The Electricity Cost of Everyday AI

Design

One experiment, four interface conditions

The intervention and the experimental design should be shown together. Each condition changes the choice architecture, not the underlying user task.

Measurement

The outcome is a frontier, not one number

Validation In The Experiment

Every claimed saving has to survive output checks

A lower-compute recommendation counts only when it produces a usable result. Objective tasks use tests or exact checks. Writing uses blind preference. Factual tasks require source support. High-stakes or uncertain tasks are abstentions, not savings.

Research Flow

From an observed mismatch to a behavioral experiment

The pilot shows what the world looks like when we read real chats as tasks. The paper becomes publishable when it proves two more things: lower-compute answers can be good enough, and users actually change choices when the interface gives them a usable alternative.

What Makes The Claim Strong

Three things cannot be blurred together

Questions

The paper should answer four questions in order

Annotation Upgrade

Stop asking annotators to jump straight to a tier

A tier by itself is too compressed. The adjudication sheet should expose the reasons: freshness, context, risk, deterministic solvability, language work, reasoning depth, and tool need. The final recommendation should fall out of those facts.

Output Validation

How we prove lower compute is sufficient

Recommendation Metrics

The recommendation rule should be evaluated like a safety-critical classifier

Experimental Arms

From resource awareness to actionable right-sizing

Outcome Dashboard

The frontier is time, quality, and carbon together

Behavioral Logic

Why recommendations may work when labels alone do not

Major Framing

What we want the paper to do

The strongest version of the paper leads with empirical magnitudes. Some observed AI use is strict overuse: search, calculation, local software, or a specialized tool would have been enough. A larger share needs language capability but not frontier AI. The behavioral question is whether users know this energy gradient and whether information makes them choose differently.

Abstract And Literature

The abstract should make one clean move: sustainable AI use is an electricity problem

Existing energy work shows that inference cost varies with model size, token length, reasoning, and serving efficiency. Our abstract should lead with what the data show: the share of strict overuse, the share that can use simpler models, the electricity multiples from reasoning overuse, and then the subject experiment.

Possible Results

The results should be stated as tradeoffs, not slogans

The paper should make the two behavioral results front and center, then connect them to the two benchmark worlds. If all tasks were standard frontier, savings are modest. If ordinary tasks default to reasoning, savings are large. The information intervention tells us whether users can be guided toward careful AI use.

Self-Critique And ROI

The next dollar should buy validation, not more labels

The current 10,000-row result is a strong pilot, but it is still classifier evidence. The highest-ROI next step is to prove that the lower-energy option actually works: no-LLM cases must be executable without an LLM, small-model cases must survive output validation, and standard-frontier cases must be shown not to need reasoning. Expanding to more raw chats before this validation would increase precision around an unverified construct.

Best Next Plan

Validate the conservation margin, then run the experiment

The plan below turns the pilot into publishable evidence. The sequence matters: first validate the task labels, then validate outputs and local energy, then test whether subjects change choices.