Get your free personalized podcast brief

We scan new podcasts and send you the top 5 insights daily.

The US believes a 10x increase in training compute will make its proprietary models 'twice as capable.' This widening performance gap is a strategic lever intended to make aligning with the American AI stack an unavoidable choice for nations seeking competitive advantages, forcing them to overlook sovereignty concerns.

Related Insights

The US focus on exporting hardware (chips, data centers) over proprietary models suggests a strategic belief that open-source AI will eventually dominate. If models become a free commodity, the most valuable and defensible part of the AI stack becomes the underlying compute infrastructure.

The White House's Michael Kratsios reframes "AI sovereignty" as owning American-built hardware and infrastructure, not renting access to US cloud models. This strategy encourages partner nations to buy the AI stack ("They build it. It's yours.") rather than remaining dependent on subscriptions.

The competition in AI infrastructure is framed as a binary, geopolitical choice. The future will be dominated by either a US-led AI stack or a Chinese one. This perspective positions edge infrastructure companies as critical players in national security and technological dominance.

The conversation around AI and government has evolved past regulation. Now, the immense demand for power and hardware to fuel AI development directly influences international policy, resource competition, and even provides justification for military actions, making AI a core driver of geopolitics.

The White House warns of a "great divergence" where AI-leading nations accelerate growth far beyond others. This same principle applies at a corporate level, creating a massive competitive gap between companies that effectively adopt AI and those that lag behind.

A nation's advantage is its "intelligent capital stock": its total GPU compute power multiplied by the quality of its AI models. This explains the US restricting GPU sales to China, which counters by excelling in open-source models to close the gap.

The US and China have divergent AI strategies. The US is pouring capital into massive compute clusters to build dominant global platforms like ChatGPT (aggregation theory). China is focusing its capital on building a self-sufficient, domestic semiconductor and AI supply chain to ensure technological independence.

The U.S. strategy treats AI not just as technology, but as a foundational tool for global influence. By creating a dominant 'tech umbrella,' it aims to forge alliances and exert power in a way analogous to how its military has secured its global standing since WWII, making AI the new core of its national power.

The new "American AI Exports Program" and "Tech Corps" initiative mirror the strategy used to compete with Huawei's 5G dominance. By offering attractive financing and on-the-ground training, the US aims to provide developing nations a complete solution to build AI capabilities with American technology.

The 2020 research formalizing AI's "scaling laws" was the key turning point for policymakers. It provided mathematical proof that AI capabilities scaled predictably with computing power, solidifying the conviction that compute, not data, was the critical resource to control in U.S.-China competition.