I built a 36-pattern checklist to catch AI writing tells in my own drafts, calibrated against everything I've published. So when a theory about why AI can't write well went semi-viral last week, I read it the way I read a bug report: I wanted to know if the explanation actually matched the failure I was seeing in my own tool's flagged patterns, or if it just sounded right.
One of the theories didn't match. It's wrong, and it's the kind of wrong that spreads because it sounds technical enough to not get checked.
The claim: inference-time optimizations — the tricks labs use to make models respond faster — are why creative writing sounds worse than it used to. Specifically, speculative decoding.
Quick version of what that is, because the claim only sounds plausible if you don't know this part. Instead of one big model generating a response token by token, you pair it with a small, fast "draft" model. The small model guesses several words ahead; the big model checks the guesses in parallel and keeps the ones it agrees with. It's a shortcut for speed. The claim going around is that this shortcut quietly degrades writing quality.
It doesn't. Speculative decoding is built to be lossless by design — the output distribution is mathematically identical to what the big model would've produced generating alone, word by word. There's no quality trade happening anywhere in the math. What's real is narrower: creative writing benefits less from the speedup, because high-temperature prose has more genuine surprise in it, so the small model's guesses get rejected more often. One inference-benchmarking writeup put creative-fiction guess-acceptance around 50-65%, versus 75-85% for code — not a peer-reviewed number, but it lines up with how the technique works. That's a story about how much faster your writing gets served, not about how good it is.
