financial pitfalls if partners drop out of the partnership. By not only creating PPPs, but also ensuring they become fully independent over time, the associated risks with PPPs decrease significantly.

By addressing these steps at both the federal and state levels, the U.S. can create a robust, scalable framework for financing biomanufacturing and the broader bioeconomy, supporting the transition from early-stage innovation to commercial success and ensuring long-term economic competitiveness. A good example of how this approach works is the DOE Loan Program Office, which collaborates with state energy financing institutions. This partnership has successfully supported various projects by leveraging both federal and state resources to accelerate innovation and drive economic growth. This model makes sense for biomanufacturing and biotechnology within the BFP in the OSC, as it ensures coordination between federal and state efforts, de-risks the sector, and facilitates the scaling of transformative technologies.

Conclusion

Biotechnology innovation and biomanufacturing are critical components of the U.S. bioeconomy which drives innovation, economic growth, and global competitiveness, but these sectors face significant challenges due to the misalignment of development timelines and investment cycles. The sector’s inherent risks and long development processes create funding gaps, hindering the commercialization of vital biotechnologies and products. These challenges, including the ‘Valleys of Death,’ could stifle innovation, slow down progress, and result in the U.S. losing its global leadership in biotechnology if left unaddressed.

To overcome these obstacles, a coordinated and comprehensive approach to de-risk the sector is necessary. The establishment of the Bioeconomy Finance Program (BFP)  within the DOD’s Office of Strategic Capital (OSC) offers a robust solution by providing targeted financial incentives, such as loans, tax credits, and volume guarantees, designed to de-risk the sector and attract private investment. These financial mechanisms would address both short-term and long-term scale-up needs, helping to bridge funding gaps and accelerate the transition from innovation to commercialization. Furthermore, building on existing government resources, alongside fostering state-level initiatives such as infrastructure development, and public-private partnerships, will create a holistic ecosystem that supports biotechnology and biomanufacturing at every stage and will substantially de-risk the sector. By empowering regions to develop their own bioeconomy strategies and leverage local federal government programs, like the EDA Tech Hubs, the U.S. can create a sustainable, scalable framework for growth. By taking these steps, the U.S. can strengthen both its economic position but also lead the world in development of transformative biotechnologies.

The U.S. should create a new non-governmental Innovation Accelerator modeled after the successful In-Q-Tel program to invest in small and mid-cap companies creating technologies that address critical needs of the United States. Doing so would directly address the bottleneck in our innovation pipeline that limits innovative companies from bringing their products to market. 

Challenge and Opportunity 

While the federal government funds basic, early-stage R&D, it leaves product development and commercialization to the private sector. This paradigm has created a so-called innovation Valley of Death: a lack of capital support for the transition to early commercialization, and one that  stalls economic growth for many innovation-driven sectors. The U.S. currently leads the world in the formation of companies, but the limitations on capital sources artificially restrict growth. For example, the U.S. currently leads the world in biotechnology and biomedical innovation. The U.S. market alone is worth $600B, and is projected to exceed $1.5 trillion.  However, international rivals  are catching up: China is projected to close the biotechnology innovation gap in 2028. The U.S. must act quickly to protect its lead.

Typically, early and mid-stage innovations are too immature for private capital investors because they present an outsized risk. In addition, private capital tends to be more conservative in rough economic times, which further dries up the innovation pipeline. Investment “fads” tend to starve other fields of capital investment for potentially years at a time So, though the U.S. government provides significant early-stage discovery funding for innovation through its various agencies, the grant lifecycle is such that after the creation and initial development of new technologies, there are few mechanisms for continued support to drive products to market. 

It is this period – after R&D but before commercial demonstration – that creates a substantial bottleneck for entrepreneurs where their work is too advanced for the usual government research and development grant funding but not developed enough to draw private investment. Existing SBIR and STTR grant programs that the government provides for this purpose are typically too small to significantly advance such innovations, while the application process is too cumbersome and slow to draw the interests of many companies. As a result, small businesses created around these technologies often fail because of funding challenges, rather than any faults of the innovations they are developing. 

The federal government, therefore, has an opportunity to make the path from lab to market smoother by establishing a mechanism for supporting smaller companies developing innovative products that will substantially improve the lives of Americans. A new U.S. Innovation Accelerator will provide R&D funding to promising companies to accelerate innovations critical to the U.S. by de-risking them as they move toward private sector funding and commercialization. 

Creating the U.S. Innovation Accelerator 

We propose creating a new federally guided entity modeled on In-Q-Tel, the government funded not-for-profit venture capital firm that invests in companies that are developing technologies that can be used by intelligence agencies. Similar to In-Q-Tel, the U.S. Innovation Accelerator would operate independently of the government, but leverage federal investments in research and development to ensure that promising new technologies make it to market. 

By having the organization live outside of the government, it will be able to pay staff a wage that is commensurate with their experience and draw top talent interested in driving innovation across the R&D spectrum. The organization would invest in the development of technology companies, and would partner with private capital sources to help develop critical technologies that are too risky for private capital entities to fund on their own. Such capital would allow innovation to flourish. In exchange, the organization could establish requirements for keeping such companies, and their manufacturing operations, in the U.S. for some period after receiving public funding (10 years, for example) to prevent the offshoring of technologies that are developed with public dollars. The agency would use a variety of funding vehicles to support companies to best match their needs and increase their chances of success. 

Scope

The new U.S. Innovation Accelerator could be established as a sector-specific entity, (for example as biotechnology and healthcare-focused fund), or it could include a series of portfolios that invest in companies across the innovation spectrum. Both approaches have merits worth exploring: a narrower biomedical fund would have the benefit of quickly deploying capital to accelerate key areas of strategic U.S. interest while proving the concept and setting the stage to expand to other sectors of the economy; alternatively, if a larger pool of funding is available initially, a broader investment portfolio would allow for targeted investments across sectors ranging from biotechnology and agriculture to advanced materials and energy.  

Sources of Capital 

The U.S. Innovation Accelerator can be funded in several ways to create a robust investment vehicle for advancing biotechnology and healthcare innovation. Two potential models include a publicly-funded revolving fund, similar to In-Q-Tel, while the other would draw capital from retirement and pension funds providing a return on investment to voluntary investors. 

Appropriations driven revolving fund. Like In-Q-Tel, Congress could kick start the Innovation Accelerator though direct appropriations. This annual investment could be curtailed and repaid to the treasury once the fund starts to realize returns on the investments it makes.

Thrift Savings Plan allocations. The federal employee retirement savings plan, the Thrift Savings Plan, holds approximately $700 billion in assets across various investment funds. By allowing for voluntary investment allocation in the Innovation Fund by federal employees, even a small percentage of TSP assets could provide billions in initial capital. This allocation would be structured as part of the TSP’s broader investment strategy, through the creation of a new specialized SBF fund option for participants.

U.S. State & Public Pension Plans. State and local government pension plans hold assets totaling roughly $6.25 trillion. The Innovation Fund could work with state pension administrators to create investment vehicles that align with their risk-return profiles and support both financial and social impact goals. These would be made available to plan participants in a similar manner to the TSP or through more traditional allocation. 

Reforming the SBIR/STTR Programs. The SBIR and STTR programs represent 3.2% of the total Federal R&D budget for 11 agencies, but struggle to attract suitable applicants. This is not because there is a lack of need in early-stage innovation. Typically, these grants are judged and awarded by program managers that have little or no private sector experience, take too long from application to award, and provide insufficient funds for many companies to consider them. Those dollars could instead be allocated to the Innovation Accelerator program, and invested in more promising small businesses through a streamlined program that creates a revolving fund through returns on initial investment that can be then reinvested in additional promising companies. The program now uses ceilings for different phases of SBIR grants. These phases are artificial, and do not reflect the reality of the needs of different types of companies and thus should be eliminated and replaced with needs-based funding. USG agencies can issue technology priority guidance to the U.S. Innovation Accelerator and completely off-load the burden of having to run multiple SBIR programs. 

Part of the proposed US Sovereign Wealth Fund. In February of this year, President Trump issued an Executive Order directing the Secretaries of Commerce and Treasury to develop plans for the creation of a sovereign wealth fund. The plan will include recommendations for funding mechanisms, investment strategies, fund structure, and a governance model. Such funds exist across many countries as a mechanism for amplifying the financial return on the nation’s assets and to leverage those returns for strategic benefit and economic growth. We propose that the U.S. Innovation Accelerator falls squarely in the remit of a sovereign fund and that the fund could serve as a sustainable source of capital to fund the development of innovative companies and products that address critical national challenges and directly benefit Americans in tangible ways. 

Structure and operations

The Innovation Accelerator program will be structured similar to a lean private venture capital entity, with oversight from the U.S. government to inform strategic deployment of capital towards innovative companies that address unmet national needs. As an  independent, non-profit organization or public benefit corporation (PBC), overhead can be kept low, and it can be guided by a small entrepreneurial Board of Directors representing innovative industries and investment professionals to ensure that the organization stays on mission. Further, the organization should collaborate with federal agencies to identify areas of national need and ensure that promising companies that originate from other federal research and development programs will have the capital necessary to bring their innovations to market, thus ensuring a stable innovation pipeline and addressing a longstanding bottleneck that has driven American companies to seek foreign capital or to offshore their operations. 

The professional investment team would include expertise in a broad set of domains and with a proven track record of commercial success. The organization would have a high degree of autonomy but maintain alignment with national technology priorities and competitive strategy. Transparency and accountability will be paramount and include constant full public accounting of all investments and strategy. 

The primary objective of the Innovation Accelerator will be to deliver game changing innovations that generate exceptional returns on investment while supporting the development of strategically important technologies. The U.S. Innovation Accelerator will also insulate domestic innovation from the delays and inefficiency caused by the private sector funding cycle.

Conclusion

The U.S. Innovation Accelerator would address a critical gap in the current U.S. innovation pipeline that was created as an artifact of the way we fund research. Most public dollars are dedicated to early-stage research but development and commercialization are normally left to the private sector, which is vulnerable to macroeconomic trends that can stall innovation for years. The U.S. Innovation Accelerator would open up that bottleneck by driving innovation and economic growth while addressing critical national needs. Because the U.S. Innovation Accelerator would exist outside of the federal government, it can be created without an act of Congress. The President could direct his administration through an executive action to develop plans and create the U.S. Innovation Accelerator as either part of the sovereign fund he has proposed or independent of that action. However, to get it initially funded and backed by the U.S. government, (see funding mechanisms above), Congress would have to appropriate dollars through an existing federal agency. Part of the charter for establishing the U.S. Innovation Accelerator could be repayment of the initial investments back to the U.S. Treasury from fund returns.

State and local failure to appropriately integrate flood risk into planning is a massive national liability – and a massive contributor to national debt. Though flooding is well recognized as a growing problem, our nation continues to address this threat through reactive, costly disaster responses instead of proactive, cost-saving investments in resilient infrastructure.

President Trump’s Executive Order (EO) on Achieving Efficiency Through State and Local Preparedness introduces a nationally strategic opportunity to rethink how state and local governments manage flood risk. The EO calls for the development and implementation of a National Resilience Strategy and National Risk Register, emphasizing the need for a decentralized approach to preparedness. To support this approach, the Trump Administration should mandate that state governments establish and fund flood infrastructure vulnerability assessment programs as a prerequisite for accessing federal flood mitigation funds. Modeled on the Resilient Florida Program, this policy would both improve coordination among federal, state, and local governments and yield long-term cost savings.

Challenge and Opportunity 

President Trump’s aforementioned EO signals a shift in national infrastructure policy. The order moves away from a traditional “all-hazards” approach to a more focused, risk-informed strategy. This new framework prioritizes proactive, targeted measures to address infrastructure risks. It also underscores the crucial role of state and local governments in enhancing national security and building a more resilient nation—emphasizing that preparedness is most effectively managed at subnational levels, with the federal government providing competent, accessible, and efficient support.

A core provision of the EO is the creation of a National Resilience Strategy to guide efforts in strengthening infrastructure against risks. The order mandates a comprehensive review of existing infrastructure policies, with the goal of recommending risk-informed approaches. The EO also directs development of a National Risk Register to document and assess risks to critical infrastructure, thereby providing a foundation for informed decision-making in infrastructure planning and funding.

In carrying out these directives, the risks of flooding on critical infrastructure must not be overlooked. The frequency and cost of weather- and flood-related disasters are increasing nationwide due to a combination of heightened exposure (infrastructure growth due to population and economic expansion) and vulnerability (susceptibility to damage). As shown in Figure 1, the cost of responding to disaster events such as flooding, severe storms, and tropical cyclones has risen exponentially since 1980, often reaching hundreds of billions of dollars annually.

Figure 1. United States Billion-Dollar Disaster Events – Flooding, Severe Storm, and Tropical Cyclone, 1980-2024 

Financial implications for the U.S. budget have also grown. As illustrated in Figure 2, federal appropriations to the Disaster Relief Fund (DRF) have surged in recent decades, driven by the demand for critical response and recovery services.

Figure 2. U.S. Nominal Dollar Disaster Relief Appropriations, FY 1964-FY2023 

Infrastructure across the United States remains increasingly vulnerable to flooding. Critical infrastructure – including roads, utilities, and emergency services – is often inadequately equipped to withstand these heightened risks. Many critical infrastructure systems were designed decades ago when flood risks were lower, and have not been upgraded or replaced to account for changing conditions. The upshot is that significant deficiencies, reduced performance, and catastrophic economic consequences often result when floods occur today.

The costs of bailing out and patching up this infrastructure time and time again under today’s flood risk environment have become unsustainable. While agencies like the Federal Emergency Management Agency (FEMA), National Oceanic and Atmospheric Administration (NOAA), and U.S. Army Corps of Engineers (USACE) maintain and publish extensive flood risk datasets, no federal requirements mandate state and local governments to integrate this data with critical infrastructure data through flood infrastructure vulnerability assessments. This gap in policy demonstrates a disconnect between federal, state, and local efforts to protect critical infrastructure from flooding risks.

The only way to address this disconnect, and the recurring cost problem, is through a new paradigm – one that proactively integrates flood risk management and infrastructure resilience planning through mandatory, comprehensive flood infrastructure vulnerability assessments (FIVAs).

Multiple state programs demonstrate the benefits of such assessments. Most notably, the Resilient Florida Program, established in 2021, represents a significant investment in enhancing the resilience of critical infrastructure to flooding, rainfall, and extreme storms. Section 380.093 of the Florida Statutes requires all municipalities and counties across the state to conduct comprehensive FIVAs in order to qualify for state flood mitigation funding. These assessments identify risks to publicly owned critical and regionally significant assets, including transportation networks; evacuation routes; critical infrastructure; community and emergency facilities; and natural, cultural, and historical resources. To support this requirement, the Florida Legislature allocated funding to ensure municipalities and counties could complete the FIVAs. The findings then quickly informed statewide flood mitigation projects, with over $1.8 billion invested between 2021 and 2024 to reduce flooding risks across 365 implementation projects. 

To support the National Resilience Strategy and Risk Register, the Trump Administration should consider leveraging Florida’s model on a national scale. By requiring all states to conduct FIVAs, the federal government can limit its financial liability while advancing a more efficient and effective model of flood resilience that puts states and localities at the fore.

Rather than relying on federal funds to conduct these assessments, the federal government should implement a policy mandate requiring state governments to establish and fund their own FIVA programs. This mandate would diffuse federal responsibility of identifying flood risks to the state and local levels, ensuring that the assessments are tailored to the unique geographic conditions of each region. By decentralizing flood risk management, states can adopt localized strategies that better reflect their specific vulnerabilities and priorities. 

These state-led assessments would, in turn, provide a critical foundation for informed decision-making in national infrastructure planning, ensuring that federal investments in flood mitigation and resilience are targeted and effective. Specifically, the federal government would use the compiled data from state and local assessments to prioritize funding for projects that address the most pressing infrastructure vulnerabilities. This would enable federal agencies to allocate resources more efficiently, directing investments to areas with the highest risk exposure and the greatest potential for cost-effective mitigation. A standardized federal FIVA framework would ensure consistency in data collection, risk evaluation, and reporting across states. This would facilitate better coordination among federal, state, and local entities while improving integration of flood risk data into national infrastructure planning.

By implementing this strategy, the Trump Administration would reinforce the principle of shared responsibility in disaster preparedness and resilience, encouraging state and local governments to take the lead in safeguarding critical infrastructure. State-led FIVAs would also deliver significant long-term cost savings, given that investments in resilient infrastructure yield a substantial return on investment. (Studies show a 1:4 ratio of return on investment, meaning every dollar spent on resilience and preparedness saves $4 in future losses.) Finally, requiring FIVAs would build a more resilient nation, ensuring that communities are better equipped to withstand the increasing challenges posed by flooding and that federal investments are safeguarded. 

Plan of Action

The Trump Administration can support the National Resilience Strategy and National Risk Register by taking the following actions to promote state-led development and adoption of FIVAs. 

Recommendation 1. Create a Standardized FIVA Framework.

President Trump should direct his Administration, through an interagency FIVA Task Force, to create a standardized FIVA framework, drawing on successful models like the Resilient Florida Program. This framework will establish consistent methodologies for data collection, risk evaluation, and reporting, ensuring that assessments are both thorough and adaptable to state and local needs. An essential function of the task force should be to compile and review all existing federally maintained datasets on flood risks, which are maintained by agencies such as FEMA, NOAA, and USACE. By centralizing this information and providing streamlined access to high-quality, accurate data on flood risks, the task force will reduce the burden on state and local agencies.

Recommendation 2. Create Model Legislation.

The FIVA Task Force, working with leading organizations such as the American Flood Coalition (AFC), and Association of State Floodplain Managers (ASFPM), should create model legislation that state governments can adapt and enact to require local development and adoption of FIVAs. This legislation should outline the requirements for conducting assessments, including which infrastructure types need to be evaluated, what flood risk scenarios need to be considered, and how the findings must be used to guide infrastructure planning and investments.

Recommendation 3. Spur Uptake and Establish Accountability and Reporting Mechanisms.

Once the FIVA framework and model legislation are created, the Administration should require states to enact FIVA laws in order to be eligible for receiving federal infrastructure funding. This requirement should be phased in on clear and feasible timelines, with clear criteria for what provisions FIVA laws must include. Regular reporting requirements should also be established, whereby states must provide updates on their progress in conducting FIVAs and integrating findings into infrastructure planning. Updates should be captured in a public tracking system to ensure transparency and hold states accountable for completing assessments on time. Federal agencies should evaluate federal infrastructure funding requests based on the findings from state-led FIVAs to ensure that investments are targeted at areas with the highest flood risks and the greatest potential for resilience improvements.

Recommendation 4. Use State and Local Data to Shape Federal Policy.

Ensure that the results of state-led FIVAs are incorporated into future updates of the National Resilience Strategy and Risk Register, as well as other relevant federal policy and programs. This integration will provide a comprehensive view of national infrastructure risks and help inform federal decision-making and resource allocation for disaster preparedness and response.

Conclusion

The Trump Administration’s EO on Achieving Efficiency Through State and Local Preparedness opens the door to comprehensively rethink how we as a nation approach planning, disaster risk management, and resilience. Scaling successful approaches from states like Florida can deliver on the goals of the EO in at least five ways:

1. Empowering state and local governments to take the lead in managing flood risks, ensuring that assessments and strategies are more reflective of local needs and conditions.
2. Distributing the responsibility for identifying and mitigating flood risks across all levels of government, reducing the burden on the federal government and allowing more tailored, efficient responses.
3. Reducing disaster response costs by prioritizing proactive, risk-informed planning over reactive recovery efforts, leading to long-term savings.
4. Strengthening infrastructure resilience by making vulnerability assessments a condition for federal funding, driving investments that protect communities from flooding risks.
5. Fostering greater accountability at the state and local levels, as governments will be directly responsible for ensuring that infrastructure is resilient to flooding, leading to more targeted and effective investments.

“Melbourne Florida Flooding” by highlander411 is licensed under CC BY 2.0.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.

Deriving insights from data is essential for effective governance. However, collecting and sharing data—if not managed properly—can pose privacy risks for individuals. Current scientific understanding shows that so-called “anonymization” methods that have been widely used in the past are inadequate for protecting privacy in the era of big data and artificial intelligence. The evolving field of Privacy-Enhancing Technologies (PETs), including differential privacy and secure multiparty computation, offers a way forward for sharing data safely and responsibly.

The administration should prioritize the use of PETs by integrating them into data-sharing processes and strengthening the executive branch’s capacity to deploy PET solutions.

Challenge and Opportunity

A key function of modern government is the collection and dissemination of data. This role of government is enshrined in Article 1, Section 2 of the U.S. Constitution in the form of the decennial census—and has only increased with recent initiatives to modernize the federal statistical system and expand evidence-based policymaking. The number of datasets itself has also grown; there are now over 300,000 datasets on data.gov, covering everything from border crossings to healthcare. The release of these datasets not only accomplishes important transparency goals, but also represents an important step toward advancing American society fairer, as data are a key ingredient in identifying policies that benefit the public. 

Unfortunately, the collection and dissemination of data comes with significant privacy risks. Even with access to aggregated information, motivated attackers can extract information specific to individual data subjects and cause concrete harm. A famous illustration of this risk occurred in 1997 when Latanya Sweeney was able to identify the medical record of then-Governor of Massachusetts, William Weld, from a public, anonymized dataset. Since then, the power of data re-identification techniques—and incentives for third parties to learn sensitive information about individuals—have only increased, compounding this risk. As a democratic, civil-rights respecting nation, it is irresponsible for our government agencies to continue to collect and disseminate datasets without careful consideration of the privacy implications of data sharing.

While there may appear to be an irreconcilable tension between facilitating data-driven insight and protecting the privacy of individual’s data, an emerging scientific consensus shows that Privacy-Enhancing Technologies (PETs) offer a path forward. PETs are a collection of techniques that enable data to be used while tightly controlling the risk incurred by individual data subjects. One particular PET, differential privacy (DP), was recently used by the U.S. Census Bureau within their disclosure avoidance system for the 2020 decennial census in order to meet their dual mandates of data release and confidentiality. Other PETs, including variations of secure multiparty computation, have been used experimentally by other agencies, including to link long-term income data to college records and understand mental health outcomes for individuals who have earned doctorates. The National Institute of Standards and Technology (NIST) has produced frameworks and reports on data and information privacy, including PETs topics such as DP (see Q&A section). However, these reports still lack a comprehensive and actionable framework on how organizations should consider, use and deploy PETs in organizations. 

As artificial intelligence becomes more prevalent inside and outside government and relies on increasingly large datasets, the need for responsible data sharing is growing more urgent. The federal government is uniquely positioned to foster responsible innovation and set a strong example by promoting the use of PETs. The use of DP in the 2020 decennial census was an extraordinary example of the government’s capacity to lead global innovation in responsible data sharing practices. While the promise of continuing this trend is immense, expanding the use of PETs within government poses twin challenges: (1) sharing data within government comes with unique challenges—both technical and legal—that are only starting to be fully understood and (2) expertise on using PETs within government is limited. In this proposal, we outline a concrete plan to overcome these challenges and unlock the potential of PETs within government.

Plan of Action

Using PETs when sharing data should be a key priority for the executive branch. The new administration should encourage agencies to consider the use of PETs when sharing data and build a United States DOGE Service (USDS) “Responsible Data Sharing Corps” of professionals who can provide in-house guidance around responsible data sharing.

We believe that enabling data sharing with PETs requires (1) gradual, iterative refinement of norms and (2) increased capacity in government. With these in mind, we propose the following recommendations for the executive branch.

Strategy Component 1. Build consideration of PETs into the process of data sharing

Recommendation 1. NIST should produce a decision-making framework for organizations to rely on when evaluating the use of PETs.

NIST should provide a step-by-step decision-making framework for determining the appropriate use of PETs within organizations, including whether PETs should be used, and if so, which PET and how it should be deployed. Specifically, this guidance should be at the same level of granularity as NIST Risk Management Framework for Cybersecurity. NIST should consult with a range of stakeholders from the broad data sharing ecosystem to create this framework. This includes data curators (i.e., organizations that collect and share data, within and outside the government); data users (i.e., organizations that consume, use and rely on shared data, including government agencies, special interest groups and researchers); data subjects; experts across fields such as information studies, computer science, and statistics; and decision makers within public and private organizations who have prior experience using PETs for data sharing. The report may build on NIST’s existing related publications and other guides for policymakers considering the use of specific PETs, and should provide actionable guidance on factors to consider when using PETs. The output of this process should be not only a decision, but also a report documenting the execution of decision-making framework (which will be instrumental for Recommendation 3).

Recommendation 2. The Office of Management and Budget (OMB) should mandate government agencies interested in data sharing to use the NIST’s decision-making framework developed in Recommendation 1 to determine the appropriateness of PETs to protect their data pipelines.

The risks to data subjects associated with data releases can be significantly mitigated with the use of PETs, such as differential privacy. Along with considering other mechanisms of disclosure control (e.g., tiered access, limiting data availability), agencies should investigate the feasibility and tradeoffs around using PETs to protect data subjects while sharing data for policymaking and public use. To that end, OMB should require government agencies to use the decision-making framework produced by NIST (in Recommendation 1) for each instance of data sharing. We emphasize that this decision-making process may lead to a decision not to use PETs, as appropriate. Agencies should compile the produced reports such that they can be accessed by OMB as part of Recommendation 3.

Recommendation 3. OMB should produce a PET Use Case Inventory and annual reports that provide insights on the use of PETs in government data-sharing contexts.

To promote transparency and shared learning, agencies should share the reports produced as part of their PET deployments and associated decision-making processes with OMB. Using these reports, OMB should (1) publish a federal government PET Use Case Inventory (similar to the recently established Federal AI Use Case Inventory) and (2) synthesize these findings into an annual report. These findings should provide high-level insights into the decisions that are being made across agencies regarding responsible data sharing, and highlight the barriers to adoption of PETs within various government data pipelines. These reports can then be used to update the decision-making frameworks we propose that NIST should produce (Recommendation 1) and inspire further technical innovation in academia and the private sector.

Strategy Component 2. Build capacity around responsible data sharing expertise 

Increasing in-depth decision-making around responsible data sharing—including the use of PETs—will require specialized expertise. While there are some government agencies with teams well-trained in these topics (e.g., the Census Bureau and its team of DP experts), expertise across government is still lacking. Hence, we propose a capacity-building initiative that increases the number of experts in responsible data sharing across government.

Recommendation 4. Announce the creation of a “Responsible Data Sharing Corps.”

We propose that the USDS create a “Responsible Data Sharing Corps” (RDSC). This team will be composed of experts in responsible data sharing practices and PETs. RDSC experts can be deployed into other government agencies as needed to support decision-making about data sharing. They may also be available for as-needed consultations with agencies to answer questions or provide guidance around PETs or other relevant areas of expertise.

Recommendation 5. Build opportunities for continuing education and training for RDSC members.

Given the evolving nature of responsible data practices, including the rapid development of PETs and other privacy and security best practices, members of the RDSC should have 20% effort reserved for continuing education and training. This may involve taking online courses or attending workshops and conferences that describe state-of-the-art PETs and other relevant technologies and methodologies.

Recommendation 6. Launch a fellowship program to maintain the RDSC’s cutting-edge expertise in deploying PETS.

Finally, to ensure that the RDSC stays at the cutting edge of relevant technologies, we propose an RDSC fellowship program similar to or part of the Presidential Innovation Fellows. Fellows may be selected from academia or industry, but should have expertise in PETs and propose a novel use of PETs in a government data-sharing context. During their one-year terms, fellows will perform their proposed work and bring new knowledge to the RDSC.

Conclusion

Data sharing has become a key priority for the government in recent years, but privacy concerns make it critical to modernize technology for responsible data use to leverage data for policymaking and transparency. PETs such as differential privacy, secure multiparty computation, and others offer a promising way forward. However, deploying PETs at a broad scale requires changing norms and increasing capacity in government. The executive branch should lead these efforts by encouraging agencies to consider PETs when making data-sharing decisions and building a “Responsible Data Sharing Corps” who can provide expertise and support for agencies in this effort. By encouraging the deployment of PETs, the government can increase fairness, utility and transparency of data while protecting itself—and its data subjects—from privacy harms.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.

Federal Advisory Committees (FACs) are the single point of entry for the American public to provide consensus-based advice and recommendations to the federal government. These Advisory Committees are composed of experts from various fields who serve as Special Government Employees (SGEs), attending committee meetings, writing reports, and voting on potential government actions.

Advisory Committees are needed for the federal decision-making process because they provide additional expertise and in-depth knowledge for the Agency on complex topics, aid the government in gathering information from the public, and allow the public the opportunity to participate in meetings about the Agency’s activities. As currently organized, FACs are not equipped to provide the best evidence-based advice. This is because FACs do not meet transparency requirements set forth by GAO: making pertinent decisions during public meetings, reporting inaccurate cost data, providing  official meeting documents publicly available online, and more. FACs have also experienced difficulty with recruiting and retaining top talent to assist with decision making. For these reasons, it is critical that FACs are reformed and equipped with the necessary tools to continue providing the government with the best evidence-based advice. Specifically, advice as it relates to issues such as 1) decreasing the burden of hiring special government employees 2) simplifying the financial disclosure process 3) increasing understanding of reporting requirements and conflict of interest processes 4) expanding training for Advisory Committee members 5) broadening  the roles of Committee chairs and designated federal officials 6) increasing public awareness of Advisory Committee roles 7) engaging the public outside of official meetings 8) standardizing representation from Committee representatives 9) ensuring that Advisory Committees are meeting per their charters and 10) bolstering Agency budgets for critical Advisory Committee issues. 

Challenge and Opportunity

Protecting the health and safety of the American public and ensuring that the public has the opportunity to participate in the federal decision-making process is crucial. We must evaluate the operations and activities of federal agencies that require the government to solicit evidence-based advice and feedback from various experts through the use of federal Advisory Committees (FACs). These Committees are instrumental in facilitating transparent and collaborative deliberation between the federal government, the advisory body, and the American public and cannot be done through the use of any other mechanism. Advisory Committee recommendations are integral to strengthening public trust and reinforcing the credibility of federal agencies. Nonetheless, public trust in government has been waning and efforts should be made to increase public trust. Public trust is known as the pillar of democracy and fosters trust between parties, particularly when one party is external to the federal government. Therefore, the use of Advisory Committees, when appropriately used, can assist with increasing public trust and ensuring compliance with the law. 

There have also been many success stories demonstrating the benefits of Advisory Committees. When Advisory Committees are appropriately staffed based on their charge, they can decrease the workload of federal employees, assist with developing policies for some of our most challenging issues, involve the public in the decision-making process, and more. However, the state of Advisory Committees and the need for reform have been under question, and even more so as we transition to a new administration. Advisory Committees have contributed to the improvement in the quality of life for some Americans through scientific advice, as well as the monitoring of cybersecurity. For example, an FDA Advisory Committee reviewed data and saw promising results for the treatment of sickle cell disease (SCD) which has been a debilitating disease with limited treatment for years. The Committee voted in favor of gene therapy drugs Casgevy and Lyfgenia which were the first to be approved by the FDA for SCD. 

Under the first Trump administration, Executive Order (EO) 13875 resulted in a significant decrease in the number of federal advisory meetings. This  limited agencies’ ability to convene external advisors. Federal science advisory committees met less during this administration than any prior administration, met less than what was required from their charter, disbanded long standing Advisory Committees, and scientists receiving agency grants were barred from serving on Advisory Committees. Federal Advisory Committee membership also decreased by 14%, demonstrating the issue of recruiting and retaining top talent. The disbandment of Advisory Committees, exclusion of key scientific external experts from Advisory Committees, and burdensome procedures can potentially trigger  severe consequences that affect the health and safety of Americans. 

Going into a second Trump administration, it is imperative that Advisory Committees have the opportunity to assist federal agencies with the evidence-based advice needed to make critical decisions that affect the American public. The suggested reforms that follow can work to improve the overall operations of Advisory Committees while still providing the government with necessary evidence-based advice. With successful implementation of the following recommendations, the federal government will be able to reduce administrative burden on staff through the recruitment, onboarding, and conflict of interest processes. 

The U.S. Open Government Initiative encourages the promotion and participation of public and community engagement in  governmental affairs. However, individual Agencies can and should do more to engage the public. This policy memo identifies several areas of potential reform for Advisory Committees and aims to provide recommendations for improving the overall process without compromising Agency or Advisory Committee membership integrity. 

Plan of Action

The proposed plan of action identifies several policy recommendations to reform the federal Advisory Committee (Advisory Committee) process, improving both operations and efficiency. Successful implementation of these policies will  1) improve the Advisory Committee member experience, 2) increase transparency in federal government decision-making, and 3) bolster trust between the federal government, its Advisory Committees, and the public. 

Streamline Joining Advisory Committees

Recommendation 1. Decrease the burden of hiring special government employees in an effort to (1) reduce the administrative burden for the Agency and (2) encourage Advisory Committee members, who are also known as special government employees (SGEs), to continue providing the best evidence-based advice to the federal government through reduced onerous procedures

The Ethics in Government Act of 1978 and Executive Order 12674 lists OGE-450 reporting as the required public financial disclosure for all executive branch and special government employees. This Act provides the Office of Government Ethics (OGE) the authority to implement and regulate a financial disclosure system for executive branch and special government employees whose duties have “heightened risk of potential or actual conflicts of interest”. Nonetheless, the reporting process becomes onerous when Advisory Committee members have to complete the OGE-450 before every meeting even if their information remains unchanged. This presents a challenge for Advisory Committee members who wish to continue serving, but are burdened by time constraints. The process also burdens federal staff who manage the financial disclosure system. 

Policy Pathway 1. Increase funding for enhanced federal staffing capacity to undertake excessive administrative duties for financial reporting.

Policy Pathway 2. All federal agencies that deploy Advisory Committees can conduct a review of the current OGC-450 process, budget support for this process, and work to develop an electronic process that will eliminate the use of forms and allow participants to select dropdown options indicating if their financial interests have changed.  

Recommendation 2. Create and use public platforms such as OpenPayments by CMS to (1) aid in simplifying the financial disclosure reporting process and (2) increase transparency for disclosure procedures

Federal agencies should create a financial disclosure platform that streamlines the process and allows Advisory Committee members to submit their disclosures and easily make updates. This system should also be created to monitor and compare financial conflicts. In addition, agencies that utilize the expertise of Advisory Committees for drugs and devices should identify additional ways in which they can promote financial transparency. These agencies can use Open Payments, a system operated by Centers for Medicare & Medicaid Services (CMS), to “promote a more financially transparent and accountable healthcare system”. The Open Payments system makes payments from medical and drug device companies to individuals, healthcare providers, and teaching hospitals accessible to the public. If for any reason financial disclosure forms are called into question, the Open Payments platform can act as a check and balance in identifying any potential financial interests of Advisory Committee members. Further steps that can be taken to simplify the financial disclosure process would be to utilize conflict of interest software such as Ethico which is a comprehensive tool that allows for customizable disclosure forms, disclosure analytics for comparisons, and process automation.   

Policy Pathway. The Office of Government Ethics should require all federal agencies that operate Advisory Committees to develop their own financial disclosure system and include a second step in the financial disclosure reporting process as due diligence, which includes reviewing the Open Payments by CMS system for potential financial conflicts or deploying conflict of interest monitoring software to streamline the process.

Streamline Participation in an Advisory Committee

Recommendation 3. Increase understanding of annual reporting requirements for conflict of interest (COI)

Agencies should develop guidance that explicitly states the roles of Ethics Officers, also known as Designated Agency Ethics Officials (DAEO), within the federal government. Understanding the roles and responsibilities of Advisory Committee members and the public will help reduce the spread of misinformation regarding the purpose of Advisory Committees. In addition, agencies should be encouraged by the Office of Government Ethics to develop guidance that indicates the criteria for inclusion or exclusion of participation in Committee meetings. Currently, there is no public guidance that states what types of conflicts of interests are granted waivers for participation. Full disclosure of selection and approval criteria will improve transparency with the public and draw clear delineations between how Agencies determine who is eligible to participate. 

Policy Pathway. Develop conflict of interest (COI) and financial disclosure guidance specifically for SGEs that states under what circumstances SGEs are allowed to receive waivers for participation in Advisory Committee meetings.

Recommendation 4. Expand training for Advisory Committee members to include (1) ethics and (2) criteria for making good recommendations to policymakers

Training should be expanded for all federal Advisory Committee members to include ethics training which details the role of Designated Agency Ethics Officials, rules and regulations for financial interest disclosures, and criteria for making evidence-based recommendations to policymakers. Training for incoming Advisory Committee members ensures that all members have the same knowledge base and can effectively contribute to the evidence-based recommendations process.

Policy Pathway. Agencies should collaborate with the OGE and Agency Heads to develop comprehensive training programs for all incoming Advisory Committee members to ensure an understanding of ethics as contributing members, best practices for providing evidence-based recommendations, and other pertinent areas that are deemed essential to the Advisory Committee process.

Leverage Advisory Committee Membership

Recommendation 5. Uplifting roles of the Committee Chairs and Designated Federal Officials

Expanding the roles of Committee Chairs and Designated Federal Officers (DFOs) may assist federal Agencies with recruiting and retaining top talent and maximizing the Committee’s ability to stay abreast of critical public concerns. Considering the fact that the General Services Administration has to be consulted for the formation of new Committees, renewal, or alteration of Committees, they can be instrumental in this change.

Policy Pathway. The General Services Administration (GSA) should encourage federal Agencies to collaborate with Committee Chairs and DFOs to recruit permanent and ad hoc Committee members who may have broad network reach and community ties that will bolster trust amongst Committees and the public. 

Recommendation 6. Clarify intended roles for Advisory Committee members and the public

There are misconceptions among the public and Advisory Committee members about Advisory Committee roles and responsibilities. There is also ambiguity regarding the types of Advisory Committee roles such as ad hoc members, consulting, providing feedback for policies, or making recommendations. 

Policy Pathway. GSA should encourage federal Agencies to develop guidance that delineates the differences between permanent and temporary Advisory Committee members, as well as their roles and responsibilities depending on if they’re providing feedback for policies or providing recommendations for policy decision-making.

Recommendation 7. Utilize and engage expertise and the public outside of public meetings

In an effort to continue receiving the best evidence-based advice, federal Agencies should develop alternate ways to receive advice outside of public Committee meetings. Allowing additional opportunities for engagement and feedback from Committee experts or the public will allow Agencies to expand their knowledge base and gather information from communities who their decisions will affect.

Policy Pathway. The General Services Administration should encourage federal Agencies to create opportunities outside of scheduled Advisory Committee meetings to engage Committee members and the public on areas of concern and interest as one form of engagement. 

Recommendation 8. Standardize representation from Committee representatives (i.e., industry), as well as representation limits

The Federal Advisory Committee Act (FACA) does not specify the types of expertise that should be represented on all federal Advisory Committees, but allows for many types of expertise. Incorporating various sets of expertise that are representative of the American public will ensure the government is receiving the most accurate, innovative, and evidence-based recommendations for issues and products that affect Americans. 

Policy Pathway. Congress should include standardized language in the FACA that states all federal Advisory Committees should include various sets of expertise depending on their charge. This change should then be enforced by the GSA.

Support a Vibrant and Functioning Advisory Committee System

Recommendation 9. Decrease the burden to creating an Advisory Committee and make sure Advisory Committees are meeting per their charters

The process to establish an Advisory Committee should be simplified in an effort to curtail the amount of onerous processes that lead to a delay in the government receiving evidence based advice.

Advisory Committee charters state the purpose of Advisory Committees, their duties, and all aspirational aspects. These charters are developed by agency staff or DFOs with consultation from their agency Committee Management Office. Charters are needed to forge the path for all FACs.

Policy Pathway. Designated Federal Officers (DFOs) within federal agencies should work with their Agency head to review and modify steps to establishing FACs. Eliminate the requirement for FACs to require consultation and/or approval from GSA for the formation, renewal, or alteration of Advisory Committees.

Recommendation 10. Bolster agency budgets to support FACs on critical issues where regular engagement and trust building with the public is essential for good policy

Federal Advisory Committees are an essential component to receive evidence-based recommendations that will help guide decisions at all stages of the policy process. These Advisory Committees are oftentimes the single entry point external experts and the public have to comment and participate in the decision-making process. However, FACs take considerable resources to operate depending on the frequency of meetings, the number of Advisory Committee members, and supporting FDA staff. Without proper appropriations, they have a diminished ability to recruit and retain top talent for Advisory Committees. The Government Accountability Office (GAO) reported that in 2019, approximately $373 million dollars was spent to operate a total of 960 federal Advisory Committees. Some Agencies have experienced a decrease in the number of Advisory Committee convenings. Individual Agency heads should conduct a budget review of average operating and projected costs and develop proposals for increased funding to submit to the Appropriations Committee.  

Policy Pathway. Congress should consider increasing appropriations to support FACs so they can continue to enhance federal decision-making, improve public policy, boost public credibility, and Agency morale. 

Conclusion

Advisory Committees are necessary to the federal evidence-based decision-making ecosystem. Enlisting the advice and recommendations of experts, while also including input from the American public, allows the government to continue making decisions that will truly benefit its constituents. Nonetheless, there are areas of FACs that can be improved to ensure it continues to be a participatory, evidence-based process. Additional funding is needed to compensate the appropriate Agency staff for Committee support, provide potential incentives for experts who are volunteering their time, and finance other expenditures.

The administration should create a federal center of excellence for state and local artificial intelligence (AI) procurement and use—a hub for expertise and resources on public sector AI procurement and use at the state, local, tribal, and territorial (SLTT) government levels. The center could be created by expanding the General Services Administration’s (GSA) existing Artificial Intelligence Center of Excellence (AI CoE). As new waves of AI technologies enter the market, shifting both practice and policy, such a center of excellence would help bridge the gap between existing federal resources on responsible AI and the specific, grounded challenges that individual agencies face. In the decades ahead, new AI technologies will touch an expanding breadth of government services—including public health, child welfare, and housing—vital to the wellbeing of the American people. An AI CoE federal center would equip public sector agencies with sustainable expertise and set a consistent standard for practicing responsible AI procurement and use. This resource ensures that AI truly enhances services, protects the public interest, and builds public trust in AI-integrated state and local government services. 

Challenge and Opportunity 

State, local, tribal, and territorial (SLTT) governments provide services that are critical to the welfare of our society. Among these: providing housing, child support, healthcare, credit lending, and teaching. SLTT governments are increasingly interested in using AI to assist with providing these services. However, they face immense challenges in responsibly procuring and using new AI technologies. While grappling with limited technical expertise and budget constraints, SLTT government agencies considering or deploying AI must navigate data privacy concerns, anticipate and mitigate biased model outputs, ensure model outputs are interpretable to workers, and comply with sector-specific regulatory requirements, among other responsibilities. 

The emergence of foundation models (large AI systems adaptable to many different tasks) for public sector use exacerbates these existing challenges. Technology companies are now rapidly developing new generative AI services tailored towards public sector organizations. For example, earlier this year, Microsoft announced that Azure OpenAI Service would be newly added to Azure Government—a set of AI services that target government customers. These types of services are not specifically created for public sector applications and use contexts, but instead are meant to serve as a foundation for developing specific applications. 

For SLTT government agencies, these generative AI services blur the line between procurement and development: Beyond procuring specific AI services, we anticipate that agencies will increasingly be tasked with the responsible use of general AI services to develop specific AI applications. Moreover, recent AI regulations suggest that responsibility and liability for the use and impacts of procured AI technologies will be shared by the public sector agency that deploys them, rather than just resting with the vendor supplying them.

SLTT agencies must be well-equipped with responsible procurement practices and accountability mechanisms pivotal to moving forward given the shifts across products, practice, and policy. Federal agencies have started to provide guidelines for responsible AI procurement (e.g., Executive Order 13960, OMB-M-21-06, NIST RMF). But research shows that SLTT governments need additional support to apply these resources.: Whereas existing federal resources provide high-level, general guidance, SLTT government agencies must navigate a host of challenges that are context-specific (e.g., specific to regional laws, agency practices, etc.). SLTT government agency leaders have voiced a need for individualized support in accounting for these context-specific considerations when navigating procurement decisions. 

Today, private companies are promising state and local government agencies that using their AI services can transform the public sector. They describe diverse potential applications, from supporting complex decision-making to automating administrative tasks. However, there is minimal evidence that these new AI technologies can improve the quality and efficiency of public services. There is evidence, on the other hand, that AI in public services can have unintended consequences, and when these technologies go wrong, they often worsen the problems they are aimed at solving. For example, by increasing disparities in decision-making when attempting to reduce them. 

Challenges to responsible technology procurement follow a historical trend: Government technology has frequently been critiqued for failures in the past decades. Because public services such as healthcare, social work, and credit lending have such high stakes, failures in these areas can have far-reaching consequences. They also entail significant financial costs, with millions of dollars wasted on technologies that ultimately get abandoned. Even when subpar solutions remain in use, agency staff may be forced to work with them for extended periods despite their poor performance.

The new administration is presented with a critical opportunity to redirect these trends. Training each relevant individual within SLTT government agencies, or hiring new experts within each agency, is not cost- or resource-effective. Without appropriate training and support from the federal government, AI adoption is likely to be concentrated in well-resourced SLTT agencies, leaving others with fewer resources (and potentially more low income communities) behind. This could lead to disparate AI adoption and practices among SLTT agencies, further exacerbating existing inequalities. The administration urgently needs a plan that supports SLTT agencies in learning how to handle responsible AI procurement and use–to develop sustainable knowledge about how to navigate these processes over time—without requiring that each relevant individual in the public sector is trained. This plan also needs to ensure that, over time, the public sector workforce is transformed in their ability to navigate complicated AI procurement processes and relationships—without requiring constant retraining of new waves of workforces. 

In the context of federal and SLTT governments, a federal center of excellence for state and local AI procurement would accomplish these goals through a “hub and spoke” model. This center of excellence would serve as the “hub” that houses a small number of selected experts from academia, non-profit organizations, and government. These experts would then train “spokes”—existing state and local public sector agency workers—in navigating responsible procurement practices. To support public sector agencies in learning from each others’ practices and challenges, this federal center of excellence could additionally create communication channels for information- and resource-sharing across the state and local agencies. 

Procured AI technologies in government will serve as the backbone of local public services for decades to come. Upskilling government agencies to make smart decisions about which AI technologies to procure (and which are best avoided) would not only protect the public from harmful AI systems but would also save the government money by decreasing the likelihood of adopting expensive AI technologies that end up getting dropped. 

Plan of Action 

A federal center of excellence for state and local AI procurement would ensure that procured AI technologies are responsibly selected and used to serve as a strong and reliable backbone for public sector services. This federal center of excellence can support both intra-agency and inter-agency capacity-building and learning about AI procurement and use—that is, mechanisms to support expertise development within a given public sector agency and between multiple public sector agencies. This federal center of excellence would not be deliberative (i.e., SLTT governments would receive guidance and support but would not have to seek approval on their practices). Rather, the goal would be to upskill SLTT agencies so they are better equipped to navigate their own AI procurement and use endeavors. 

To upskill SLTT agencies through inter-agency capacity-building, the federal center of excellence would house experts in relevant domain areas (e.g., responsible AI, public interest technology, and related topics). Fellows would work with cohorts of public sector agencies to provide training and consultation services. These fellows, who would come from government, academia, and civil society, would build on their existing expertise and experiences with responsible AI procurement, integrating new considerations proposed by federal standards for responsible AI (e.g., Executive Order 13960, OMB-M-21-06, NIST RMF). The fellows would serve as advisors to help operationalize these guidelines into practical steps and strategies, helping to set a consistent bar for responsible AI procurement and use practices along the way. 

Cohorts of SLTT government agency workers, including existing agency leaders, data officers, and procurement experts, would work together with an assigned advisor to receive consultation and training support on specific tasks that their agency is currently facing. For example, for agencies or programs with low AI maturity or familiarity (e.g., departments that are beginning to explore the adoption of new AI tools), the center of excellence can help navigate the procurement decision-making process, help them understand their agency-specific technology needs, draft procurement contracts, select amongst proposals, and negotiate plans for maintenance. For agencies and programs with high AI maturity or familiarity, the advisor can train the programs about unexpected AI behaviors and mitigation strategies, when this arises. These communication pathways would allow federal agencies to better understand the challenges state and local governments face in AI procurement and maintenance, which can help seed ideas for improving existing resources and create new resources for AI procurement support.

To scaffold intra-agency capacity-building, the center of excellence can build the foundations for cross-agency knowledge-sharing. In particular, it would include a communication platform and an online hub of procurement resources, both shared amongst agencies. The communication platform would allow state and local government agency leaders who are navigating AI procurement to share challenges, learned lessons, and tacit knowledge to support each other. The online hub of resources can be collected by the center of excellence and SLTT government agencies. Through the online hub, agencies can upload and learn about new responsible AI resources and toolkits (e.g., such as those created by government and the research community), as well as examples of procurement contracts that agencies themselves used. 

To implement this vision, the new administration should expand the U.S. General Services Administration’s (GSA) existing Artificial Intelligence Center of Excellence (AI CoE), which provides resources and infrastructural support for AI adoption across the federal government. We propose expanding this existing AI CoE to include the components of our proposed center of excellence for state and local AI procurement and use. This would direct support towards SLTT government agencies—which are currently unaccounted for in the existing AI CoE—specifically via our proposed capacity-building model.

Over the next 12 months, the goals of expanding the AI CoE would be three-fold:

1. Develop the core components of our proposed center of excellence within the AI CoE. 

• Recruit a core set of fellows with expertise in responsible AI, public interest technology, and related topics from government, academia, and civil society for a 1-2 year placement; 
• Develop a centralized onboarding and training program for the fellows to set standards for responsible AI procurement and use guidelines and goals; 
• Create a research strategy to streamline documentation of SLTT agencies’ on-the-ground practices and challenges for procuring new AI technologies, which could help prepare future fellows. 

2. Launch collaborations for the first sample of SLTT government agencies. Focus on building a path for successful collaborations: 

• Identify a small set of state and local government agencies who desire federal support in navigating AI procurement and use (e.g., deciding which AI use cases to adopt, how to effectively evaluate AI deployments through time, what organizational policies to create to help govern AI use); 
• Ensure there is a clear communication pathway between the agency and their assigned fellow; 
• Have each fellow and agency pair create a customized plan of action to ensure the agency is upskilled in their ability to independently navigate AI procurement and use with time. 

3. Build a path for our proposed center of excellence to grow and gain experience. If the first few collaborations show strong reviews, design a scaling strategy that will: 

• Incorporate the center of excellence’s core budget into future budget planning;
• Identify additional fellows for the program; 
• Roll out the program to additional state and local government agencies. 

Conclusion

Expanding the existing AI CoE to include our proposed federal center of excellence for AI procurement and use can help ensure that SLTT governments are equipped to make informed, responsible decisions about integrating AI technologies into public services. This body would provide necessary guidance and training, helping to bridge the gap between high-level federal resources and the context-specific needs of SLTT agencies. By fostering both intra-agency and inter-agency capacity-building for responsible AI procurement and use, this approach builds sustainable expertise, promotes equitable AI adoption, and protects public interest. This ensures that AI enhances—rather than harms—the efficiency and quality of public services. As new waves of AI technologies continue to enter the public sector, touching a breadth of services critical to the welfare of the American people, this center of excellence will help maintain high standards for responsible public sector AI for decades to come.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.