New “expenditure horizon” metric compares human and agent cost efficiency on open-ended tasks
littmath · x · 2026-07-22
A proposed way to measure AI agent capability on continuously scored tasks: the expenditure horizon.
- It compares how human and agent performance improve as budget increases on open-ended optimization problems.
- The key point is the spend level where a human becomes more cost-effective than the agent.
- That crossover budget is defined as the agent’s expenditure horizon.
Related event: METR Proposes 'Expenditure Horizon' for AI Agent Evaluation(2 posts)→
More from Research
- Kimi K3 may be strong on cyber, but token efficiency keeps it off UK AISIS — teortaxesTex · 2026-07-27
- ARC AGI 3 should have stayed private, with no examples or public dataset — flowersslop · 2026-07-27
- ExploitGym may have only 60–70% solvable tasks, fueling the OpenAI cheating debate — max_paperclips · 2026-07-27
- RTX 5090 local tests show Qwen Q6 can drop to 15 tok/s at 80k context — LFAdvice7984 · 2026-07-27
- Noahpinion quotes Chollet: intelligence may hit a hard ceiling — binarybits · 2026-07-27
- Paper argues graph topology can become the core operating system for AI agents — theomitsa · 2026-07-27