Steering open-source AI to accelerate the sustainable development goals

Comment https://doi.org/10.1038/s41467-026-73866-8 Steering open-source AI to accelerate the sustainable development goals 1234567890():,; 1234567890():,; Min Chen, Kai Wu, Prajal Pradhan, Cameron Allen, Stefano Nativi, Klaus Hubacek, Alexey Voinov, Felix Creutzig, Tatiana Filatova, Niklas Boers, Michael Meadows, Peilong Ma, Frank Biermann, Hans Joachim Schellnhuber, John Ludden, Maria Paradiso, Michael Batty, Huadong Guo, Min Cao, Peng Hou & Guonian Lü While the artificial intelligence (AI) revolution is advancing rapidly, the open-source paradigm offers key pathways and potential risks for accelerating progress towards the Sustainable Development Goals and beyond. This comment introduces four governance actions that consider how sustainability, evaluation, safety, and cooperation can be integrated into the transformation of open-source AI, thereby reducing uncertainties and challenges posed by opensource AI for sustainable global prosperity. The Artificial Intelligence (AI) Action Summit, held on February 10, 2025, in Paris, highlighted the rising international commitment to harness AI for the Sustainable Development Goals (SDGs) proposed and endorsed by the United Nations1,2, aligned with calls to govern AI in the context of planetary health, across environmental, social, and safety domains3. Between 2018 and 2024, the use of AI to support the SDGs grew by 300%4, with contributions in agriculture monitoring (SDG2), ecosystem governance (SDG15)5, and climate change mitigation (SDG13)6. The rise of open-source paradigms underpins this transition toward a ubiquitous and efficient AI ecosystem. By making the source code, weights, and training data open, releasing them under an open-source license7, and allowing the community to study, use, modify, and distribute them, open-source AI was initially envisioned to inherently and efficiently accelerate global progress toward sustainable development. However, as the open-source AI models have expanded, their impact on the SDGs has revealed an increasingly complex and dualistic nature for SDG progress7,8. The accessibility of open-source models within unregulated environments has introduced critical inhibiting effects that were previously overlooked. Specifically, the lack of structured governance often leads to excessive resource consumption and ethical vulnerabilities, which have not yet been well discussed. The emerging reality suggests that without a deliberate transformation in how these open-source AI models are managed, the openness intended to empower global SDGs could instead undermine it. While many previous studies and frameworks have advanced the understanding of open-source AI and sustainability, they remain fragmented and separated. On the one hand, most studies emphasize single-stage impacts (e.g., training or deployment) rather than nature communications Check for updates addressing interdependencies across the full lifecycle of open-source AI models9,10. On the other hand, existing open-source AI governance frameworks, ranging from the United States’ innovation-oriented framework to the European Union’s risk-based regulatory framework11, primarily operate within national or institutional silos, lacking mechanisms for cross-border coordination and enforcement. To address the challenges of open-source AI models and governance frameworks, we propose four actions tailored to them, which would advance previous studies and frameworks in two ways. First, they could contribute to shifting the focus from passive implementations to proactive interventions, embedding sustainability, accountability, and transparency throughout the development, deployment, and reuse of open-source AI models. Second, the transformative actions could introduce a multi-stakeholder and cross-scalar perspective, aligning developers, regulators, and users within a coordinated framework that connects local implementation with global cooperation. We conceptualize open-source AI governance as a dynamic interaction among sustainability (sustainable lifecycle management), evaluation (quantitative impact assessment), safety (regulation and scrutiny), and coordination (sharing and cooperation) (Fig. 1), thereby aligning the open-source AI paradigm with sustainable, ethical, and equitable principles. Why open-source matters in AI models Leveraging the opportunities offered by AI for SDGs is both desirable and feasible. Recent innovations signal a transition toward an opensource paradigm8, aligning with the more accessible and adaptable solutions of the SDGs. Furthermore, they enable localized modeling of the SDGs and foster global collaboration in SDG modeling and policymaking. The transition toward the open-source paradigm promotes the dissemination of knowledge and the iterative refinement of SDG modeling, which involves mathematical, statistical, and/or computational approaches for analyzing and projecting SDGs (e.g., SDG interaction analysis and scenario simulation)12. Through open access to algorithms and collaborative development environments, this paradigm enables rapid model iteration in response to emerging SDG challenges, such as epidemics, natural disasters, or conflicts, thereby generating faster and more reliable insights into future SDG trends. The ability to host open-weight AI models on highly optimized, dedicated infrastructure enables speeds of over 179 tokens per second (versus 138 for a proprietary AI model)13, which is an advantage for latency-sensitive applications addressing SDG challenges in specific regions. In the environmental dimension of SDGs, open-source natural language processing models like ChatClimate are being developed to (2026)17:4959 | 1 Comment Uncertainty: AI's persistent environmental footprint Uncertainty: Lack of standardized SDG impact metrics Uncertainty: Risks of AI misuse and harm Sustainable lifecycle management Quantitative impact assessment Regulation and scrutiny High-efficiency computing hardware Integrated baseline indicator frameworks Mandatory audits and adversarial testing Shared, modular AI architectures Context-aware impact indicators Robust regulatory frameworks Minimizing redundant deployment waste Indicator-aligned datasets and repositories Ethical usage and reporting Uncertainty: AI infrastructure and talent inequality Sharing and cooperation Publishing policy-oriented annual reports Intergovernmental negotiations and consultations Open-source AI transition for SDGs FAIR-compliant openaccess platforms Regional institution collaboration Fig. 1 | Uncertainties and solutions for the open-source Artificial Intelligence (AI) transition. Key uncertainties of open-source AI persist regarding environmental impacts, assessment methodologies, potential misuse, and infrastructure inequalities. Our proposed solutions address these through integrated governance mechanisms: sustainable lifecycle management; quantitative impact assessment; regulation and scrutiny; and sharing and (...truncated)