Can AI research become a self-sustaining feedback loop?

Tom Cunningham and coauthors at the Elasticity Institute ask a precise version of the recursive self-improvement question: can AI become sufficiently useful at improving AI that capability growth speeds up without continued growth in outside inputs? Their model represents the relevant pathways as feedback loops, where the strength of each link is an elasticity. The paper argues that AI research automation alone is not enough: limits in people, compute, data, investment, verification, and diminishing returns can weaken the loop. It also separates narrow gains on AI R&D tasks from broad improvements that translate into economic impact. Using currently available evidence, the authors’ rough calibration finds feedback below the threshold for a self-sustaining acceleration, while stressing that the key parameters are uncertain and may be rising. They propose concrete measurements—especially AI’s effect on effective research effort and inference-compute use—to make the debate more empirical.