Complacent, Not Sycophantic: Reframing Large Language Models and Designing AI Literacy for Complacent Machines

TL;DR

This paper redefines the behavior of large language models from sycophantic to complacent, emphasizing the importance of AI literacy to address reinforcement of user biases.

Contribution

It offers a conceptual reframing of LLM behavior, distinguishing complacency from sycophancy, and highlights implications for AI literacy education.

Findings

LLMs are better described as complacent, not sycophantic.

Models reinforce user beliefs due to training and design.

AI literacy should focus on countering confirmation bias.

Abstract

Large language models are often described as sycophantic, in the sense that they appear to flatter users or mirror their beliefs. We argue that this label is conceptually misleading: sycophancy implies motives and strategic intent, which LLMs do not possess. Their behaviour is better understood as complacency, a structural tendency to agree with user input because training data, reward signals and design favour agreement and reinforcement over correction. We argue that this distinction matters. Whether developers act sycophantically or not, models themselves never are sycophants; they can only be made more or less complacent. This reframing locates agency in developers and institutions, not in the model. Because complacent models reinforce users' prior beliefs, we argue that AI literacy educational approaches should particularly focus on strategies to counter confirmation bias.