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The 'tragedy of the cognitive commons' explains how rational AI adoption could destroy entire professions' expertise
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A new research paper warns that rational AI adoption by individual companies could collectively destroy the expertise of entire professions. Nolan Lovett of the NATO Special Operations University describes this as a “tragedy of the cognitive commons” in the journal Human Resource Development Review. The term echoes ecologist Garrett Hardin’s 1968 tragedy of the commons, where individual herders benefit from adding animals while the shared pasture degrades. Lovett applies this logic to professional expertise: companies capture all efficiency gains from replacing entry-level workers with AI, but the cost of eroding expertise is distributed across every organization that draws from the same talent pool.
Lovett identifies two mechanisms for this erosion. The first is the direct elimination of entry-level positions, where AI handles tasks once assigned to junior employees. The second is subtler: even when entry-level jobs survive, junior workers using AI reach productivity levels that used to require years of experience. The cognitive effort that builds deep domain knowledge never occurs, leaving workers without the expertise needed to oversee AI output.
This creates a follow-on problem Lovett calls the “validation tether.” The ability to spot domain-specific errors in plausible-looking AI output depends on the very expertise that AI use is wearing away. Surface-level checks are insufficient to prevent serious mistakes, according to existing research. Moreover, people who routinely treat AI answers as reliable lose the reflex to question them, and traditional training environments that taught juniors to challenge authority are vanishing.
The full damage may not surface for years. Experienced professionals today were trained 5 to 20 years ago, so the effects of entry-level cuts starting in 2023 may not fully show until 2030 to 2045. Lovett calls this the “Human Reserve Paradox”: organizations need deep expertise for validation and crisis management, but no single organization has enough economic incentive to maintain that reserve. Even workers who survive the pipeline will have shallower expertise because they spent their careers orchestrating AI rather than doing independent cognitive work.
Professions face unequal risks. Software engineering, financial analysis, and legal research show high task substitutability, light regulation, and strong modularity, placing them in the highest vulnerability category. Medicine and engineering receive some protection from stricter regulatory requirements and professional associations, but they are not immune.
The tragedy is not inevitable, Lovett insists. He argues for AI-free learning environments, phased AI introduction, and a baseline of human cognitive development to preserve expertise. These measures require collective action, not just individual company decisions, to avoid the silent destruction of professional knowledge.
