GUIDES
The Real Data on AI’s Energy Use 📚
If you’ve been holding off on using AI because of the environmental impact, the data tells a very different story than what’s been spreading online. Here are.
Why I Wrote This
I started this account because I don’t want women to get left behind in this AI era. The people who use AI the most are getting promoted, building careers, starting side hustles, making more money.
One of the biggest reasons I hear from people who haven’t started: guilt about the environment. So I went and pulled the actual data — from primary sources, not viral tweets. Here it is.
The 3 Numbers That Matter
The International Energy Agency — the most respected energy research body in the world — published Energy and AI in April 2025. Direct from the report:
Yes, data centers use a meaningful amount of energy. Yes, that number is growing. But the slice coming from individual people asking AI questions is genuinely tiny. The growth is enterprise: companies running AI on their own data, GPU training for new frontier models, big infrastructure deployments.
What Your Personal Use Looks Like
The most-shared comparison online is “one AI question = one second of microwave.” That’s roughly right — but most people are crediting it to the IEA, which never said it. Here’s where the number actually comes from.
Epoch AI, an independent research org, found a typical AI query uses about 0.3 watt-hours. A 1,000W microwave running for 1 second uses ~0.28 Wh. The math lines up. Hannah Ritchie at Our World in Data popularized the comparison.
100 × 0.3 Wh = 30 Wh total. The average US home pulls about 1.2 kW. So 30 Wh / 1.2 kW = about 90 seconds of normal household use. That’s your daily AI energy footprint — a minute and a half of your home running normally.
Older viral claims pegged AI queries at 3 Wh per question. The IEA report calls those numbers roughly 90× too high. So if anyone is sending you a graphic with the old 3 Wh number — that math has been formally rejected by the most credible source on energy.
Not Coming From You
The IEA is clear about where the energy use is actually scaling: GPUs running enterprise AI workloads. “Accelerated servers” (the GPU-heavy systems training and running large models) account for about half of the net data center energy increase through 2030.
Translation: a hyperscaler training a frontier model for 3 months uses orders of magnitude more energy than every casual user combined for that same period. The aggregate data-center number is real, but the per-person guilt is misplaced. Your AI use is a rounding error compared to the enterprise side of the equation.
3 Steps to Begin
If environmental impact has been your reason to wait — the data doesn’t back the guilt. Here’s the simplest start.
Go to claude.ai. Free plan, no credit card. Have a real back-and-forth conversation. The point is just to see what’s good about it.
Pick the most boring or repetitive part of your work. Use Claude for that — only that — for 7 days. Don’t try to do everything. Get good at one thing.
Once you’ve used it for a week, you’ll know if it’s worth more time. If yes, the rest of the guides on this site walk you through every direction you can take it next.
In outbound marketing and career positioning, generic applications have near-zero conversion. Treat yourself as a high-ticket solution: identify the company's pressing operational pain points, build a mini-audit or work sample using AI before you apply, and bypass crowded channels by reaching out directly to the decision-maker with structured value.