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Would you see these as analogous?

The people who create the models and the people that use them.

The people who create the programming languages and the people that use them.

I think because it's a relatively 'younger' field, there is a bit more need to know about the foundations in AI than in programming. You hit the perimeters a bit more often and need to do a bit of research to modify or create a model.

Whereas it's unlikely in most programming jobs you would need to do any research into programming language design.

I agree with you. Being a corporate department head, I've led exactly one project that's had me digging through my DS&A textbook. But it's much more common to need to go beyond the limits of an off-the-shelf deep learning algorithm. Plus many of the cutting edge deep learning advancements have been fairly simple to implement but required serious effort to create, and being able to understand an Arxiv paper can have a direct impact on the job you're currently working on, whereas being able to read all of TAOCP will make you a better coder, but in a more abstract way.

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