As Congress continues to consider comprehensive AI legislation, states are increasingly stepping in to regulate artificial intelligence across issues ranging from employment and consumer protection to safety, procurement, and government use. This rapid expansion of state-level AI laws has created a dynamic but complex patchwork of requirements, raising concerns around compliance costs, uncertainty for developers and deployers, and prompting growing calls for federal intervention. In response to these concerns, President Trump signed the Executive Order on Ensuring a National Policy Framework for Artificial Intelligence in December 2025, signaling a significant shift in the federal-state balance of power over AI governance. This session will examine the constitutional, economic, and practical implications of this evolving landscape. Can federal preemption reduce fragmentation without undermining valuable state experimentation? Will executive action withstand legal scrutiny around the separation of powers and interstate commerce? And what does this uncertainty mean for businesses, consumers, startups, and regional AI ecosystems?

The discussion will also explore the persistent geographic unevenness of AI readiness in the United States. As AI capabilities concentrate in a limited number of regions, states and regions remain critical to building AI clusters, developing talent, and supporting place-based adoption. Speakers will examine how the U.S. can balance national coherence with regional dynamism, and what that balance means for innovation, trust, and global AI leadership.

• To what extent does the current patchwork of state AI laws materially hinder innovation, or does it serve as a testing ground for best practices?
• How are businesses, especially SMBs, navigating diverging state requirements while also complying with global AI regimes? What governance structures should organizations implement now to remain resilient amid federal-state realignment?
• What principles could form the basis of a “minimally burdensome” yet effective national AI framework?
• What role should states play in assessing their unique strengths and building intentional AI ecosystems around talent, research capacity, and adoption? How can federal and state governments coordinate to build inclusive AI ecosystems beyond major tech hubs?
• In the absence of clear federal standards, how can consumers be assured of consistent protections across states?
• What would a durable federal AI framework need to include to align innovation, consumer protection, and national security objectives?

As artificial intelligence becomes central to economic growth, national security, and global technological leadership, infrastructure is emerging as both the primary bottleneck and the defining opportunity of the AI era. Scaling next-generation AI will require unprecedented investment in gigawatt-scale compute, hyperscale cloud capacity, secure semiconductor supply chains, reliable, resilient and affordable energy systems, and modernized digital connectivity networks.

This session will explore how federal and state governments, utilities, cloud providers, and industry are responding to surging demand for data centers, advanced compute clusters, domestic chip manufacturing, and modernized energy and communications infrastructure. It will examine efforts to accelerate permitting and project timelines, expand electricity generation and transmission, deploy next-generation energy technologies, and strengthen fiber, broadband, and network resilience to support AI-driven cloud and edge applications.

The discussion will also assess how policy and regulatory frameworks can enable long-term financing and investment while managing risks to grid reliability, consumer electricity costs, environmental and water impacts, workforce shortages, and cybersecurity vulnerabilities. As AI infrastructure investment reshapes regional development, the session will highlight strategic trade-offs around co-location of energy and compute, trusted supply chains, and ensuring that the benefits of AI-driven growth extend beyond major tech hubs.

• What infrastructure investments are most urgent to support next-generation AI compute, cloud expansion, and high-speed connectivity, and how can they be delivered faster and at scale?
• How can permitting reforms, including NEPA streamlining and use of federally owned land, accelerate deployment of data centers, grid upgrades, and energy infrastructure while addressing environmental and community concerns?
• What role should public-private partnerships play in unlocking large-scale AI infrastructure development and long-term project financing?
• How can the U.S. expand electricity generation, transmission, and grid capacity fast enough to meet AI demand while maintaining reliability, affordability, and climate objectives?
• How can policymakers balance the trillion-dollar opportunity in AI infrastructure with risks to grid stability, rising electricity prices, and cost spillovers to consumers?
• Can domestic semiconductor manufacturing scale quickly enough to meet AI compute needs, and how can the CHIPS Act implementation strengthen trusted supply chains?
• What investments are needed to strengthen fiber networks, broadband expansion, subsea cable resilience, and secure 5G/6G connectivity to support AI-driven cloud and edge services?
• How can the U.S. improve security and resilience across AI, energy, cloud, and communications infrastructure, and what role should the AI-ISAC play?
• How can federal land strategies support data center and energy co-location, and should the U.S. prioritize distributed regional compute hubs beyond major tech centers?
• What policies are needed to address workforce shortages, manage water and environmental constraints, and ensure AI infrastructure investment delivers broad community and economic benefits?
• How can federal R&D investments and initiatives like NAIRR democratize access to compute and datasets beyond major AI labs?
• Should AI infrastructure policy explicitly prioritize distributed regional compute hubs tied to research institutions and SMEs?

As the United States rolls out its ambitious AI Action Plan, a central strategic question emerges: how can America compete effectively with geopolitical rivals in the global AI race while strengthening cooperation with specific regions and countries? This session explores the evolving U.S. approach to global AI leadership, as outlined in the Trump Administration’s Executive Order on “Promoting the Export of the American AI Technology Stack” and the newly launched American AI Exports Program. These initiatives position full-stack AI exports as both a major economic opportunity and a national security priority. Washington’s goal is to offer trusted partners a secure alternative to Chinese technology ecosystems, while reinforcing U.S. influence over the global development and deployment of AI. However, this strategy also exposes fundamental tensions. The U.S. must navigate competing priorities: openness versus protectionism, alliance-building versus market dominance, and values-based partnerships versus pragmatic commercial interests. At the same time, policy debates in Washington reflect these divisions, particularly around whether the U.S. should further tighten chip export restrictions to China or maintain commercial engagement to preserve leverage in critical supply chains. With AI rapidly becoming a defining arena of global power-shaping economic competitiveness, national security, and geopolitical alignment-the U.S. now faces a broader test. As countries across the world, including emerging economies and the Global South, pursue AI capabilities to meet their own development and sovereignty goals, America must determine whether it can build lasting AI alliances that go beyond transactional export relationships.

This session will examine whether the United States can translate its AI leadership into long-term geopolitical advantage by strengthening alliances, shaping global standards, and supporting an open and competitive AI ecosystem amid intensifying strategic rivalry. What is the current state of global AI competition, and how does U.S. innovation compare with China and other emerging AI powers?

• How might exporting full-stack American AI solutions reshape U.S. influence in the global technology ecosystem? Can U.S.-curated AI stacks anchor long-term partnerships without crowding out local innovation or the participation of allies? 
• What mechanisms can ensure that AI export partnerships create mutual value and shared growth, rather than dependency?
• How should the U.S. balance export controls and national security priorities with the need for global AI diffusion, market access, and supply-chain influence?
• How can the U.S. promote regulatory interoperability internationally while avoiding restrictive AI regulations that disadvantage American companies?
• How can the United States offer the Global South a credible, long-term AI value proposition that prioritizes capacity-building, economic development, and local agency over binary geopolitical alignment?
• Can the United States sustain AI leadership without “going it alone,” and what would a cooperative yet competitive global AI order look like?

Artificial intelligence is no longer a promise for the future: it is already reshaping industries, accelerating scientific discovery, modernizing public services, and redefining how organizations operate. Yet a persistent gap remains between AI’s transformative potential and its adoption at scale. While early deployments show measurable gains in productivity, innovation, and efficiency, most organizations, public and private alike, remain stuck in experimentation mode.

This session examines what it takes to move from AI ambition to sustained deployment across the economy and government. It explores how leading organizations and public agencies are embedding AI into core workflows, redesigning operating models, and generating repeatable value. Drawing on concrete public and private-sector case studies, the discussion will examine how AI is augmenting work rather than simply automating it; how generative and autonomous systems are reshaping decision-making and productivity; and how procurement, data strategy, cloud migration, and regulatory alignment can either accelerate or bottleneck adoption.

The focus will be on the role of government, not only as a regulator but also as a market-shaping buyer and operator of AI. Indeed, as the world’s largest technology purchaser, the US federal government is redefining AI acquisition and governance through evolving procurement rules, risk-management standards, and directives around accountability, competition, and “truth-seeking” AI principles. These frameworks have the potential not only to transform public-sector deployment but also increasingly to set de facto standards for industry.

• What does durable AI adoption look like in both the private and public sectors? How is AI enabling not just efficiency gains, but entirely new business and operating models? Which industry domains are emerging as key sectors for AI-driven competitive advantage, and what is at stake for those that fall behind?
• What business, operational, or shared infrastructure can unlock AI at scale?
• How are evolving federal AI procurement and risk-management requirements reshaping what it takes to sell AI to the government, and how are agencies interpreting them in practice?
• To what extent can the federal government’s purchasing power effectively set AI governance standards for the wider domestic and global market?
• What sector-specific regulations, data policies, and procurement strategies can accelerate responsible AI adoption without creating innovation bottlenecks?
• How can federal, state, and local governments strengthen coordination and guidance to move from pilots to sustained deployment?
• Is the focus on AI Talent Exchanges, secondments, and Chief AI Officer networks sufficient to rebuild and retain AI expertise inside government?
• What lessons can be drawn from early public and private-sector AI deployments that have delivered measurable results, and how should they shape future procurement and operating models?

As artificial intelligence transforms every sector of the U.S. economy, the success of America’s AI strategy will depend on one factor above all: whether the workforce is ready. This session explores the administration’s emerging Worker-First AI Agenda and the initiatives outlined in the AI Action Plan, including large-scale investments in training, tax incentives for upskilling, and the ambitious goal of one million new apprenticeships annually.

Speakers will examine how public-private partnerships can deliver AI skills at scale, support small and medium-sized businesses, and address growing talent shortages in critical fields such as engineering and computer science. The discussion will also consider how workforce policy, education, immigration, and employer commitments must align to ensure AI adoption leads to better jobs, higher wages, and long-term competitiveness.

As artificial intelligence accelerates across the U.S. economy, trust has emerged as the defining challenge, and opportunity, of the AI era. While innovation continues at pace, the United States still lacks comprehensive federal safeguards for data privacy, copyright, and algorithmic accountability, relying instead on voluntary standards and fragmented enforcement. This governance gap is increasingly eroding public confidence, exposing vulnerable populations to harm, and creating legal uncertainty for companies building and deploying AI systems.

This session explores how trust can be rebuilt without sacrificing U.S. competitiveness. Panelists will examine the growing tension between AI’s demand for vast, high-quality data and the absence of clear rules governing its use. The discussion will address emerging policy responses to AI-driven harms, including addictive algorithms affecting children and the surge in non-consensual intimate imagery, and assess whether targeted, harm-based interventions can meaningfully restore public trust. The conversation will also turn to the future of AI governance. Moving beyond reactive compliance, speakers will explore “agile governance” approaches, such as third-party certification regimes, regulatory sandboxes, and multi-stakeholder policymaking, that aim to deliver transparency, accountability, and measurable outcomes while keeping innovation onshore. With organizations increasingly treating privacy and data governance as strategic enablers rather than regulatory burdens, the session will consider how trust itself is becoming a source of competitive advantage in the global AI race.

• How does the lack of a clear U.S. framework for data privacy and intellectual property affect trust, investment, and America’s position in the global AI race?
• Can the United States sustain AI leadership through voluntary standards and post hoc enforcement alone, or does the absence of enforceable safeguards risk eroding trust, competitiveness, and international influence?
• In the absence of comprehensive federal legislation, what role do regulators play in shaping AI accountability and consumer protection?
• How can policymakers and companies balance AI’s growing demand for high-quality data with rising expectations around privacy, consent, and data minimization?
• Should AI companies be allowed to train models on copyrighted works without explicit permission, or does protecting creators require new legal boundaries on data use? What transparency and disclosure obligations should apply when personal or proprietary data is shared with third parties for AI training or deployment?
• Are targeted, harm-based interventions, such as the TAKE IT DOWN Act, a viable model for addressing specific AI harms without over-regulating the technology itself?
• Can certification regimes, third-party audits, and regulatory sandboxes provide a credible middle ground between heavy regulation and self-regulation?
• Can open-weight and open-source models expand trust and resilience, or do they introduce systemic risks?
• What role do open standards play in ensuring interoperability and safe deployment of next-generation AI agents?