Center for Data Innovation Blog

Data centers can deliver substantial tax revenue, jobs, infrastructure investment, and lower utility costs when communities adopt policies that capture their benefits while managing local impacts.

The United States will not realize the full benefits of AI if policymakers treat it primarily as a technology that needs to be constrained.

Fixed compute thresholds for “frontier” AI models are quickly becoming outdated, and policymakers should adopt a dynamic, capability-based definition that keeps regulation focused on models posing truly exceptional risks.

Debate over AI’s workforce impact focuses too narrowly on whether it creates or eliminates jobs. Rather than impose new reporting requirements on businesses, policymakers should commission studies and expand existing surveys to capture how AI transforms work, not just changes in headcount.

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.

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.