AI Researchers' Views on Automating AI R&D and Intelligence Explosions

TL;DR

Leading AI researchers believe that automating AI research could trigger an intelligence explosion, but they disagree on timelines and governance, with most expecting advanced AI R&D to remain internal and emphasizing transparency.

Contribution

This study provides empirical insights into expert perspectives on AI automation, recursive improvement, and associated risks, highlighting diverging views on timelines and governance.

Findings

20/25 researchers see automating AI research as a major risk.

Most predict AI will become more capable at coding and math.

Majority expect advanced AI R&D to stay internal to organizations.

Abstract

Many leading AI researchers expect AI development to exceed the transformative impact of all previous technological revolutions. This belief is based on the idea that AI will be able to automate the process of AI research itself, leading to a positive feedback loop. In August and September of 2025, we interviewed 25 leading researchers from frontier AI labs and academia, including participants from Google DeepMind, OpenAI, Anthropic, Meta, UC Berkeley, Princeton, and Stanford to understand researcher perspectives on these scenarios. Though AI systems have not yet been able to recursively improve, 20 of the 25 researchers interviewed identified automating AI research as one of the most severe and urgent AI risks. Participants converged on predictions that AI agents will become more capable at coding, math and eventually AI development, gradually transitioning from `assistants' or `tools' to `autonomous AI developers,' after which point, predictions diverge. While researchers agreed upon the possibility of recursive improvement, they disagreed on basic questions of timelines or appropriate governance mechanisms. For example, an epistemic divide emerged between frontier lab researchers and academic researchers, the latter of which expressed more skepticism about explosive growth scenarios. Additionally, 17/25 participants expected AI systems with advanced coding or R&D capabilities to be increasingly reserved for internal use at AI companies or governments, unseen by the public. Participants were split as to whether setting regulatory ``red lines" was a good idea, though almost all favored transparency-based mitigations.