Beyond linearity: reimagining AI as a participant in circular bioeconomies

As artificial intelligence transitions from industry-exclusive tool to public-facing technology, society faces critical decisions about its integration into socioecological systems. This paper proposes a reimagining of AI as a synthetic participant in the circular bioeconomy (CBE)-a regenerative model emphasizing cyclical flows of resources, information, and energy. Drawing on Bruno Latour's Actor-Network Theory and Donna Haraway's posthumanism, we reconceptualize AI as a non-living organism capable of functioning within multispecies systems, analogous to viruses that shape ecosystems without conventional life. Conventional, in that it meets the standard biological criteria for like: metabolism, reproduction, and homeostasis. AI, like viruses, does not meet this biological criteria. Current AI applications in CBE-from biowaste recycling to precision agriculture-demonstrate both transformative potential and ethical concerns. While AI enables unprecedented efficiency through advanced algorithms and embodied robotics, it risks perpetuating extractive logics that treat information as a resource to be mined rather than circulated. Critical ethical challenges emerge including algorithmic bias amplifying inequalities, epistemic opacity eroding stakeholder trust, blurred accountability for AI-driven harm, displacement of human labor, and marginalization of indigenous and local ecological knowledge. Through examples in medicine and remote sensing, we argue that AI becomes a "friend" to the Circular Bioeconomy (CBE) only when designed as circular and relational rather than linear and extractive. This requires synthetic datasets preserving privacy, multimodal architectures enabling dimensional understanding, and human-machine-ecosystem feedback loops replacing terminal outputs with ongoing accountability. Ultimately, AI's role depends on intentional design grounded in justice and multispecies dignity-transforming it from extractive tool into participant in shared regenerative futures.