If we achieve AGI-level performance using an LLM-like approach, the training hardware will be capable of running ~1,000,000s concurrent instances of the model.

Definitions

Although there is some debate about the definition of compute overhang, I believe that the AI Impacts definition matches the original use, and I prefer it: "enough computing hardware to run many powerful AI systems already exists by the time the software to run such systems is developed". A large compute overhang leads to additional risk due to faster takeoff.

I use the types of superintelligence defined in Bostrom's Superintelligence book (summary here).

I use the definition of AGI in this Metaculus question. The adversarial Turing test portion of the definition is not very relevant to this post.

Thesis

Due to practical reasons, the compute requirements for training LLMs is several orders of magnitude larger than what is required for running a single inference instance. [...]

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Outline:

(00:15) Definitions

(00:54) Thesis

(02:20) From AGI to ASI

(03:07) Counterpoints

(03:37) Conclusion

(04:03) Appendix - Training Data Volume

The original text contained 1 footnote which was omitted from this narration.

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Narrated by TYPE III AUDIO.