Having just finished rewriting a 20-pager that went from outline -> slop -> finished product, I can say with certainty that it’s not a case of AI being too intelligent.
It could be a case of AI being differently intelligent, though. Here are some of the patterns I encountered:
Repetition: AI never breaks a sweat repeating the same thing over and over. This is fine with code because there are more tools to catch and eliminate it and even so, it matters less as long as the code works.
Jumbled statement order: AI has a glossary of facts and it is not very careful about the order of… shall I say, revelation… of facts. We have a certain way of building understanding in deductive steps. AI writing disregards that. (Aside: I actually started using an Answer Set Programming tool to keep the LLM on track.)
Mixing attribution and control: we have a sense of what things are passive, what are active, what controls and what follows or reacts in a situation. AI mixes it up badly and suddenly you have stuff like “the lead shade is red but it has nothing to show for its presence around orange” instead of saying that the red accent doesn’t contrast well against orange.
Post-training flavours: I think this is clear from how Claude models write vs. other trainers’. Even accounting for intelligence differences, Claude has an empirically worse writing style.
Having just finished rewriting a 20-pager that went from outline -> slop -> finished product, I can say with certainty that it’s not a case of AI being too intelligent.
It could be a case of AI being differently intelligent, though. Here are some of the patterns I encountered:
Repetition: AI never breaks a sweat repeating the same thing over and over. This is fine with code because there are more tools to catch and eliminate it and even so, it matters less as long as the code works.
Jumbled statement order: AI has a glossary of facts and it is not very careful about the order of… shall I say, revelation… of facts. We have a certain way of building understanding in deductive steps. AI writing disregards that. (Aside: I actually started using an Answer Set Programming tool to keep the LLM on track.)
Mixing attribution and control: we have a sense of what things are passive, what are active, what controls and what follows or reacts in a situation. AI mixes it up badly and suddenly you have stuff like “the lead shade is red but it has nothing to show for its presence around orange” instead of saying that the red accent doesn’t contrast well against orange.
Post-training flavours: I think this is clear from how Claude models write vs. other trainers’. Even accounting for intelligence differences, Claude has an empirically worse writing style.