Most impact organisations use AI either in secret or with a guilty conscience. Neither is the right approach. The real question is not ‘whether’, but ‘where’.
June 2026 · Noted by · reads in ~3 minutes
Most impact organisations I come across either use AI without mentioning it publicly, or do so with a slight twinge of guilt. I consider both approaches to be inadequate. Not because AI’s resource consumption is irrelevant, but because the question is being asked in the wrong way. It’s not about whether you use AI. It’s about where the energy for it comes from and whether you’re prepared to take an active role in shaping that.
A typical text query consumes around 0.3 Wh of electricity – the same as an LED bulb in two minutes. The figures most frequently cited in the press are overestimated by a factor of 10, because a single estimate from 2023 is being passed on uncritically. Per-prompt consumption is a real factor, but it is not the problem the headlines suggest.
The problem is scaling: more users, longer contexts, more autonomous processes. And: exactly where the data centres are located.
This is the crux of the latest UN report on the subject (UNU-INWEH, June 2026), and it is rarely understood: low-carbon electricity is not automatically low-water or low-land. Every energy source has a different profile.
Wind power and photovoltaics have very low water consumption, but require land. Nuclear power is low-carbon, but water-intensive. Gas-fired power stations emit more CO₂, but use less water than coal. The three footprints – carbon, water and land – do not always move in the same direction.
What this means in practical terms is that a data centre in Iceland, powered by geothermal energy, has a different environmental profile to one in Texas, which is fed by the gas grid. Two-thirds of the new AI data centres built in the US since 2022 are located in semi-arid or drought-prone regions. The European electricity mix already has a significantly higher share of renewable energy.
Location decisions are made by the providers, not the users. But users have more influence than one might think.
Anyone using AI APIs can make enquiries or make an active choice: Anthropic and OpenAI offer EU server regions. European servers run on an electricity mix with a significantly higher proportion of renewables than many US locations. This is a decision that can be documented and communicated as part of one’s own AI strategy.
Three further patterns drive consumption disproportionately and can be directly controlled.
AI images and videos. A single high-resolution AI image consumes as much energy as a full smartphone battery charge. Anyone who uses image generation on autopilot will quickly end up using many times the amount of energy required for text processing.
Autonomous agents without termination conditions. Processes without a defined end point multiply consumption exponentially. Clear boundaries and manual approval steps are essential in every AI workflow.
Unnecessarily long contexts. Token length is the direct cost driver. A 100,000-token context consumes around 130 times as much as a typical query.
An organisation that uses AI in a targeted manner, actively chooses its server location, communicates its usage transparently and channels the freed-up capacity into its mission is in a better position, both environmentally and ethically, than one that does so secretly or avoids it altogether. This is not mere self-reassurance. It is a justifiable stance.
All sources, figures and methodological background information can be found on the resources page for this topic.
45 minutes, one screen shared, an honest assessment. Not a sales pitch, I show you where I see leverage and where I do not.