The Tipping Point Has a Formula: What the GWU Study Actually Measured
The Tipping Point Has a Formula: What the GWU Study Actually Measured Two physicists at George Washington University have done something the AI safety world has been waiting for: they derived a mathematical formula for the moment an AI system's output flips from good to bad. Not from true to false. From desirable to dangerous — answers that can be factually correct and still harmful. A nudge toward self-harm. Misleading advice to a doctor, a soldier, a lawyer. The paper is "Competition for attention predicts good-to-bad tipping in AI," published in the journal *Patterns*, and its core claim is startlingly concrete: the flip isn't random. It follows a tipping-point dynamic buried in the model's attention mechanism, and you can compute when it happens. How they found it Neil F. Johnson and Frank Yingjie Huo started from the smallest working part of the machine: a single attention head. Their move is the classic physicist's move — understand one representative at...