Recursive AI self-improvement may require specialized data, human expertise and capital

As AI models become more capable, the focus in data shifts from volume to precision and quality. The analysis argues that recursive self-improvement (RSI) cannot rely only on changes to training code and hyperparameters: compute, energy, data, human expertise and real-world input can also be bottlenecks. A fully realized RSI system may even need to raise capital and handle capital formation.

The need for human input varies by task. Mathematics has abundant data and verification approaches such as Lean, while workplace writing—such as a Slack post—has sparser data, less-defined rewards and requires nuanced context.