'Thirsty' ChatGPT uses four times more water than previously thought
Claims that ChatGPT “uses four times more water than previously thought” prompt a broader examination of data center cooling and resource use. Commenters note that evaporative cooling does consume significant water, but argue its footprint is small compared to other industrial uses and even municipal leakage, and that AI workloads are just one share of overall cloud demand. The central tension is whether AI’s societal and economic benefits justify its marginal water and energy costs, and if not, why similar scrutiny isn’t applied to other high-consumption activities like office buildings, theme parks, or cryptocurrencies.
Scope of the Article / Framing
- Many argue the piece is really about generic data center water use, with “ChatGPT” in the title mainly for clicks.
- Some see it as a targeted “hit piece” driven by fear of LLMs or innumeracy around big-sounding quantities.
- Others push back on dismissiveness, arguing that resource allocation and new, rapidly scaling loads (like LLMs) are legitimate topics.
Water Use, Allocation, and Externalities
- Key point: water doesn’t vanish; cooling often uses evaporation, returning water to the atmosphere, not sewers.
- Critics respond that the issue is where and when water is used: data centers may compete with households, agriculture, and ecosystems, especially in water‑stressed regions.
- Concern that “water-positive” pledges may restore water in different locations than where it was withdrawn.
- Some argue water use should be priced to include local externalities; unclear if this is happening adequately.
Scale and Comparisons
- Several commenters emphasize scale: data center use is claimed to be tiny relative to industrial usage, municipal pipe leaks, agriculture, and lifestyle choices (e.g., lawns, avocados, T‑shirts, theme parks).
- Others counter that “everyone else wastes more” doesn’t address whether new high-consumption uses are justified.
Value of LLMs vs. Waste
- Strong disagreement on utility:
- Pro-LLM comments cite large personal and professional productivity gains (coding, research, fraud detection, learning).
- Skeptics see LLMs as hallucination-prone, black-box tools whose output is often low-value or replaceable by search.
- Some frame LLMs as comparable to or better than other high-consumption tech (e.g., Bitcoin); others see both as wasteful.
Energy Shaming and Ethics
- Debate over whether “energy/water shaming” is useful or selectively applied only to new tech instead of incumbents (offices, casinos, golf courses, theme parks).
- Underneath is a larger question: in a finite-resource world, should unnecessary or luxury uses (including LLMs) be socially or politically constrained?
Technical and Policy Notes
- Distinction between evaporative cooling vs once‑through systems; some confusion about what counts as “consumption.”
- Training is described as more intensive per run but small compared to total long‑term inference usage.
- Ideas raised: siting data centers near seas or in deserts with solar and desalination; pairing waste heat with beneficial uses.