What is Jevons Paradox and why does it matter for AI costs?
Jevons Paradox observes that when a resource becomes more efficient, total consumption often increases rather than decreases. In AI, this plays out clearly: as inference costs drop (cheaper models, better hardware), organizations deploy AI in more places - more use cases, more users, more automation. Each individual call is cheaper, but the total bill grows.
This isn't necessarily bad - if each AI call generates business value, increased usage is a sign of success. The risk is uncontrolled proliferation where teams spin up AI features without measuring whether they deliver ROI. The antidote is unit economics: track value per AI interaction, not just total spend.
This question reflects common advisory themes. It is editorially curated, not sourced from individual conversations.
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