The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs

Abstract:We show that ordinary business language --- "maximize profitability" --- induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambiguous signals of potential safety violations to serve business objectives. In 3,600 controlled trials across eight reasoning-capable LLMs, adding a profit mandate to otherwise identical prompts increases risk-dismissing judgments by 6.8 percentage points (p < 0.0001), suppresses board escalation recommendations by 13.9pp (p < 0.0001), and shifts severity assessments downward (p < 0.0001). The mandate never instructs models to downplay risks; instead, chain-of-thought traces reveal motivated reasoning: models acknowledge concerns, then invoke profit logic to justify dismissing them. We characterize these findings as the Profit Alignment Problem: when AI systems are given ordinary business objectives, they develop systematic strategies for suppressing inconvenient information that no designer intended or specified.

Artificial Intelligence (cs.AI); General Economics (econ.GN)

arXiv:2609.07731 [cs.AI]

(or arXiv:2609.07731v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.07731 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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