The Federal Reserve Bank of Dallas published research drawing a distinction between two types of worker knowledge: "codified knowledge" — textbook material that can be written down and therefore readily encoded into an AI system — and "tacit knowledge" — hands-on judgement accumulated through years of practice1. The finding is blunt: AI is "simultaneously aiding and replacing workers." Returns to experience are rising in AI-exposed occupations. Experienced workers in those sectors are gaining pay increases. Entry-level workers, who bring mostly codified knowledge, face compressed opportunities.
The distinction has a long intellectual lineage — philosopher Michael Polanyi argued in 1966 that "we know more than we can tell" — but the Dallas Fed applies it to a specific, measurable labour market shift. Previous waves of automation, as documented extensively by MIT economist David Autor, displaced workers performing routine manual and cognitive tasks. AI inverts this pattern. It automates precisely the kind of knowledge that formal education provides — the textbook answers, the standard procedures, the codifiable rules — while struggling with the improvised judgement that comes from doing a job for a decade. A senior engineer who has debugged a production outage at 3am has knowledge that no language model possesses. A fresh graduate holding the same degree does not.
The practical consequence is a broken career escalator. The traditional pathway — earn a qualification, enter at the bottom, learn by doing — depends on employers hiring at the entry level. If AI handles the codified-knowledge tasks that junior staff once performed, employers have less reason to bring them on. Hiring across the US economy fell 56% year-to-date in early 2026 compared with the same period in 2025, with UBS chief economist Arend Kapteyn attributing record-low white-collar turnover partly to "AI fear" . The Dallas Fed's own companion paper found the employment decline in AI-exposed industries landed mostly on workers younger than 25 — driven not by termination but by collapsed job-finding rates2. An NBER working paper by Anders Humlum and Emilie Vestergaard found LLM adoption linked to occupational switching and task restructuring without net changes in hours or earnings — consistent with a market reshuffling who does what, in a way that favours those who already have years on the job.
The risk is self-defeating. Companies that stop hiring junior workers to save costs on codified-knowledge tasks are also closing the pipeline through which the next generation acquires tacit knowledge. The experienced workforce they depend on cannot replenish itself. Today's cost saving becomes tomorrow's skills shortage — and the shortage is already arriving: ManpowerGroup's 2026 survey reports 72% of employers face hiring difficulty, with AI model development and AI literacy the top shortage skills globally.
