From Innovation Race to Algorithmic Dependence: AI Geopolitics and the Future of Emerging Economies

AI has stopped being just a technology story. It’s a power story now. The US, China, and the EU are racing to lead it, and much of the rest of the world is navigating what that means for them — often on unfavorable terms. The problem for Global South countries isn’t getting access to AI. It’s what that access costs. Building national infrastructure on imported systems you can’t inspect, modify, or fully understand means you’re not just buying software. You’re exposing behav- ioral data, ceding influence over how decisions get made, and — gradually — losing the ability to chart your own course. Some researchers call this data colonialism. The label is contested, but the dynamic it describes is real. This paper examines how that plays out across different countries and governance approaches, with India as the main case study. India is worth looking at closely: it has the technical depth and political will to push back against dependency, and it’s actively doing so. The analysis pulls from policy documents and concrete examples, read through both realist and constructivist lenses — who holds structural power, and how shared narratives shape what countries think is possible. The pattern that shows up repeatedly is this: states that control AI infrastructure set the rules and harvest the data. States that don’t end up structurally subordinate — not through force, but through the slow accumulation of dependencies. The paper calls this an ”algorithmic hierarchy.” India’s experience suggests that domestic investment and deliberate sovereignty-building can change the equation, though not easily. The framework tying it all together — the ”Algorithmic De- pendence Model” — is the paper’s main theoretical contribution. It links geopolitics, AI adoption, and dependency theory in a way that’s meant to be practically useful for policymakers, not just analytically tidy.