Honestly, it's a workflow issue, so there is no single right answer.I agree that an ADR should be concise, for example. However, if your user memory or custom instructions are already set to prefer conciseness, the information density will naturally be high. In my opinion, the fact that an AI adds rhetorical flourishes and unnecessary elaboration alongside essential information is fundamentally a configuration issue.
Furthermore, I suspect what you are referring to is its tendency to output overly accommodating explanations or mechanically neutral phrasing. However, I believe this changes completely if you provide sufficient source material. I think AI is capable of highly complex logical development. I felt this, for instance, when looking at Terence Tao's conversation logs with AI.
I consider using AI to be like pouring water into a tank. If you build the "tank" using academic paper data or strict constraints as your input, it fills that tank with water of much higher purity than most humans could. In fact, it produces drafts of higher purity than if I were to write them myself.
The reason I think this is simple. If standard AI outputs were inherently illogical, there would be no way to explain why it is showing such outstanding results in mathematics, the most logical of all disciplines.
Based on AI papers, my understanding is that the model maps to the word with the highest probability in the semantic space for the next token. Because it selects the semantic word with the highest probability, it completes the sentence based on the statistical likelihood in its dataset following that specific context. Naturally, if you use semantically deep words in your prompt, the output becomes equally deep. Humans are fundamentally inconsistent in maintaining this balance across different domains, but AI operates with perfect homogeneity.
An LLM's core mechanism is predicting the probability distribution of the next token conditioned on the current context, combined with techniques like sampling. However, when you use formal terminology commonly found in academic papers or words with deep semantic weight, the subsequent sentences and structural techniques actually unfold in a highly rigorous and logical manner.
In fact, if we define being "logical" as "faithfully adhering to a procedural development without logical leaps," then I believe LLMs are more logical than humans.
Humans can write at length about subjects they know well, but they falter in areas they do not. AI, on the other hand, can write about other fields with the exact same depth as my own area of expertise, to the point where it eventually generates code that even I cannot understand.
Conversely, if AI is truly nothing more than a "bullshitter," are its recent achievements in mathematics simply a scam? I don't believe that's the case at all.
Ultimately, it is true that our experience varies depending on our workflow and our own expertise. However, I have already seen too much proof to simply dismiss it as bullshit.