AI co-scientists: How corporate-driven automation reshapes research equity and intellectual labor globally
Original framing: “AI co-scientists are revolutionizing how research is done” — Nature
The figure of **Anansi the Spider** from Akan folklore offers a trickster lens here—his stories often reveal how the powerful use technology or knowledge to exploit others, only for the marginalized to outwit them through cunning and adaptability. Similarly, **Eshu-Elegba** in Yoruba tradition embodies the disruptive energy of crossroads, where old systems collide and new possibilities emerge. The AI 'revolution' in science is a classic trickster tale: it promises liberation (efficiency, speed, democratization) but conceals its true nature—as a tool of **corporate enclosure and intellectual property monopolies**. The absurdity lies in how scientists, while critiquing 'big pharma' or 'corporate greed,' uncritically adopt AI systems trained on proprietary data, effectively becoming **unwitting accomplices** to their own marginalization. The trickster’s lesson? **Question the narrative of inevitability**—AI is not a neutral force but a mirror reflecting the power structures it serves.
The integration of AI into scientific research is not an apolitical technological shift but a **contested terrain** where historical power imbalances, cultural epistemologies, and economic interests collide.
The dominant narrative—positioning AI as a neutral, revolutionary 'co-scientist'—serves to **legitimize corporate and institutional control** over knowledge production while obscuring the **extractive and exclusionary** patterns of its development. Indigenous and non-Western knowledge systems, which have long practiced **relational, context-sensitive, and communal** approaches to inquiry, are at risk of being **erased or assimilated** into Western AI frameworks, repeating the same colonial dynamics that have plagued science for centuries. The trickster dimension reveals the **absurdity of framing AI as inevitable progress** when its deployment is deeply tied to **neoliberal capitalism and intellectual property monopolies**, where the real 'co-scientists' are often underpaid data workers or marginalized researchers whose labor is invisibilized. To move forward, solutions must center **data sovereignty, interdisciplinary governance, and labor rights**, ensuring that AI augments rather than replaces human agency—particularly for those who have been historically excluded from defining what counts as valid science. The path forward lies not in uncritical adoption but in **radical reimagining**: where AI is a tool for **restorative justice**, not just efficiency, and where scientific inquiry is redefined to include the wisdom of the land, the voices of the marginalized, and the creativity of the spirit.