AI is not one thing. Neither is its environmental impact.

We need decision making in business and policymaking focused on the problems we’re trying to solve. On the outcomes we’re trying to achieve and the society we want to build.

AI can be machine learning, Grok, or basic (mislabelled) data analytics and simple process automation software.

Its impact on sustainability can of course be environmental – its emissions, energy use, water consumption, effect on the local environment, or through the physical materials and supply chains that build datacentres and all manner of other infrastructure.

AI’s impact can also be social and economic – on employment, education, policing, for creatives, or on local community cohesion.

When considering governance, often used as the third pillar of sustainability (and the acronym ESG) AI opens up new questions of accountability from self-driving cars to corporate management.

I’m not going to rank these impacts. Or pretend that in one blog post I can fully account for their breadth and complexity. But as a starting point for our collective future conversations, here are some numbers, questions, and counterarguments to consider…

The whole technology industry, including chip manufacturing and electronics supply chains, is 2-3% of global emissions, based on an analysis combining sources like Our World In Data and the World Economic Forum. This is, it’s worth noting, a variable estimate depending on how you define “the sector” as is the case with all carbon accounting. The International Energy Agency (IEA) reports that AI is far less than the 0.5% of global emissions generated by all current datacentres, which provide storage and processing for our digital lives far beyond AI. Now that footprint will grow with the rapidly increasing number of datacentres before it comes back down as electricity is decarbonised. And given emissions saved now are especially vital in arresting global warming (vs emissions saved years and decades down the line) the construction of new gas and coal to fuel datacentres should be stopped wherever possible – in line with most all climate science and viable pathways to limiting global warming to between 1.5-2 degrees. In the long run the energy transition is building critical mass: over $2 trillion will be spent this year on clean energy vs $1 trillion in fossil energy. Agriculture, steel, cement, and other sectors are more worrying as far as a successful transition to net-zero is concerned. But all this said, we must address both the now and long term.

Regarding electricity, it’s a major headache for planners and the grid. Numbers across NESO, DESNZ, and National Grid in the UK project datacentres as 2-3% of our electricity demand in 2026. A recent London Assembly report cited that will grow to between 30-71TWh by 2050 (roughly between 1 and 3 Hinkley Point C nuclear plants) – an immensely variable number and likely supplied by heavily decarbonized electricity.

The impact of the physical infrastructure follows the concerns of the broader tech and infrastructure sector: security of critical mineral supply, refining ability, and human rights abuses in those opaque supply chains. Something we desperately need to address in legislation but not uniquely to AI.

Water is typically used in closed loop systems in the UK and this could easily be mandated in planning. Internationally it is more concerning but still presents a need for clear context as worldwide datacentre water withdrawal sits at around 0.1-0.2% according to the IEA… but it is often concentrated in water-scarce areas. In the UK, 64% of datacentres use less water than a typical leisure centre, suggests a TechUK report published in collaboration with the Environmental Agency. Compared with heavy industry and agriculture, AI’s water footprint remains minimal, although it still requires us to minimize specific hyper-local impacts.

Again, I’m not going to go into a major ranking exercise (for now). Beyond also accounting for the social and economic impacts, the environmental positives and negatives alone need wide-ranging assessments by wide-ranging contributors… and a clear picture of your own decision-making context, responsibility, and sphere of influence – which I explored more last year.

For example, are you a Chief Technology Officer in charge of specifically mitigating AI and digital technology’s environmental footprint – and maximizing its positive application for the organisation and broader stakeholders? Are you a policymaker considering how AI can provide the platform for people and communities to thrive and live their unique versions of best lives? Or are you thinking in your own personal capacity, trying to minimize your footprint as best you can… while at the same time looking toward what game changing applications for your life, career, and the world that AI could help you discover?

AI can be shaped to provide a platform for people to flourish. But it needs better conversations and better decisions in every sphere of influence.

There are so many of these conversations to come in this space, with people and organisations in business and politics… do reach out!

This article is based on the original policy-focused post which you can read here.