AI’s data colonialism: How venture capital’s boom-and-bust cycles extract intellectual property from failed startups for large-scale model training
Original framing: “AI labs are buying data from dead startups to train their models” — MIT Technology Review
Marginalised voices—particularly those of failed startup founders, open-source contributors, and developers of colour—are entirely absent from the original framing. These individuals are often the ones most vulnerable to data extraction, as they lack the legal, financial, or social capital to contest IP violations. For example, studies on the racial and gender disparities in venture capital funding show that marginalised founders are more likely to experience startup failure, making them prime targets for AI data extraction. The lack of representation in the narrative erases the human cost of this practice, reducing it to a faceless economic transaction. Including these voices would reveal how data colonialism disproportionately harms those already excluded from the benefits of innovation, deepening systemic inequalities.
The story of AI labs buying data from dead startups is not just about individual financial windfalls or market transactions—it is a systemic manifestation of data colonialism, where the tech industry extracts intellectual property from marginalised and precarious actors under the guise of innovation.
This practice is rooted in historical patterns of corporate raiding and the speculative nature of venture capital, which creates a pipeline of failed startups whose data becomes a resource for AI training. The original framing obscures the power asymmetries at play, reducing a complex issue to a transactional one while ignoring the ethical, legal, and cultural dimensions of data ownership. Cross-culturally, this extraction conflicts with Indigenous and communal traditions that treat knowledge as sacred and bound by ethical duties, highlighting the colonial dimensions of AI’s global expansion. Future modelling suggests that unchecked, this trend will accelerate the concentration of IP in the hands of a few tech giants, stifling diversity in innovation and deepening systemic inequalities. To address this, solutions must centre ethical data governance, alternative funding models, and the amplification of marginalised voices in tech policy—ensuring that the benefits of innovation are shared equitably rather than extracted at the expense of those who create it.