Anthropomorphism: As we are all aware, providers are incentivized to post-train anthropomorphic behavior in their models - it increases engagement. My regret is that instructing a model at prompt time to "reduce all niceties and speak plainly" probably reduces overall task efficacy since we are leaving their training space.

Deference: I view the trustworthiness of LLMs the same as I view the trustworthiness of Wikipedia and my friends: good enough for non-critical information. Wikipedia has factual errors, and my friends' casual conversation certainly has more, but most of the time that doesn't matter. For critical things, peer-reviewed, authoritative, able-to-be-held-liable sources will not go away. Unlike above, providers are generally incentivized to improve this facet of their models, so this will get better over time.

Abdication of Responsibility: This is the one that bothers me most at work. More and more people are opening PRs whose abstractions were designed by Claude and not reasoned about further. Reviewing a PR often involves asking the LLM to "find PR feedback" and not reading the code. Arguments begin with "Claude suggested that...". This overall lack of ownership, I suspect, is leading to an increase in maintenance burden down the line as the LLM ultimately commits the wrong code for the wrong abstractions.

Many weaker or non-native writers might use AI for that "editor's eye" without realizing that they are being driven to sound identical to every other blog post these days. And while I'm certainly growing tired of constantly reading the same LLM style, it's hard to fault someone for wanting to polish what they publish.

Related: I also suspect that this is one reason we get so little information about the exact data used to train Meta's Llama models ("open weights" vs "open source").

[1]: https://www.annas-archive.org/llm

reference for those unaware: https://commons.wikimedia.org/wiki/File:Nice_here._(Nett_hie...