Watch now for a panel discussion on how data and technology can create safer, healthier, and more productive workplaces, and the policies needed to encourage adoption while protecting workers.

Watch ITIF's Center for Data Innovation discussion on how the United States can build a durable AI alliance that creates strategic partnerships that are mutually beneficial and contains China’s growing technological influence in the Global South.

Watch now for a timely discussion by ITIF's Center for Data Innovation and Public First surrounding new polling data exploring how U.S. public sentiment toward AI is evolving. The conversation unpacks where Americans see promise or peril, how their views have shifted over the past year, and what these perspectives mean for lawmakers, business leaders, and the future of AI policy.

Watch now for a panel discussion by ITIF's Center for Data Innovation on what the rise of agentic AI means for innovation, competition, and policy, how prepared the United States is to keep pace, and what policy shifts might be needed to ensure consumers and businesses can successfully develop and deploy AI agents.

Watch now for the Center for Data Innovation's discussion on the potential consequences of the UK government’s proposed option and how creating a more permissive text and data mining exception would advance the UK’s goals of being competitive in AI without undermining the rights of creators.

The DMA’s forced interoperability undermines platform differentiation, weakens security and reliability, and ultimately leaves European consumers with degraded versions of global technologies.

The EU’s digital rulebook, often praised as global leadership, instead forces many non-EU countries into costly regulatory alignment that stifles local innovation and entrenches global digital inequality, underscoring the need for more flexible, locally tailored frameworks.

In an op-ed for The Dispatch, Daniel Castro argues that banning superintelligent AI is misguided because it’s based on speculation, would undermine U.S. innovation and security, and should be replaced with strong oversight—not restrictions on advancing knowledge.

In China Daily, Daniel Castro argues that the U.S. and China face AI risks—like models enabling biological threats or cyberattacks—that are too great for either to manage alone, and can be mitigated through coordinated safety measures such as joint research, incident reporting, and red-team testing.

Fifty conflicting state AI laws create a fragmented, innovation-crushing patchwork, which federal preemption can solve by establishing a single, coherent national framework for AI regulation.

The proposed AI-Related Job Impacts Clarity Act would create misleading, unhelpful data and unfairly stigmatize AI adoption, diverting attention from more effective ways to measure technology’s real impacts and support workers.

Senator Blackburn’s call to shut down Google’s AI over a false claim is misguided, as the error came from a small, open developer model not designed for factual accuracy, and the incident does not demonstrate political bias or systemic failure.