System-to-model innovation is an emerging innovation pathway in artificial intelligence that has driven progress in several prominent areas over the last decade. System-level innovations advance with the diffusion of AI and expand the base of contributors to leading-edge progress in the field. Countries that can identify and harness system-level innovations faster and more comprehensively will gain crucial economic and military advantages over competitors. This paper analyzes the benefits of system-to-model innovation and suggests a three-part framework to navigate the policy implications: protect, diffuse, and anticipate.

Wuhan, China’s inland metropolis, is paving the way for a nationwide rollout of “embodied” artificial intelligence meant to fast-track scientific discovery, optimize production, streamline commerce, and facilitate state supervision of social activities. Grounded in real-world data, the AI grows smarter, offering a pathway to artificial “general” intelligence that will reinforce state ideology and boost economic goals. This report documents the genesis of Wuhan’s AGI initiative and its multifaceted deployment.

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.

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.

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.

How to govern artificial intelligence is a concern that is rightfully top of mind for lawmakers and policymakers.To govern AI effectively, regulators must 1) know the terrain of AI risk and harm by tracking incidents and collecting data; 2) develop their own AI literacy and build better public understanding of the benefits and risks; and 3) preserve adaptability and agility by developing policies that can be updated as AI evolves.

Since 2019, the U.S. government has imposed restrictive export controls on Huawei—one of China’s leading tech giants—seeking, in part, to hinder the company’s AI chip development efforts. This data snapshot reveals how exactly Huawei’s latest AI chip—the Ascend 910B—improves on the prior generation and demonstrates how export controls are likely hindering Huawei’s production.