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I'm finding the comparison to the previous wave of ML absolutely fascinating.

I tinkered with ML for a few years, and it took a LOT of work to get anything useful out of it at all.

Now with LLMs I can literally type out a problem in English and there's a reasonably good chance I'll get a useful result!

It really does feel like an entirely new set of capabilities to me.

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That's the big difference in this round. Before you had to have the ML expertise and the expertise to understand the implication of say a MNIST classifier example. Now anyone can "get" it because you're prompting and getting inference back in English. Underneath the fundamentals aren't all that different though, it has the same novelty factor and the same limitations apply.

I think the fundamentals are radically different, just due to the ease of applying this stuff.

I used to be able to train and deploy a ML model to help solve a problem... if I put aside a full week to get that done.

Now I tinker with LLMs five minutes at a time, or maybe for a full hour if I have something harder - and get useful results. I use them on a daily basis.

Exactly. LLMs are useful out of the box—and that enables tons of use-cases that just weren't economical (or even possible) with older models.

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