One Is Not Enough: How People Use Multiple AI Models in Everyday Life
Researchers studied how people use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting models based on perceived strengths. Findings show users build shifting hierarchies between primary and secondary models across personal and professional contexts. They employ coordination strategies like task-stage assignment and cross-checking to manage effort, latency, and output credibility.