Data Driven NYC

AI Is Already Building AI — Google DeepMind’s Mostafa Dehghani

In this episode of the Matt Podcast, DeepMind researcher Mostafa Dehghani explains how the AI field is moving from human‑driven model design to a regime where models iteratively build the next generation of models. He frames the shift as a series of "loops"—micro‑level loops that add compute at inference time and macro‑level loops that automate the entire development pipeline, effectively removing the human bottleneck that has historically limited progress. Dehghani highlights that today’s leading labs already train new architectures on the outputs of previous generations, but full‑automation and long‑horizon self‑improvement remain unfinished. He argues that once models can evaluate and update their own weights without external supervision, a dramatic acceleration—recursive self‑improvement—will follow, provided sufficient compute and robust evaluation metrics are in place. The conversation cites concrete examples such as the Kapathies auto‑research project, where models began contributing to research engineering tasks, and discusses formal verification as a promising, though incomplete, tool to ensure safe feedback loops. Dehghani also warns of model collapse when a closed loop lacks external grounding, defining it as loss of generalization after over‑optimizing on self‑generated data. If these challenges are solved, enterprises could see their data pipelines and retrieval‑augmented generation systems rebuilt around continuously learning AI, reshaping competitive dynamics and raising new governance questions about safety, evaluation, and control.