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ZENODO
Report . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Report . 2025
License: CC BY
Data sources: Datacite
ZENODO
Report . 2025
License: CC BY
Data sources: Datacite
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Cognitive Divergence Theory of AI Adoption

Teoria Divergenței Cognitive a Adopției IA
Authors: STAN, Adrian;

Cognitive Divergence Theory of AI Adoption

Abstract

Abstract (EN):This paper introduces the Cognitive Divergence Theory of AI Adoption, arguing that for complex tasks, productivity gains from Artificial Intelligence are not evenly distributed but are proportional - often exponentially - to the user's cognitive capacity. Challenging the prevailing "equalizer effect" narrative (which holds true only for short-term, simple tasks), the study integrates empirical behavioral data with a Monte Carlo simulation of 10,000 agents. The findings demonstrate that within 24 months of adoption, the labor market undergoes not gradual stratification, but categorical divergence.The model predicts a productivity collapse for the bottom 50% of the cognitive distribution (due to error rates and rework costs) alongside a threefold output surge for the top 25%, resulting in a massive 71-fold productivity gap. The paper forecasts the emergence of three distinct economic classes by 2028: Homo Symbioticus (the cognitive elite in symbiosis with AI), the Precarious Middle, and the Displaced. Special attention is paid to the specific risks for Eastern European economies, where AI acts as a "Brain Drain Amplifier." Finally, the paper proposes a technical intervention framework: the Mechanism of Cognitive Stimulation (MCS 2.0) to mitigate cognitive atrophy.This paper continues the article: https://zenodo.org/records/17734010 Published on December 1st, 2025 - Romania's National Day. This work is dedicated to the resilience of human intelligence. Rezumat (RO):Acest articol prezintă Teoria Divergenței Cognitive a Adopției IA, demonstrând că, în cazul sarcinilor complexe, câștigurile de productivitate generate de Inteligența Artificială nu sunt distribuite uniform, ci sunt proporționale - uneori exponențial - cu capacitatea cognitivă a utilizatorului. Contrazicând narativul popular al „efectului de egalizare” (care se aplică doar pe termen scurt și la sarcini simple), lucrarea integrează date comportamentale empirice (GitHub, Stack Overflow) cu o simulare Monte Carlo (10.000 de agenți) pentru a arăta că, în decurs de 24 de luni de la adopție, piața muncii nu se stratifică gradual, ci suferă o divergență categorială.Rezultatele simulării indică un colaps al productivității pentru ultimele 50% din distribuția cognitivă (din cauza erorilor și a costurilor de refacere) și o triplare a producției pentru top 25%, creând un decalaj de productivitate de 71x. Lucrarea prognozează emergența a trei clase economice distincte până în 2028: Homo Symbioticus (elita cognitivă integrată cu IA), Clasa de Mijloc Precară și Cei Strămutați (Displaced). De asemenea, este analizat impactul specific asupra Europei de Est, unde IA acționează ca un amplificator al exodului de creiere („Brain Drain”), și este propus un cadru de intervenție tehnică: Mecanismul de Stimulare Cognitivă (MCS 2.0).Acest articol continuă articolul: https://zenodo.org/records/17734010 Publicată pe 1 decembrie 2025 - Ziua Națională a României. Această lucrare este dedicată rezilienței inteligenței umane.

Keywords

Artificial intelligence, MEG Initiative, Artificial Intelligence, Artificial Intelligence/economics, Artificial Intelligence/ethics, Cognitive Divergence, Minimal Ethical Governance, Labor Economics, Artificial Intelligence/trends, Homo Symbioticus

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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