[finebalance]

Not a clue.

I'm a decent engineer working as a DS in a consulting firm. In my last two projects, I checked in (or corrected) so much more code than the other two junior DS's in my team, that at the end some 80%-90% of the ML-related stuff had been directly built, corrected or optimized by me. And most of the rest that wasn't, was mostly because it was boilerplate. LLMs were pivotal in this.

And I am only a moderately skilled engineer. I can easily see somebody with more experience and skills doing this to me, and making me nearly redundant.

[busterarm]

You're making the mistake of overvaluing volume of work output. In engineering, difference of perspective is valuable. You want more skilled eyeballs on the problem. You won't be redundant just as your slower coworkers aren't now.

It's not a race, it's a marathon.

[markus_zhang]

For most of the business, they don't really need exceptionally good solutions. Something works is fine. I'm pretty sure AI can replace at least do 50% of my coding work. It's not going to replace me right now, but it's there in the foreseeable future, especially when companies realize they can have some setup like 1 good PM + a couple of seniors + bunch of AI agents instead of 1 good PM + a few seniors + bunch of juniors.

[TechDebtDevin]

Once again, this seems to only apply to Python / ML SWEs. Try to get any of these models to write decent Rust, Go or C boilerplate.

[finebalance]

I can't speak to Rust, Go or C, but for me LLMs have greatly accelerated the process of learning and building projects in Julia.

[mbm]

Have been writing Rust servers with Cursor. Very enjoyable.