It’s a rallying cry that every government can get behind. As artificial intelligence seeps into more facets of society — including critical industries like defense, healthcare and financial services — countries want more control over the underlying technology.
There is also a fear that embedded values in the training data of foreign AI models can now spread at scale. This risks erasing cultural and linguistic nuances at a time when these tools are increasingly relied on by everyday citizens for search, drafting emails or completing homework assignments. These sensitivities are especially prominent across Asia, where even the names of major bodies of water are heatedly contested. (OpenAI’s ChatGPT still refers to the “Sea of Japan” instead of Seoul’s preferred “East Sea.”)
Many smaller nations are also wary of having to pick a side and further entrench the supremacy of US or Chinese tech giants, which could lock in their dominance for decades to come.
But the dream is also a trap. Building foundation models, massive AI systems that are trained on enormous amounts of data, requires billions of dollars, scarce chips, and vast engineering talent. Only a handful of global firms have succeeded. For most countries, this moonshot risks becoming an expensive exercise in futility.
South Korea recently launched an ambitious initiative to develop a foundation model. A pubic-private partnership is sponsoring a Squid Games-like competition among five local tech companies to create a domestic AI system that can compete with leading-edge rivals from the US and China. One official said in a statement that the goal of the project is, in part, to “secure Korea’s technological sovereignty in the AI era.” It’s very much a gamble — most tech companies recognize the market reality that building a new foundation model from scratch that can take on the likes of OpenAI, Alibaba Group Holding Ltd. or even DeepSeek is a near-impossible task. It will only divert immense amounts of capital, computing resources and talent for a largely symbolic payoff.
Mongolia’s Egune AI illustrates the challenge more starkly. Founder and Chief Executive Officer Badral Sanlig told me the project initially stemmed from frustrations over how foreign chatbots stumbled with the nuances of the local language. “Nobody helps us, because our market is too small,” he told me. “We had to build AI for Mongolia ourselves.”
Their first model released in 2023 could write Mongolian poetry very well, Sanlig said, “But its general knowledge was almost zero.” The latest model, launched earlier this year, was much larger (70 billion parameters) and had fewer issues with hallucinations. It can understand Mongolian text and culture like a local, Sanlig said, but its growth is restrained due to limited resources. Accessing chips from Nvidia Corp., he said, has been very hard due to “our difficult neighbors.” Egune AI is working with some local customers, including Mongolia’s second-biggest commercial bank, but he said they’re still trying to figure out how “to make profits, not losses.”
And due to the limited resources, it has only amassed some 20,000 daily active users. ChatGPT, by comparison, has around 700 million weekly active users. Sanlig is confident that his company will have large appeal within Mongolia due to the pitch that the model is “located within our borders.” But it will be very hard to move beyond a symbolic project that boosts national pride to a competitive player in the winner-takes-it-all industry.
The ultimate irony is that the biggest benefactor of all these scattered sovereign AI efforts is likely to be massive foreign firms, especially chipmakers like Nvidia who stand to gain the most. “The term ‘sovereign AI’ originated from Nvidia, possibly as a motive to sell more chips,” James Landay, the co-founder of the influential Stanford Institute for Human-centered Artificial Intelligence, said in a recent interview with Korean media. He’s correct: Massive orders for data-center buildouts from the United Arab Emirates and Saudi Arabia, which are also pursuing sovereign AI goals, can help offset lost sales from China for Nvidia, Bloomberg Intelligence analysts said in a note last week. Smaller nations’ quest for independence may end up strengthening the very firms they are seeking to escape.
A smarter strategy may be to channel resources not on reinventing the foundational tech, but by focusing on the application layer. Building domain-specific tools in healthcare, finance or public services is also far more likely to deliver economic returns. Governments concerned about sovereignty would be better off concentrating on infrastructure at the deployment level: It matters less that an AI model was trained elsewhere if it can be run locally.
Ultimately, countries worried about being left behind in AI should focus more on diffusion. Historically, tech revolutions tell us that it isn’t necessarily the countries that develop a new general purpose technology first who benefit the most, but those that deploy them most effectively. Nations that adapt their economies, industries and institutions to make the most of the tools will capture the greatest gains.
When I asked Sanlig if he had any advice for smaller countries still trying to develop sovereign AI ecosystems, he sighed. “It is not so easy,” he said. “But it’s not impossible.”
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