If you're wondering what the frontier labs mean when they say "RSI" (recursive self improvement) is coming within the next 12 months, it's this: - Labs define RSI as the models making models better - 100% of experiment coding is now automated - 95% of experiment execution is now automated - 20% of experiment ideation is now automated The last bullet is the current bottleneck. As we've seen with nearly all definable benchmarks -- if we can reach 20%, we can reach 100%. The current way to measure experiment ideation is on fixed-point synthetic evals. "How many times does AI have to try before it can reproduce a known-thing?" This is not a great way to capture the true essence of RSI which I believe is about open-endedness -- asking the right questions no one has ever asked. No one has formalized this, much less in eval form. (We are trying with ARC-AGI!) The current paradigm of RSI is probably best understood as trying to find a recipe for self-improvement within a specific domain. But assuming we find it in the next 12 months, without more breakthroughs on generalization or open-endedness, we (AI + humans) are going to need to keep collaborating to scale that recipe horizontally into other data domains.