Introduction
The global AI race is no longer just about who builds the most powerful models. It is about who supplies the most widely adopted ones. As closed, proprietary AI dominates enterprise usage but remains expensive, open models are becoming the strategic choice for countries and institutions that need affordability, control, and sovereignty. This shift is turning AI into a new arena of geopolitical competition - where influence flows through infrastructure, standards, and ecosystems, not just raw capability. This is an evolving story, and we covered it earlier here
12 key points
1) Power follows adoption, not just capability
Geopolitical leverage accrues to the models embedded across governments, education, startups, and public services. Widespread use creates standards, dependencies, and long-term influence.
2) Cost asymmetry reshapes global uptake
Open models offer near-frontier performance at a fraction of the cost of closed systems. This price gap structurally pushes emerging economies toward open alternatives.
3) Digital sovereignty favours openness
Open models can be hosted locally, audited, and customized. This aligns with national goals around data control, compliance, and independence from foreign platforms.
4) Closed models dominate enterprises, for now!
Proprietary systems lead in large enterprises due to reliability, support, and perceived safety. But their cost and lock-in limit adoption beyond wealthy markets.
5) Open models create “infrastructure diplomacy”
Countries that fund and export open AI stacks embed their technology into others’ digital infrastructure, shaping standards and long-term dependencies.
6) China’s open/semi-open push expands influence
Affordable models and ecosystem building position China to grow AI adoption across Africa, Asia, and the Global South where budgets are constrained.
7) The U.S. risks influence loss via premium-only strategies
If leadership remains concentrated in high-cost closed models, U.S. firms may retain capability leadership but lose adoption leadership globally.
8) Data gravity compounds geopolitical advantage
Models embedded in workflows accumulate local data and feedback loops, improving faster in-region and deepening technological dependence.
9) Regulation and trust are strategic levers
Closed models offer centralized compliance and security assurances. Open models require strong governance frameworks to earn state-level trust.
10) Switching costs slow rebalancing
Ecosystems built around closed models create inertia. Migration to open alternatives carries short-term costs that delay geopolitical realignment.
11) Performance gaps are closing faster
Open models rapidly approach closed-model performance after releases, shrinking the premium justification for many use cases.
12) The real contest is control of workflows
Whoever controls orchestration layers, data pipelines, and integration standards controls economic value—models become utilities over time.
Read more on AI geopolitics; click here
Summary
AI geopolitics is shifting from a race for the best model to a race for the most embedded model. Open systems, by virtue of cost, deployability, and sovereignty alignment, are poised to shape global adoption- especially across emerging economies. Closed models will remain critical in high-stakes enterprise contexts, but influence at scale will flow to those who supply affordable “AI infrastructure.” In the next phase of the AI race, standards, ecosystems, and workflow control will matter as much as raw intelligence.
