Das Jevons-Paradoxon
When you make knowledge work cheaper to produce, you do not get the same amount of output with less effort. You get more output, more decisions to review, more meetings to debrief, ...
Good morning. Let’s start this Tuesday together. Because, as always, “It is perfectly possible to be both rational and wrong.”
A company rolls out a new AI writing tool. The promise: less time spent on routine text, more time for real thinking. Four months later, the volume of internal reports has tripled. Nobody is writing less. Everyone is writing more, faster, about more things. The time savings dissolved before anyone noticed they had arrived.
This is not an IT problem. It is a behavioral pattern that the British economist William Stanley Jevons identified in 1865 while watching coal consumption rise after the steam engine became more efficient. More efficiency, more use, net consumption up. The Jevons Paradox.
How Does It Work?
When a resource becomes cheaper or easier to use, the threshold for using it drops. Demand increases, often past the original level. The mechanism runs through two channels. First, the direct rebound: the tool costs less effort, so people use it for tasks they previously skipped. Second, the systemic rebound: the freed capacity gets reallocated to new activity, generating fresh demand for the same resource. The efficiency gain is real. The net reduction in load is not.
Why This Is Important?
Most organizational efficiency programs are built on a simple model: reduce friction, reduce consumption, free up time. The Jevons Paradox breaks that model. When you make knowledge work cheaper to produce, you do not get the same amount of output with less effort. You get more output, more decisions to review, more meetings to debrief, more reports to read. The cognitive load on the system increases precisely because individual tasks became easier. Leaders who invest in efficiency tools and then measure success by adoption rates are watching the wrong variable.
And Now?
The lever is not efficiency alone. It is efficiency combined with a hard constraint on volume. Before deploying a productivity tool, define what will stop being produced, not just what will be produced faster. Replace the question “How much time will this save?” with “What will we actively not do anymore?” Without a deliberate reduction target attached to every efficiency gain, the freed capacity fills automatically. Jevons was describing thermodynamics, not laziness. The system expands to absorb available energy.
Core knowledge: Improving efficiency without constraining volume does not reduce organizational load. It increases it.
Identify one tool or process introduced in the last 12 months to save time
Measure whether total output in that category went up or down
For every new efficiency, define one thing the team will stop doing
Treat capacity freed by automation as a decision, not a windfall
Ask whether your current AI rollout has a volume constraint built in
Where are management decisions made every day that assume efficiency tools automatically reduce workload? Where can you be a Chief Behavioral Officer yourself this week?
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