The Moving Bottleneck: What Actually Limits AI Progress?
AI progress is supposed to be running into limits almost everywhere at once. Frontier models are consuming much of the readily available high-quality public text; giant training clusters strain semiconductor supply chains and electricity infrastructure; more elaborate reasoning systems consume increasing amounts of computation after training; and AI agents can perform impressive individual tasks while remaining unreliable over long workflows. Yet frontier AI is not behaving like a technology at its ceiling. Stanford’s 2026 AI Index reports a roughly 30 percentage-point improvement in a single year on Humanity’s Last Exam, a benchmark deliberately designed to remain difficult for advanced models. On OSWorld, which tests an agent’s ability to operate computers, performance rose from roughly 12 percent to 66.3 percent. The same report captures the other side of the picture: agents still fail roughly one in three structured benchmark attempts, while systems capable of solving extremely ...