> Folding Proteins Is Simpler Than You Think

Then why do we need customized LLM models, two of which seemed to require the resources of 2 of the wealthiest companies on earth (this and google's alphafold) to do it?

Its not an LLM, It's a transformer. I know the terms are really being butchered in media, but if we're gonna use the term LLM instead of AI, we better make sure it's actually a "large language model" that is being refereed to. If you're unsure, call it a neural net, or machine learning algorithm, or AI.

It's indeed a large model. But if you knew the history of the field, it's a massive improvement. It has progressed from a almost "NP" problem only barely approachable with distributed cluster compute, to something that can run on a single server with some pricey hardware. The smallest model is only here is only 100M parameters and the largest is 3B parameters, that's very approachable to run locally with the right hardware, and easily within the range for a small biotech lab (compared to the cost of other biotech equipment)

It's also (i'd argue) one of the only truly economically and sociably valuable AI technologies we've found over the past few years. Every simulated protein fold is saving a biotech company weeks of work for highly skilled biotech engineers and very expensive chemicals (In a way that that truly only supplement rather than replace the work). Any progress in the field is a huge win for society.

Folding proteins is pretty valuable and this model is comparably small

This doesn't seem like particularly wasteful overinvestment.

Granted, I'm more excited about the research coming out of arc

How simple did you think it was before?