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Traditional benchmarks have long been the standard for evaluating AI performance, but a new research approach by METR suggests there may be a more direct way to measure meaningful progress: tracking the length of tasks AI systems can reliably complete.

In this episode of AI Deep Dive, we explore METR’s recent findings, which show an exponential increase in AI task-handling capability over the past six years—doubling roughly every seven months. This metric offers a fresh perspective on how AI is advancing in ways that directly affect productivity and real-world impact.

According to METR’s projections, within the next five years, AI could take on many software development tasks that currently require days or weeks of human effort, completing them in a fraction of the time. The research team has also open-sourced their methods and data to encourage further forecasting and analysis.

🔹 What We Cover:

• How task length
• The implications of a 7-month doubling time in AI capabilities
• Why this model could be more useful than traditional AI benchmarks
• Potential future impact on software engineering and creative work
• How open-sourcing the data can support greater transparency and collaboration

Whether you’re an AI researcher, engineer, policymaker, or enthusiast, this episode offers timely insights into how we measure what AI can really do—and how fast that’s changing.

🎧 Tune in now to explore the future of AI capability through METR’s lens on task completion.

Source: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks