If you’ve ever wondered how close we are to AI systems that can design better versions of themselves, Vals AI just built the scoreboard. The company’s Recursive Self-Improvement Index, or RSI Index, attempts to quantify something that until now has lived mostly in the realm of theoretical worry: how capable are today’s frontier models at conducting the research and development needed to build their successors?
The top-performing model on the index, Claude Fable 5.1, scores 35.03%. Which sounds low until you understand the scale.
How the RSI Index actually works
Vals AI designed the index around five specific tasks that mirror the real workflow of AI development. Models are evaluated under fixed compute and time constraints, meaning they can’t just brute-force their way to a good score by burning through unlimited resources.
The scoring system is anchored to reference points rather than arbitrary grades. A score of 0 represents a baseline, 0.5 maps to performance levels already documented in published research, and a theoretical optimum sits at 1.
Claude Fable 5.1’s score of 35.03% means it’s performing meaningfully but still falls short of replicating techniques already known to researchers. The models can outperform existing techniques in experiment execution but generally lag in producing novel techniques.
Why this matters now
The RSI Index launched in August 2026 through a collaboration between Vals AI and CoreWeave, timed alongside the company’s $40 million Series A funding round. That round valued Vals AI at $400 million.
CEO Rayan Krishnan has positioned the index as filling a critical gap. Major AI labs have already started referencing recursive self-improvement potential in their model cards, but without a standardized framework for comparison, those references lack a common basis for evaluation.
In the six months following its funding round, Vals AI’s revenue grew 8x compared to all of 2025. Its customer base doubled. Its staff tripled.
The governance question
Krishnan has been vocal about what the RSI Index implies for regulation. The concern isn’t that a model scoring 35% is about to go rogue. It’s that the trajectory from 35% to higher scores could be steep, and the development of these capabilities is happening inside a handful of labs with limited external visibility.
Krishnan has emphasized the need for international governance structures to keep pace with these advancements. The RSI Index gives it empirical grounding: it’s harder to dismiss calls for oversight when you can point to a concrete metric showing measurable progress toward autonomous self-improvement.