OpenAI’s closed-access biology LLM entrenches corporate control over life sciences data, deepening extractive AI monopolies

Indigenous knowledge systems often treat biological data as sacred and communal, governed by principles of reciprocity and consent rather than proprietary control. The closed-access model of GPT-Rosalind contradicts these values by privatising biological insights, which are often derived from Indigenous and local communities without benefit-sharing mechanisms. This reflects a broader pattern of biopiracy, where corporate entities extract knowledge without acknowledging or compensating its origins. Indigenous perspectives would advocate for data sovereignty and collective ownership of biological information.

The privatisation of biological knowledge through AI mirrors historical patterns of enclosure, such as the 19th-century patenting of plant varieties and the mid-20th-century corporate consolidation of pharmaceutical research. Silicon Valley’s closed-access AI models echo the enclosure of the commons in the digital age, where publicly funded data is repackaged as proprietary tools. This trend also parallels the enclosure of genetic resources during colonialism, where Indigenous knowledge was extracted and commercialised without consent. The current model risks repeating these injustices in the digital realm.

In contrast to Silicon Valley’s extractive approach, many Global South initiatives prioritise open-access frameworks to address local health and environmental challenges. For example, the African Centre of Excellence for Genomics uses open-source tools to tackle infectious diseases, demonstrating how collaborative models can drive innovation. Similarly, Indigenous-led bioinformatics projects in Latin America and Oceania emphasise data sovereignty and community control. These models highlight the limitations of closed-access AI in addressing global inequities and offer alternative pathways for scientific collaboration.

Scientifically, closed-access AI models like GPT-Rosalind risk limiting the reproducibility and transparency of biological research, as proprietary systems obscure methodological details. Open-access alternatives, such as those developed by the European Bioinformatics Institute, have demonstrated that collaborative models accelerate scientific discovery. Additionally, the lack of peer review in proprietary AI tools raises concerns about bias and error propagation in biological research. The scientific community has long advocated for open data and reproducible methods to ensure robust and equitable progress.

From an artistic and spiritual perspective, the privatisation of biological knowledge reflects a mechanistic worldview that separates humans from nature, treating life as a commodity to be exploited. Indigenous and spiritual traditions often view biological data as part of a sacred web of life, where knowledge is shared for the collective good rather than hoarded for profit. The closed-access model of GPT-Rosalind embodies a reductionist approach that divorces science from its ethical and spiritual dimensions. Artistic movements, such as bioart, challenge this paradigm by reimagining the relationship between humans, technology, and nature.

If unchecked, the closed-access model of GPT-Rosalind could lead to a future where a handful of corporations control the tools for biological innovation, exacerbating global inequities in science and medicine. Scenario planning suggests that open-access alternatives could democratise biological research, enabling Global South scientists to contribute to and benefit from AI-driven discoveries. However, the current trajectory risks entrenching a techno-feudal system where corporations dictate the terms of scientific progress. Future models must prioritise collaboration, transparency, and equity to avoid these pitfalls.

Marginalised voices, including researchers from the Global South and Indigenous communities, are systematically excluded from access to tools like GPT-Rosalind due to cost, licensing barriers, and lack of infrastructure. This exclusion perpetuates historical inequities in science, where Western institutions have long dominated knowledge production. The closed-access model also sidelines the contributions of women and minority scientists, who are underrepresented in AI development. Amplifying these voices requires structural changes, such as funding open-access initiatives and supporting community-led research.