Research and News
The below database includes IAPS’ public research reports, responses to government requests for information, blog posts, and more.
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- Compute governance 14
- International governance & China 12
- Frontier Security 3
- Policy and Standards 18
- Responses to RFIs/RFCs 6
- Linkpost 10
- Erich Grunewald 9
- Asher Brass 2
- Bill Anderson-Samways 5
- Jam Kraprayoon 6
- Oscar Delaney 2
- Oliver Guest 9
- Shaun Ee 2
- Tao Burga 1
- Renan Araujo 3
- Sumaya Nur Adan 1
- Issue Brief 5
- Joe O'Brien 3
- Onni Aarne 1
AI systems are advancing at breakneck speed and already reshaping markets, geopolitics, and the priorities of governments. Frontier AI systems are developed and deployed using compute clusters of hundreds of thousands of cutting-edge AI chips housed in specialized data centers. These AI data centers are likely tempting targets for sophisticated adversaries like China and Russia, who may seek to steal intellectual property or sabotage AI systems underpinning military, industry, or critical infrastructure projects.
Adapted from a section of a report by Erich Grunewald and Christopher Phenicie, this blog post introduces the core concepts and background information needed to understand the AI chip-making process.
Computational power (“compute”) is a strategic resource in the way that oil and steel production capacity were in the past. Like oil, and like steel production capacity, compute is scarce, controllable, concentrated, and highly economically and militarily useful. Just as oil and steel were and remain strategic resources to some extent, compute is now also a strategic resource of very high importance.
The most advanced AI systems remain hidden inside corporate labs for months before public release—creating both America's greatest technological advantage and a serious security vulnerability. IAPS researchers identify critical risks and propose lightweight interventions to lessen the threat.
Alongside its AI Action Plan, the Trump administration published an executive order (EO) for Promoting the Export of the American AI Technology Stack.
In our Differential Access report, we provided a strategic framework to help developers give defenders an advantage by shaping access to AI-powered cyber capabilities. In a new policy memo, we outline government actions that can enable Differential Access and promote AI adoption for cyber defense.
The growing impacts of artificial intelligence (AI) are spurring states to consider international agreements that could help manage this rapidly evolving technology. The political feasibility of such agreements can hinge on their verifiability—the extent to which the states involved can determine whether other states are complying. This report, published by the Oxford Martin School at the University of Oxford analyzes several potential international agreements and ways they could be verified.
The Trump Administration unveiled its comprehensive AI Action Plan on Wednesday. Experts at the Institute for AI Policy and Strategy reviewed the plan with an eye toward its national security implications. As AI continues to accelerate towards very powerful artificial general intelligence, our researchers discuss promising proposals for addressing critical AGI risks, offer key considerations for government implementation, and explore the plan's gaps and potential solutions.
The most powerful AI systems are used internally for months before they are released to the public. These internal AI systems may possess capabilities significantly ahead of the public frontier, particularly in high-stakes, dual-use areas like AI research, cybersecurity, and biotechnology. To address these escalating risks, this report recommends a combination of technical and policy solutions.
This post contains IAPS’s response to the Request for Information from Senators Heinrich and Rounds as part of the American Science Acceleration Project (ASAP), a national initiative to accelerate the pace of American technical innovation.
This is a linkpost for a policy memo published by the Federation of American Scientists, which proposes scaling up a significantly enhanced “CAISI+” within the Department of Commerce.
The emergence of the China AI Safety and Development Association (CnAISDA) is a pivotal moment for China’s frontier AI governance. How it navigates substantial domestic challenges and growing geopolitical tensions will shape conversations on frontier AI risks in China and abroad.
A Whistleblower Incentive Program to Enforce U.S. Export Controls: "A program modeled on the successful SEC program would help America overcome its export control enforcement woes.”
The Center for a New American Security (CNAS), in collaboration with the Institute for AI Policy and Strategy, has released a new working paper which catalogues evidence that substantial quantities of advanced artificial intelligence (AI) chips are being smuggled into China, undermining U.S. national security.
A memo to outline a strategic, coordinated policy approach supporting R&D to address urgent assurance and security challenges relating to frontier AI systems.
“Differential access” is a strategy to tilt the cybersecurity balance toward defense by shaping access to advanced AI-powered cyber capabilities. We introduce three possible approaches, Promote Access, Manage Access, and Deny by Default, with one constant across all approaches — even in the most restrictive scenarios, developers should aim to advantage cyber defenders.
Our survey of 53 specialists across 105 AI reliability and security research areas identifies the most promising research prospects to guide strategic AI R&D investment.
Adding location verification features to AI chips could unlock new governance mechanisms for regulators, help enforce existing and future export controls by deterring and catching smuggling attempts, and enable post-sale verification of chip locations. This paper is meant to serve as an initial introduction to location verification use-cases for AI chips with comparison of different methods.
As the administration works towards a strong, streamlined successor to the diffusion rule, we offer recommendations for BIS across three core objectives: (1) Steer the global distribution of American compute to preserve America’s lead in AI; (2) Ensure importing countries—including allies—uphold US export controls or face strict import limits, and use existing technology to address enforcement challenges such as illegal AI chip reexports; and (3) Secure key AI models stored on foreign soil, as model weight theft represents a major potential “compute shortcut” for adversaries.
There has been significant recent speculation about whether the US government will lead a future project to build and acquire advanced AI, or continue to play a more arms-length role. We conducted a forecasting workshop on this question, employing the IDEA protocol to elicit predictions from six professional forecasters and five experts on US AI policy.
This report is an accessible guide to the emerging field of AI agent governance, including an analysis of the current landscape of agent and their capabilities, novel and enhanced risks posed by more agentic systems, and major open questions and agent interventions.
While attention focuses on publicly available models like ChatGPT, the real risk to U.S. national interests is the theft of unreleased “internal models.” To preserve America’s technological edge, the U.S. government must work with AI developers to secure these internal models.
Our comments focus on ways the US AI Action Plan can build trust in American AI, deny advantages to adversaries, and prepare to adapt as the technology evolves.
If the US is serious about outcompeting China in AI, it needs to strengthen, not weaken, its AI chip export regime. A crucial first step is eliminating the widespread occurrence of AI chip smuggling.
This is a linkpost for an article written by IAPS researchers Oscar Delaney and Oliver Guest.
This is a linkpost to a piece that Tao Burga, an IAPS fellow, co-authored with researchers from CNAS (Center for a New American Security).
This paper, published by the Oxford Martin AI Governance Initiative, explores how to determine which actors are best suited to develop AI model evaluations. IAPS staff Renan Araujo, Oliver Guest, and Joe O’Brien were among the co-authors.
This is a link post for a paper which was led by researchers from the Oxford Martin AI Governance Initiative, and on which IAPS researcher Oliver Guest was one of the authors.