I agree, and I very strongly dislike it, to be polite about it. It contributes absolutely nothing and is an excellent way of hand-waving away literally anything an AI model does. Saying "well people do this too" is a great way to rationalize away anything you can imagine that an AI model would be capable of, because "humans do it too so what's the big deal, guys?"

I can just immagine the response to a headline "LLM chooses mass death: thousands killed in horrific AI accident" being something like "lots of humans have caused mass death too."

Maybe we can think up a (personal) rule we can follow?

A naked "natural intelligences do this too" might be a bit too short to be useful. But if we can add when/where, cite papers, or show ways in which the parallel operates, then it might be useful.

Compare, eg, talking about a robot arm, and someone goes "a natural arm does this too". You can tell about the fact that it has the same degrees of freedom in the same places, or how this pertains to inverse kinematics, or etc...

Same way here, "this happens to be how natural intelligences seem to solve this too! According to Foo, Bar, Baz et al (2026) the gadget is always twiddled beforehand in macaque apes. " or "Same for natural intelligence: I've noticed I use the same general algorithm myself. I've always considered this the correct way to do translation between languages". --

A more concrete example of a useful answer here.

Natural intelligence does this too! When given the question "explain your reasoning" humans are indeed quite prone to post-hoc confabulation. [1]

[1] https://home.csulb.edu/~cwallis/382/readings/482/nisbett%20s... "Telling More Than We Can Know: Verbal Reports on Mental Processes" (this citation is quite old and may have been superseded, mostly just to illustrate how the rule might work)

It's the grounded portion of a feedback loop searching for the 'why is this happening' thinking. I'd imagine most of my own comments in this area boil down to "GIGO" most of the time.

It's a relevant comment in this instance because we're discussing concepts you need to be both trained and practiced in to reason about, and that our discipline has traditionally been blind to. Plenty of people working with LLM context issues who've never been exposed to the idea of 'subtext' or could tell you why it would matter to their direction of effort.

You think, "this problem" is qn LLM problem?

or rather, its not productive to "we should do better with artificial intelligence"

Many who watched the last decade knows just because its possible to noticed mistakes and change, its clearly not a reliable process.