Edit: from the other HN article on this topic:
> We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks
https://deepmind.com/blog/article/alphafold-a-solution-to-a-...
In other words, it's less like GPT3 and more like ImageNet.
Yes, because they must release sufficient information for others to recreate the AI model, according to the rules of entering CASP.
> Did AlphaFold2 also have the biggest budget? :)
And then the parent
> Actually, no! Or at least, the budget they used (<$100k at retail prices to train the model) is well within the feasible range for other research institutions.
I'm not sure how the cost of replicating the model in the future is relevant in this context. We appear to be discussing the cost of developing this model from scratch, such as what it would have taken an alternate team to create and submit this if DeepMind never got involved.
Well, I've worked in some academic deep research labs and they did not have the money to do the experiments they wanted to do.