Colonial Genealogy of the Digital

In The You On AI Encyclopedia

The argument operates on multiple levels simultaneously. At the material level, the physical infrastructure of AI — the cobalt in the batteries, the lithium in the data centers, the rare earths in the chips — is extracted from the same geographic zones that colonial powers extracted rubber, diamonds, and copper from a century ago. The Democratic Republic of Congo, which produces more than 70 percent of the world's cobalt, is the same Congo that Leopold II turned into a death-world for rubber extraction. The continuity is not metaphorical; it is the same mines, in many cases the same families laboring in them, producing different commodities for different industries.

At the labor level, the outsourcing of content moderation, data labeling, and AI evaluation to Kenya, the Philippines, Colombia, and India follows the geographic logic of the call-center wave of the 1990s and 2000s, which itself followed the logic of manufacturing offshoring, which itself followed the logic of colonial extraction. Each iteration updates the technology; the underlying map remains recognizable.

At the epistemic level — which is where Mbembe's analysis is most distinctive — the training corpora that define what AI models consider knowledge reproduce the hierarchy of knowledge that colonial universities established in the nineteenth century. English-language, Western, male-authored, digitized text is abundantly represented. Oral traditions, minority languages, indigenous knowledge systems are absent or marginal. The model is not neutral; it is trained on a specific intellectual archive, and that archive is the colonial archive.

At the governance level, the regulatory frameworks that attempt to discipline AI — the EU AI Act, the U.S. executive orders, the emerging frameworks in Singapore and Brazil — are written in metropolitan capitals by bureaucrats and technologists who have no accountability to the populations most affected by the technology's deployment. The developer in Lagos is governed by terms she did not write, enforced by courts she cannot access, in languages she may not read. This is colonial governance updated for the platform age.

Recognizing this genealogy is not a counsel of despair. It is a precondition for meaningful intervention. You cannot decolonize what you do not recognize as colonial. Mbembe's work, and the work of the broader decolonial AI movement that has drawn on it, offers a framework for seeing the present clearly enough to imagine alternatives: indigenous training corpora, participatory governance structures, the assertion of African and Asian AI infrastructure on terms that do not require permission from the metropolitan center.

Origin

The colonial-genealogy framework has been developed in dialogue across postcolonial studies, decolonial theory, and critical technology studies. Key contributors include Walter Mignolo, Sylvia Wynter, Ramón Grosfoguel, Syed Mustafa Ali, Shakir Mohamed, Marie-Therese Png, and William Isaac.

Key Ideas

Continuity, not rupture. AI reproduces colonial patterns in updated form rather than constituting a break from them.

Material, labor, epistemic, governance. The continuity operates at multiple levels simultaneously and can only be understood by attending to all of them.

The same geographies recur. The map of AI extraction is substantially the same map as earlier extraction regimes.

Recognition precedes intervention. Decolonizing AI requires first seeing the colonial structures that the industry's self-description denies.

Alternatives are possible. The framework points toward specific forms of resistance and reconstruction, not only critique.