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This author makes broad sweeping claims, supprting them with numerous references that (in all instances that I checked) actually counter their argument. I'm not even sure the author knows _what subject they want to talk about_, never mind what argument to present.

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Whatever it is, it should be antifragile though.

Yep... didn't read past the headline of the article.

I waded through half of it hoping a coherent point would emerge before heading over to hn comments to confirm my suspicions.It’s a mash up of a couple of different pop opinions on the state of ML without any real insight.

at least one of them is true. The effort spending "cleaning data" which I think by her description she means "connecting pipes" is underestimated. I work for a company that deploys an ML model that works on a "lowest common denominator" between multiple different downstream SAASes, and this week I have been struggling with a field that should be an integer, but is a string in one SAAS provider, (and there are entries that are not parsable as integer). I can't simply convert it to a string because the ML model is not expecting it.

Yes. "AI" (read Deep Learning/LogReg/SVM models) do indeed perform better given more data. I can vouch for this myself. And there was also a paper regarding this.

Everyone is an expert in AI now!

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