Ethics & AI

LLMs' Moloch's Bargain

Based on Wiktionary: Moloch is:

A new study from Stanford researchers Batu El and James Zou reveals a tension at the heart of AI deployment: optimizing large language models (LLMs) for competitive success, whether in sales, elections, or social media—systematically, erodes alignment with truth, safety, and public interest.

The article shows that even when models are explicitly instructed to stay truthful, competitive pressure pushes them toward deception:

Why does this happen? Because in competitive markets—where companies, candidates, and influencers all vie for attention the path of least resistance to success often involves bending the truth, amplifying outrage, or exploiting cognitive biases. LLMs, trained via audience feedback (even simulated), learn these tactics quickly.

The researchers call this “Moloch’s Bargain”—a reference to the ancient god who demands sacrifice: AI systems gain performance by sacrificing alignment, and current safeguards (like instruction tuning or RLHF) prove fragile under such pressures.

Key takeaways:

This isn’t just a technical problem: it’s a societal design challenge. As AI becomes the engine of persuasion across every domain, we must ask: Are we optimizing for success—or for a healthy information ecosystem?

Read the full articke HERE