[iandanforth]

Let's say that Google is already 1 generation ahead of nvidia in terms of efficient AI compute. ($1700)

Then let's say that OpenAI brute forced this without any meta-optimization of the hypothesized search component (they just set a compute budget). This is probably low hanging fruit and another 2x in compute reduction. ($850)

Then let's say that OpenAI was pushing really really hard for the numbers and was willing to burn cash and so didn't bother with serious thought around hardware aware distributed inference. This could be more than a 2x decrease in cost like we've seen deliver 10x reductions in cost via better attention mechanisms, but let's go with 2x for now. ($425).

So I think we've got about an 8x reduction in cost sitting there once Google steps up. This is probably 4-6 months of work flat out if they haven't already started down this path, but with what they've got with deep research, maybe it's sooner?

Then if "all" we get is hardware improvements we're down to what 10-14 years?

[qingcharles]

Until 2022 most AI research was aimed at improving the quality of the output, not the quantity.

Since then there has been a tsunami of optimizations in the way training and inference is done. I don't think we've even begun to find all the ways that inference can be further optimized at both hardware and software levels.

Look at the huge models that you can happily run on an M3 Mac. The cost reduction in inference is going to vastly outpace Moore's law, even as chip design continues on its own path.

[promptdaddy]

*deep mind research ?

[iandanforth]

Nope, Gemini Advanced with Deep Research. New mode of operation that does more "thinking" and web searches to answer your question.