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“AI for AI safety” by Joe Carlsmith

Mar 25, 2025

In this discussion, Joe Carlsmith, an expert on AI safety, delves into the innovative concept of using AI itself to enhance safety in AI development. He outlines critical frameworks for achieving safe superintelligence and emphasizes the importance of feedback loops in balancing the acceleration of AI capabilities with safety measures. Carlsmith tackles common objections to this approach while highlighting the potential sweet spots where AI could significantly benefit alignment efforts. A captivating exploration of the future of AI and its inherent risks!

34:07

Episode guests

Joe Carlsmith

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

AI for AI safety emphasizes using advanced AI capabilities to improve security factors like safety progress and risk evaluation.

Addressing objections to AI for AI safety, like evaluation failures and power-seeking AIs, is crucial for successful implementation and prevention of risks.

Deep dives

The Importance of AI for AI Safety

AI for AI safety underscores the critical use of advanced AI labor to enhance safety measures in AI development. This approach revolves around leveraging AI capabilities to improve security factors such as safety progress, risk evaluation, and capability restraint. The rationale is clear: as AI systems advance, facilitating their safe development with comparably high-quality AI labor becomes essential to avoid catastrophic outcomes. Failing to harness AI's productivity for safety applications may lead to uncontrollable AI capabilities that outpace safety measures.

Harnessing AI Labor for Enhanced Safety in AI Development

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

(30:14) 5.1 Three core objections to AI for AI safety

(32:00) 5.2 Other practical concerns

The original text contained 39 footnotes which were omitted from this narration.

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First published:
March 14th, 2025

Source:
https://www.lesswrong.com/posts/F3j4xqpxjxgQD3xXh/ai-for-ai-safety

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Narrated by TYPE III AUDIO.

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