What's the environmental footprint of your AI use?

Last May I used AI to create a simple environmental impact calculator, to provide rough orders of magnitude estimates of what an individual's AI use would likely translate to in terms of energy and water usage and carbon impact.

This January, I created some infographics to try and illustrate the water and carbon footprint of AI at both a macro and individual level.

Today, I’m sharing an updated individual use calculator I’ve created using more recent data and Claude Fable (the most advanced publicly-available AI model from Anthropic).

As with my previous efforts, this isn’t intended to minimise the environmental impact of the development and operation of AI models, which is significant and growing.

Rather, it’s intended as a tool to help contextualise the impact of our individual AI use relative to other aspects of our day-to-day lives.

My takeouts from using it to estimate the impact of my own usage and the impact of those with different usage profiles (e.g. developer, video creator):

• Regular use of AI chatbots has a negligible impact relative to other non-AI aspects of our daily lives.

• AI video generation is many orders of magnitude more energy intensive than text interactions and image generation.

• For most of us, travel, heating and diet are much bigger levers in managing our personal environmental impact.

However, this shouldn’t be a cause for complacency about AI's aggregate footprint - it's growing rapidly, and the proliferation of more agentic models, capable of spinning up armies of sub-agents, introduces a worrying multiplier effect. Fortunately, easing individual guilt and pushing for systemic accountability aren't mutually exclusive.

So, where should we direct our anxiety/anger at the global environmental impact of AI? I’d suggest primarily at governments, who desperately need to legislate. We can’t rely on tech companies to regulate themselves on this (or, let’s be honest, anything).

I’m very much a layperson in this area but it strikes me governments should be legislating for:

• Full transparency of the energy and water consumption of different AI models and the data centres they run on.

• Binding targets for data centres. Germany's approach (mandating a rising renewable electricity share, reaching 100% by 2027) is one live example of what this can look like in practice.

• Material consequences for missed targets, financial and regulatory, not just reputational risk.

• Incentives for the development of more efficient models and on-device processing.

Of course, this agenda doesn't remotely align with the current US or Chinese administrations, both of which are prioritising speed over environmental impact. The US's current approach favours streamlined permitting and voluntary frameworks over binding rules, whilst China is actively subsidising electricity for data centres as part of its national strategy. Neither is a promising sign for anyone hoping meaningful regulation arrives soon. But the EU and a handful of individual countries are demonstrating that at least parts of this agenda are politically possible. Europe's biggest contribution to the AI race might not be a frontier model, but a working example of how to regulate them.