Complementarity, in labor economics, describes the relationship between automation and human labor where the two are complements rather than substitutes. When AI is complementary to a human skill, automating related tasks increases the marginal value of that skill — making the humans who possess it more productive and more valuable, not less.
The classic example is spreadsheet software. Spreadsheets automated much of the mathematical calculation that bookkeepers performed by hand — yet the number of people working with financial data grew, because spreadsheets made financial analysis cheaper and faster, expanding demand for the underlying analytical judgment that humans provided.
In the AI era, complementarity is strongest in roles where AI produces outputs that require expert human interpretation, quality control, and contextual application. A radiologist using AI diagnostic tools needs deeper pattern recognition expertise to catch the cases AI misclassifies — AI doesn't reduce the value of expert radiological training, it amplifies it. Similarly, AI coding assistants make senior engineers who can review, architect, and integrate code more valuable, even as they reduce demand for junior engineers writing boilerplate.
Understanding which of your skills are complementary to AI — rather than replaceable by it — is one of the most important career strategy exercises in 2026. Skills that sit in the path of AI output (reviewing, directing, applying) benefit from complementarity. Skills that sit in parallel with AI capabilities (producing the same outputs AI produces) face substitution.