The turn to digital sovereignty, and now somewhat more plausibly to AI sovereignty, is an attempt to impose some framework of purpose on a technological and economic stage of development that threatens otherwise to reduce national and supra-national (as in the West) self-determination to a memory. The corporate reactions from OpenAI, Palantir, DeepSeek, Mistral, and others are attempts to ride this wave, giving political meaning to business activities. But by seeking to acquire public missions that advance sovereignty rather than destroy it, corporations are hitching their fortunes to one state (or a collection of states) that puts them in opposition to another state and the competitors who serve it.
What are the counter-vailing trends? One option that makes more sense than might be obvious is sovereignty-as-a-service. Major US tech companies insisted for many years that what they were doing was beyond the reach or understanding of mere nation-states. That changed for a host of reasons, including strong Chinese competition, the Indian mode of playing foreign tech multinationals off each other, European digital regulation, and a much stronger economic-nationalist cast to US tech policy beginning in the first Trump administration. Tech multinationals eventually learned that sovereignties created a market they could sell into, for example with sovereign data clouds.
A combination of the Indian model of digital public infrastructure (DPI) and data localization, together with some regulatory requirements in the European manner and security-oriented foreign-investment rules in the American one, can create a rough version of a national sovereign “stack.” AI corporations, and others, can then create products that service this stack.
It may seem almost paradoxical that multinationals should offer national sovereignty as a service. But it seems to be the political price that must be paid to have a transnational product. It at least preserves the possibility of the multinational selling across barriers of national values or social missions.
Another counter-vailing trend that operates against LLMs implementing social missions is theft. Anthropic, like OpenAI, is not available in China (or Russia, Iran, North Korea, Afghanistan, Cuba). But when Anthropic, which does not enforce socialist values as Chinese LLMs are required to do, accidentally leaked the source code to its Claude Code product, Chinese developers seized the opportunity anyway. Anthropic and OpenAI also both believe there has been very substantial theft of their IP by Chinese companies. So whatever social mission Chinese AI companies are meant to pursue does not keep them from using non-Chinese AI product, including LLMs, even if these are developed under a rubric of “democratic AI.” This occurs at the consumer level as well, as seen in the ferocious adoption of Austrian AI product OpenClaw in China earlier this year — to the annoyance of the Communist government.
A third counter-vailing trend to the assignment of national missions to AI companies is the tradition of open source. The leading open-source LLMs are Chinese (DeepSeek, Mimo, Kimi) alongside Google’s Gemma. The Chinese government has long advocated open source as a catch-up (to the US) strategy with a values veneer. But because Chinese AI firms do need to make money, and Chinese venture-capital markets do not have anything close to the size of their US counterparts, the open-source window might be closing, which would tend to work in favor of AI nationalism. We shall see.
A fourth counter-vailing trend has to do with how companies actually use LLMs. For most, AI is a design tool. Designers use LLMs to develop software methods for doing existing processes differently and to design new processes. It’s an iterative approach involving multiple pieces of software from various sources; the selection of components for the resulting software stack is part of the process. And over time those components can change. The .md files that accumulate as a project takes shape are part of the process content. Those files change too as the process is refined. The data being used is often proprietary and sitting on the AI user’s machine or the company’s machine, and the data can change. The final result also is subject to change. This is not using a chatbot. It’s using an LLM as a design partner to make something that is unique — something that the LLM could not have conceived “on its own” — to meet a particular commercial purpose. All of the design files and documentation and even data of the process can be transferred from one LLM to another, or exposed to one rather than another, with some tweaks. So there is a real limit to the vendor lock-in of an LLM, at least in the context of corporate product design. That means that “AI-generated” products can travel across borders as software. This will frustrate nationalist designs because it limits the power of LLMs and therefore the power of states to shape products built with LLMs to their values.
And finally there is cross-border competition for customers. Chinese AI companies may have to advance socialist values at home, and American AI companies might want to advance democratic values in the US or the West, but none are going to therefore abandon customers outside their preferred borders because the customers are not socialists or democrats. Anthropic only stays out of half a dozen countries, which is far from an exhaustive list of authoritarian states. American programmers definitely use Chinese AI products and Chinese programmers use American ones, even when their respective states don’t want them to. No AI company shows any signs of wanting to be limited to the home market, although they are very happy to have their home market protected. AI companies will continue to press beyond the borders of their own social-mission statements as long as they can get away with it because there is money to be made.
For investors, the key things are to identify companies that have the capacity to adjust to nationalist and other values demands without sacrificing commercial vigor, and to identify sectors (like advanced manufacturing rather than media or edutech) where AI can do incredible things with minimal political exposure, including exposure on job loss.