Overhang and unhobbling

Plain English. Capability that already exists but isn't yet usable. An overhang is latent capacity waiting to be released — compute built but not applied, or abilities sitting inside current models that nobody has drawn out. Unhobbling, a term from Leopold Aschenbrenner's "Situational Awareness", is the release mechanism: better harnesses, tools, memory and prompting that unlock what the weights could already do. It is why products keep improving between training runs, without any new model.

Why it moves money. Overhang breaks the intuition that AI progress equals model releases. A serious slice of recent gains came from harnesses, not weights — so even if frontier training stalled tomorrow, years of deployable improvement would remain, which reshapes both the bear case (a training plateau is not an industry plateau) and the safety debate (a development pause does not pause capability growth). It also says where application-layer margin comes from: unhobbling gains are captured by whoever ships the harness, not whoever trained the model.

What to watch. Capability gains at a fixed model — time-per-task and eval movement attributable to harness changes alone — and deliberate hobbles being priced: guardrail and safety taxes measured, argued over, and walked back.

From the signals. A US$100,000 contest asks what happens if AI progress stopped today — the overhang counterfactual, funded seriously. Fable's guardrail walk-back framed safety as a measurable harness tax.

Further reading. Leopold Aschenbrenner, "Situational Awareness".