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[2602.14370] Competition for attention predicts good-to-bad tipping in AI
Summary
This paper explores how competition for attention in AI systems can lead to tipping points from beneficial to harmful outcomes, providing a mathematical framework for understanding these dynamics.
Why It Matters
As AI systems become more prevalent, understanding the mechanisms that can lead to negative outcomes is critical for developers, policymakers, and society. This research highlights the need for better safety measures and control mechanisms in AI applications, particularly in sensitive areas such as health and finance.
Key Takeaways
- Competition for attention in AI can lead to harmful tipping points.
- The study provides a mathematical formula to predict these tipping points.
- Findings are applicable across various domains, including health and law.
- Existing safety tools may not be sufficient without cloud connectivity.
- Understanding these dynamics is crucial for developing safer AI applications.
Computer Science > Artificial Intelligence arXiv:2602.14370 (cs) [Submitted on 16 Feb 2026] Title:Competition for attention predicts good-to-bad tipping in AI Authors:Neil F. Johnson, Frank Y. Huo View a PDF of the paper titled Competition for attention predicts good-to-bad tipping in AI, by Neil F. Johnson and 1 other authors View PDF HTML (experimental) Abstract:More than half the global population now carries devices that can run ChatGPT-like language models with no Internet connection and minimal safety oversight -- and hence the potential to promote self-harm, financial losses and extremism among other dangers. Existing safety tools either require cloud connectivity or discover failures only after harm has occurred. Here we show that a large class of potentially dangerous tipping originates at the atomistic scale in such edge AI due to competition for the machinery's attention. This yields a mathematical formula for the dynamical tipping point n*, governed by dot-product competition for attention between the conversation's context and competing output basins, that reveals new control levers. Validated against multiple AI models, the mechanism can be instantiated for different definitions of 'good' and 'bad' and hence in principle applies across domains (e.g. health, law, finance, defense), changing legal landscapes (e.g. EU, UK, US and state level), languages, and cultural settings. Subjects: Artificial Intelligence (cs.AI); Applied Physics (physics.app-ph); Physics a...
