Center for Data Innovation Blog

Adversarial AI distillation poses a growing threat to U.S. technological leadership, national security, and AI safety by enabling foreign actors to extract the capabilities of frontier models without authorization. Policymakers should strengthen legal and technical defenses against industrial-scale model theft while ensuring any response preserves legitimate AI research, innovation, and the responsible use of distillation.

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.

As AI-powered cyber threats become more advanced, the federal government should modernize the CyberCorps SFS program by integrating AI-security training, reforming cyber hiring pipelines, and expanding training infrastructure to build a stronger cybersecurity workforce.

OECD data shows there is a consistent, positive relationship between the share of firms using AI and a country’s GDP per hour worked.

Requiring government approval before releasing advanced AI models would slow innovation, politicize AI development, and weaken U.S. competitiveness. Instead, policymakers should focus on collaborative safety efforts and strengthening cybersecurity.

France’s sweeping effort to replace foreign technology providers with European alternatives prioritizes digital sovereignty and domestic protectionism over productivity, despite no public evidence the transition will improve government performance or reduce costs.

Lawmakers risk misregulating grocery prices by targeting dynamic and algorithmic pricing tools, and should instead focus on enforcing existing laws against clearly defined deceptive practices.

Japan should revise its draft AI IP code to remove technically infeasible disclosure mandates and instead adopt workable, pro-innovation transparency standards aligned with international efforts like the Hiroshima AI Process to preserve U.S. alignment and avoid deterring AI investment.