I know using LLMs on LessWrong is often frowned upon (probably for good reasons) but given that this post is about using AIs to generate and evaluate AI research I thought it would be appropriate to use an LLM here.
I asked o1 pro to evaluate this post and this was its response:
This post offers an intriguing perspective on delegating the hardest parts of AI safety research to AI itself, rather than expecting human researchers to handle every step all the way up to superintelligence. The central idea—“passing the buck” to AI—strikes me as a pragmatic stance if you anticipate that AI systems will soon outstrip humans in many relevant research and engineering capacities. Notably, the author does not claim that autonomous “buck passing” is trivially safe or guaranteed to work out of the box; instead, they provide a structured framework for why it could improve safety relative to keeping humans in the loop.
A central strength is the post’s recognition that AI developers might reach a point where human oversight becomes practically irrelevant: sheer speed and complexity of AI-led R&D can eclipse what any human team can meaningfully supervise. The proposed solution is a multi-layered approach of alignment, control, and incentive design—each method contributing partial safeguards that, ideally, reinforce each other. The emphasis on continuous testing, local verifiability, iterative evaluations, and “multi-party integrity” structures also underscores a realistic appreciation that no single safety measure is foolproof.
That said, many open challenges remain. The post takes on big assumptions: for example, that we can rely on thorough testing or incremental “bootstrapping” to detect (and correct) misalignment before it becomes catastrophic. While such approaches are viable in principle, adversarial dynamics at very high capability levels can be harder to test than the post acknowledges. Another concern is that the solution is still highly conceptual; significant engineering details would be required to make “buck passing” arguments robust against advanced adversarial behavior. Overall, though, the post is refreshingly direct about its core premise—using AI to solve the alignment problem—and outlines multiple thoughtful ways to try to make that premise safe.
I thought its response was pretty helpful and I would rate it as 7⁄10.
I know using LLMs on LessWrong is often frowned upon (probably for good reasons) but given that this post is about using AIs to generate and evaluate AI research I thought it would be appropriate to use an LLM here.
I asked o1 pro to evaluate this post and this was its response:
I thought its response was pretty helpful and I would rate it as 7⁄10.