Artificial intelligence (AI) is no longer defined by future possibilities, but by the pace of innovation. While AI models and software capabilities are advancing at unprecedented speed, the underlying compute — chips, systems, and infrastructure — must evolve just as quickly. The industry is moving to meet this moment, rethinking how AI systems are designed, deployed, and governed so innovation can scale responsibly.

The US continues to lead in foundational AI development, but global momentum around terms and guardrails for its responsible use is accelerating. The European Union has launched the world’s first comprehensive AI Act. China and Singapore have launched their own centralized and human-centric models, respectively. As governance frameworks become more complex and fragmented, the US has a unique opportunity to lead by steering a strategic, innovation-first path grounded in American strengths. Policy decisions made now will determine whether the US can scale AI innovation or whether infrastructure, energy, and regulatory friction become binding constraints.

Business leaders are embracing AI and are eager to move forward. According to Arm’s AI Readiness Index, 83 percent of global executives say it’s urgent to embrace AI, and nearly nine in ten have already allocated budgets to do so. Businesses are looking to Washington for strategic direction that supports innovation and builds long-term trust.

Federal actions reflected this need. The Administration’s executive order addressing state-level AI laws signals an important effort to reduce fragmentation and ensure innovation is not slowed. At the same time, Initiatives such as the Genesis Mission Executive Order underscore a broader commitment to leadership in advanced computing, science, and infrastructure — the foundations for AI leadership.

The White House AI Action Plan marked a critical step toward a coordinated national approach. By focusing on AI safety, security, innovation, and infrastructure, it creates a framework that can unlock private-sector investment and accelerate responsible growth.

Building on that momentum, the strategy can go further by focusing on three priorities:

First, prioritize innovation as a national advantage. America’s leadership in semiconductors, research institutions, and startup ecosystems thrives when policy creates clear, stable conditions for investment. A forward-leaning national strategy should champion open markets, long-term R&D, and collaboration between government, academia, and industry.

Second, advance hardware and infrastructure readiness. AI software and models evolve in months; hardware, infrastructure, and power systems evolve over years. Regulation must account for this mismatch to enable innovation rather than constrain progress. 

Infrastructure investments — from data center modernization to R&D in energy-efficient architectures — are essential to meeting the Action Plan’s goals. Ensuring US companies and developers have access to efficient compute should be central to US competitiveness. The Arm-SCSP joint position paper outlines how policy and innovation can accelerate AI energy efficiency and power the next wave of US competitiveness.

Third, strengthen global engagement. As countries define their own AI frameworks, the US can work to promote interoperable standards across borders. Active leadership reduces the risk of global fragmentation on frameworks and standards, supports cross-border innovation, and positions the US industry to compete in global markets shaped by shared principles.

One of America’s regulatory strengths is its use of sector-specific approaches. Tailoring oversight to real-world use cases can accelerate innovation by providing clarity and confidence. One-size-fits-all rules risk slowing progress and innovation. Rather than broad, sweeping mandates, the US has historically applied tailored guardrails where risks and use cases differ — a model well-suited to AI.

A diagnostic model used in healthcare carries different societal stakes than a creative tool used for marketing, and treating them identically can slow low-risk innovation without improving safety. By focusing on context, not categories, policymakers can target real risks without slowing safe, beneficial applications. This mirrors the US approach to healthcare, finance, and transportation: risk-based oversight with accountability, flexibility, and practical safeguards. 

Approaches like regulatory sandboxes, evaluation frameworks, and promotion of voluntary technical standards can help agencies introduce oversight without creating barriers for emerging applications. These tools complement the goals of the AI Action Plan by encouraging responsible development, enabling experimentation, and continuous improvement.

Global partners are looking to the US for technological leadership and governance models that enable innovation responsibly. The most powerful AI systems of the future will be evaluated not only by capability, but by trust and efficiency.

The AI Action Plan creates the right foundation. Building on it will require clear interagency guidance, infrastructure investments, security-by-design principles across the ecosystem, global leadership that shapes interoperable frameworks, and a skilled workforce prepared for long-term AI integration.

As AI capabilities accelerate faster than the infrastructure and systems that support them, US leadership will depend on policies flexible enough to evolve with technology and promote private-sector risk-taking without losing sight of safety, security, and trust. The US has the opportunity and momentum to set the global standard for trusted, energy-efficient, innovation-driven AI.