Max Headroom 
UNCLASSIFIED
Adam Tooze recently shared a piece from The Economist about Brazil’s push for what it calls “medical sovereignty,” the determination to make its own vaccines and the active ingredients that go into its medicines rather than depend on supply chains it doesn’t control. Brazil already produces a large share of its own medicines through public institutions like Fiocruz and Butantan, but a lot of the underlying inputs still come from abroad, and the pandemic made clear the cost of that dependence. So the country is trying to build the capacity to make the things it most needs to survive. The economist behind a lot of this thinking is Mariana Mazzucato, whose mission-oriented approach treats public procurement as a tool to build national capacity rather than just buy finished goods. (Foreign Policy has a good overview.)
I think we’re going to see a lot more of this, and not only in medicine. The same impulse is driving the quest for sovereign AI, as countries decide they don’t want their access to a foundational technology to run through a handful of American or Chinese companies. You can see it too in Europe’s and Japan’s new willingness to take responsibility for their own military destiny rather than assume the United States will always be there.
Most commentators describe all of this as decoupling, the unwinding of a connected world. That reading is too narrow.
Free trade was an architecture of participation that broke
Much like open source software and the World Wide Web, free trade was supposed to have what I call “an architecture of participation.” The most important thing about the web and open source wasn’t openness for its own sake. It was that there were no central gatekeepers. Anyone could add to the richness of the system without asking permission as long as they followed the rules of the communication protocols that allowed independently-developed pieces to work together. In addition, value circulated among the participants instead of being extracted to a center, and the system got better the more people used it. That is a very different thing from a system that is merely large and connected.
Free trade was also supposed to work like that. The theory, going back to Smith and Ricardo, was that specialization and exchange would make everyone better off, and that the connections would be mutual. What we actually got over the past few decades looks more like the platform dominance we see in big tech than the original vision of a commons built around shared exchange. A handful of large and powerful countries and firms set the terms and the smaller players are forced to take what is on offer. Despite the language of free trade, the experience for many countries was closer to colonialism, just with a new narrative.
Overall, under the neoliberal order (whose reign, as Gary Gerstle explains, is now ending), free trade became far less egalitarian, inclusive, and generative than it could have been. Less powerful countries ended up in roughly the position that small businesses occupy on Amazon, or developers occupy on the app stores: free to participate, on terms they don’t control, with much of the value they create flowing back to the hub.
Brazil’s response (and that of many others) should not be seen as a retreat from the world. It is a refusal to be participate only as a buyer, or as a source of raw materials.
That’s why decoupling is the wrong word. Decoupling means cutting the connections. What these countries seem to want is to stay connected but to build real capacity of their own, so that no single supplier can switch them off. That’s closer to federation than to separation. A federated system is still a system, and its nodes still interoperate. But no node is wholly at the mercy of another, and value circulates among them rather than collecting at the center. A trading order in which the gains pool at a few hubs is brittle and eventually illegitimate, in the same way that a platform economy that strip-mines its participants eventually provokes regulation and revolt.
I put the increasingly visible quest for sovereign AI, and the role of open source models and open source agentic protocols and harnesses in enabling that sovereignty, into the same bucket. I remember back in the early days of open source software when Michael Tiemann, whose pioneering open source company Cygnus Solutions had just been acquired by Red Hat, told me “What we really sell at Red Hat is control. The ability to control your own destiny.”
As companies are increasingly at the mercy of unexpected token pricing changes by the big centralized players, this same quest for sovereignty is playing out at the level of organizations. Open source AI, including not just open source and open weight models but open agentic protocols, agentic harnesses, and portable memory, are increasingly an essential part of the sovereignty toolkit.
The national technology sovereignty movements should take a lesson from the open source movement. The heart of open source is its architecture of participation. It is a force for innovation and value creation to the extent that it frees up the ability of people to solve their own problems and contribute their solutions to a low-friction global commons.
Is capture the inevitable fate of any architecture of participation?
The pattern of open architectures leading to a wave of innovation, winners emerging, consolidating their power and then turning to the dark side seems to be a natural part of the technology cycle. The web broke Microsoft’s dominance over the personal computer software ecosystem only to give rise to a new generation of gatekeepers. Cory Doctorow called this cycle “enshittification.” I’ve told my own version of that story using the language of economics in “Rising Tide Rents and Robber Baron Rents.”
The instinct after capture is to try to rebuild the thing that got captured, only this time with better rules. Mastodon and Bluesky tried to rebuild Twitter’s social layer with cleaner governance, and neither has succeeded at the scale they hoped for. Critics might say that it was because Mastodon stayed pure and never made itself easy enough to use, while Bluesky looked federated without really being so. But more importantly, reinventing what we used to have, or what we think we used to have, is rarely the path forward. You have to build something new.
Each country building its own answer to the latest frontier models is the Mastodon move. The winning move is to operate at a layer the centralized model structurally can’t reach. Open agent protocols that let services from different providers interoperate (the work that MCP and the emerging agent stack are beginning to do) are one such layer. AI accountable to local democratic and legal institutions is another such layer. Domain-specific AI built around problems the global market won’t serve (the tropical disease vaccine analogue) is another. None of these is a smaller copy of what the hyperscalers offer. But there’s one more important layer to consider: infrastructure.
Where are the servers?
Ilan Strauss made a useful point in our conversation about these ideas. Ilan noted that AI is one of the most global forms of capital we’ve ever built, trained on the whole of the internet and runnable more or less anywhere, and the sovereignty rhetoric is partly an attempt to give something inherently placeless a place. The technology wants to be everywhere at once. The people who live with its consequences want some say over it where they are.
The placelessness of AI is only half of the truth, though. The other half is that AI is physically place-bound. The model weights are placeless. The data centers, the chips, the electrical grid, and the water for cooling are very much somewhere.
The comparison with Brazil’s medical sovereignty reinforces this point. Brazil’s challenge isn’t to invent new drugs to compete with Pfizer, but to build the capacity to manufacture existing vaccines, and eventually to build the capacity to invent vaccines for diseases the West ignores. Fiocruz and Butantan matter not because they hold patents but because they are physical institutional capacity rooted in Brazilian soil: the labs, the cold chains, the regulatory capacity, the trained workforce, and access to the active pharmaceutical ingredients. That’s what medical sovereignty really means in practice. It is infrastructure plus the institutions that run it.
The same is becoming true for AI. Open weights matter. They’re closer, though, to the patent than to the lab. Even if Qwen, Kimi, DeepSeek, Llama, Gemma, Granite, and whatever comes next are fully open, running them at scale requires data centers that cost tens of billions to build, chips whose supply chains a handful of countries control, and electricity grids that have to be expanded substantially to carry the load. The countries pursuing sovereign AI seriously seem to understand this. The EU’s AI Gigafactories program, India’s IndiaAI mission, the Gulf compute buildouts, the Singapore and Japan strategies, are all infrastructure plays first and model plays second.
Infrastructure is the layer where capture is hardest to undo. You can distill or fine tune a model far more easily than you can build a new continent’s worth of data centers or conjure the necessary electricity from a fragile power grid. If the architecture of participation for AI is defined only at the model layer, the infrastructure layer below will quietly recapture, over years, everything that was won above. Open weights running on three companies’ servers is not sovereignty.
Building physical infrastructure capable of carrying a generation’s worth of economic activity is exactly the kind of mission the public sector used to take on, before we convinced ourselves the market would handle it. Mazzucato’s argument is that public procurement and public capacity-building are the real engines of foundational technology. AI sovereignty without industrial policy is wishful thinking.
Industrial policy should aim to reinvent 20th century infrastructure, not just copy it. Can we use the enormous rebuild of infrastructure for the AI era to leapfrog the past? The analogy with centralized power grids and decentralized solar reminds us that local control does not have to be a localized version of the hyperscaler pattern. Might we envision a future where there is an intelligence grid that seamlessly uses frontier models in massive data centers and local models controlled by the user as dictated by considerations like cost, privacy, specialized knowledge, and user preferences? Creating the software to manage such an interoperable intelligence grid should be a high priority for the AI open source community. We need an orchestrator not just for agents but also for models and even for data center capacity.
Could federated AI give us a new pattern for the economy?
In a previous piece about AI and markets, “The Third Artificial Intelligence” I picked up Richard Danzig’s argument that markets and the bureaucracies that underpin nation states are themselves artificial intelligences, information-processing mechanisms older than the machine kind. The question with all three is who designs and builds them, what they optimize for, and what feedback loops govern them.
We’re about to spend a lot of effort working out how AI should be organized both across nations and across organizations, whether it concentrates in a few firms and a few countries or whether it can be built as something more federated, where smaller players have genuine capacity and the value they create flows back to them. The choices we are now making about how AI is organized, at the model layer, the protocol layer, and the infrastructure layer, are also choices about how economic activity will be organized for at least a generation. If we manage to get that architecture right for AI, it may give us a working pattern for the thing we’ve so far failed to get right for trade. If we get it wrong, we’ll most likely reproduce, at the level of intelligence itself, the same concentration that free trade has produced in goods and the existing internet platforms produced online.
The technology wants to be everywhere at once. The people who live with its consequences want some say over it where they are. The infrastructure that resolves that tension will be a federation of models, a federation of protocols and code, and a federation of capacity. We need an architecture of participation all the way down the stack, and all the way up.
The final section of this piece benefited greatly from questions and comments raised by Ilan Strauss and Mike Loukides, as well as from previous conversations with Richard Danzig.
AI & ML, Commentary
Radar
