Electricity In, Emotional Energy Out
With great power comes… a power bill (unless you can do it locally)
The headlines are loud right now. “Data center demand will push power use to record highs.” (Reuters) “Hundreds of groups want a pause on new data centers.” (The Guardian) “Artificial intelligence is draining water.” (WIRED)
All fair questions. The part that usually goes missing is scale.
The zoomed-out answer
In 2024, data centers used about 415 terawatt-hours, around 1.5% of the world’s electricity. (IEA)
In the International Energy Agency Base Case, they reach about 945 terawatt-hours by 2030, just under 3%. (IEA)
That is meaningful growth. It is not “everything.”
Electricity, in perspective
Sources for the table: data center electricity from the International Energy Agency. (IEA) Cooling share from the International Energy Agency. (IEA)
Water, in perspective
Globally, the International Energy Agency estimate that data centers consume about 560 billion liters of water per year today, rising to about 1,200 billion liters by 2030 in the Base Case. (GOV.UK)
For context, global freshwater withdrawals were just under 4,000 cubic kilometers in 2021, and agriculture is 72% of that. (UNESCO Documents)
Sources for the table: data center water from the International Energy Agency estimate (summarized in a UK government report). (GOV.UK) Global withdrawals and agriculture share from the United Nations World Water Development Report 2025. (UNESCO Documents)
Also, “data center water” is not one single thing. Cooling designs vary, and the International Energy Agency notes that options like direct liquid cooling and immersion cooling can reduce direct water consumption. (IEA Blob Storage)
Power, Water, Heat, Panic
When people talk about AI and the environment, the conversation usually sounds like this: power, water, heat, panic. Fair. Those inputs matter.
But the question we keep coming back to is simpler:
If we’re going to spend energy, what do we get for it?
With great power comes… a power bill. Unless you can do more of the work locally.
That sentence is basically the quiet spine of this whole series.
In the beginning, we met the “muscle.” Cat, GPU, GPT was the moment you realize the modern era runs on a specific kind of hardware scale. GPUs made the leap possible. Scale is why electricity and water entered the chat.
Then we met the “habit.” The guessing game learned to listen wasn’t really about fancy magic. It was about the model getting less scattered. It stopped treating everything as equally important and started holding onto the thread.
And then we finally got the “skill.” Attention was the 2017 shift that made language work at scale: not more words, but better focus learning what to highlight in a messy paragraph. (Vaswani et al., 2017) ([9])
That’s the bridge from electricity to usefulness.
Because the question is not “Can the model generate more text?”
The question is: Does the compute turn into something that gives a human being energy back?
Here’s an example of what we mean: a small cognitive support that helps a person see a pattern clearly enough to interrupt it and make a positive change.
A Common Anxiety Moment, From Overwhelm to Clarity
“Sunday night hits and my chest tightens. I start thinking about work tomorrow. I replay everything I might mess up. I keep checking my calendar and messages. Then I feel exhausted and mad at myself.”
For most people, the problem here isn’t a lack of insight. It’s that everything shows up at once.
In moments like this, more words don’t help. What helps is compression: turning a fog of experience into a recognizable pattern. When the swirl gets smaller, the nervous system can finally orient.
Seen clearly, this moment has a simple shape: a predictable trigger, an immediate body response, a loop of worry and reassurance seeking, and a narrow window where interruption is possible. Nothing is “fixed.” But the fear becomes specific instead of everywhere.
That’s the “emotional energy” return. Not because a tool fixed them but because it reduced the experience to a size their nervous system could hold.
From “I’m trapped in it” → to “I see the loop, and I know where I can step in.”
And yes: doing that kind of sorting takes compute. (With great power comes… a power bill.)
This is only one example. Not a defense of all AI, and not a claim that technology is the answer to human suffering. But it is a concrete case of something that matters: energy spent turning into human capacity returned. When AI does that when compute becomes clarity, orientation, or relief it earns its place. The question isn’t whether AI uses resources. It’s whether those resources come back as something that genuinely helps people live better.
When this patterning can happen locally, it avoids unnecessary trips to data centers for every turn. That’s not about purity. It’s about treating compute as a shared resource and using it carefully when less is enough.
We are designing Simcha to be cost-efficient and resource-aware. Not because energy use is bad, but because context matters. In cases like this, local models simply make sense.
Next article
Next: how to carry that “small loop, small step” energy between sessions so the plan doesn’t evaporate by Tuesday, and progress shows up in the week, not just in the room.
References
International Energy Agency. (2025). Executive summary – Energy and AI. https://www.iea.org/reports/energy-and-ai/executive-summary (IEA)
International Energy Agency. (2025). Energy demand from AI. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai (IEA)
International Energy Agency. (2018). Air conditioning use emerges as one of the key drivers of global electricity demand growth. https://www.iea.org/news/air-conditioning-use-emerges-as-one-of-the-key-drivers-of-global-electricity-demand-growth (IEA)
UNESCO World Water Assessment Programme. (2025). The United Nations World Water Development Report 2025: Mountains and glaciers: Water towers. https://unesdoc.unesco.org/ark%3A/48223/pf0000393090 (UNESCO Documents)
UK Government. (2025). Water use in data centre and AI report (executive summary). https://assets.publishing.service.gov.uk/media/688cb407dc6688ed50878367/Water_use_in_data_centre_and_AI_report.pdf (GOV.UK)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762 (IEA Blob Storage)
Reuters. (2025). Data center demand to push US power use to record highs in 2025-26, EIA says. (Reuters)
WIRED. (2025). You’re Thinking About AI and Water All Wrong. (WIRED)
The Guardian. (2025). More than 200 environmental groups demand halt to new US datacenters. (The Guardian)