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Anthropic announces the results of the second phase of the Project Fetch experiment

1 reports1 story updatesUpdated Jun 18 08:00

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On June 18, 2026, Anthropic announced the results of the second phase of the Project Fetch experiment. The experiment showed that Claude Opus 4.7, running Claude Code without human assistance, completed tasks that human teams had done last year about 20 times faster than the fastest human team. On every task that at least one human team completed, Opus 4.7 was at least 10 times faster; on tasks completed by all four human teams, it was on average more than 37 times faster than teams without Claude and more than 18 times faster than teams with Claude, while generating nearly ten times less code than Team Claude. Anthropic also noted that the model still fails at the closed-loop control step of precisely pushing the beach ball, that this is not a solved robotics problem, and that the improvements came from general capability scaling rather than robot-specific training.

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Anthropic Project Fetch Phase 2: Opus 4.7 Operates Robot Dog Independently, Faster Than Human Teams

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  1. Anthropic announces results of Project Fetch Phase 2 experiment

    Source:Anthropic:The Institute

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Jun 18

  1. Anthropic:The InstituteFirst-party

    Anthropic Project Fetch Phase 2: Opus 4.7 Operates Robot Dog Independently, Faster Than Human Teams

    Anthropic published the Phase 2 experiment of Project Fetch: Claude Opus 4.7, running Claude Code without human assistance, completed the tasks that human teams did last year about 20 times faster than the fastest human team. On every task completed by at least one human team, Opus 4.7 was at least 10 times faster; on tasks completed by all four human teams, it was on average more than 37 times faster than teams without Claude and more than 18 times faster than teams with Claude, while generating nearly ten times less code than Team Claude. The model still fails at the closed-loop control step of precisely pushing the beach ball; Anthropic says this is not a solved robotics problem, and that the improvement comes from general capability scaling rather than robot-specific training.

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