Toby Ord explores a mathematical model explaining the declining success rates of AI agents on longer tasks, suggesting that each agent can be characterized by its own "half-life." The findings from Kwa et al. (2025) indicate that as task duration increases, the probability of success decreases exponentially, with implications for understanding AI capabilities over time. The study highlights the importance of measuring performance across various tasks and the challenges of generalizing results beyond the specific task suite used in the research.

A mathematical model explains the performance decline of AI agents on longer-duration tasks, suggesting an exponentially decreasing success rate characterized by a unique half-life for each agent. This model indicates that task complexity increases with the number of subtasks, where failure in any subtask leads to overall task failure. Further research is needed to explore the model's applicability across different task suites.