We investigate the scale of attack and defense mathematically in the context of AI's possible effect on cybersecurity. For a given target today, highly scaled cyber attacks such as from worms or botnets typically all fail or all succeed.

Unlike other domains of conflict, and unlike other fields with high anticipated risk from AI, the cyber domain is intrinsically digital with a tight feedback loop between AI training and cyber application. Cyber may have some of the largest and earliest impacts from AI, so it is important to understand how the cyber domain may change as AI continues to advance. Our approach reviewed the literature, collecting nine arguments that have been proposed for offensive advantage in cyber conflict and nine proposed arguments for defensive advantage.

The Carnegie Classification of Institutions of Higher Education is simplifying its top-tier R1 research criteria this year. Recognizing the strategic importance of historically Black colleges and universities, Congress passed Section 223 of the 2023 National Defense Authorization Act to increase defense research capacity by encouraging the most eligible among these institutions to seek the highly coveted R1 status. This in-depth analysis examines the 2025 classification changes, their effect on eligible HBCUs, and strategies for Congress to maintain progress.

As new advanced AI systems roll out, there is widespread disagreement about malicious use risks. Are bad actors likely to misuse these tools for harm? This report presents a simple framework to guide the questions researchers ask—and the tools they use—to evaluate the likelihood of malicious use.

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.

As artificial intelligence permeates the economy, the demand for AI talent with all levels of educational attainment will expand in kind. Apprenticeships are an effective education and training pathway for other industries, but are they suitable for AI-related roles? This report analyzes trends in AI-related apprenticeships across the United States from 2013 through 2023. It explores the growth of these programs, completion rates, demographic and geographic information, and the organizations sponsoring these programs.

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.

The emergence of artificial intelligence as a general-purpose technology could profoundly transform work across industries, potentially affecting a variety of occupations. While previous technological shifts largely enhanced productivity and wages for white-collar workers but led to displacement pressures for blue-collar workers, AI may significantly disrupt both groups. This report examines the changing landscape of workforce development, highlighting the crucial role of community colleges, alternative career pathways, and AI-enabled training solutions in preparing workers for this transition.

Automation bias is a critical issue for artificial intelligence deployment. It can cause otherwise knowledgeable users to make crucial and even obvious errors. Organizational, technical, and educational leaders can mitigate these biases through training, design, and processes. This paper explores automation bias and ways to mitigate it through three case studies: Tesla’s autopilot incidents, aviation incidents at Boeing and Airbus, and Army and Navy air defense incidents.

Artificial intelligence models have become increasingly adept at generating computer code. They are powerful and promising tools for software development across many industries, but they can also pose direct and indirect cybersecurity risks. This report identifies three broad categories of risk associated with AI code generation models and discusses their policy and cybersecurity implications.