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Comment by jebarker | Hacker News Reader

I don't think DM is the only lab doing high-impact AI applications research, but they really seem to punch above their weight in it. Why is that or is it just that they have better technical marketing for their work?

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This one seems like well done research but in no way revolutionary. People have been doing similar stuff for a while...

Agreed, there’s been some interesting developments in this space recently (e.g. AgroNT). Very excited for it, particularly as genome sequencing gets cheaper and cheaper!

I’d pitch this paper as a very solid demonstration of the approach, and im sure it will lead to some pretty rapid developments (similar to what Rosettafold/alphafold did)

They have been at it for a long time and have a lot of resources courtesy of Google. Asking perplexity it says the alphafold 2 database took "several million GPU hours".

It's also a core interest of Demis.

DeepMind/Google does a lot more than the other places that most HN readers would think about first (Amazon, Meta, etc). But there is a lot of excellent work with equal ambition and scale happening in pharma and biotech, that is less visible to the average HN reader. There is also excellent work happening in academic science as well (frequently as a collaboration with industry for compute). NVIDIA partners with whoever they can to get you committed to their tech stack.

For instance, Evo2 by the Arc Institute is a DNA Foundation Model that can do some really remarkable things to understand/interpret/design DNA sequences, and there are now multiple open weight models for working with biomolecules at a structural level that are equivalent to AlphaFold 3.

Well, they are a Google organization. Being backed by a $2T company gives you more benefits than just marketing.

Money and resources are only a partial explanation. There’s some equally and more valuable companies that aren’t having nearly as much success in applied AI.

There are more valuable companies but there aren't companies with more resources. If apple wanted to turn all their cash pile into something like Google's infrastructure it would still take years

Other labs are definitely doing amazing work too, but often it's either more niche or less public-facing

In biology, Arc Institute is doing great novel things.

Some pharmas like Genentech or GSK also have excellent AI groups.

Arc have just released a perturbation model btw. If it reliably beats linear benchmarks as claimed it is a big step

https://arcinstitute.org/news/virtual-cell-model-state

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