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
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."
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.
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.
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.
Why Is Everyone In Tech So Sad?
A lot of people seem to be realizing that knowledge work is mostly pointless. AI might give us the pleasure of finding out what happens if an entire class of workers loses faith in their careers.
On Software, or the Persistence of Visual Knowledge
Wendy Hui Kyong Chun; On Software, or the Persistence of Visual Knowledge. Grey Room 2005; (18): 26–51. doi: https://doi.org/10.1162/1526381043320741
From gifted to high potential and twice exceptional: A state-of-the-art meta-review
Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.
A World Unobserved
Humans have always generated knowledge and judged it. That's changing fast.
Ever thought we acquire generalizable knowledge by discarding details and compressing our experiences? In a new BBS paper, @sabinasloman.bsky.social and I argue otherwise, proposing a novel way of studying human learning inspired by double descent in ML. Disagree? Propose a commentary by May 15 :)
"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…
