Data Innovation

As AI systems gain greater autonomy, policymakers should prioritize research and safeguards that make dangerous behavior harder to initiate, easier to detect, and faster to contain. Emergency shutdown capabilities can be one useful tool in that effort, but a government‑mandated kill switch is neither universally feasible nor sufficient to address the broader challenges posed by autonomous AI.

In an op-ed for National Review, President Daniel Castro argues that the United States should address specific AI risks without sacrificing the economic freedom, competition, and market-driven innovation that give America an edge over China in the global AI race.

AI presents real risks that warrant serious investment in security, testing, monitoring, and accountability, but claims that it poses an imminent existential threat lack a clear empirical basis. Rather than slowing AI development, policymakers should focus on evidence-based safeguards that make increasingly capable AI systems safer and more reliable.

Daniel Castro moderates a panel on how the United States can responsibly govern the rapid development of data centers at the 2026 Internet Governance Forum USA hosted by the Internet Society Washington DC Chapter.

The temporary shutdown of Anthropic's Fable 5 and Mythos 5 highlighted the uncertainty surrounding the government's authority to restrict access to frontier AI models and the costs that uncertainty can impose on innovation and trust. To provide greater clarity for developers, customers, and allies, Congress should establish a transparent statutory framework for AI shutdown authority with clear standards and proportionate safeguards.

Adversarial AI distillation poses a growing threat to U.S. AI leadership and national security, and Congress is right to address it through the Deterring American AI Model Theft Act (DAAMTA). But before passing the bill, lawmakers should narrow its scope, strengthen evidentiary standards, protect legitimate research, and pair it with technical safeguards and international cooperation to better combat large-scale AI model theft without hindering innovation.

As employers increasingly seek workers who can apply AI alongside domain expertise, universities should integrate AI across disciplines—not just standalone AI or computer science programs—to prepare graduates for the modern workforce.

Cities that are successfully scaling AI are investing in workforce upskilling alongside governance and technology deployment. Case studies from Washington, DC, San Jose, Seattle, and Cleveland show that employee training and AI literacy are critical to turning pilot projects into lasting improvements in public service delivery.

Unlike training-data disputes, CNN's lawsuit against Perplexity alleges near-verbatim reproduction of its journalism through AI search products. Policymakers should favor targeted enforcement—not sweeping AI restrictions.

Five states—Utah, Connecticut, Ohio, Texas, and North Carolina—are showing how centralized AI sandboxes, oversight frameworks, and clear evaluation metrics can help governments move beyond isolated pilot programs and scale AI tools to deliver measurable improvements in public services.