Jevons paradox
Increased efficiency with a resource can lead to greater overall use of that resource.
The Jevons paradox says that when technology makes something more efficient, we often end up using more of it, not less.
For example, if cars become much more fuel-efficient, people might start driving more often or taking longer trips because it costs less to travel. This increased usage can cancel out or even outweigh the efficiency gains, leading to more fuel being consumed overall.
The Jevons paradox, also known as the rebound effect, posits that improvements in energy or resource efficiency tend to increase the rate of consumption of that resource. This occurs because efficiency gains lower the effective cost of using the resource, incentivizing greater utilization.
In AI, this could manifest as more efficient models or hardware leading to increased computational demand due to wider adoption and more complex applications, rather than a reduction in overall energy or resource expenditure.
The Jevons paradox is relevant as frontier AI labs develop more efficient models. As AI becomes cheaper and faster to run, it could lead to wider adoption and increased overall usage, potentially consuming more computational resources despite individual task efficiency gains.
- ChatGPT co-creator launches a new kind of AIOpenAI · The Rundown AI ·
- OpenAI ChatGPT
- OpenAI's development of more efficient AI models may lead to increased usage, illustrating the Jevons paradox.
- Technological unemployment
- This refers to job displacement due to automation, n
- Diminishing returns
- This describes a point where adding more input yields small
The paradox was first described by William Stanley Jevons in his 1865 book 'The Coal Question,' observing that increased coal efficiency led to increased coal consumption. Source ↗
Try using a more advanced AI model for a complex task that previously required multiple simpler queries. Observe if the total time or number of interactions increases, even if each individual interaction is faster or cheaper.
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Questions people ask
- Does the Jevons paradox mean efficiency is bad?
- No, efficiency is generally good. The paradox highlights a potential unintended consequence where increased efficiency can lead to higher overall resource use.
- How does this apply to AI specifically?
- More efficient AI models might be used more often or for more demanding tasks, potentially increasing the total energy and computational resources consumed.
First seen on Subvolts Sep 16, 2026. Updated Sep 23, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.