As geopolitical fragmentation accelerates in this post-globalization era, technology sits squarely at the fault line.

Key cutting-edge domains—semiconductors, AI, communications, quantum computing, and biotechnology—are no longer just catalysts for innovation and economic growth, but conduits for countries’ political power, national security, and strategic advantage. Governments are stepping in more forcefully, actively influencing and directing the flow of capital, talent, and intellectual property. Technological self-reliance (to the extent it’s possible) is becoming a more urgent priority for nations worldwide, partly as a means of protecting themselves in case tech-leading countries wield their control over essential technologies—cloud computing, mission-critical business software, defense—as a geopolitical cudgel.

For several years, these dynamics have created constant, unpredictable challenges for technology executives. Two issues are rising fast on boardroom agendas: the near-term effects of tariffs on technology supply chains, and the longer-term business implications of governments’ accelerating push for “sovereign” AI.

Navigating this more complex and fundamentally different tech environment will require updated strategies, new bets, and a high tolerance for ambiguity.

The decoupling core: Semiconductors and the electronics supply chain 

Semiconductors are the pressure point at the epicenter of today’s geopolitical tensions. Aiming to protect its advantage in leading-edge compute and the technologies it powers, the US has steadily tightened export controls on advanced chips, chipmaking tools, electronic design automation software, and high-bandwidth memory chips destined for China.

The restrictions and tariffs implemented by the US on China beginning in 2018 sparked a wave of supply chain diversification. Many companies adopted a “China Plus One” strategy that shifted manufacturing to countries such as Mexico and Vietnam. Now, the second Trump administration has enacted or proposed more extensive tariffs on a much wider set of countries.    

For many tech executives, this raises significant supply chain questions, given the complex, global nature of the electronics value chain. No longer can the answer simply be China Plus One; a broader set of options must now be considered to ensure supply and cost stability. The only real hedge against unpredictable shocks to the system is continued regionalization or even nationalization; supply chains will become even more dispersed.

China, for its part, is racing toward self-reliance. Since 2019, it has invested more than $250 billion in semiconductor manufacturing, tripling its domestic production capacity to a projected nearly 3 million wafers per month this year; that’s roughly 20% of global capacity. While most of this growth is in mature, lagging-edge semiconductor nodes, China is also making progress in the production of more advanced chips smaller than 28 nanometers, now accounting for around one-fifth of global output of logic chips and a quarter of memory chips (see Figure 1).

China’s strides are challenging the notion that market leadership in semiconductors is solely defined by the leading edge. While still critical, memories of the semiconductor shortage in 2021 and 2022 remind executives that a lack of lagging-edge chips can keep a company from shipping final products. China’s strong position in less advanced chips, which have a larger global supplier base, initially led many to assume that customers would eagerly switch to vendors outside of China. But overcapacity in mature nodes and the perceived flexibility to change suppliers later have kept the status quo intact longer than expected.

It’s yet another signal that decoupling isn’t linear, and it’s far from over. 

Sovereign AI: Leveling the playing field 

The concept of sovereign AI has rapidly evolved from theory to geopolitical imperative. Sovereign AI systems are trained on domestic or culturally appropriate data, hosted by nationally or regionally controlled data centers (if possible), and increasingly rely on open-source foundation models developed domestically, which allows governments and institutions to audit the systems for bias, transparency, and misuse.

This isn't just about privacy or control. It’s about data security and aligning AI outputs with national values, regulatory standards, and strategic priorities, all while reducing dependence on foreign tech ecosystems. Sovereign AI capabilities are increasingly seen as a strategic advantage on par with economic and military strength.

The race between the Chinese and American tech ecosystems is at the forefront of the decoupling movement. Both countries are advancing swiftly. The US leads in high-performance chips and foundation models, while China is expanding its AI capabilities through initiatives including the DeepSeek-R1 model and Huawei’s Ascend 910C chip—all developed with minimal US tech. China is also investing heavily in physical AI, such as humanoid robotics. Meanwhile, South Korea dominates in high-bandwidth memory chips, illustrating the complexity of the global ecosystem and the fact that it remains difficult for any one country to become completely self-reliant (see Figure 2). While hardware, models, and apps are decoupling, open-source technologies and talent continue to cross-pollinate.  

However, sovereign AI is a global priority. The EU’s €200 billion InvestAI initiative, launched in February, includes €20 billion to build AI gigafactories—data centers equipped with at least 100,000 graphics processing units (GPUs) each. In a related project, Germany-based Deutsche Telekom has partnered with Nvidia to build an industrial AI cloud for European manufacturers. Saudi Arabia’s new AI firm, Humain, plans to build domestic data centers with a combined capacity of 500 megawatts. It’s starting with a 50-megawatt data center housing 18,000 Nvidia GPUs, slated to launch in 2026. Humain also aims to build one of the most powerful multimodal Arabic large language models.

AI goals vary. In China, it’s about end-to-end control. In Europe, it’s more about regulatory alignment and data localization. In the Middle East, it’s participation in the global ecosystem. Practicality trumps purity: For most countries, it’s simply not feasible to achieve full-stack independence, at least not today, given the realities of where semiconductor fabs are clustered and which countries control the best AI models.

That divergence will complicate everything for tech companies. As AI becomes embedded in business operations—from customer engagement to supply chain management—multinational firms will need to localize not just compliance, but technology architecture. A single AI workflow may need to be retooled for different markets, with varying models, training data, data usage practices, and infrastructure requirements.

And global AI standards? Unlikely. From content censorship to data labeling to acceptable uses, definitions of “responsible AI” differ widely and likely won’t converge. AI systems are becoming more like national or regional products, shaped by the political and cultural norms in which they’re developed.

Strategic implications: Rethinking how and where to compete

Executives are starting to recognize this isn’t a passing phase. It’s a new world order with profound consequences for everything a business does. To steer their organizations effectively through this new era, leading tech companies are focusing on four key principles.

Think in operating models, not just product lines. Some countries will build their own AI capacity or treat it as a strategic lever. Others will simply buy from the cheapest available source. That creates a patchwork of environments in which companies must tailor how they build, deploy, and monetize technology. Will local laws demand new infrastructure, retrained models, or local data partnerships? If so, what trade-offs are acceptable?

Don’t assume the global tech race is over. It’s very much still on and has a long way to go, as tariffs and export controls haven’t slowed China as much as expected. The journey will be volatile and bumpy, with big unknowns surrounding possible trade deals and the knock-on effects of China’s accelerated investments in technology manufacturing capacity. Leading companies will closely follow developments, particularly in generative AI and humanoid robotics.

Don’t mistake relocation for resilience. For multinational technology companies, moving production out of China is a start, but it’s no longer enough. Supply chains must be regionalized, flexible, and built for continued political volatility. Establishing a stronger presence in the most important end markets is becoming more crucial, particularly for semiconductors and other strategic, capital-intensive sectors.

Make decisions with optionality. Executives won’t get every bet right. Where confidence is high, move boldly. Where the future is murky, prioritize flexibility. For some companies, that might mean setting up neutral-region tech hubs. (Will Dubai become the next Singapore?) For others, it may mean delaying or skipping certain markets altogether because they’re too costly or complex to serve.