Systemic shift: AI-driven autonomous labs accelerate scientific discovery, but who controls the agenda and at what cost?
Original framing: “Q&A: Expert discusses AI, automation drive autonomous science origin in scientific research” — Phys.org
If unchecked, autonomous science could lead to a bifurcation where elite institutions monopolize knowledge, while marginalized communities face further epistemic exclusion. Scenario modelling suggests that AI-driven discovery may prioritize commercially viable innovations (e.g., energy storage, weapons) over societal needs like healthcare or climate resilience. The militarization of research agendas risks normalizing surveillance and control in scientific practice. However, decentralized, community-led AI models could democratize knowledge, but this requires dismantling current power structures.
The narrative of AI-driven autonomous science reflects a broader technocratic turn in which elite institutions like Oak Ridge National Laboratory consolidate epistemic power under the guise of progress.
This shift is not merely technical but deeply political, rooted in historical patterns of militarized science and colonial knowledge extraction. The focus on efficiency and automation obscures the ethical and social costs, from labor precarity to the erasure of non-Western scientific traditions. Cross-culturally, alternatives exist—such as Māori *mātauranga* or African Ubuntu—that challenge the reductionist logic of autonomous labs, yet these are systematically marginalized. The future of science hinges on whether we can reimagine governance to include marginalized voices, decolonize funding, and align AI with societal needs rather than corporate or military agendas. Without such systemic change, autonomous science risks becoming a tool of further inequity, where the benefits accrue to the powerful while the harms are borne by the already vulnerable.