AI-driven drug discovery accelerates but risks reinforcing extractive pharmaceutical paradigms; systemic inequities in access and ecological costs remain unaddressed
Original framing: “AI diffusion models tailor drug molecules to custom-fit protein targets, speeding drug development and evaluation” — Phys.org
It correctly foregrounds the long‑standing extractive practices of Big Pharma, linking current AI‑driven pipelines to colonial‑era patent regimes and monopolistic pricing. By situating the AI surge within this lineage, the piece reveals a continuity of profit‑first logic that predates the technology.
By weaving together historical patterns of pharmaceutical extraction, scientific realities of energy‑intensive AI, and the silenced perspectives of indigenous and marginalized communities, the analysis reveals a systemic paradox: the very tools hailed as accelerators also reinforce the inequities they could dissolve.
A trickster reading exposes this irony, urging a redesign of the drug discovery ecosystem that embeds green standards, open licensing, and culturally inclusive knowledge. Only through coordinated future modelling and policy reform can AI become a catalyst for equitable, sustainable medicine rather than a new layer of extractive profit.