Jemist's Shortform

J Bostock

This is a special post for quick takes by J Bostock. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.

So Sonnet 3.6 can almost certainly speed up some quite obscure areas of biotech research. Over the past hour I've got it to:

Estimate a rate, correct itself (although I did have to clock that it's result was likely off by some OOMs, which turned out to be 7-8), request the right info, and then get a more reasonable answer.
Come up with a better approach to a particular thing than I was able to, which I suspect has a meaningfully higher chance of working than what I was going to come up with.

Perhaps more importantly, it required almost no mental effort on my part to do this. Barely more than scrolling twitter or watching youtube videos. Actually solving the problems would have had to wait until tomorrow.

I will update in 3 months as to whether Sonnet's idea actually worked.

(in case anyone was wondering, it's not anything relating to protein design lol: Sonnet came up with a high-level strategy for approaching the problem)

I think you might find this paper relevant/interesting: https://aidantr.github.io/files/AI_innovation.pdf

TL;DR: Research on LLM productivity impacts in material disocery.

Main takeaways:

Significant productivity improvement overall
Mostly at idea generation phase
Top performers benefit much more (because they can evaluate AI's ideas well)
Mild decrease in job satisfaction (AI automates most interesting parts, impact partly counterbalanced by improved productivity)

Jemist's Shortform

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Jemist's Shortform

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