Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
I’m not sure the 300-bit → 1,000-bit framing applies in all instances. The 300 bits may be a compressed cue to a much fuller idea. The AI can combine that cue with its prior knowledge to help reconstruct what the prompter was trying to express, with the prompter then verifying whether it’s right. Without the relevant prior knowledge for reconstruction, or the prompter for verification, it becomes much harder to know whether you’ve reconstructed the intended idea.
This is true - but also, models are absolutely terrible at writing articles and I don’t want to read them.
The issue isn’t that a 300 bit idea is padded with 15 KB of content. You can take any human-written article and reduce it by 90% with next to no information loss. What you lose is what makes the article a compelling read instead of a fact table.
I think the reality is that we will see quality long form AI-written content at some point. It doesn’t even feel like labs are particularly interested in chasing that now; code sells way more tokens. Right now the trend is that subsequent models degrade in writing quality as long as that pulls them up on coding benchmarks.
> You can take any human-written article and reduce it by 90% with next to no information loss.
You've unintentionally circled the error here. The "purpose" of an article extends beyond "convey this essential information".
By analogy, a textbook contains far more words than a spec sheet, but attempts to train the human to be able to easily interpret spec sheets. The so-called "information" content of both might be equivalent, yet one does a better job of teaching students.
hatthew · · focus · HN ↗
Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
zefalt · · focus · HN ↗
throwuxiytayq · · focus · HN ↗
The issue isn’t that a 300 bit idea is padded with 15 KB of content. You can take any human-written article and reduce it by 90% with next to no information loss. What you lose is what makes the article a compelling read instead of a fact table.
I think the reality is that we will see quality long form AI-written content at some point. It doesn’t even feel like labs are particularly interested in chasing that now; code sells way more tokens. Right now the trend is that subsequent models degrade in writing quality as long as that pulls them up on coding benchmarks.
IAmBroom · · focus · HN ↗
You've unintentionally circled the error here. The "purpose" of an article extends beyond "convey this essential information".
By analogy, a textbook contains far more words than a spec sheet, but attempts to train the human to be able to easily interpret spec sheets. The so-called "information" content of both might be equivalent, yet one does a better job of teaching students.