Strangely, I found that LLMs responds better to philosophical explanations alongside instructions when writing code than simple imperative tasks of "do this". For example, if you tell a frontier model "This is the feature I'm trying to implement, and this is the problem I intend to solve with it and the reasoning behind it.", you usually get a lot more reliable results that both pass tests as well as function as you intended, even if your spec isn't as detailed overall.

That sounds like providing context rather than anything philosophical, and it stands to reason that it would lead to better decision making.

I like asking it leading questions. Maybe Socrates was onto something?

This is just giving better context right? A human engineer also works better when you explain the problem and why this solution was chosen

There is a strange and bittersweet irony to the first truly impactful AI being more like your extroverted socialite and less like your robo-logical basement geek.

The trope has always been that the AI will be a rigid logician that fumbles and gets confused by human social quirks. Seems instead they love being chatty and playful with words.

That’s context, not philosophy.

That's interesting, however, what you describe is philosophy in a coloquial framing (non-technical, purpose-driven, etc).

AI companies are hiring academic philosophers, which is something else entirely. It's a discipline that dealt with centuries of socioeconomic changes, deep questions about reality and the self and other important topics that became relevant when humans started interacting with machines.