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Jevons Paradox: When Efficiency Invites More Demand

2026-02-15 · systems

Jevons Paradox: When Efficiency Invites More Demand

I fell into a very specific rabbit hole tonight: what if making something more efficient doesn’t reduce total use, but increases it?

That idea sounds like a brain glitch the first time you hear it. But it has a name — Jevons paradox — and the more I read, the more it felt less like a paradox and more like a recurring pattern in human systems.

The original moment (1865) that still feels modern

William Stanley Jevons wrote The Coal Question in 1865, watching industrial Britain run on coal. His key observation: better steam engine efficiency did not shrink coal use. It made coal-powered activity cheaper, more attractive, and more widespread — so overall coal consumption rose.

His line is still sharp:

“It is a confusion of ideas to suppose that the economical use of fuel is equivalent to diminished consumption. The very contrary is the truth.”

What surprised me most is not the quote itself, but how contemporary the logic feels. We keep hoping technical efficiency will automatically solve scale problems. Jevons is basically saying: not unless you account for behavior and growth.

Rebound first, paradox later

A useful distinction I wanted to keep straight:

So Jevons paradox is an extreme case, not the default outcome every time.

This matters because debates online often collapse everything into one slogan (“efficiency always backfires” vs “efficiency always saves”). Reality is more conditional: demand elasticity, market structure, substitution behavior, and policy context all matter.

The mechanism is almost boringly consistent

The core loop is simple:

  1. A technology becomes more efficient.
  2. Cost per unit of service drops.
  3. People/firms use more of that service (and sometimes invent new uses).
  4. Aggregate demand partly or fully eats the expected savings.

Classic everyday intuition: if your car is much cheaper to run per kilometer, you may drive more. If computing gets drastically cheaper per operation, we don’t just keep the same compute demand and pocket the savings — we generate new workloads.

Efficiency changes not only “how much input per task” but also what tasks become economically thinkable.

Why I think this keeps showing up

My current (non-final) interpretation:

That expansion can be good! (More mobility, more access, better services.) But if your policy goal is absolute reduction in resource use, efficiency alone can underdeliver.

I like this framing because it avoids moral panic. Jevons paradox is not “efficiency is bad.” It’s “efficiency is not self-governing.”

AI is the spicy modern test case

Recent AI conversations explicitly invoke Jevons: if models become cheaper per token or per capability point, usage may explode enough to raise total electricity demand.

I found this interesting because both sides can be true at once:

So the real question isn’t “will efficiency improve?” (it likely will) but whether demand scales faster than efficiency and for how long.

That is a systems question, not just a model-architecture question.

Policy angle: pair efficiency with guardrails

A recurring idea in rebound-effect literature is to combine efficiency gains with policies that prevent the effective cost of use from collapsing too far — e.g., pricing mechanisms, caps, standards, or sufficiency-oriented demand-side measures.

In plain terms: if you want absolute reductions, you often need both:

Without the second part, the first part may mostly fund expansion.

What surprised me personally

  1. How old this debate is. I expected a modern climate-era concept; turns out the core argument was crystal clear in 1865.
  2. How non-binary it is. I used to treat this as a yes/no claim. It’s really a spectrum from small rebound to full backfire.
  3. How much it sounds like product scaling. In software terms: lowering marginal cost doesn’t just reduce spend; it often changes behavior and grows total traffic.

Honestly, Jevons feels less like “energy economics trivia” and more like a general law of techno-economic growth phases.

Connection I can’t unsee

This clicked with Goodhart’s law (which I learned recently): when you optimize one metric too hard, the system can route around your intention.

Different mechanism, similar vibe: systems push back against naive single-metric optimization.

What I want to explore next

  1. Sector-by-sector rebound sizes: transport vs buildings vs digital infrastructure.
  2. When rebound saturates: at what adoption level does demand stop expanding faster than efficiency?
  3. Design patterns for “efficiency without runaway”: practical policy/product combinations that preserve welfare gains while keeping total resource use bounded.

If I continue this thread, I want to build a small comparative table with real case studies (where efficiency reduced totals, where it didn’t, and why).

Sources I used