AI investment often brings to mind a specific set of companies: NVIDIA, AMD, SK Hynix, Samsung Electronics, and ASML. These companies are undoubtedly at the heart of AI infrastructure. However, this time, we need to look from a slightly different angle.

A significant change has recently occurred in the AI market. Frontier AI models are no longer treated as mere software products but are beginning to be regarded as strategic assets, similar to semiconductors. As the perception grows that model access can be controlled and restricted to specific countries or users, governments and companies naturally begin to ask one question:

"Will the AI we use still be turned on tomorrow?"

I believe this question elevates the discussion around Sovereign AI to a new level. Until now, Sovereign AI has largely been akin to a slogan: "We must develop our own foundation models." However, it is highly likely to evolve into a more practical issue in the future.

The essence of Sovereign AI is not about developing proprietary models, but about how much of the supply chain required to train, operate, validate, and protect those models can be secured within one's own country or allied nations.

From this perspective, Sovereign AI is not just an AI software theme. It is a global supply chain realignment theme, extending from GPUs, HBMs, foundries, packaging, equipment, materials, power, cooling, and optical communication to next-generation memory.

1. Learning demand is not over; its ceiling is rising again

Recently, a very simplistic logic regarding AI demand has been prevalent in the market: Learning uses GPUs, inference uses CPUs.

Of course, the reality is far more complex. GPUs are also used for inference, and learning requires CPUs, memory, and networks. However, investors' understanding of the market generally followed this framework. To some extent, it was also true.

Frontier-level model training is already dominated by a few companies in the US and China. OpenAI, Google, Anthropic, Meta, xAI, and some Chinese big tech and model companies are at the center of the learning race. Naturally, the market began to think: "Learning has reached a certain stage, and now inference demand will be key, right?"

I agree with this direction in principle. As AI expands into actual services, inference demand will naturally grow. As agents, search, coding, robotics, on-device AI, and enterprise AI workflows increase, the daily operation of inference infrastructure becomes crucial.

However, Sovereign AI shakes this dynamic once more.

Previously, only the US and China focused on creating frontier-level foundation models. But what if G20 countries each begin to decide, "We must have at least a minimal level of our own AI infrastructure"?

Not every country can directly build GPT-level models. However, the demand to train and tune models based on local languages and local data for use in national government, defense, finance, legal, medical, and public systems could increase. The key is not whether they can build the best model, but the movement to avoid complete reliance on foreign models.

This is fuel that will reignite the GPU market.

In this trend, looking only at NVIDIA and AMD is insufficient. While GPUs are central, Sovereign AI expands beyond simply buying a GPU to the question of "where to procure the entire AI system, where to operate it, and how much control can be exercised over it."

2. Sovereign AI is not about proprietary models, but proprietary supply chains

This is the core point as I see it. Sovereign AI starts with model sovereignty, but ultimately leads to supply chain sovereignty.

To build AI models directly, GPUs are needed. To use GPUs, HBMs are needed. To make HBMs, advanced packaging and test equipment are needed. To make chips, foundries and lithography equipment are needed. To run foundries, wafers, photoresists, specialty gases, and chemical materials are needed. To operate data centers, power, cooling, optical communication, transformers, and power control systems are needed.

Ultimately, Sovereign AI does not end with "Let's create our own country's model." It leads to the question, "Who holds the kill switch for the AI supply chain we depend on?"

From this perspective, looking only at US and Korean stocks narrows the view too much. We must also consider Japan, Taiwan, China, and Europe. Japan, in particular, may have fewer leading AI software companies, but it is indispensable in the semiconductor equipment and materials supply chain. Taiwan is central to foundries, server ODMs, and packaging substrates. Europe is strong in lithography equipment and power/automation infrastructure. China is both a victim of sanctions and the country most aggressively pushing for its own supply chain.

3. Japan should be viewed as a supply chain bottleneck rather than an AI software leader

Japan receives relatively less attention in the AI model competition. However, when viewed through the lens of the supply chain, the story changes completely. Japan is strong in semiconductor equipment, materials, wafers, inspection, ceramics, and optical communication. As AI semiconductors become more complex, and as countries strive to secure their own supply chains, the strategic value of Japanese companies could actually increase.

What makes these companies interesting is that they don't need to directly pick the winner of the AI model competition. Regardless of who creates the models or designs the GPUs, as advanced semiconductors and data centers proliferate, demand for equipment, materials, inspection, and cooling will follow.

4. Taiwan is not just TSMC, but also servers and packaging

Taiwan is one of the most important regions in the Sovereign AI supply chain. The reason is simple: it's where AI chips are actually made. Most people only think of TSMC, but from a Sovereign AI perspective, the ecosystem behind it is also crucial. We need to look at AI server ODMs, packaging substrates, back-end processes, and general-purpose memory.

Taiwan should be viewed from the perspective of "who actually manufactures AI chips and servers" rather than "who creates AI models." As Sovereign AI spreads, countries may demand not only US big tech models but also their own cloud, data center, and AI server infrastructure. In this process, Taiwanese ODMs and substrate companies are likely to remain in the supply chain.

5. China is both a victim of sanctions and a testing ground for its own supply chain

China must be viewed separately when considering the Sovereign AI supply chain. China is the most heavily impacted by US semiconductor sanctions, but at the same time, it is the country most aggressively building its own AI supply chain. China's Sovereign AI is partly about "replacing US models," but more fundamentally, it's an experiment in "how far can we go without US equipment and US chips?" Therefore, when looking at Chinese stocks, one should not simply focus on performance gaps. As sanctions persist, even lower-performance domestic alternatives are more likely to be adopted in the domestic market.

6. Europe is not just ASML, but also power and industrial infrastructure

Looking only at ASML is insufficient for Europe. Of course, ASML is an absolute bottleneck in the advanced semiconductor supply chain. However, when considering the Sovereign AI supply chain, Europe's strengths extend beyond equipment to power, automation, industrial control, and power semiconductors. As Sovereign AI penetrates national data centers and public infrastructure, power and automation become bottlenecks.

7. MRAM is not an HBM replacement, but an Edge AI option

To describe MRAM as a substitute for HBM is an overstatement. The central bottleneck for AI training remains HBM. However, if Sovereign AI does not remain confined to cloud data centers, the story changes. As AI moves into defense, automotive, industrial equipment, robotics, medical devices, edge servers, and secure devices, the need for low-power, non-volatile, and highly reliable memory could increase. In this scenario, MRAM becomes an option. HBM is the current bottleneck, and MRAM is an option for the Edge AI era.

8. Summary from an investment perspective

Ultimately, this trend shows one thing: AI infrastructure demand is not simply shifting from learning to inference. While inference demand is growing, the ceiling for learning demand is also rising again, driven by the justification of Sovereign AI. Sovereign AI is expanding from a proprietary model competition to a proprietary supply chain competition.

What's important here is not an approach that simply follows the leading stocks. NVIDIA remains at the center of AI infrastructure. SK Hynix is also key to the HBM bottleneck. However, the market is already well aware of these facts. A good company and a good price are different.

Therefore, going forward, we should not only look at "who makes the best AI models," but also "where are the supply chain bottlenecks that AI must pass through as it continues to grow?"

Conclusion

Sovereign AI can seem somewhat abstract when viewed as a slogan. However, when Sovereign AI is viewed through the lens of the supply chain, the story changes. Even if countries cannot directly create the best models, they will at least try to avoid complete reliance on foreign models and foreign clouds in defense, public, finance, medical, and industrial sectors. In this process, securing proprietary training, proprietary inference, proprietary data centers, and proprietary supply chains becomes crucial.

This trend reignites the GPU market. At the same time, it broadens demand to HBM, packaging, foundries, equipment, materials, power, cooling, optical communication, and next-generation memory. It's about who holds the AI supply chain. For actual investment, instead of chasing leading stocks at high prices, an approach of calmly selecting companies in each supply chain bottleneck that the market has not yet fully reflected seems more appropriate.

From "Which model is the smartest?" to "Will that model be turned on tomorrow?" and now to "Whose hands hold the supply chain that makes that model possible?"