Governments worldwide plan to invest $1.3 trillion in AI infrastructure by 2030, with the explicit goal of achieving "sovereign AI" through domestic data centers and locally trained models. The premise is straightforward: national control over AI capabilities requires national ownership of the computational substrate. But this infrastructure-first approach reveals a fundamental misunderstanding of how algorithmic capability actually develops in organizational contexts.

The sovereign AI movement assumes that computational infrastructure creates competence. Build the data centers, train the models on local data, and organizational capability follows naturally. This mirrors the classical coordination theory assumption that Kellogg et al. (2020) identify: that competence exists ex-ante and simply needs to be properly allocated. But platform coordination research demonstrates the opposite. Capability develops endogenously through participation in algorithmically-mediated environments, not through access to infrastructure alone.

Why Infrastructure Access Does Not Solve the Variance Puzzle

Consider the empirical reality from platform labor markets. Workers with identical access to algorithmic systems show dramatically different outcomes, with power-law distributions emerging not from differential infrastructure access but from algorithmic amplification of initial differences in how workers engage with those systems (Schor et al., 2020). The variance puzzle cannot be solved by ensuring everyone has the same computational resources.

The sovereign AI investment thesis assumes that providing domestic infrastructure solves a supply constraint. But the actual constraint is coordinative, not computational. Organizations within these sovereign AI ecosystems will face the same awareness-capability gap that platform workers face: knowing that locally trained models exist does not translate to knowing how to deploy them effectively in organizational contexts. Gagrain et al. (2024) document this precisely in their research on algorithmic media use, where awareness of algorithmic systems correlates poorly with effective engagement.

The Structural Feature These Investments Miss

What sovereign AI initiatives actually encode is a topographical solution to a topological problem. They focus on the specific instantiation of infrastructure (where servers are located, whose data trains the models) rather than the structural features that determine whether organizations can develop adaptive expertise in algorithmic coordination.

The distinction matters because routine expertise transfers poorly across contexts while adaptive expertise transfers well (Hatano & Inagaki, 1986). Building domestic infrastructure optimizes for routine expertise: organizations learn procedures specific to their national AI stack. But when those systems change, when new coordination challenges emerge, or when cross-border algorithmic coordination becomes necessary, that procedural knowledge provides no scaffolding for novel problems.

Schema induction targeting structural features would suggest a different approach. Rather than optimizing for sovereign control of specific computational infrastructure, the focus should be on developing organizational understanding of how algorithmic coordination mechanisms function as a class. This means understanding how algorithms mediate information asymmetries, how they create path dependencies through feedback loops, and how they amplify or attenuate organizational signals (Rahman, 2021).

The Counterintuitive Implication for AI Governance

The $1.3 trillion investment embeds a prediction: that platform-specific infrastructure development produces better organizational capability than general understanding of algorithmic coordination principles. This is precisely the opposite of what transfer theory would suggest. Organizations trained on general structural features of algorithmic systems should outperform organizations trained on procedures specific to their domestic AI infrastructure, even if the infrastructure-specific training produces faster initial performance.

The question is not whether nations should invest in AI capability development. The question is whether that investment should target computational infrastructure or coordinative competence. Current sovereign AI initiatives bet heavily on the former. But if algorithmic coordination capability develops endogenously through understanding structural features rather than through access to specific infrastructure, these investments risk creating expensive routine expertise that fails precisely when adaptive expertise is most needed.

The sovereignty framing itself may be the problem. It imports geopolitical logic into a coordination challenge, optimizing for control over specific instantiations rather than transferable understanding of underlying mechanisms. Whether nations spend $1.3 trillion learning to navigate one particular algorithmic landscape or learning to recognize the topology that all such landscapes share will determine whether this investment builds capability or merely purchases dependence on today's infrastructure configurations.