Producing sentences was never the part that made writing worth reading. When a model can produce clean, well-structured prose about anything in seconds, the value of an author stops being the prose and comes down to the one thing the model cannot supply: a reason the words should exist at all.
Writing Was Always Two Jobs #
For as long as writing was hard, two very different jobs were fused inside it. One was mechanical: choosing words, ordering sentences, hitting the register a reader expects. The other was everything that made the writing worth a stranger’s time: deciding what was true, which detail mattered, what to assert and what to leave out, what claim I would stand behind if someone pushed back.
The two jobs looked like one task because the same person did both, and because the judgment was invisible unless the prose was done well. LLMs did not replace the author; they split the author in two and automated only the half that was always the easier one to learn.
The split looks like this:
The Model Is Fluent and Has Nothing to Say #
A model trained on the corpus of everything humans have written can generate plausible text on any subject, and that fluency looks like understanding from the outside.
It is closer to a lossy compression of everything already said, as Ted Chiang argued in calling ChatGPT a
“blurry JPEG of the web”.
The model can only recombine what exists; it has nothing of its own to add. It has not run the experiment, taken the risk, held the opinion, or been wrong in public and had to fix it. Fluency without experience produces prose that could have been written by anyone, which is exactly the prose the internet now has too much of.
What the Author Brings That the Model Cannot #
Four things do most of the work, and each is scarce for the same reason: it is paid for in something other than typing.
Something to say. A real piece of writing starts from an observation, a result, a mistake, or a conviction that did not exist in the corpus in that form. The author has lived something the model has not. The trace of that experience, a number that was actually measured, a failure that actually happened, a stance the author will defend, is the only material the model cannot manufacture.
Judgment about what is true. The model will produce a confident answer either way, which is why its confidence is worth almost nothing as a signal. An author commits: this is right, this matters, this is the claim I am making. That willingness to be wrong, narrowing a general cloud of plausibility down to one assertion a reader can check or reject, is the act the model cannot perform, because the model has no stake in being correct.
Stakes and accountability. Writing with a name on it can be wrong, criticized, quoted against its author, and remembered. That risk is precisely what gives the words weight to a reader, who is deciding whether to trust a person, not a process. A model has nothing to lose from a false sentence: the sentence costs it nothing, costs an author something, and readers sense that asymmetry even when they cannot name it.
The thinking that writing forces. Writing is not the transcription of finished thought; for most authors, myself included, it is how the thought gets finished at all. Offload the whole job and I keep the artifact but lose the part that changed me, the slow, uncomfortable work of discovering what I actually believe by being forced to say it precisely.
The Trap of Offloading the Wrong Half #
Prose generation is so cheap and so good that I am tempted to also let the model decide what to say, and then edit a draft into something shippable. Let the model choose the claim on every piece and the work becomes indistinguishable from the flood, because the work is the flood, recompressed from the same corpus by a slightly different prompt.
The moment you let the model choose the claim, you have stopped being the author and started being the first reader of the model’s writing. That arrangement produces a thinner kind of work, the kind that does not earn the trust that makes anyone return.
The useful line is not “human-written” versus “AI-written,” and never was (see Written by for how this blog labels AI involvement). The useful line is whether a human mind made the decisions that determine whether the piece is worth reading: what to assert, what to omit, what experience anchors it, and whether the claim is one the author will stand behind.
What to Do Next #
Decide what the piece is for and what claim it is making before the model writes a single sentence, because that decision is the work and the prose is downstream of it.
Anchor every piece in something the corpus does not contain. A measurement you took, a failure you caused, an opinion you hold and can defend, a reader you are specifically addressing, is the only material that keeps the writing from being echo.
Put your name behind it and mean it. The accountability is not a cost you pay for publishing; it is the source of the weight the writing carries. A reader can feel its presence or absence in the first paragraph.
The prose is now the cheapest output. The judgment behind it is the entire product.
See also #
- Getting Noticed in the LLM Flood - the producer’s mirror of this piece: once the writing is good enough to be the floor, attention moves to trust and distribution
- The Shifting Bottleneck - the pattern of the constraint moving one level up when a lower one is automated, here from prose production to judgment
- The Acceptance Gap - the parallel problem in code: the model produces, deciding it is acceptable is the human part that stays
- Written by - this blog’s disclosure policy, which exists because the author-versus-echo distinction matters
- Keeping Up With AI Is a Losing Strategy - the reader’s side: why more fluent content is the wrong thing to optimize for
References #
Ted Chiang, “ChatGPT Is a Blurry JPEG of the Web” - the model as lossy compression of the corpus, which is why it can recombine but not originate
Sturgeon’s law (Wikipedia) - “ninety percent of everything is crud,” sharpened by the fact that the crud is now fluently written