Technology as a geopolitical instrument
Technology is now at the center of global power. It’s no longer just about driving innovation or improving efficiency. It’s about control, security, and independence. Countries are starting to treat technologies, like AI, semiconductors, biotech, and quantum computing, not as tools, but as assets of national interest. That changes how governments think and act.
They’re not standing on the sidelines. They’re stepping in, redirecting capital, regulating access, pushing domestic development, and protecting talent. The goal is simple: reduce dependencies. In a world where advanced tech determines economic and military outcomes, any country that relies on another for critical capabilities creates significant risk exposure for itself.
So now you’ve got strategic investment pouring into local research, manufacturing, and infrastructure. Export controls, data localization laws, subsidies, these are no longer isolated moves. They’re part of a bigger shift toward what we can call “technological sovereignty.” And if tech leaders don’t adapt with the same urgency as governments, they will fall behind.
C-suite leaders need to act accordingly. Thinking globally still matters, if you want to scale, but it can’t come at the expense of resilience. Building local strength and reducing single points of failure has become core strategy, not contingency planning.
Semiconductors and supply chain realignments
Semiconductors chips drive almost every meaningful piece of modern technology. Because of that, they’re deeply tied to geopolitics. Right now, semiconductors are the front line of tech decoupling.
Since 2018, the U.S. has been tightening export restrictions on high-performance chips and the tools needed to manufacture them. China’s innovation trajectory triggered a deliberate response. Many companies responded with the “China Plus One” strategy, distributing manufacturing to Vietnam, Mexico, and elsewhere. But that’s not enough. Wider tariffs under the second Trump administration mean companies now face a landscape where broader supply chain bets are required.
The simple truth: one region can’t be the answer. You need optionality. The semiconductor supply chain is inherently complex, it stretches across dozens of countries and hundreds of suppliers. So when one part is disrupted, the entire operation feels the shock. That’s why regionalization is gaining momentum. Not for efficiency, but for resilience.
Now, look at China. They’re not sitting still. Since 2019, over $250 billion has gone into boosting domestic semiconductor capacity. As of this year, they’re producing nearly 3 million wafers per month, that’s about 20% of total global output. Most of these are mature nodes, but increasingly China is producing advanced chips below 28 nanometers, accounting for 20% of global logic chip output and 25% of memory.
This dents the idea that only bleeding-edge chips matter. The 2021–22 chip shortage proved that you can’t build or ship product if you don’t have stable access to even the basic silicon. So executives need to think beyond cutting-edge. Mature-process chips are still indispensable. And right now, China owns a big chunk of that space.
One decision won’t solve this. C-suite leaders should think in phases, spread production, create regional fallback plans, partner in neutral territories. You may not be able to outpace the geopolitical dynamics, but you can outmaneuver them.
The emergence of sovereign AI
Artificial Intelligence used to be about speed and scale. Now, it’s about control. Countries want AI systems that match their values, laws, and strategic goals. This idea, called sovereign AI, is being built, funded, and deployed.
Sovereign AI is straightforward. Build models on domestic data. Host them in local or regionally controlled infrastructure. Prioritize open-source frameworks that you can audit and align to national standards. The purpose isn’t just privacy, it’s independence. Countries don’t want to depend on systems they can’t influence. They want AI that reflects their rules, not someone else’s.
We’re already seeing momentum. The U.S. is ahead in high-performance chips and general-purpose foundation models. China is investing heavily too, focusing on AI hardware and scaled model training, DeepSeek-R1 and Huawei’s Ascend 910C are just part of a broad national push that’s been designed from the ground up to minimize reliance on U.S. tech. And while China tightens end-to-end control of its full AI stack, Europe is steering toward regulation, trust, and data autonomy.
South Korea is excelling in memory chips, essential for AI workloads, and the Middle East is entering the race through investment. The EU’s €200 billion InvestAI initiative, which includes €20 billion to fund massive GPU-based AI data centers, signals how seriously regional governments are taking this. Saudi Arabia is moving quickly too: its national AI firm, Humain, is building a 500MW domestic data center footprint. Their pilot project, a 50MW site with 18,000 Nvidia GPUs, comes online in 2026.
For business leaders, this is now an operational challenge. AI deployment plans can’t assume uniform infrastructure, model access, or regulation. Teams will need to localize not just compliance but execution, training data, model behavior, and deployment strategy all have to match the region. If you’re supporting customers in five markets, your tech might need five different configurations. Full-stack independence may be out of reach in the short term, but intelligent adaptation isn’t.
The limits of complete technological self-reliance
Self-reliance sounds clean, but in execution, it’s messy. The idea of one country owning the full tech stack, hardware, data, models, and infrastructure, doesn’t hold up under real-world conditions. What we’re seeing now is a hybrid model. Nations push for more independence but still rely on global partnerships, supply chains, and open-source development.
The U.S. dominates in GPUs, foundational AI models, and semiconductor design. China is narrowing the gap through incremental innovation and vertical integration. But there are areas of specialization that don’t overlap. South Korea remains the leader in high-bandwidth memory production, a category vital to AI training. No single country owns or controls all critical components.
Even with decoupling accelerating, there’s ongoing cross-pollination, especially in open-source AI communities and international R&D collaborations. So while the narrative frames AI and semiconductors as battlegrounds, the system is still interconnected. That’s not a weakness, it’s just how the tech works right now.
Executives should recognize the gap between ambition and reality. Total self-sufficiency, if it comes, is years away for every player involved. What matters now is strategic positioning, where to invest, who to partner with, and how to structure operations to reduce risk without losing access to core innovations. Stability doesn’t come from isolation. It comes from being adaptive, connected, and ready to shift strategy as the global tech balance evolves.
Strategic adaptation in a fragmented tech ecosystem
The global tech environment has fundamentally changed. We’re not returning to uniform rules or shared systems. Fragmentation isn’t temporary, it’s structural. For companies operating across borders, that means one thing: strategy needs to evolve, fast.
Startups can move quickly with limited exposure, but large tech firms, and any company relying on AI or semiconductors, must design with complexity in mind. A single product line or infrastructure strategy no longer works across all regions. You’ll need custom deployment methods, flexible compliance pathways, and infrastructure that meets local laws around data, AI behavior, and security controls.
Some governments will demand AI models that reflect national standards. Others will want domestic compute. Some end markets will legislate against foreign hosting or imported training data. Still others will prioritize cost over control and look for off-the-shelf global tech. Every region is moving on its own terms. That’s the operational reality.
This change doesn’t mean growth stops. It just shifts where and how growth happens. Companies with modular operations, not just modular products, will win the next phase of this tech era. That also means rethinking supply chain strategy. Relocating manufacturing out of China is no longer edge-case planning, it’s the starting line. But beyond relocating, companies need regional manufacturing options, redundancy in key geographies, and tighter focus on proximity to high-growth markets.
Add in the uncertainty of trade dynamics, and flexibility becomes essential. Not every bet will be right, that’s part of the calculation. Executives should prioritize decision structures that allow for fast adjustment. That might mean setting up in neutral hubs, like Dubai. In some markets, it may mean deliberate absence, skipping high-cost, high-friction regions until conditions improve.
This isn’t about hedging. It’s about building with resilience and actionability in mind. Companies that wait for conditions to “settle” will struggle. Conditions aren’t settling, they’re reshaping. And the winners will be the ones moving with speed, awareness, and clear tolerance for dynamic complexity.
Key takeaways for leaders
- Technology as power strategy: Tech is now central to national security and economic independence. Leaders should expect tighter government control over IP, talent, and capital, and align company strategies accordingly.
- Semiconductor supply chains are political: Chip production is ground zero for geopolitical shifts. Executives should regionalize supply chains and reduce single-market exposure to manage long-term volatility.
- Sovereign AI is a strategic priority: Nations are building AI systems aligned with local laws and values. Companies deploying AI globally must prepare for market-by-market customization across models, data, and infrastructure.
- Full self-reliance isn’t realistic, yet: No nation controls every layer of the AI or semiconductor stack. Leaders should focus on flexible partnerships and modular operations that absorb supply shocks without losing innovation access.
- Strategize for a fragmented future: Uniform global models no longer work. Decision-makers should rethink tech architectures, localize go-to-market approaches, and build optionality into where and how they operate.
