The Jevons paradox AI jobs story is not what anyone predicted.

AI was supposed to eliminate call center jobs.

Instead, nearly two million people in the Philippines now work in call centers.

Up every year since 2016.

Through the entire AI boom.

The technology that was supposed to shrink the industry is fueling its expansion.

This is not an anomaly. It is Jevons paradox, one of the most important economic principles for understanding what AI actually does to labor markets. And it has a 160-year track record.

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What the Jevons Paradox AI Jobs Story Actually Tells Us

In 1865, economist William Stanley Jevons observed something counterintuitive about the steam engine.

As steam engines became more efficient, coal consumption went up, not down.

More efficiency meant lower cost per unit. Lower cost per unit meant more uses made economic sense. More uses meant more total consumption, not less.

The same logic is playing out in customer service right now.

As AI makes call center work cheaper and faster, companies are not buying less of it. They are buying more. Lower cost per interaction does not mean fewer interactions. It means more customers served. More channels opened. More markets worth reaching that were not worth reaching before.

The technology that was supposed to shrink the industry is funding its expansion.

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The Nuance That Leaders Need to Hold

This is not a clean “AI creates jobs” story. And treating it as one would be its own leadership error.

The displacement may be real, just not where most leaders are looking. Entry-level positions may migrate to other parts of the world rather than disappear entirely from the global economy. The labor economics of agentic AI, the next wave of autonomous agents conducting work without human direction, remain an open and genuinely uncertain question.

But the first-order narrative, “AI automates the task, humans become redundant, demand collapses,” has a consistent track record of being wrong about total employment.

It was wrong after the loom. Wrong after the steam engine. Wrong after the computer. Wrong after the internet.

The reason it keeps being wrong is Jevons paradox. Cheaper means more consumed. More consumed means more demand. More demand means more work, though often different work, in different places, requiring different capabilities.

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The Question Jevons Actually Asks

Here is the productive version of the AI disruption question, the one that leads to preparation instead of paralysis.

When AI cuts the cost of serving your customer in half, what happens to demand in your industry?

Is the answer fewer interactions? Or more markets worth reaching?

Is the answer less work? Or different work, requiring the five capabilities that AI cannot replicate?

The organizations making workforce decisions based only on the first-order narrative, “AI eliminates the task, so it eliminates the role,” are not seeing the full picture. And the workers inside those organizations who are building only task capability, with no investment in judgment, evaluation, and distinctly human contribution, are the ones most exposed.

Not because the fear is irrational. Because the analysis is incomplete.

Why the Jevons Paradox AI Jobs Pattern Keeps Repeating

William Stanley Jevons identified this pattern in 1865. When steam engines became more efficient, he noticed something counterintuitive: coal consumption went up, not down. More efficiency meant lower cost per unit. Lower cost meant more demand. More demand meant more consumption — not less.

The economists who predicted AI would eliminate call center jobs were not wrong about the technology. AI did make call center work faster and cheaper. Their error was assuming cheaper meant less.

It meant more.

Lower cost per interaction opened markets that were previously uneconomical to serve. Companies that could not justify the expense of a full support operation suddenly could. Small businesses in markets that lacked infrastructure got access to services they could not previously afford. The demand expanded to fill the new capacity, and then kept expanding.

This is the Jevons paradox AI jobs dynamic in its most visible form. And it is playing out across industries simultaneously.

The Question Your Organization Needs to Ask

The first-order analysis of AI is almost always displacement. Which jobs disappear. Which roles get automated. Which functions get compressed into a software update.

The second-order analysis is where the real strategic question lives.

When AI makes your core function cheaper and faster, what happens to demand? Does your market shrink, or does it expand into territory that was previously out of reach?

For call centers, the answer was expansion. The same technology that was supposed to eliminate the industry funded its growth.

The organizations that understood this dynamic early built for scale. The ones that waited for the displacement to arrive discovered that the expansion had already happened around them.

The 5-Ingredient Kryptonite Defense is built on this second-order thinking. IDEAS, the ability to see what the technology enables before the consensus does, is the ingredient that separates the organizations that rode the Jevons curve from the ones that were caught by it.

The prepared side of this equation does not require prediction. It requires the discipline to ask the question one level deeper than everyone else.

Those prepared need not fear the forces at work.

Is your organization asking the right questions about AI? Take the Kryptonite Scorecard: realmikeevans.com/scorecard