Well that's the difference between data and information. The writing is _data_ dense but not _information_ dense. Data is just the numbers. Information is what comes out of the data when you analyze it and provide context. The text being data dense, information sparse is why it's so hard to read. You're continually being bombarded with irrelevant noise.

I agree, well put. Data dense but information sparse.

One is bombarded with facts and struggle to get what the text is actually saying.

I have tried getting the LLM (Opus 4.8 or some 5.x and some other model) to write more human like, and also analyze its own text. It was hilarious, it could describe the problem quite well, but was helpless to actually do anything about it. "Oops I did it again, sorry!" LOL

Tons of extraneous information. You see this in code, architecture, test suites and etc all the time.

And absolutely we know models themselves get bogged down with too much extraneous reducing accuracy.

This is reflected in classifier test suites.

Didn't Dario exclaim earlier in the year getting an LLM to simplify a design was like pulling teeth?

The LLM doesn’t know what’s relevant or important. It puts tokens together that are likely to make sense together. It can reason a bit, but it doesn’t think in the way you and I do, and therefore would find it difficult to guess what YOU consider relevant.

In many cases like this it's requirements from the prompt that are leaking trough. It's usually stuff that matters for the model itself writing the text/code, but are irrelevant to the end user and add noise

Yeah I find that when adding a feature they’ll often add irrelevant stuff to the documentation, stuff that mattered during the conversation like “we removed feature X” - which was only added in the current commit and was never actually deployed, or “feature Y does not support use case Z” which is just something I told it, nobody is going to be interested in what this thing doesn’t do. The most recent models seem a bit better at this I find.