Why scientists should lead the shift away from AI mega data centres | Nature

Email Bluesky Facebook LinkedIn Reddit Whatsapp X Save article View saved research Activists in San Marcos, Texas, protested against proposed data centres that would power artificial-intelligence systems. Credit: Sara Diggins/The Austin American-Statesman via Getty As public opposition grows against the soaring energy and water demands of data centres powering the artificial-intelligence boom, some technology companies are talking about putting these facilities in space. For instance, businessman Elon Musk’s AI and rocket firm, SpaceX, is one of a handful of companies planning to deploy constellations of satellites in low-Earth orbit that act as data centres. The logic is seductive: such facilities could tap abundant solar energy and avoid opposition from communities. But the premise that massive data centres are a prerequisite for enabling AI-driven scientific advances is incorrect. The infrastructural needs of science are fundamentally different from those of consumer AI platforms built to serve millions of users. Researchers with the necessary technical know-how should champion an alternative vision: one centred on open-weight AI models — those with publicly available parameters — that can be deployed locally while prioritizing the efficient use of computing resources. Such an approach would make the use of AI tools more sustainable and better aligned with public interest. How much energy will AI really consume? The good, the bad and the unknown Data centres have supported the Internet economy for decades. However, those being built to support AI models require a massive amount of power. The world’s data centres used about 485 terawatt-hours of electricity last year, similar to that used by Germany, and the International Energy Agency expects that to double by 2030. Five technology companies — Amazon, Alphabet, Microsoft, Meta and Oracle — are expected to spend a total of more than US$600 billion on AI infrastructure this year; a decade ago, the same five companies spent less than $40 billion. Data centres concentrate this extraordinary energy demand on the electricity grids of the specific communities where they are built, despite concerns about water use, noise and equity. But this expansion is facing mounting resistance. A poll published by Gallup in May found 71% of Americans opposed the construction of a data centre in their local area (20% were somewhat in favour of it). As scientists who rely on AI in our own work, we think a more practical solution exists on Earth. Researchers must pioneer the adoption of open-weight AI models that run locally on institutional servers. Here, we outline a vision for a more decentralized approach to AI — one that allows researchers to deploy these tools in a more accountable manner, while reducing reliance on massive data centres. Decentralize AI Although precise numbers are difficult to obtain, most of the billions of queries made to AI chatbots each day are currently handled by data centres run by large tech companies. Open-weight models offer comparable capabilities to those of closed-weight, proprietary models in many cases, but using them often requires technical know-how. This use of chatbots has fostered the misconception that advanced AI can operate only in vast, centralized data centres. This is not true, based on our experience. The history of personal computing offers a useful analogy. Early computers filled entire rooms before shrinking into desktop computers and laptops. The AI era is just a few years old, but signs of a similar shift are already visible. For example, the chipmaker NVIDIA has announced a new laptop chip, the RTX Spark, to run powerful AI models (such as Google’s Gemma 4) on laptops from Dell, HP and other providers; Apple’s laptop chips have supported a range of local AI models for years. These devices are currently expensive, but the trend is clear. Similarly, a version of Google’s flagship AI model, Gemini, is designed to run within an organization’s own facilities . A rack of servers the size of a mini fridge can support around 50 users simultaneously sending prompts to an AI model and receiving responses. Data centres are often opposed because of their location and energy and water use. Credit: Erik S Lesser/EPA/Shutterstock Many research teams would have enough expertise to set up a similar server loaded with an open-weight model, rather than buying a subscription from Google. Academic institutions are not currently investing in such an approach, but they must, because neither the environmental costs of AI nor the subsidized fees for access can go on indefinitely. Scientific communities, which have long led the adoption of open-source software — with freely available code, training data and model parameters — should help to drive this shift, setting an example for wider public use of AI tools that is more energy efficient and sustainable. Open-source AI tool beats giant LLMs in literature reviews — and gets citations right In practical terms, a model that runs on a laptop today can match or exceed the performance that top proprietary systems achieved just a year ago. Epoch AI — a research institute in San Francisco, California, that investigates AI trends — found that open-weight models take around four months to match the capabilities of frontier proprietary systems. Massive AI data centres will continue to be built by technology firms, particularly to support proprietary services and harvest user data to train future models. But this is not the only path forwards. Personal computers are designed for efficiency: they consume little power when idle and can be cooled without large amounts of energy or water. Scientific communities must focus on building a future that consciously rejects the need for an ever-greater reliance on massive data centres. The agentic future The performance of large language models (LLMs) increasingly depends not just on the models themselves, but also on the external software and tools surrounding them, a process often described as agentic AI. Early LLMs struggled with mathematics tasks because they were limited to generating one token, or word fragment, at a time, based on patterns learnt from the training data. Because these were incomplete segments of equations, they made mistakes. That is changing. Data centres will use twice as much energy by 2030 — driven by AI Enjoying our latest content? Log in or create an account to continue Access the most recent journalism from Nature's award-winning team Explore the latest features & opinion covering groundbreaking research Access through your institution or Sign in or create an account Continue with Google Continue with ORCiD Nature 656 , 296-298 (2026) doi: https://doi.org/10.1038/d41586-026-02451-2 Reprints and permissions Competing Interests The authors declare no competing interests. Related Articles How much energy will AI really consume? The good, the bad and the unknown Why open-source generative AI models are an ethical way forward for science Open-source AI tool beats giant LLMs in literature reviews — and gets citations right Data centres will use twice as much energy by 2030 — driven by AI Subjects Environmental sciences Sustainability Technology Research data Latest on: Environmental sciences Sustainability Technology Why environmental treaties must also address mental health Comment 10 AUG 26 Fires in Europe are breaking records: here’s what the future holds News 30 JUL 26 Artificial algal blooms could remove atmospheric carbon — but can they be used responsibly? News & Views 29 JUL 26 Food systems transformation would reshape global agriculture Article 15 JUL 26 How a rush for minerals is causing deforestation in tropical regions News & Views 03 JUN 26 Analysis of trade-offs of post-sorting plastic packaging Article 03 JUN 26 Will AI make our dreams all look the same? Correspondence 11 AUG 26 Seven tips for a graceful lab exit Technology Feature 11 AUG 26 Levitating sensor for magnetic fields could detect ultrafaint brain activity News 06 AUG 26 Jobs Associate or Senior Editor, Nature Progress Brain Health Job Title: Associate or Senior Editor, Nature Progress Brain Health Location: Shanghai, Beijing or Pune, Hybrid Working Model Application Deadl... Shanghai, Beijing or Pune, Hybrid Working Model Springer Nature Ltd Research Scientist in AI for Healthcare The post-holder will play a key role in OSCAR's research, supervised by Prof. Clifton, Chair of Clinical Machine Learning, University of Oxford. Suzhou, Jiangsu (CN) Oxford Suzhou Centre for Advanced Research Global Recruitment of Outstanding Young Talents We are seeking exceptional candidates who qualify for the National Science Fund for Excellent Young Scholars (Overseas) 1068 Xueyuan Avenue, Shenzhen 518055 P. R. China Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences（CAS) Seeking Global Talents at All Levels - SIAT We find Distinguished PIs, Senior PIs, Junior PIs, Senior Engineer, Junior Engineer, Post-doctoral fellow, Assistant research fellow. 1068 Xueyuan Avenue, Shenzhen 518055 P. R. China Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences（CAS) Postdoctoral fellow Postdoctoral positions at University of California, San Francisco in the Ma lab are available for highly motivated candidates with PhD training in ... San Francisco, California University of California, San Francisco Colitis Center