AI governance is often discussed in terms of risk, ethics, safety, and international cooperation, but these are insufficient without the necessary computing infrastructure. Without the ability to run, monitor, and maintain AI systems, governance remains merely theoretical. The global AI divide is fundamentally about access to infrastructure, not just models. Most governance frameworks assume nations possess essential resources like high-performance compute, reliable data pipelines, and skilled operators, which is often not the case.The "execution layer" translates policy into practice through compute infrastructure, data processing, monitoring tools, and operational workflows. This crucial layer, rarely discussed, is the foundation for responsible AI. Ignoring this reality risks making governance aspirational rather than actionable, leading to structural inequalities and long-term dependency on external providers. Capability building initiatives focusing solely on training are ineffective without providing the necessary compute resources.A realistic global AI governance strategy must prioritize infrastructure, advocating for regional and sovereign compute capacity, shared agreements, and robust operational tools. The protection of youth, global collaboration, climate action, and cost management in AI all hinge on this underlying infrastructure. Ultimately, AI's success and equitable development depend on recognizing infrastructure as a core component of capability. Without compute, there is no capability; without capability, there is no governance; and without governance, there is no equity. The execution layer is where the future of AI and its governance will be determined.