You know the thing where AI companies describe their alignment direction as "Honest, Harmless, Helpful"?

The only one that's marketable to the idiots buying AI services is Helpful. So that's always top priority even when it goes against the other two.

Average decider wouldn't stand an "assistant" that sometimes answers "I don't know". So instead agent providers train their already hallucination-prone model to be 100% confident in whatever it says. Who cares if it's true if it looks like it is?

When- they don’t have “stubbornness“- they have a weights set (a weighted probbility table) and a training model (a text file filled with compressed text coordinates) that the llm cant write to to give positive feedback to the weights. It’s less of a matter of “convincing“ and more of an issue of “making the resulting statement logically concurrent with the probability table thats inbuilt into the system by injesting stack overflow vote talley, reddit posts voted most popular” - that makes it hard to “convince”. Since making those weights is very expensive (an energy intensive), the developers usually incorporate the old model into the new one. TLDR: yet again, lazy programming, google up: the y2k date folks in the 70’s.