Digital Habits, Water & Energy

Calculate Your Digital Habits

Calculate Your Digital Habits

Estimate a day of cloud-connected activity, then interrogate the assumptions behind the numbers.

Calculator overview

The left side of the tool is where you enter rough amounts for the activities in a typical day. Rows are grouped into AI tasks, media and search, and meetings so you can build your own mix instead of relying on one average use case.

The summary panel updates as you type. It shows server + network energy, total-system energy, direct water, and total water, then translates those totals into everyday anchors like bulb time, laptop batteries, drops, cups, and gallons.

The lower table is the assumptions and sources layer. It shows the quantity for each active row, the status label on the estimate, the energy and water values used for that activity, and the source links behind the figures. The method note at the bottom explains where the estimates are strongest and where broader conversions are being used.

This tool shares some lineage with Jon Ippolito's What Uses More?, which was an early blueprint for comparing AI tasks with everyday digital activity. The angle here is different. His calculator is built for pairwise comparison with factor switches. This one is built to inventory a full day's mix of habits while keeping cloud-side versus total-system energy and direct versus total water visible at the same time.

Parts of the tool

  • Inputs: enter prompts, hours, meetings, or daily blocks for the rows that match your day.
  • Starter mixes: load a sample pattern, then adjust it instead of starting from zero.
  • Summary and anchors: compare the four totals and use the reference conversions to make them easier to read.
  • Assumptions and sources: use the lower table to check confidence labels, per-row values, and linked sources before citing a number.

AI tasks

Media and search

Meetings

Assumptions and sources

The lower table shows each activity's current row values, confidence label, and source links.

Activity Qty Status Server + network Total energy Direct water Total water
AI text prompts Uses an everyday text-prompt estimate of about 0.3 Wh while keeping a separate prompt-specific water estimate of 0.26 mL direct and 1.3 mL total.
Sources Epoch AI (2025) Jegham et al. (2025)
0 prompts Estimated 0 Wh 0 Wh 0 mL 0 mL
High-reasoning prompts Uses the long high-reasoning 33.8 Wh benchmark rather than the much smaller medium-query comparison value, so this row stays an intentional outlier.
Sources Jegham et al. (2025) Internal synthesis (2026)
0 prompts Inferred 0 Wh 0 Wh 0 mL 0 mL
AI image generation Uses an open-model image-generation average and the same water-conversion rule used across the broader comparison set.
Sources Roucy-Rochegonde et al. (2025) Internal synthesis (2026)
0 images Inferred 0 Wh 0 Wh 0 mL 0 mL
Coding-agent use Uses the current one-hour coding-agent benchmark and adds a 30 Wh device allowance in the total-system column.
Sources Couch (2026) Internal synthesis (2026)
0 hours Estimated 0 Wh 0 Wh 0 mL 0 mL
TikTok or short-video scrolling Built from device-side social-video measurement plus streaming-style infrastructure assumptions; keep this row estimated until a platform-specific cloud-side benchmark exists.
Sources Greenspector (2021) Holter (2025) Kamiya (2020) Internal synthesis (2026)
0 hours Estimated 0 Wh 0 Wh 0 mL 0 mL
Instagram scrolling This remains an analogy-based estimate built from social-media measurement plus streaming comparisons, useful for relative scale rather than platform accounting.
Sources Greenspector (2021) Kamiya (2020) Internal synthesis (2026)
0 hours Estimated 0 Wh 0 Wh 0 mL 0 mL
Snapchat Another low-confidence social-media estimate built from broader comparison logic rather than a platform disclosure.
Sources Greenspector (2021) Internal synthesis (2026)
0 hours Estimated 0 Wh 0 Wh 0 mL 0 mL
YouTube watching Uses the same CDN-style video benchmark as Netflix so the calculator keeps an apples-to-apples streaming anchor for the cloud-side and total-system columns.
Sources Kamiya (2020) Internal synthesis (2026)
0 hours Inferred 0 Wh 0 Wh 0 mL 0 mL
Netflix streaming Uses the IEA/Kamiya 22 Wh cloud-side and 77 Wh total benchmark used throughout the site's streaming comparisons.
Sources Kamiya (2020) Internal synthesis (2026)
0 hours Inferred 0 Wh 0 Wh 0 mL 0 mL
Email, cloud docs, and browsing This is a teaching bundle for light browsing, email, and cloud documents rather than a per-click benchmark.
Sources Internal synthesis (2026)
0 daily blocks Estimated 0 Wh 0 Wh 0 mL 0 mL
Zoom as participant Uses a one-hour participant meeting estimate in which network plus platform routing form the cloud-side figure and participant device energy is added back for the total-system column.
Sources Mytton (2023) Verdecchia et al. (2022) Internal synthesis (2026)
0 hours Estimated 0 Wh 0 Wh 0 mL 0 mL
Zoom as host Uses a hosted Zoom estimate that counts all participant devices in the total-system column.
Sources Mytton (2023) Verdecchia et al. (2022) Internal synthesis (2026)
0 meetings Estimated 0 Wh 0 Wh 0 mL 0 mL

Method note: The first energy column is a cloud-side view that usually combines server and network demand, not a pure server-only accounting. The tool keeps direct-versus-total water explicit, but many non-AI rows still use broader electricity-based water conversions because most platforms do not disclose their own water use.