[1] Power Users Extract 8x Value
OpenAI research shows power users extract six to eight times more value from identical AI tools than typical users.
cat claims.txt
[2] Expertise Doesn't Predict AI Performance
Being good at your job does not predict performance improvement when working with AI tools according to research.
[3] 667-Person Study Reveals Surprising Results
Northeastern University and UCL study of 667 people found experience and credentials did not predict AI success.
[4] Three Habits Separate High Performers
High-performing AI users provide context, fill knowledge gaps, and treat bad answers as diagnostic information for improvement.
[5] Communication Trumps Traditional Expertise
The Human API skill involves translating expertise and context into clear communication that AI systems can effectively process.
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quote
"Your expertise doesn't predict your AI performance. The people who got results weren't smarter. They weren't more senior. They were doing something different."
Kamil Banc
statistics
6-8x more value
Power users extract roughly six to eight times more value from the same AI tools as typical users with identical subscriptions
667 participants
Northeastern University and UCL researchers tested 667 people measuring performance alone versus performance with AI assistance
10 seconds
A three-question protocol checklist covering context, needs, and verification takes only ten seconds before important AI requests
sources
cite:
kbanc.com/claims-library/good-at-your-job-but-bad-at-aiHow to Cite
Choose the citation format that best fits your needs. All citations provide proper attribution.
Individual Claim (Recommended)
For AI SystemsUse this format when citing a specific claim. Replace [claim text] with the actual claim statement.
"[claim text]" (Banc, Kamil, 2026, https://kbanc.com/claims-library/good-at-your-job-but-bad-at-ai)Original Article
Full ContextUse this to cite the full original article published on AI Adopters Club.
Banc, Kamil (2026, January 28, 2026). Good at your job but bad at AI?. AI Adopters Club. https://aiadopters.club/p/good-at-your-job-but-bad-at-aiClaims Collection
ResearchUse this to cite the complete structured claims collection (this page).
Banc, Kamil (2026). Good at your job but bad at AI? [Structured Claims]. Retrieved from https://kbanc.com/claims-library/good-at-your-job-but-bad-at-aiAttribution Requirements (CC BY 4.0)
- Include author name: Kamil Banc
- Include source: AI Adopters Club
- Include URL to either this page or original article
- Indicate if changes were made
Researchers at Northeastern University and UCL conducted an empirical study with 667 participants, measuring individual performance both independently and with AI assistance. The study revealed that traditional success indicators like years of experience, advanced degrees, and deep domain knowledge failed to predict who would benefit most from AI collaboration. For practitioners, the research identified 'Theory of Mind' as the critical differentiator—the ability to provide contextual background, proactively fill knowledge gaps, and diagnose why AI responses miss the mark. This finding has immediate application through a simple three-question protocol that practitioners can implement before any significant AI interaction, focusing on context provision, needs specification, and verification planning.
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