Could semantic caching create a Jevons paradox in AI, where cheaper inference ultimately causes total computation to inc

Yes, this is a plausible application of the Jevons paradox.

The paradox occurs when technological improvements increase the efficiency of a resource, leading to a decrease in its per-unit cost, which subsequently triggers an increase in total consumption.

In the context of AI:

1. Lowering the Barrier: Semantic caching makes individual inference faster and cheaper.
2. Increased Latency Sensitivity: As inference becomes cheaper, developers may integrate AI into more granular, high-frequency processes that were previously considered too expensive or slow to justify.
3. Elastic Demand: If the cost drops significantly, latent demand is unlocked. Features that might have been "nice-to-haves"—such as real-time predictive text, background reasoning, or massive-scale automated data curation—become economically viable.

The result is that while the energy cost per query drops, the total volume of queries could rise exponentially, potentially resulting in a higher aggregate energy footprint for the sector. Whether this manifests depends on whether the growth in query volume outpaces the efficiency gains provided by the caching mechanism.