It's rude to show AI output to people

It gets interesting once you start a discussion about a topic with someone who had ChatGPT doing all the work. They often do not have the same in-depth understanding of what is written there vs. someone who wrote it themselves. Which may not come as a surprise, but yet - here we are. It‘s these kind of discussions I find exhausting, because they show no honesty and no interest by the person I'm interacting with. I usually end these conversations quickly.

If someone uses AI to generate an output, that should be stated clearly.

That is not an excuse for it being poorly done or unvetted (which I think is the crux of the point), but it’s important to state any sources used.

If i don’t want to receive AI generated content, i can use the attribution to filter it out.

Obviously the answer is to send them back an AI generated response

Previously there was some requirement for novel synthesis. You at least had to string your thoughts together into some kind of argument.

Now that's no longer the case and there are lazy or unthoughtful people that simply pass along AI outputs, raw and completely unprocessed, as cognitive work for other human beings to deal with.

LLMs are very very good at adding words in a way that looks "well written" (to our current mental filters) without adding meaning or value.

I wonder how long it will be before LLM-text trademarks become seen as a sign of bad writing or laziness instead? And then maybe we'll have an arms race of stylistic changes.

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Completely agree with the author:

Earlier this week I asked Claude to summarize a bunch of code files since I was looking for a bug. It wrote paragraphs and had 3 suggestions. But when I read it, I realized it was mostly super generic and vague. The conditions that would be required to trigger the bug in those ways couldn't actually exist, but it put a lot of words around the ideas. I took longer to notice that they were incorrect suggestions as a result.

I told it "this won't happen those ways [because blah blah blah]" and it gave me the "you are correct!" compliment-dance and tried again. One new suggestion and a claimed reason about how one of its original suggestions might be right. The new suggestion seemed promising, but I wasn't entirely convinced. Tried again. It went back to the first three suggestions - the "here's why that won't happen" was still in the context window, but it hit some limit of its model. Like it was trying to reconcile being reinforcement-learning'd into "generate something that looks like a helpful answer" with "here is information in the context window saying the text I want to generate is wrong" and failing. We got into a loop.

It was a rare bug so we'll see if the useful-seeming suggestion was right or not but I don't know yet. Added some logging around it and some other stuff too.

The counterfactuals are hard to evaluate:

* would I have identified that potential change quicker without asking it? Or at all?

* would I have identified something else that it didn't point out?

* what if I hadn't noticed the problems with some other suggestions and spent a bunch of time chasing them?

The words:information ratio was a big problem in spotting the issues.

So was the "text completion" aspect of "if you're asking about a problem here, there must be a solution I can offer" RL-seeming aspect of its generated results. It didn't seem to be truly evaluating the code then deciding so much as saying "yes, I will definitely tell you there are things we can change, here are some that seem plausible."

Imagine if my coworker had asked me the question and I'd just copy-pasted Claude's first crap attempt to them in response? Rude as hell.