David Autor's 'Polanyi's paradox' names why tasks that demand tacit judgment resist automation while codifiable tasks do not

David Autor, in "Polanyi's Paradox and the Shape of Employment Growth" (2014), named and applied Michael Polanyi's observation that "we know more than we can tell" to the economics of automation. His claim: "Tasks that have proved most vexing to automate are those that demand flexibility, judgment and common sense — skills that we understand only tacitly." The inverse holds too — tasks that can be reduced to explicit, codifiable procedures are exactly the ones that machines can execute. Codifiability, not complexity per se, is what sorts tasks into automatable and non-automatable.

Autor also glosses the machine-learning response to this limit: contemporary computer science "seeks to overcome Polanyi's paradox by building machines that learn from human examples," substituting statistical pattern-matching for explicit rule-writing where the rules themselves cannot be articulated.

This is the automation leg of a broader structural pattern: codification is the single property that governs both what a machine can execute and what a rival can copy. See observation-codification-is-the-shared-hinge-of-automation-and-imitation for the full bridge, which pairs this claim with the imitation-barriers literature (claim-causal-ambiguity-from-tacitness-raises-imitation-barriers) and applies it to claim-canonic-slop-is-a-verdict-of-domain-expertise and claim-canonic-deliverable-is-an-append-only-evidence-ledger.