The Reality of AI Power Usage

AI power usage generates significant controversy. Headlines paint it as an environmental catastrophe waiting to happen. The reality proves more nuanced and potentially more optimistic than these dire warnings suggest.

A ChatGPT query uses 10 times more energy than a Google search. This sounds alarming until one realises it equates to running your hairdryer for six seconds. The entire data centre industry, including all AI operations, accounts for just 1.5% of global electricity consumption.

Here is a rundown of the more pressing issues with AI power usage.

We can power AI with clean energy

Data centres power everything from email to streaming services to artificial intelligence. The total energy footprint of all data centres in the world remains remarkably modest at 1.5% of global electricity use. This figure includes all AI operations, from training to inference.

To put this in perspective, AI operations consume approximately 54 TWh annually, representing just 0.2% of global energy output. By comparison, Bitcoin mining alone uses 175 TWh, nearly triple AI usage, accounting for 0.6% of global energy. Unlike AI which drives technological progress and can help solve climate challenges, cryptocurrency mining provides questionable utility while consuming significant resources.

Another comparison: 1.5% roughly matches aviation industry emissions. However, aviation remains heavily dependent on fossil fuels, whereas data centres can transition to renewable energy sources. The tech industry has unprecedented opportunities to lead in sustainable operations.

Solving climate challenges requires abundant clean energy rather than solely focusing on reduced consumption. Human behaviour and technological progress make consumption reduction an unrealistic primary strategy. Instead, we must focus on transitioning to sustainable power sources and optimising resource usage.

We can use AI to solve climate challenges

AI serves as a powerful ally in our climate fight. Energy companies utilise artificial intelligence to optimise renewable energy systems, maximising their efficiency. AI can aid its own power transition to sustainable energy sources - no other use of power has this potential.

Other environmental applications already show promising results. Teams use AI systems to detect methane leaks from space, enabling rapid response to emissions. Agricultural researchers employ machine learning to breed heat-resistant crops, securing our food supply against climate change. Materials scientists harness AI to design new carbon capture compounds.

The real challenge is not power, but water

The environmental impact of AI extends beyond electricity consumption. Modern data centres require substantial water resources for cooling, and many new facilities are being constructed in regions already experiencing severe drought conditions. This presents a more immediate environmental challenge than power consumption.

Unlike electricity, which increasingly comes from renewable sources and benefits from rapidly advancing solar technology, fresh water remains a finite and unevenly distributed resource. The suggestion to reopen closed coal power plants for AI operations fundamentally misunderstands the challenge: power generation will become increasingly sustainable, but water scarcity cannot be solved through technological advancement alone.

The Path Forward

The solution lies not in abandoning AI development but in pursuing it responsibly through several key strategies:

Sustainable Power Sources

Our data centres must run on clean, renewable power sources. This transition requires strategic planning and investment but remains technically feasible. The rapid advancement of solar technology makes this increasingly achievable.

Strategic Water Management

Data centre locations need careful selection based on sustainable water availability. Regions facing drought conditions cannot support additional cooling requirements. This geographical constraint may shape the future distribution of AI infrastructure more than power availability.

Efficiency and Purpose

Continuous improvement in model efficiency reduces resource consumption while maintaining capabilities. This includes both training and inference optimisation. More importantly, we must prioritise AI applications that directly combat climate change. The technology possesses unique capabilities to address environmental challenges at scale.

AI power usage warrants attention but not panic. The technology presents both environmental challenges and opportunities. Our task involves maximising its potential for climate solutions while minimising its resource footprint. The real challenge lies not in power consumption, which renewable energy can increasingly address, but in responsible water usage and ensuring AI development serves meaningful environmental purposes.

I would like to thank Roisi Proven, Romilly Cocking, Tadas Tamošauskas, and Aimee Rivers for their thoughtful contributions to this discussion. Additional valuable insights came from Ajay Prabandham and Guillaume Roy.