[2601.23045] The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?

Computer Science > Artificial Intelligence arXiv:2601.23045 (cs) [Submitted on 30 Jan 2026 (v1), last revised 10 Apr 2026 (this version, v2)] Title:The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? Authors:Alexander Hägele, Aryo Pradipta Gema, Henry Sleight, Ethan Perez, Jascha Sohl-Dickstein View a PDF of the paper titled The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?, by Alexander H\"agele and 4 other authors View PDF HTML (experimental) Abstract:As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task scope. It is therefore important to understand how extremely capable AI models will fail: Will they fail by systematically pursuing goals we do not intend? Or will they fail by being a hot mess, and taking nonsensical actions that do not further any goal? We operationalize this question using a bias-variance decomposition of the errors made by AI models: An AI's \emph{error-incoherence} on a task is measured over test-time randomness as the fraction of its error that stems from variance rather than bias in task outcome. Across all tasks and frontier models we measure, the longer models spend reasoning and taking actions, \emph{the more incoherent} their failures become. Error-incoherence changes with model scale in a way that is experiment dependent. However, in several settings, larger, more capab...