AI makes radiology and software work cheaper, which drives more imaging and more engineering, proving efficiency creates demand and amplifies the value of human expertise.
Jevons Paradox tells us that when a resource becomes more efficient, total consumption rises. The article shows how AI lowered the cost of reading medical images, yet radiologist numbers grew from 30,723 in 2014 to 36,024 in 2023 and are projected to keep rising. Rather than cutting staff, hospitals used faster AI triage to order more scans, expanding the overall demand for expertise.
Automation shifted radiologists from simple image readers to detectives who interpret ambiguous cases, validate false positives, and integrate multimodal data. The human role became more complex, not less, because the easier tasks were handed to machines while the remaining work required deeper clinical judgment.
The same pattern repeats in software engineering. AI code generators, low-code platforms, and cloud services reduce the cost of building prototypes, so teams ship ten times more products. Engineers spend less time typing and more time designing architecture, ensuring reliability, and handling edge-case reasoning. The net effect is a larger, more demanding engineering workforce, not a shrinking one.
For technical leaders the lesson is clear: automation is a complement, not a substitute. Expect scope creep, invest in upskilling, and focus on higher-order problem solving. When tools make work cheaper, demand explodes, and the value of skilled humans rises.
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