National-Scale AI: The Blueprint for U.S. Advantage and Security
Published:
AI is no longer a “software story.” It’s a national infrastructure story.
Most people talk about AI in terms of demos—chatbots, copilots, and clever automations. Leaders can’t afford to think that way. Real advantage comes from what’s underneath: compute, energy, data pipelines, security, governance, and the ability to deploy at scale.
In my latest paper, “AI for National Security,” I make a straightforward case: if the U.S. wants durable economic and strategic advantage, we have to treat AI like critical infrastructure—not a collection of pilots, not a procurement line-item, and not something we outsource by default.
The core issue isn’t imagination. It’s execution. Frontier AI increasingly concentrates in a handful of organizations with the capital, compute, and talent to build at scale—while public-sector missions face slower acquisition, fragmented data, and uneven access to serious infrastructure.
This paper lays out a practical blueprint: national-scale compute access, modern data architecture, secure deployment patterns, and acquisition models that match the speed of threats. It’s a build plan—focused on capability, accountability, and time-to-field.
All of this information is publically available. My paper just summarizes hundreds of research papers, corporate and academic websites, and market analysis.
Read the paper + exec summary here:
Final thought:
If you lead in government, defense, critical infrastructure, or enterprise AI, this is the framework for moving from “AI interest” to AI power.