this post was submitted on 01 Jul 2025
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I actually work in the field of protein crystallography. Contrary to newspaper reporting by people who don't understand the field and just repeat what the people who developed the tool say about it, it has made just a small improvement to analysing experimental data which we could have easily made using traditional algorithmic approaches with a similar amount of resources spent. And this is one of its biggest legitimate impacts - it absolutely hasn't "revolutionised plenty of other sciences", or you'd be able to list more things than just alphafold.
It doesn't improve programmer productivity, it increases the lines of code created, which is a really bad metric for productivity. There is good evidence that its use is already leading to increased code churn, that means someone is having to go back and revisit the additional new errors introduced by AI tools, which is obviously less productive.
As a software engineer, I can tell you that it absolutely has increase productivity. Especially for small tasks without too much complexity. AI is really good for prototyping. The problems you hear about are mostly people who have no idea how to write propper code trying to mask their incompetence by writing AI code.
I usually outright reject code that is obviously AI. But I employ plenty of AI in my own coding. The trick is to always double check and rewrite segments that aren't good enough.
The huge amount of garbage AI PRs ate an enormous problem. Especially for small open source projects. But the benefits are also pretty obvious.
So what you are saying is that AI is actually useful since it has improved analyzing experimental data.
Thanks for proving my point.