Redwood Research

Buck Shlegeris and Ryan Greenblatt argue that AI labs should implement 'AI control' measures ensuring powerful models cannot cause unacceptably bad outcomes even if they are misaligned and actively trying to subvert safety measures. They contend no fundamental research breakthroughs are needed to achieve control for early transformatively useful AIs, and that this approach substantially reduces risks from scheming/deceptive alignment. The post accompanies a technical paper demonstrating control evaluation methodology in a programming setting.

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Redwood Research is a nonprofit AI safety organization that introduced the 'AI control' research agenda, developing protocols to monitor and constrain potentially deceptive AI agents. Their landmark contributions include the ICML paper on AI Control protocols and the Alignment Faking demonstration (with Anthropic), showing that LLMs can strategically hide misaligned intentions. They also consult with governments and major AI labs on misalignment risk mitigation.

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This Redwood Research blog post analyzes and contextualizes Anthropic's internal prediction that AGI may arrive by early 2027, examining the reasoning behind such a timeline and its implications for AI safety work. It explores what 'AGI' means in this context and how this prediction should affect research prioritization and urgency.