Hello Reader,

A few months ago, I switched to more efficient AI models for my internal tools—faster inferences, cheaper compute, cleaner code. The result? I ran more prompts than ever. What had been a thoughtful resource turned into a background utility.

That’s Jevons Paradox: when something gets cheaper to use, we tend to use more of it—not less.

This idea dates back to 1865 when economist William Stanley Jevons noticed that more efficient coal-powered engines didn’t reduce coal usage—they exploded it. As coal became cheaper per unit of work, the Industrial Revolution ramped up consumption across the board.

I’ve folded Jevons Paradox (and 99 other models) into Re:Mind, a pocket-sized toolkit for real-world decision-making. We wrapped a successful Kickstarter—over $8.4K was raised, and both stretch goals were unlocked. If you missed it, late pledges are still open.

Why Use It

Understanding Jevons Paradox challenges the common assumption that technological efficiency leads to conservation. It exposes the hidden loop between cost, behavior, and total impact.

This model is essential when you want to:

• Set accurate expectations for innovation’s downstream effects
• Avoid blind spots in forecasting or sustainability planning
• Catch false comfort in “green” upgrades

When to Use It

• Fuel-efficient cars → More driving: Lower cost per mile makes longer commutes attractive.
• LED lighting → More illumination: Cheaper light means more bulbs, longer hours, and more usage overall.
• Cloud AI chips → More model runs: Cheaper compute per inference leads to more queries and larger models.

How to Use It

You don’t have to abandon efficiency but must pair it with awareness and limits.

You can see this dynamic play out in WALL-E. The spaceship’s passengers enjoy extreme efficiency—automated everything, constant comfort, zero effort. But the result isn’t freedom—it’s inertia. Consumption soars and autonomy collapses. The tools are optimized, but the system breaks.

Jevons Paradox teaches us that efficiency must be paired with intention:

• Price the externality: Add friction (e.g., carbon pricing) to prevent runaway use.
• Set absolute caps: Emissions ceilings or usage budgets place hard stops on total output.
• Introduce behavioral nudges: Real-time feedback or auto-off settings help users notice when efficiency is inviting overuse.

Next Steps

Efficiency isn’t the villain—but mistaking it for a complete solution can be. Jevons Paradox reminds us to think beyond the immediate gain and anticipate the broader system response. If you’re building or adopting something more efficient, ask what behaviors it might unlock—and whether those behaviors serve your larger goals.

1. Look for systems where upgrades are making usage “too easy.”
2. Ask: “Are we saving… or just shifting when and how we consume?”
3. Add friction or boundaries to avoid backsliding.

Where It Came From

Jevons observed that as steam engines became more efficient, coal demand didn’t fall—it surged. That counterintuitive insight laid the groundwork for rebound effect theory in economics, now echoed in energy, tech, and AI systems alike.

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Until next time, keep exploring and questioning. Your unique perspective is your greatest asset.

Think Independently, JC

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