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If AI makes judgment faster and cheaper, people may use it for more decisions—but that possibility is a hypothesis, not an established “Jevons paradox of judgment.” Jevons’s paradox describes what can happen when greater efficiency in using a resource leads to enough additional demand to offset expected savings. Whether the same pattern applies to human decisions, and whether more AI-assisted decisions would be better or worse, requires separate evidence.
What is the Jevons paradox?
In 1865, economist William Stanley Jevons argued that more efficient use of coal could increase total coal consumption: efficiency made coal useful and economical in more applications, expanding demand. The key idea is not simply that each task uses fewer resources. It is that the lower effective cost may prompt enough additional use to reduce or even erase the expected total savings. Blake Alcott’s historical account discusses Jevons’s argument in “Jevons’ paradox”.
Energy analysts distinguish between rebound, where increased use offsets some expected savings, and backfire, the stronger case in which total use rises beyond the counterfactual level it would have reached without the efficiency improvement. Backfire is not inevitable; its size and even its presence at the economy-wide level remain subjects of empirical debate. Steve Sorrell’s review stresses the difficulty of testing the claim and says the evidence was far from conclusive, while suggesting broader rebound could be larger than commonly assumed: “Jevons’ Paradox revisited”.
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Applied to judgment, the analogy would be that AI lowers the time, effort, or money needed to reach a decision, so people seek judgments for more situations. For example, someone might ask an AI system not only to compare two job offers, but also to assess routine emails, small purchases, everyday disagreements, and low-stakes choices they would previously have handled without assistance.
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That increase in decision volume would not by itself prove backfire. To establish a Jevons-style effect, researchers would need to define the relevant cost and counterfactual, then show that a specific efficiency gain caused total demand for judgment to increase enough to offset an expected saving. The comparison also has to specify what “total” means: more decisions, more computing, more human time, or something else. These outcomes can move in different directions.
Does AI make people think less for themselves?
There is evidence that people can change their behavior when they know their choices are being used to train AI, but it does not show that access to AI increases total decision demand. A 2025 Proceedings of the National Academy of Sciences paper reports five experiments using an ultimatum-game task. Participants who knew their choices would train AI became more punitive toward low offers than control participants, and the behavior change persisted in a later task that was no longer used for training. The authors write, “However, our work challenges this assumption.” Read in context, the finding concerns the effect of knowing one’s choices contribute to AI training—not a general effect of using AI for advice: “The consequences of AI training on human decision-making”.
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Research on cognitive demand offers a possible mechanism to consider: people may simplify tasks or shift control demands to their environment. But that work is not a study of current generative AI, and it does not establish that AI use causes lasting loss of unaided judgment skill: “Decision Making and the Avoidance of Cognitive Demand”.
Does easier AI judgment mean worse decisions?
Not necessarily. Decision volume, quality, effort, and skill retention are separate outcomes. More AI-assisted decisions could mean that previously neglected questions receive useful attention; it could also mean that people spend time on low-value choices or accept poor recommendations. Neither possibility follows automatically from lower cost.
To assess whether easier judgment is helpful or harmful, ask what is changing:
- Decision volume: Are people making more decisions, or merely handling the same decisions faster?
- Decision quality: Do outcomes improve, worsen, or stay unchanged against a meaningful comparison?
- Unaided skill: Does performance change when the person must decide without AI, and over what time period?
- Resources: Does lower effort for the user correspond to more computing, energy, money, or time elsewhere?
- Value: Do additional judgments address unmet needs, or create activity without meaningful benefit?
What does energy rebound tell us—and what does it not?
The United Nations Development Programme’s Human Development Report 2025 states: “Evidence from dozens of studies suggests that economywide rebound effects following energy efficiency gains exceed 50 percent, on average.” That figure is about economy-wide energy rebound following energy-efficiency gains. It is not a measurement of AI-assisted judgment, decision quality, or cognitive skill: Human Development Report 2025.
AI’s own environmental rebound is a related but distinct issue. More efficient computing can lower the cost or energy per computation, while increased demand or more complex models may offset some marginal savings. A 2025 FAccT paper frames potential effects as material or physical, economic, and social or behavioral, but notes that direct comparisons and impacts remain under-explored. This makes AI rebound a live research question, not proof of a net rebound in every setting: “From Efficiency Gains to Rebound Effects”.
What evidence would establish a Jevons paradox of judgment?
A convincing test would track more than whether AI makes an individual decision faster. It would compare people or settings with and without a defined efficiency improvement, then measure what changes over an appropriate period:
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- Define the outcome: Count decisions, measure computing or other resource use, assess quality, record time saved, or test unaided skill. Do not treat these as interchangeable.
- Set the counterfactual: Estimate what the same people would have decided or consumed without the efficiency gain.
- Track time: Separate immediate substitution—AI replacing another method—from longer-term changes in demand and habits.
- Identify who bears the cost: A user may save time while another person, organization, or computing system takes on additional cost or risk.
- Assess usefulness: Establish whether additional decisions answer questions that mattered or simply increase low-value decision volume.
- Use an appropriate design: Distinguish causal findings from correlations, illustrative examples, and theoretical possibilities.
The available studies cited here do not establish that AI has made total human judgment rise enough to constitute backfire, or that AI assistance degrades unaided judgment over time. A decision-specific rebound claim needs evidence about decision demand; a claim about skill loss needs longitudinal evidence about performance without assistance. Neither follows from energy statistics or from experiments about people whose choices train AI.
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