On July 31, 2025, the Trump administration released “Winning the Race: America’s AI Action Plan.” CSET has broken down the Action Plan, focusing on specific government deliverables. Our Provision and Timeline tracker breaks down which agencies are responsible for implementing recommendations and the types of actions they should take.

Recent op-eds comparing the United States’ and China’s artificial intelligence (AI) programs fault the former for its focus on artificial general intelligence (AGI) while praising China for its success in applying AI throughout the whole of society. These op-eds overlook an important point: although China is outpacing the United States in diffusing AI across its society, China has by no means de-emphasized its state-sponsored pursuit of AGI.

Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the third blog post in a three-part series explaining some key elements of how LLMs function. This blog post explains how AI developers are finding ways to use LLMs for much more than just generating text.

Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the second blog post in a three-part series explaining some key elements of how LLMs function. This blog post explores fine-tuning—a set of techniques used to change the types of output that pre-trained models produce.

This guide provides a run-down of CSET’s research since 2019 for first-time visitors and long-term fans alike. Quickly get up to speed on our “must-read” research and learn about how we organize our work.

This blog post by CSET’s Executive Director Dewey Murdick explores two different metaphorical lenses for governing the frontier of AI. The "Space Exploration Approach" likens AI models to spacecrafts venturing into unexplored territories, requiring detailed planning and regular updates. The "Snake-Filled Garden Approach" views AI as a garden with both harmless and dangerous 'snakes,' necessitating rigorous testing and risk assessment. In the post, Dewey examines these metaphors and the different ways they can inform approaches to AI governance strategy that balances innovation with safety, all while emphasizing the importance of ongoing learning and adaptability.

Recent announcements from both Pentagon and Congressional leaders offer significant opportunity for rapidly delivering autonomous systems technology at-scale for U.S. Warfighters well into the future. Dr. Jaret Riddick, CSET Senior Fellow and former Principal Director for Autonomy in USD(R&E) offers his perspective on DOD’s Replicator Initiative and recent legislative proposals about DOD autonomy.

How can we measure the reliability of machine learning systems? And do these measures really help us predict real world performance? A recent study by the Stanford Intelligent Systems Laboratory, supported by CSET funding, provides new evidence that models may perform well on certain reliability metrics while still being unreliable in other ways. This blog post summarizes the study’s results, which suggest that policymakers and regulators should not think of “reliability” or “robustness” as a single, easy-to-measure property of an AI system. Instead, AI reliability requirements will need to consider which facets of reliability matter most for any given use case, and how those facets can be evaluated.

Machine learning advances have powered the development of new and more powerful generative language models. These systems are increasingly able to write text at near human levels. In a new report, authors at CSET, OpenAI, and the Stanford Internet Observatory explore how language models could be misused for influence operations in the future, and they provide a framework for assessing potential mitigation strategies.

CSET's Daniel Chou provides an update on previous CSET research exploring China's security forces' AI research portfolio.