LessWrong (30+ Karma)

“AI for AI safety” by Joe Carlsmith

Mar 15, 2025

Joe Carlsmith, author of the insightful essay on AI safety, dives into the intriguing concept of using AI itself to enhance AI safety. He discusses crucial security factors necessary for developing smarter AI without compromising safety. Carlsmith contrasts his views with traditional alignment approaches and addresses common objections to this innovative perspective. The conversation highlights the potential of AI feedback loops and the importance of finding a 'sweet spot' for practical implementation.

34:08

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Joe Carlsmith

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Quick takeaways

AI for AI safety highlights the importance of utilizing advanced AI labor to enhance alignment research and improve safety measures effectively.

The concept of the 'AI for AI safety sweet spot' underscores the need for balance between advancing AI capabilities and implementing safety countermeasures to ensure security.

Deep dives

The Importance of AI for AI Safety

AI for AI safety emphasizes the strategic use of advanced AI labor to enhance our societal capabilities regarding the alignment problem without relying solely on human efforts. This strategy revolves around two important feedback loops: the AI capabilities feedback loop, where capabilities drive rapid AI advancements, and the AI safety feedback loop, where safe access to capable AIs improves security measures. The ability to harness AI labor for safety improvements could help mitigate the risks posed by accelerating AI capabilities, particularly if efforts to make AI safer do not match the pace of advancement in AI technology. Without these measures, there's a significant risk that we will encounter disasters as AI systems become too powerful to control.

Leveraging AI for Enhanced Safety Measures

34min

(Audio version here (read by the author), or search for "Joe Carlsmith Audio" on your podcast app.

This is the fourth essay in a series that I’m calling “How do we solve the alignment problem?”. I’m hoping that the individual essays can be read fairly well on their own, but see this introduction for a summary of the essays that have been released thus far, and for a bit more about the series as a whole.)

1. Introduction and summary

In my last essay, I offered a high-level framework for thinking about the path from here to safe superintelligence. This framework emphasized the role of three key “security factors” – namely:

Safety progress: our ability to develop new levels of AI capability safely,
Risk evaluation: our ability to track and forecast the level of risk that a given sort of AI capability development involves, and
Capability restraint [...]

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Outline:

(00:27) 1. Introduction and summary

(03:50) 2. What is AI for AI safety?

(11:50) 2.1 A tale of two feedback loops

(13:58) 2.2 Contrast with need human-labor-driven radical alignment progress views

(16:05) 2.3 Contrast with a few other ideas in the literature

(18:32) 3. Why is AI for AI safety so important?

(21:56) 4. The AI for AI safety sweet spot

(26:09) 4.1 The AI for AI safety spicy zone

(28:07) 4.2 Can we benefit from a sweet spot?

(29:56) 5. Objections to AI for AI safety