Who owns the handoff?
Benchmarks are supposed to help teams decide where a model is reliable enough to use and where a person still needs to step in. That gets harder when the scores stop separating systems in a way that maps cleanly to operational risk. Ethan Mollick said major benchmarks for expert-level work, including ARC-AGI, GDPval, and METR Long Tasks, are completely saturated in his April 2025 post. If the gauge is pinned at the top, the score no longer tells a manager, reviewer, or operator where the cliff is.
The problem also runs in the other direction. Ethan Mollick said 0% benchmark scores can hide economically meaningful differences in success rates in his April 2025 post. A score that looks equally bad across models can still conceal differences that matter to a budget, a queue, or a customer-facing workflow. The benchmark can flatten the comparison right when the practical consequences start to diverge.
That is why more model shopping is often the wrong first response. SemiAnalysis launched GDPx, an agentic capability benchmark across 11 domains, and its top reported score was 12% for Opus 5.5, as noted in the April 2025 Techmeme post citing SemiAnalysis. Low scores may still be useful for research and tracking progress, but they do not remove the need to decide how long a system should act on its own before a person reviews the work.
That decision matters more now because the horizon of autonomous work has moved. Ethan Mollick said GPT-4 could do about 4 minutes of independent work with a 50% chance of success, while current models can do multiple weeks of work in his April 2025 post. The loss here is not abstract. When a system can keep going for much longer, accountability can drift unless someone defines where the work must pause, what must be checked, and who takes the handoff.
Write the stop rules first
A useful response is to set a decision window. A decision window is a written rule for when an AI task must stop and return to a person, and at work it looks like a short list in the runbook: pause after a certain duration, after a certain spend, after a failed check, or before any external action.
This does not require predicting which frontier model will stay ahead. It requires deciding which points in a workflow still belong to a human because the benchmark no longer marks them clearly enough. If a system is drafting code, the handoff might be before deployment. If it is handling support, the handoff might be before account changes or refunds. If it is doing research, the handoff might be before a claim is published or sent to a client. The pattern is the same across tools: define the moment where authorship, approval, or risk becomes human again.
The point is not to slow everything down. The point is to prevent long autonomous runs from creating silent failures, unreviewed commitments, or unclear ownership. Benchmarks can still inform procurement and evaluation. They are just no longer sufficient as the only control surface when capability spreads across longer tasks and uneven success rates.
This week, define the exact conditions that force an AI task to stop and come back to a person. Write them as explicit review gates, handoff points, and failure triggers for the work you already run.
Human Runtime Core™ is designed for the mental edge you need to survive in an AI-run world.