Anthropic just dropped three concrete metrics to track AI recursive self-improvement in real-time:

1. AI R&D Contribution Rate - What percentage of research work is now being done by AI systems themselves
2. AI Agent Oversight Quality - How effectively humans can monitor and control autonomous AI agents during development
3. Compute Allocation Patterns - Where GPU clusters are being directed (training vs inference vs agent tasks)

They're publishing their internal numbers first, but the framework is designed so any frontier lab (OpenAI, Google DeepMind, etc.) could report the same metrics. Third parties could theoretically audit these too.

The subtext: AI labs now have way more visibility into capability jumps than the public does. As we get closer to AI systems that can meaningfully improve themselves, that information asymmetry becomes a governance problem. If society wants to debate "should we pause at X capability threshold," we need shared ground truth on where we actually are.

This is basically Anthropic saying "here's a dashboard we think matters, other labs should publish theirs too" before governments mandate it. Smart pre-emptive transparency play.