The curse of knowledge, also called the curse of expertise[1] or expert's curse, is a cognitive bias that occurs when a person who has specialized knowledge assumes that others share in that knowledge.[2]
The Use of Knowledge in (AGI) Society
How to build to break the intelligence curse
Theory and Memory: Two Forces Shaping Software Team Knowledge
How insights from cognitive science and social psychology explain why software knowledge is so hard to preserve
Copying Better: How To Acquire The Tacit Knowledge of Experts
Much of expertise is tacit: that is, it cannot be captured through words alone. We look at techniques, drawn from the field of Naturalistic Decision Making, designed to acquire the tacit knowledge of experts.
AI May Be Atrophying Our Brains, Professor Warns
AI may soon ruin our ability to make decisions for ourselves — an outcome that would be, one expert warns, "catastrophic."
Why Tacit Knowledge is More Important Than Deliberate Practice
What tacit knowledge is, and why it is the most interesting topic in the study of expertise today.
Eliezer Yudkowsky Is Frequently, Confidently, Egregiously Wrong
It is bad when people who are consistently egregiously wrong become deferred to on everything
AI Epistemic Risks: Emerging Mechanisms & Evidence
<p>Advances in artificial intelligence pose risks to humanity's collective capacity to form accurate beliefs, reason well, and maintain a healthy information en
People are not friction
The Gell-Mann Amnesia Effect of AI is a pretty well documented phenomenon: The Gell-Mann amnesia effect is a cognitive bias describing the tendency of individuals to critically assess media reports in a domain they are knowledgeable about, yet continue to trust reporting in other areas despite recognizing similar potential inaccuracies.
Du privilège exorbitant à l’ignorance invincible
traduction française du billet de blog "From exorbitant privilege to invincible ignorance" de Paul Krugman
The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise
Artificial intelligence is reshaping cognitive work, but Human Resource Development scholarship has treated this transformation as an organizational training challenge, leaving the collective regeneration of professional expertise unexamined. This conceptual paper introduces the Cognitive Commons framework, integrating commons theory, HRD scholarship, and distributed cognition to explain how rational AI adoption decisions can deplete the shared expertise pool professions require for renewal. The framework distinguishes Internalized Mastery (deep domain knowledge from sustained practice) from Distributed Mastery (orchestrating human-AI systems), and develops the Validation Tether: effective AI oversight depends on the expertise AI adoption may undermine. Early labor market and clinical evidence suggests possible disruption to expertise-regeneration pathways in highly AI-exposed sectors, though adoption is recent and the strongest signals come from leading sectors rather than all professions. Five factors determine occupational vulnerability, and governance arrangements may form across organizational, professional-association, and policy levels. The paper reframes expertise development as collective stewardship rather than organizational optimization, with implications for HRD theory and workforce policy.
Ignorance got a bad rap
One of the best skills to develop as an entrepreneur? Ignorance. I think a lot of folks are spending way too much energy trying to know it all. They’re trying to be over-informed. Soaking up every …
AI overuse could spark "brain fry," new research finds
The mental strain associated with AI carries "significant costs," researchers find.
Agnotology : the making and unmaking of ignorance
viii, 298 p. : 24 cm; "This volume emerged from workshops held at Pennsylvania State University in 2003 and Stanford University in 2005"--P. vii; Includes bibliographical references and index
The shape of a knowledge worker
In a recent post, I threw around the term 'cognitive exponent' a bunch. Today I'd like to talk about a thing that might help us frame our investigation of what puts someone on the right side of that exponential graph.
"Powerful AI can statically help human decision-makers, but can harm collective knowledge building... it can lead to what we call “knowledge collapse” whereby in the long-run all human knowledge is ultimately destroyed.” economics.mit.edu/sites/default/files/2026-02/A…
