Is AI the new breadwinner?

We often talk about AI as though it is on an inevitable path to job destruction. But what AI can do at the frontier, and how human-like it becomes, may be less economically relevant than the order in which it progresses – what it masters first, what it struggles to grok, or perhaps what it never acquires.

This piece is the first part of a short series exploring how there remain parts of human labour that resist codification, and why the shape of AI capabilities matters as much as the frontier. A large share of economically valuable activity depends on tacit knowledge—the kind that cannot simply be written down. From artisanal sourdough baking to whisky blending, semiconductor fabrication, and clinical medical diagnosis, many tasks rely on sensory discrimination, pattern recognition, or contextual judgement that resists decomposition into rules.

This kind of tacit knowledge is not a rounding error. As we consider the economic deployment of AI, the gap between performing a task in controlled benchmark conditions and performing it reliably in the real world may be wider and more persistent than capability models imply. Real work runs on tacit knowledge, and the more knowledge an economy codifies, the higher the returns to the tacit skills needed to interpret and apply that information in context.