But the word pioneer comes from French pionnier, literally “foot soldier”, a soldier who goes ahead to prepare the way.

If you don't publish you may be advancing, but you're not preparing anyone's way.

I find just juxtaposing these two normal words flows much more nicely from the tongue than the comparatively awkward "LLM".

For the AI study real data from "3.4 million people who submit 4 million job applications to 1,700 job postings across 150 employers and 11 industry sectors" was used.

I have the feeling it probably teaches you something, or at least it should. Something not too unlike epistemic humility, maybe.

The only answer that makes sense is they wanted the model to be competent and usable in these fields, just not by you, which is why they had to bolt on a badly functioning crippling device after the fact.

I can speak from myself as an example (although n=1): I am incredibly open to machine learning and the advances it brings. On the other hand I am extremely conscious of the fact that the current LLMs do often write bad code, which becomes especially obvious once projects go much beyond "private toy" size. For me personally the consequence is that I try to make my projects even more modular, and the modules even more clearly delineated. With proper guidance, current LLMs work mostly fine when working on isolated modules. That doesn't mean they don't sometimes fail even there, but they also on rare occasions come up with surprisingly clever solutions when you let them loose on a code base, as long as the problem you want them to fix is mostly isolated in a couple of modules.

So long story short, you can be all for LLMs and still be conscious of their shortcomings, and that just vibe coding applications you intend to let loose on unsuspecting customers is probably a bad idea, and possibly outright immoral. We have already seen more than enough examples of vibe coded applications dumping sensitive user data into the lap of anyone who is inclined to pick up a stick and prod them a little bit.