The Diplomat author Mercy Kuo regularly engages subject-matter experts, policy practitioners, and strategic thinkers across the globe for their diverse insights into U.S. Asia policy. This conversation with Jouko Ahvenainen – founder and chairman of Mission Grey and serial entrepreneur in data analytics, fintech, and external intelligence – is the 528th in “The Trans-Pacific View Insight Series.”
In what ways is artificial intelligence shaping geopolitical trends in the Asia-Pacific?
AI is increasingly connecting technology policy, national security, industrial policy, and geopolitics. Nowhere is this more visible than in the Asia-Pacific.
It is important, however, not to see the region simply through a China-U.S. lens. Taiwan is critical to advanced semiconductor manufacturing; South Korea and Japan have major positions in semiconductors, materials, and industrial technology; India is building its own AI capabilities; and Singapore and other ASEAN countries are developing their own approaches to AI infrastructure, governance, and investment.
China is particularly important because of its ability to combine technology development with hardware manufacturing, industrial scale, and rapid deployment. We already see this with electric vehicles and many other technologies: Chinese companies are increasingly selling globally, including throughout Europe despite trade frictions. AI will become another layer in this industrial and commercial expansion.
This creates a much more complex geopolitical network. Countries are asking where their compute comes from, whose models they use, where their data resides, what hardware and infrastructure they depend on, and how dependent they want to become on any single technology ecosystem.
I divide my own time between Asia, Europe, and the Americas, and one thing I see clearly is that AI development is happening in all of these regions. The differences are often in emphasis, speed, business culture, and the relationship between government and industry rather than simply in technological capability.
For businesses, these developments are no longer abstract geopolitical questions. They can quickly affect suppliers, market access, regulation, cybersecurity and investment. This is why at Mission Grey we see external intelligence becoming increasingly important: technology signals need to be understood together with political, economic, industrial, and supply chain signals.
Share your perspective on a recent comment from European technology chief Henna Virkkunen that “AI is a geopolitical weapon.”
I understand her point, and the concern behind it is real: Europe’s dependency on foreign models and infrastructure, and the leverage that dependency creates. I share that concern. But I would not describe AI itself as a weapon.
It is a general-purpose technology that is becoming strategically very important. There is a useful comparison with aviation and drone technologies. They can clearly have major military applications, but the technologies themselves are much broader than weapons. They also transform transportation, logistics, agriculture, communications, and many other areas. AI is similar, although potentially even broader in its impact.
AI can certainly be weaponized. It can strengthen military systems, intelligence capabilities, cyber operations, and information influence. Access to advanced chips, computing infrastructure, cloud services, or models can also be restricted and used as geopolitical leverage. This is where Virkkunen’s warning is most relevant, and it is a legitimate one.
But AI’s strategic importance goes much further. It will influence productivity, scientific research, industrial competitiveness, education, healthcare, and the ability to create new companies and services. Its geopolitical significance may ultimately come as much from these economic and societal effects as from direct military applications.
I would say AI is becoming a strategic capability and source of geopolitical power. That is broader, and in my view more important, than simply calling it a weapon.
What is the geopolitical impact of Asia’s AI developments on the United States and Europe?
The first impact is that Asia should no longer be viewed simply as a manufacturing base or market for Western technology. It is increasingly a source of technology, capital, standards, products, and AI ecosystems itself.
China is the most obvious example. Its strength is not only in AI models, but also in combining software with hardware, manufacturing capacity, and the ability to scale products very quickly. Its electric vehicle industry, which I mentioned earlier, shows how rapidly this can become a global commercial force. Similar dynamics can emerge around robotics, autonomous systems, and AI-enabled products.
But the broader Asian picture matters just as much. Japan and South Korea remain major technology powers, Taiwan has an exceptional position in semiconductors, India is building significant AI capabilities, and Singapore has positioned itself as an international technology and AI hub.
For the United States, maintaining leadership therefore increasingly depends not only on domestic innovation but also on partnerships and supply chains throughout Asia.
Europe faces a somewhat different challenge, but I think the common description of Europe as mainly a regulator is too simplistic. There is substantial AI innovation in Europe. France has become an important AI hub, while Sweden has a strong technology startup ecosystem, with companies such as Lovable reaching a multi-billion valuation within about two years and demonstrating how quickly new AI businesses can emerge. Both countries are also significant in defense technology.
Europe also brings another dimension to the discussion: individual control over data and technology. Concerns about data are not simply regulatory bureaucracy. People and companies are genuinely asking who controls their information, how large technology companies or governments may use it, and whether they retain the freedom to choose between different services, models and jurisdictions.
These issues will increasingly become geopolitical as well as commercial.
Examine the geopolitical stakes of pursuing technological sovereignty.
Technological sovereignty is becoming increasingly important, but the concept is often misunderstood. Sovereignty should not mean trying to manufacture everything domestically or disconnecting from global technology networks.
No major economy is genuinely independent across the entire AI stack. Advanced AI depends on an international network of semiconductor design, fabrication, memory, manufacturing equipment, cloud infrastructure, energy, data, software, and talent.
The practical objective should therefore be strategic optionality.
A country, company, or even an individual should understand which dependencies are acceptable, which are strategically critical, and where alternatives are necessary. Government systems, defense applications, and critical infrastructure naturally require particularly strong safeguards.
But sovereignty also has another level that receives less attention: the position of the user.
People and organizations increasingly depend on a small number of technology platforms for data, identity, communication, and AI. The ability to control your own data, move between services, and choose where your information is processed is therefore becoming part of technological sovereignty as well.
This is not only about Europe versus the United States or China. Different jurisdictions will make different choices concerning privacy, data access, national security, and technology governance. Users and businesses may increasingly want the ability to choose between them.
Too little sovereignty creates strategic dependence. Too much isolation can increase costs, fragment markets, and slow innovation. The objective should therefore be resilience and freedom of choice rather than complete technological independence.
Ultimately, sovereignty means maintaining the ability to make your own decisions when circumstances change.
Assess how policymakers and business leaders should understand the China-U.S. AI race and its implications for technological dominance in the global economy.
The biggest mistake is to treat the China-U.S. AI competition as a race on a single track with one finishing line.
The United States has exceptional strengths in frontier AI companies, capital, computing infrastructure, and semiconductor design. China has major strengths in hardware integration, manufacturing, engineering capacity, industrial deployment, and the ability to scale technology rapidly into real-world products.
We should also remember that China increasingly competes globally. Its technology companies are not developing only for the Chinese domestic market. They sell industrial and consumer products throughout Asia, the Middle East, Latin America, Africa, and Europe, and AI-enabled products will follow the same routes.
At the same time, a two-player framing hides much of what actually matters. Europe has significant AI and defense technology capabilities. India is becoming increasingly important. Japan, South Korea, and Taiwan remain critical parts of the technology ecosystem. Smaller countries such as Singapore can have influence far beyond their size.
The global AI landscape therefore contains many gray zones. Countries may use American cloud infrastructure, Asian hardware, European industrial technology, and locally developed models simultaneously. Companies will often make decisions based on capability, price, reliability, data requirements, and market access rather than geopolitical ideology alone.
For policymakers and business leaders, the key is therefore not simply to ask who is ahead.
They need to understand dependencies, emerging capabilities, export restrictions, technology alliances, standards, talent flows, data rules, and changes in global supply chains. Most importantly, they need to recognize early signals showing when those relationships are beginning to change.
That is also central to how we think at Mission Grey. In a world where geopolitics, technology, regulation, business, and supply chains increasingly interact, the challenge is not simply obtaining more information. It is understanding which developments matter, how they connect, and what they could mean for decisions under different scenarios.
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