Will recursive self-improvement be observed in AI by 2030?

This market asks whether, by the end of 2030, an AI will publicly demonstrate the complete, human-independent R&D loop needed to produce a more generally capable version of itself—not merely improve code, optimize a benchmark, or assist researchers. Current systems can modify agent scaffolds, run autonomous post-training, and generate research artifacts, but the strongest evidence still finds them engineering optimizers that depend on human-set objectives, evaluators, scaffolding, or bounded tasks. Recent process-level evaluations and reporting specifically weaken the claim that open-ended research judgment and reliable feedback control have already been achieved. The remaining four-plus years leave substantial upside from rapid capability progress and investment, but the strict evidence and recognition requirements make a modest reduction from the existing 65-cent quote appropriate; external sentiment provided no usable exact-match probability for this question.