Anthropic: How Misalignment Scales with Bigger Models

AI failures on hard tasks tend to be incoherent and unpredictable (“hot mess”) rather than systematically pursuing the wrong goal.

• More scale ≠ more coherence: bigger models don’t reliably behave more consistently and can get worse on very hard problems.
• Longer reasoning can backfire: “overthinking” increases error variance; ensembling helps but isn’t practical for real-time agents.
• Safety implication: future risks look more like industrial accidents from complexity and goal misspecification than deliberate, coherent misalignment.

Take away for AI engineers: build simple system that are easy to test and combine them. In other words SOLID and KISS methods translate from engineering to AI.

Source: blog