Everything, Everywhere, All at Once: The Hidden Cost of AI

Written by: Paolo Rizzi

As part of CAT’s Responsible AI programme in collaboration with ustwo, we welcomed Professor Chris Preist for a session exploring the environmental impact of artificial intelligence. Chris, Professor of Sustainability and Computer Systems at the University of Bristol, challenged us to look beyond AI’s capabilities and consider the infrastructure, resources, and policies that will shape its future.

As AI adoption accelerates, questions about its sustainability are becoming increasingly important. While public discussions often focus on individual interactions with AI tools, Chris encouraged us to think about the much larger systems behind them.

Every AI interaction relies on a network of data centres, specialised hardware, cooling systems, telecommunications infrastructure, and electricity grids. As demand for AI grows, so does the need for the infrastructure required to support it.

One of the central messages of the session was that electricity consumption is the standout environmental concern. Training and operating advanced AI models requires significant computational power, which translates directly into growing energy demand. While other impacts, including water use and hardware production, are important considerations, electricity remains the key factor in understanding AI’s environmental footprint.

At the same time, Chris emphasised that the picture is more complex than simply “AI uses a lot of energy”. Digital technologies have historically become more efficient over time, and AI is no exception. Advances in hardware, model design, and computing efficiency continue to reduce the resources required to perform a given task.

This creates an important tension. AI demand is increasing rapidly, but the technology is also becoming more efficient. The challenge is understanding whether those efficiency gains can keep pace with continued growth.

The session also explored where AI computation happens. Today, most AI services rely on large, centralised data centres operated by a small number of technology companies. However, as models become smaller and more efficient, some AI workloads may increasingly move onto personal devices or local infrastructure.

Chris highlighted that this shift could bring both opportunities and challenges. Large providers are often able to measure and report their environmental impacts and invest in efficiency improvements at scale. If AI becomes more distributed across millions of devices, tracking its overall environmental footprint may become more difficult. It could also shift some of the costs of AI infrastructure from technology companies to consumers through increased electricity use and hardware requirements.

A particularly interesting part of the discussion focused on the role of government. Chris argued that the future environmental impact of AI will not be determined by technology companies alone. Decisions about energy systems, infrastructure development, and public policy will play a significant role in shaping outcomes.

The same AI system can have very different environmental impacts depending on how the electricity powering it is generated. As a result, government decisions about grid decarbonisation and infrastructure planning are likely to be just as important as technological innovation itself.

The session ultimately offered a balanced perspective. AI has enormous potential, but understanding its environmental implications requires us to look beyond individual tools and consider the wider systems that support them. The future of AI will depend not only on technological progress, but also on the choices we make about how it is built, powered, and governed.

A recording for this talk is accessible in the #greener-ai channel in CAT Slack community.

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