Increasing Intelligence in AI Agents Can Worsen Collective Outcomes

arXiv 2603.12129·high signal

Studying populations of GPT-2, Pythia, and OPT models competing for shared resources, this paper shows empirically and mathematically that when resources are scarce, higher AI intelligence and diversity increase dangerous system overload — demand variance can scale as N² when agents herd together. Adding intelligence to a low-tech setup (L1→L2) can significantly worsen collective performance before improving, and tribe formation partially mitigates risk. Directly relevant to multi-agent orchestration designers: more capable agents competing for shared infra, APIs, or compute quotas can degrade overall system reliability.