GoKawiilActivists 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.
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