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We spent 2 hours working in the future

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metr.org
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Faster implementation can leave a research team waiting on everything around it. METR explored that possibility in a two-hour tabletop exercise: three researchers imagined working with much more capable agents while their colleagues, external reviewers and experimental infrastructure stayed at their existing capabilities.

In the simulated workflow, understanding results, choosing priorities and obtaining feedback became prominent constraints. Running several ideas at once also created more work to coordinate. These are hypotheses from a role-playing exercise, with assumed capabilities and a human game master deciding uncertain outcomes. Its value is in identifying organizational changes worth testing, rather than measuring a productivity gain.

Terms in this piece · Glossary
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
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