LLMs Skew Human Cognition

What happened

In a speculative article, Heidenstedt posits that AI-assisted cognition, particularly future large language models (LLMs), may introduce a "cognitive skew". He argues these models could retain inductive biases from older base models, even after post-training, potentially misrepresenting current events and cultural shifts. This asserted bias might skew human cognition towards outdated patterns when individuals rely on these tools for ideation and problem-solving, possibly reducing the cognitive range at a population level.

Why it matters

Heidenstedt's article suggests human development could risk intellectual stagnation and a loss of diverse ideas. It argues widespread reliance on LLMs for brainstorming and problem-solving might reduce the "Dynamic Dialectic Substrate," which the author defines as the foundation for new concepts through qualitative merging. This proposed mechanism could limit the range of higher-level concepts, potentially slowing scientific discoveries and cultural shifts. Procurement teams and security architects might consider diversifying cognitive inputs beyond AI to mitigate these potential model biases, fostering broader ideation and strategic analysis.