A perspective piece in Nature Machine Intelligence describes how organisations build up a knowledge debt when staff adopt AI output without verifying it. The gap between what is presented and what can actually be defended grows with every further step built on top.
Authors Irene Unceta, Paula Subias-Beltran and Oriol Pujol from ESADE Business School and the University of Barcelona introduce the term epistemic debt in a piece published on 26 August 2026. It describes a gap that opens when someone adopts output from a generative system without spending time or effort to verify it. That gap widens as further work is built on top of the unchecked result, much like a financial debt that accrues interest over time.
For companies, the concept offers a rationale for tighter review and approval steps before AI-generated content feeds into business decisions, reports or customer communication. The idea matters most where output passes through several processing stages, such as analysis, proposals or internal decision papers. Governance teams can use the term to frame verification not as friction but as debt repayment.
It remains unclear how epistemic debt can be measured in practice and at what point review effort cancels out the efficiency gains from generative AI. The authors offer a way of thinking about the problem, not a testing method. Firms still have to decide which types of decisions need full traceability and where a lighter review level is acceptable.
What this means for decision-makers
- Define which types of decisions require full verification of AI output and which do not.
- Add an approval step before AI-generated content moves into reports or customer communication.
- Train staff to actually understand AI output before building further work on top of it.
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