Empires of Intelligence: What the Colonial Past Can Teach Us About the AI Future
The tech superiority in shipping and gunpowder warfare, combined with the appetite for distant resources and captive markets, aided Spain and Portugal in colonizing the Americas from the 1490s. The British and Dutch East India Companies were chartered around 1600, extending this colonization.
Then came the Industrial Revolution in 18th century: a handful of brilliant machines, the steam engine, the spinning jenny, the power loom, burst out of Britain in the second half of the eighteenth century, raced across Western Europe and the Atlantic, and remade human productivity forever.
What the Industrial Revolution did was not invent extraction — it industrialized it. Railways now hauled raw cotton out of colonized land and finished cloth back in, at a speed and scale no sailing ship could match. The telegraph let a handful of colonial offices in London coordinate an empire on which the sun never set. Steamships and the Maxim gun turned a slow, centuries-long process of colonization into the frantic, decades-long "Scramble for Africa." The machines did not create the logic of empire. They gave an existing logic ‘industrial teeth’.
That distinction matters, because it is also the more useful lens for thinking about artificial intelligence today. The worry is not that AI will cause a new colonization from nothing. It is that AI is industrializing a concentration of power that is already visible, already forming — and that, exactly as happened two centuries ago, the architecture being built right now will determine who spends the next century as a builder of intelligence and who spends it as a tenant.
The New Raw Material, the New Merchant Fleet
Colonial extraction needed three things: a resource worth taking, a fleet capable of moving it, and a captive market for what came back. Frontier artificial intelligence has its own version of all three. The resource is data - the accumulated digital exhaust of human behavior, conversation, and transaction. The fleet is compute: the small number of companies and countries that control advanced semiconductor fabrication, the data centers, and the energy to run them. And the captive market is everyone else, every individual, enterprise, and government that will consume intelligence as an API call rather than build it as sovereign infrastructure.
Look at where frontier foundation models actually get built today. A handful of firms in the United States and China account for nearly all of the models capable of general reasoning at scale. Building one requires not just world-class research talent but hundreds of millions to billions of dollars in compute, energy contracts that rival those of small nations, and access to a semiconductor supply chain concentrated in a handful of firms and geographies. Export controls on advanced chips are, in effect, the modern equivalent of a colonial power controlling who gets gunpowder. None of this is a moral accusation against the companies involved — they are responding rationally to the economics of the technology. But the structural resemblance to the old pattern of resource, fleet, and market is difficult to unsee once you look for it.
The Questions Policymakers Cannot Defer
This raises three questions that deserve to be asked plainly, even though the honest answers are uncomfortable.
How many countries, realistically, will ever have the capital, energy infrastructure, chip access, and research talent needed to build and maintain a frontier foundation model? The number today is small (probably fewer than half a dozen) and the barriers to entry are rising, not falling, as the frontier moves toward ever larger training runs.
What happens to the nations and enterprises that cannot cross that threshold? Do they simply rent intelligence indefinitely, the way a colonized economy once exported raw cotton and imported finished cloth at a price set elsewhere? Renting is not inherently ruinous; nations rent all kinds of capability today, from cloud infrastructure to vaccine manufacturing, without becoming colonies. But renting the layer that increasingly mediates commerce, education, healthcare, and governance is a different order of dependency, because it is not a discrete purchase; it is a permanent tax on every future transaction, and the terms of that tax are set entirely by the renter.
And is "intellectual colonization" too strong a phrase for this, or is it precisely the right one? Colonization was not merely economic dependency, it involved the erasure of local systems of knowledge and their replacement with the colonizer's categories, language, and worldview. A world in which every culture's laws, medicine, and commerce are mediated through a handful of models trained overwhelmingly on the historical and linguistic corpus of a few countries risks something structurally similar: not a flag planted in the ground, but a worldview quietly planted in the model weights that every other nation's citizens interact with daily.
These are not rhetorical questions asked for effect. They are the kind of question that, if left unanswered for another decade, answers itself by default, in favor of whoever already holds the compute.
Two Ways to Diffuse the Power
If concentration is the risk, diffusion is the countermeasure, and there are two distinct architectural choices policymakers and technologists can push toward, both of which already have working precedents.
The first is pushing intelligence to the edge instead of pooling it at the center. Today's default architecture treats a handful of giant, universally trained models, updated continuously from everyone's interactions, as the intended destination for every query, every business process, every personal decision: an omniscient friend, philosopher, and guide for each individual and enterprise, with the full transcript of the relationship flowing back to a central server. An alternative already exists in embryonic form. Federated learning, a technique in which a model on a device learns from local data and sends back only aggregated, anonymized updates rather than the raw data itself has been used for years in consumer products like predictive keyboards, precisely because it lets a system improve without every keystroke leaving the phone. Extend that logic further: a model that lives on a phone, a home router, or an enterprise gateway can handle the great majority of everyday reasoning locally, drawing on a general model only when it genuinely needs broader context, and sharing back to any central knowledge base only what the user or enterprise explicitly consents to share. The transaction stays local by default; participation in the global commons becomes an opt-in choice rather than an automatic surrender.
The second is resisting the pull toward one all-purpose model and instead building highly specialized models for individual domains like health, education, law, financial services, each trained deeply enough in its own field to outperform a generalist model at the tasks that actually matter to citizens in that domain, and each able to keep improving through use within that domain rather than through indiscriminate absorption into a universal corpus. The risk of leaving specialization there is that it simply recreates ten small walled gardens instead of one large one. The answer is open standards and protocols that let these specialized models interact with each other on a consent basis, a health model calling a financial-inclusion model to check affordability, an education model calling a language model to translate content into a local dialect, without any of them needing to defer to, or route through, a single dominant global model to reason well.
This is not a hypothetical. India's own experience with open digital protocols is a working demonstration of the underlying principle, even though it was built for commerce rather than AI. Before the Open Network for Digital Commerce, digital commerce in India was consolidating toward the same pattern seen almost everywhere else: a small number of platforms that owned both the buyer relationship and the seller relationship, with every transaction and every unit of pricing power flowing through their walled infrastructure. ONDC instead created an open, interoperable protocol, built on the Beckn protocol, that let any compliant buyer app discover and transact with any compliant seller app, with no single platform sitting in the middle extracting rent from every exchange. The lesson generalizes directly to AI: an open protocol layer for model-to-model interaction could do for intelligence what an open commerce protocol did for retail - letting specialized, smaller players interoperate on equal footing instead of every interaction defaulting to whichever platform happens to be largest.
Both of these architectural choices carry a second, more practical benefit that should appeal to any finance ministry worried about the cost of the AI transition: they are cheaper. Routing the bulk of everyday reasoning through small, local, or narrowly specialized models rather than a giant universal model every single time reduces the number of tokens processed, the compute cycles consumed, and the electricity drawn from the grid. Diffusion is not only a safeguard against concentration of power; it is very plausibly the more economically sustainable path as AI usage scales into billions of daily interactions.
Why Digital Public Infrastructure Is the Precondition, Not an Afterthought
Neither of these architectural interventions works unless the underlying data exists in a usable, trustworthy, and interoperable form in the first place. A local model on a phone in a country where identity, land records, health records, and financial transactions are still paper-based, fragmented, or locked inside proprietary corporate databases has nothing meaningful to reason over. This is precisely the argument for treating digital public infrastructure — the interoperable, open "rails" for identity, payments, and data exchange that countries like India have built through systems such as Aadhaar and the Unified Payments Interface — not as a separate policy agenda from AI, but as its precondition.
The alternative to open, interoperable rails is not the absence of digitization; digitization is happening everywhere regardless. The alternative is digitization captured inside walled gardens controlled by a handful of private platforms, each sitting on a pool of data large enough to train a proprietary model, each with every incentive to prevent that data from ever becoming interoperable with a competitor's, and each able to charge rent on that data's use indefinitely. Digital public infrastructure, built on open standards with consent-based data sharing at its core, is what allows every country, not just the handful that can afford frontier compute, to accumulate a well-structured, contextually rich pool of its own data, and to let smaller, local, or open-source models be trained meaningfully on that data instead of being permanently dependent on a foreign model's second-hand understanding of local context.
This is also, not incidentally, the strongest antidote to rent-seeking. A market with open, interoperable rails and many interoperating specialized models is a market with real competition, which pushes the cost of intelligence down for everyone. A market of walled gardens converging on two or three global models is a market that, however impressive the technology, will behave like a monopoly, because eventually, it will be one.
The Choice Is Being Made Now
The colonizing nations of the eighteenth and nineteenth centuries did not sit down and vote on empire; the choice was made, cumulatively, by which ships got built, which trading companies got charters, and which technologies got industrialized first, long before most of the affected societies had any say in the matter. By the time the consequences were fully visible, the architecture was already locked in, and undoing it took centuries.
The architecture of artificial intelligence is being decided now, in this decade, in choices that look small: which protocols become standards, whether edge inference is subsidized or taxed, whether digital public infrastructure is built as an "open commons" or licensed out to whichever platform arrives first with capital. None of these choices individually looks like a decision about empire. Collectively, they are exactly that. The lesson of the last industrial revolution is not that the machines were the problem, it is that by the time everyone understood what the machines had made possible, the terms had already been set by whoever built them first.
Policymakers have a narrower window than they think to make sure that this time, the terms are set by more than a handful of hands.
“Every empire, however, tells itself and the world that it is unlike all other empires, that its mission is not to plunder and control but to educate and liberate." -Columbia University professor Edward Said.