In a variety of conversations about AI misalignment risks, I find that it is important to be able to clearly point at different levels of AI capability. My current favorite approach is to talk about how much the AI accelerates AI R&D[1] labor.

I define acceleration of AI R&D labor by Y times as "the level of acceleration which is as useful (for making more powerful AIs) for an AI company as having its employees run Y times faster[2] (when you allow the total inference compute budget for AI assistance to be equal to total salaries)". Importantly, a 5x AI R&D labor acceleration won't necessarily mean that research into making AI systems more powerful happens 5x faster, as this just refers to increasing the labor part of the production function, and compute might also be an important input.[3] This doesn't include acceleration of hardware R&D (as a pragmatic [...]

---

Outline:

(04:28) What do I think these levels of capability look like?

(08:58) Alternative capability breakdowns

(09:01) t-AGI

(09:42) Anthropics ASL levels

(10:05) Purely qualitative breakdowns

(10:29) Total AI progress speedup or total AI software progress (including compute bottlenecks)

(10:53) Will all these levels be passed at once?

(11:27) Conclusion

The original text contained 12 footnotes which were omitted from this narration.

---

First published:

Source:

Narrated by TYPE III AUDIO.