👑 Sovereign AI is not a model, but a supply chain problem

AI investment often brings to mind a specific set of companies: $NVDA NVIDIA, $AMD, $K000660 SK Hynix, $K005930 Samsung Electronics, and $E: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 $NVDA 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.

Viewed this way, Sovereign AI is not simply a story about US big tech and Korean HBM. Rather, it provides an opportunity to re-examine the bottlenecks in each region that constitute the AI 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.

However, many Japanese companies are already highly valued in the market as high-quality supply chain players. A good company and a good price are different. Therefore, rather than viewing them as short-term theme stocks, it is more appropriate to consider them as supply chain bottleneck stocks to keep in the candidate pool during corrections.

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 $TW2330 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.

However, the Taiwanese supply chain carries significant geopolitical risk. Therefore, as Sovereign AI expands, efforts to reduce reliance on Taiwan may also increase. This is both an opportunity and a risk for $TW2330 TSMC. The trend of diversifying production bases to the US, Japan, and Europe should also be considered.

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 faces restrictions in high-performance GPUs and cutting-edge equipment. However, it cannot be ignored in foundries, packaging, communication equipment, optical communication, rare metals, and domestic data centers.

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.

Of course, there are also significant risks. The advanced process gap, HBM shortages, equipment restrictions, geopolitical risks, and accounting transparency issues are all present. When considering the Chinese AI supply chain, distinguishing between good and bad companies is important, but one must always consider the significant impact of sanctions and policies.

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

Looking only at $E: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.

AI data centers do not run on GPUs alone. They require stable electricity supply, heat dissipation, automated facilities, and improved power efficiency. European companies are strong in these areas.

As Sovereign AI penetrates national data centers and public infrastructure, power and automation become bottlenecks. From this perspective, companies like $E:SU Schneider Electric, $E:SIE Siemens, and $E:ABB could be re-evaluated not just as power equipment companies, but as AI infrastructure supply chain players.

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

MRAM is a point worth including in this article. However, its position must be accurately defined.

To describe MRAM as a substitute for HBM is an overstatement. The central bottleneck for AI training remains HBM. In large-scale GPU clusters, memory bandwidth and capacity are key, and $K000660 SK Hynix, $K005930 Samsung Electronics, and $MU Micron are central in this area.

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.

MRAM can retain data even when power is off, can be advantageous in terms of leakage current compared to SRAM, and has potential for use as embedded memory. It is an interesting candidate, especially in environments like edge AI, industrial AI, and defense AI, where constant cloud connectivity is not guaranteed or power constraints are significant.

However, MRAM is still more of an "option" than the "mainstream of AI memory." Its market size is small compared to HBM, and challenges remain in writing energy, process integration, cost, yield, and expanding applications.

Therefore, MRAM should be viewed this way:

HBM is the current bottleneck, and MRAM is an option for the Edge AI era.

Investors currently focused on AI training demand should look at HBM and packaging. But if one considers the trend of Sovereign AI moving into defense, industrial, automotive, and robotics, then next-generation memories like MRAM are also worth including in the candidate pool.

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.

And the more important change is this:

Sovereign AI is expanding from a proprietary model competition to a proprietary supply chain competition.

From this perspective, the AI supply chain should be divided as follows:

What's important here is not an approach that simply follows the leading stocks.

$NVDA NVIDIA remains at the center of AI infrastructure. $K000660 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?"

Those bottlenecks are not only in US GPUs. They are spread across Korean HBM, Taiwanese foundries and substrates, Japanese equipment and materials, European power and automation, China's optical communication and domestic supply chain, and next-generation memory options like MRAM.

Conclusion

Sovereign AI can seem somewhat abstract when viewed as a slogan. Not every country can create an OpenAI. Not every company can train its own foundation model.

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.

Therefore, this Sovereign AI trend is not a simple AI model theme.

It's about who holds the AI supply chain.

From this perspective, the companies to continue watching are $NVDA, $AMD, $K000660 SK Hynix, $K005930 Samsung Electronics, $MU, $TW2330 TSMC, $TW2382 Quanta, $TW3037 Unimicron, $J8035 Tokyo Electron, $J6857 Advantest, $J4063 Shin-Etsu, $J4185 JSR, $E:ASML, $E:SU Schneider Electric, $E:SIE Siemens, $H00981 SMIC, $S600584 JCET, $S600487 Hengtong Optic-Electric, and $MRAM Everspin Technologies.

However, this is not a statement that "they will definitely rise." Sovereign AI is a politically strong justification, but economically a very expensive choice. Therefore, 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.

As AI grows, the important questions are increasingly changing.

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?"