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Canada's AI strategy document mentions "indigenous" more than "GPUs." This focus on ideological alignment over the core technology required for AI development signals a disconnect from reality and may explain the country's lagging economic performance.

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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 US AI strategy is dominated by a race to build a foundational "god in a box" Artificial General Intelligence (AGI). In contrast, China's state-directed approach currently prioritizes practical, narrow AI applications in manufacturing, agriculture, and healthcare to drive immediate economic productivity.

Relying solely on imported AI technology from superpowers like the US and China is a path to economic and political dependency. Governments must foster local AI innovation and infrastructure to maintain economic sovereignty and global competitiveness.

The primary constraint on AI development is not software or algorithms but the physical infrastructure required to support it: power, data centers, and supply chains. Policy will focus on this area regardless of election outcomes, though the specific approach may differ.

Former White House advisor Ben Buchanan argues that contrary to the popular phrase "data is the new oil," computing power is the true bottleneck and driver of AI progress. This physical reality—advanced chips primarily made by democracies—creates a powerful geopolitical lever to influence nations like China.

The feeling that AI development is a "race" is unique to this tech era. According to Aetherflux founder Baiju Bhat, this urgency is fueled by geopolitical competition between the U.S. and China, who both view AI leadership as a national strategic priority, unlike previous consumer-focused tech waves.

The AI competition is not a race to develop the most powerful technology, but a race to see which nation is better at steering and governing that power. Developing an uncontrollable 'AI bazooka' first is not a win; true advantage comes from creating systems that strengthen, rather than weaken, one's own society.

While AI leaders are preoccupied with public lawsuits and personal disputes, they are failing to articulate why AI development is crucial for the country (e.g., competing with China). This narrative vacuum allows public backlash against necessary infrastructure, like data centers, to grow unchecked.

A core motivation for Poland's national AI initiative is to develop a domestic workforce skilled in building large language models. This "competency gap" is seen as a strategic vulnerability. Having the ability to build their own models, even if slightly inferior, is a crucial hedge against being cut off from foreign technology or facing unfavorable licensing changes.

Companies fail at AI strategy because their leaders haven't invested in understanding the technology's core capabilities, such as reasoning and multimodality. Without this literacy, any strategic plan for org charts, tech stacks, or workflows will be suboptimal and incomplete.