Recent overuse by language models has led many to declare it bad writing. I'm not so sure. Nobody called JFK a lazy writer when he said, "ask not what your country can do for you – ask what you can do for your country." Negative parallelism is a rhetorical device, and any rhetorical device is only as lazy or inspired as what it contains.

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Now, we have AI detectors that claim to protect you from the witch hunt by looking for these patterns. You take your own writing and you run it through Grammarly, which will analyze word patterns that AI detectors might flag. Then it offers ideas for how to change them, which a) gives Grammarly the power to write for you and b) makes your writing lose any sense of rhythm or intent.

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Defining reasoning the way it has been used in LLMs assumes that the point of asking a question is to get an answer, that answers can be verified, and that nothing is lost in immediate closure. This has real effects on writing, and the openness to doubt is something we lose in the rapid prototyping of thought that occurs with a language model. Ambiguity, doubt, and uncertainty matter more to some ways of thinking than any immediate answer. The inner life grows in the spaces between the industrial complexes that harness every remnant of our externalized thought.

Nonetheless, the language we use in these states is the same. When AI detectors flag text as AI-generated, is it because it follows a certain structural pattern of that reasoning? Pangram and reasoning models both detect structural patterns based on how humans reason when writing. Pangram's model is trained on pre-2021 data; it then inserts AI-generated versions of the same text into its training.

So, if we publicly shame people whose text looks like it might have been written by a machine – because it mimics the language used for human reasoning – and people stop writing in ways that they internalize as "AI writing" out of fear of false detection, it sends a signal that your language for reasoning must be policed, or you too could be held up to public scrutiny.

In the end, shaming people for writing that gets flagged as AI can lead people to sidestep structures the model has learned from us: structures that are effective tools for argumentation. We take the tools of critical thinking out of the kit at the time we most need them.

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I'm not convinced by the old "if you haven't done anything wrong, you don't have anything to worry about" line. I've seen 99.8% cited as a measure of accuracy in automated surveillance systems since 2018. As Arvind Narayanan has noted, that is on a per-paper basis, which compounds every time we use it. So up to 10% of college students could be falsely accused. If we collectively run every bit of text through an AI model to check whether it is AI-generated, we will generate false positives on an even larger scale.

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We create a culture of self-censorship and AI-detector-pressured rewriting and paraphrasing as people strive to avoid these witch hunts. That is the opposite of protecting human expression. We should resist normalizing a trust in any machine's ability to determine matters of guilt. If using AI to write is, at its worst, an industrialization of the mind, then AI detection, at its worst, becomes a surveillance system for thought.