MIT AI Risk Repository · Risk Sub-Category · 63.05.03

Homogeneity and correlated failures

Category: Network Effects

Description

"Homogeneity and Correlated Failures. The current paradigm driving the state of the art in AI is the ‘foundation model’ (Bommasani et al., 2021): large-scale ML models pre-trained on broad data, which can be repurposed for a wide range of downstream applications. The costs required to create such models (and continuing returns to scale) means that only well-resourced actors can create cutting- edge models (Epoch, 2023; Hoffmann et al., 2022; Kaplan et al., 2020), making them relatively few in number. If current trends continue, it is likely that many AI agents will be powered by a small number

From Multi-Agent Risks from Advanced AI (Hammond2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
Other
Intent
Other
Timing
Other

Subdomain definition: Risks from multi-agent interactions, due to incentives (which can lead to conflict or collusion) and/or the structure of multi-agent systems, which can create cascading failures, selection pressures, new security vulnerabilities, and a lack of shared information and trust.

How other frameworks describe this risk

Other entries from Hammond2025