Maintaining long-term U.S. leadership in artificial intelligence will require policymakers to foster a diversified, contestable, and competitive market for AI systems. Today, however, incumbent technology companies maintain a distinct advantage in the production of large AI models, and they have the means and motion to use their control over key chokepoints in the AI supply chain (compute, data, foundation models, distribution channels) to stifle competition. This report explores the associated economic and national security risks, and offers recommendations for maintaining an open and competitive AI industry.

In response to the Office of Science and Technology Policy's request for input on an AI Action Plan, CSET provides key recommendations for advancing AI research, ensuring U.S. competitiveness, and maximizing benefits while mitigating risks. Our response highlights policies to strengthen the AI workforce, secure technology from illicit transfers, and foster an open and competitive AI ecosystem.

Large generative models are widely viewed as the most promising path to general (human-level) artificial intelligence and attract investment in the billions of dollars. The present enthusiasm notwithstanding, a chorus of ranking Chinese scientists regard this singular approach to AGI as ill-advised. This report documents these critiques in China’s research, public statements, and government planning, while pointing to additional, pragmatic reasons for China’s pursuit of a diversified research portfolio.

This paper presents two new methods for identifying research relevant to emerging technology. The authors developed and deployed technology topic classification and targeted research field scoring over a corpus of scientific literature to identify research relevant to cybersecurity, LLM development, and chips fabrication and design—expanding CSET’s existing set of topic classifications for AI, computer vision, NLP, robotics, and AI safety. The paper summarizes motivation, methods, and results.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series uses data from a variety of sources to track how three cloud providers—Amazon, Alphabet, and Microsoft—distribute their financial resources to create and sustain demand for their cloud services. By investing in data centers & workforce training, the large tech platforms of Amazon, Google, and Microsoft draw developers, companies, and governments to their tools & services.

Maintaining U.S. technological leadership in the years ahead will require policymakers to promote competition in the AI market and prevent industry leaders from wielding their power in harmful ways. This brief examines trends in U.S. mergers and acquisitions of artificial intelligence companies. The authors found that AI-related M&A deals have grown significantly over the last decade, with large U.S. tech companies being the most prolific acquirers of AI firms.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series uses data from a variety of sources to track how three cloud providers—Amazon, Alphabet, and Microsoft—distribute their financial resources to create and sustain demand for their cloud services. By investing in data centers & workforce training, the large tech platforms of Amazon, Google, and Microsoft draw developers, companies, and governments to their tools & services.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series uses data from a variety of sources to track how three cloud providers—Amazon, Alphabet, and Microsoft—distribute their financial resources to create and sustain demand for their cloud services. By investing in data centers & workforce training, the large tech platforms of Amazon, Google, and Microsoft draw developers, companies, and governments to their tools & services.

The Chinese Academy of Sciences is among the most important S&T organizations in the world and plays a key role in advancing Beijing’s S&T objectives. This report provides an in-depth look into the organization and its various functions within China’s S&T ecosystem, including advancing S&T research, fostering the commercialization of critical and emerging technologies, and contributing to S&T policymaking.

A core question in policy debates around artificial intelligence is whether federal agencies can use their existing authorities to govern AI or if the government needs new legal powers to manage the technology. The authors argue that relying on existing authorities is the most effective approach to promoting the safe development and deployment of AI systems, at least in the near term. This report outlines a process for identifying existing legal authorities that could apply to AI and highlights areas where additional legislative or regulatory action may be needed.