Rather than trying to predict specific geopolitical crises, Siemens builds resilience by creating separate technology stacks for different regions. For instance, its industrial AI for China is trained on Chinese LLMs, while its US counterpart uses American models, creating independent and compliant systems.
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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.
To overcome the data scarcity problem for industrial AI, Siemens formed an alliance with competing German machine builders. These companies agreed to pool their operational data, trusting Siemens to build powerful, shared AI models that are more effective than any single company could create alone.
As countries from Europe to India demand sovereign control over AI, Microsoft leverages its decades of experience with local regulation and data centers. It builds sovereign clouds and offers services that give nations control, turning a potential geopolitical challenge into a competitive advantage.
The push for sovereign AI clouds extends beyond data privacy. The core geopolitical driver is a fear of becoming a "net importer of intelligence." Nations view domestic AI production as critical infrastructure, akin to energy or water, to avoid dependency on the US or China, similar to how the Middle East controls oil.
The open vs. closed source debate is a matter of strategic control. As AI becomes as critical as electricity, enterprises and nations will use open source models to avoid dependency on a single vendor who could throttle or cut off their "intelligence supply," thereby ensuring operational and geopolitical sovereignty.
Roland Bush asserts that foundational LLMs alone are insufficient and dangerous for industrial applications due to their unreliability. He argues that achieving the required 95%+ accuracy depends on augmenting these models with highly specific, proprietary data from machines, operations, and past fixes.
For many companies, 'AI sovereignty' is less about building their own models and more about strategic resilience. It means having multiple model providers to benchmark, avoid vendor lock-in, and ensure continuous access if one service is cut off or becomes too expensive.
A key risk to OpenAI's trillion-dollar valuation is not just market competition, but the rise of a state-backed, parallel AI ecosystem in China. This creates a future where global AI leadership could be fragmented along geopolitical lines, challenging long-term dominance.
Siemens mitigates geopolitical risks and tariffs not just by being global, but by being hyper-local. Its CEO reveals that 85-87% of its production in major markets like the US and China is for that market, minimizing cross-border dependencies and the direct impact of trade wars.
To escape platform risk and high API costs, startups are building their own AI models. The strategy involves taking powerful, state-subsidized open-source models from China and fine-tuning them for specific use cases, creating a competitive alternative to relying on APIs from OpenAI or Anthropic.
