About a year and a half ago, I began deliberately deleting em dashes from my writing. It was for exactly the reason you’re thinking—because AI chatbots tend to overuse them; their presence was becoming a tell. Even if the words were 100% human-generated, I thought anyone reading might raise an eyebrow if they saw them and wonder, Is this AI?
You can’t blame the world for its suspicion. Synthetically generated articles, social posts, and emails are everywhere. An analysis by Graphite found that AI-generated articles now account for roughly half of everything published online, running dead even with the human-written share. And even though the presence of AI text doesn’t necessarily mean the content is “slop,” most people use it as a proxy for quality, or, more precisely, whether or not it’s worth their time.
Recently, AI detectors have received a lot of attention because they’re ostensibly supposed to fix, or at least mitigate, the problem. They haven’t, for three reasons: First, they can be unreliable, sometimes producing false positives. Second, the presence of tells—even ones that are human-originated—is still a problem, and it plays out in the reader’s mind, where no detection software gets a vote. And third, there isn’t agreement on what the exact problem even is.
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The trial of Stanley Druckenmiller
That third point was thrust into the spotlight this past week when billionaire Stanley Druckenmiller told NOTUS, a government and politics news site, that he had used AI to write a guest column for The Wall Street Journal. The admission came after economist Claudia Sahm ran the column through the AI detector Pangram and posted that the entire text came back as machine-written.
But instead of apologizing and sheepishly retreating into the bushes in embarrassment, Druckenmiller declared his use proudly: Of course he had used AI, he said, for the same reason he uses a calculator to do math. He confessed to not being a gifted writer, so he outsourced the work to AI, something he says he does all the time. The important thing, he said, was that he vetted the piece and stood by all the words.
The editorial page editor at The Journal, Paul Gigot, agreed: “AI is a fact of modern life. People will use it to assist in their work and their writing, including with research, checking grammar, editing and more. The question for us is whether what we publish from contributors reflects an author’s original argument, and if the author has the standing and credibility to make it.”
In the wake of the incident, Semafor did its own analysis of how often AI writing appears from op-ed contributors to The Journal, The New York Times, and The Washington Post. It turns out the frequency is relatively low, with about 3% of the articles analyzed coming up as entirely AI-written. The Journal’s portion is slightly higher than the others at 5%, which tracks with Gigot’s position. Why is there such an outsize reaction to something that’s barely happening?
At the heart of this is the simple equation in people’s heads when they see what they perceive as an AI tell—in this case, the AI-flavored transition from Druckenmiller’s piece, “There is a quieter cost, too.” Once you question whether someone used AI to write, you start to question the effort. The NOTUS piece quoted a journalism professor saying that AI use “raises questions about how much time and thought actually went into the piece.” Nobody was arguing that Druckenmiller’s piece was wrong. They accused it of being cheap. Style became a proxy for effort, and effort became a proxy for credibility.
Convicted by punctuation
There are problems with using style to convict someone of whatever this crime is. One of them is that AI writing—and the methods to find and evade its hallmarks—keeps evolving. A recent study found that of the major AI models, only Claude still uses em dashes more than human writers. The tell has been around long enough that ChatGPT, Gemini, and others now proactively avoid them.
That inverts the signal, and with it the defensive edit I described earlier. If you strip out em dashes, you’re now writing more like ChatGPT, not less. These days I don’t shy away from dashes as much, but I have a new habit: closing the spacing around them, since AI tends to put spaces in by default (you may have noticed). I may need to alter that one at some point, too.
Perhaps the best indicator of the scale of this problem is Wikipedia’s “Signs of AI writing” page, which details the many, many tics that have, at one point or another, been a telltale signal that the words are synthetic. The page even acknowledges that publishing a guide in fact exacerbates the arms race, since anything predictable enough to be documented can be systematically avoided.
That’s the trap. The more people get wise to tells like negative parallelisms (“It’s not this. It’s that.”), front-loaded transitional words (“Moreover . . .”), or three-item lists separated by Oxford commas, the easier it is for models—and humans—to prune them. In effect, a tell becomes useless the moment you realize it’s a tell.
This goes double if you have even modest prompting skills for telling the AI to, well, not sound like AI. Ever since custom instructions have existed in AI apps, I’ve included a long list of words to avoid when responding: delve, revolutionize, unleash, et al. I sometimes show these off in the AI classes I teach with the caveat that I think the list has been obsolete for a while, since the models now avoid those words by default. But that hasn’t hurt the demand for guides to tells, and methods to avoid them.
What bylines don’t say
That’s the same instinct behind the Druckenmiller fallout: We’re all terrified of an AI tell flipping a switch in the reader’s head, leading them to dismiss the work and, by extension, our credibility. Formal AI detection is almost beside the point. And it’s not reliable anyway, especially for writers whose first language isn’t English. The most popular AI detectors, Pangram and GPTZero, have nontrivial error rates. Humans do worse. The people best at spotting AI writing turn out to be heavy AI users, who get it right about 90% of the time. Everyone else lands close to a coin flip.
The byline was supposed to settle this. As I wrote a few weeks ago, the use of AI in writing doesn’t need to be a scarlet letter. If you use it, vet the text with human judgment, and stand by all the words, it theoretically shouldn’t matter that AI was involved in producing them.
Yet Druckenmiller did all that, complete with his editor’s stamp of approval, and it didn’t matter. That’s because a judgment layer sits underneath all this, and it exists in the reader’s mind. It doesn’t wait for some kind of accountability test. It just delivers a verdict before anyone gets to argue about standing. This manifests in the contradiction of disclosure: 94% of readers say they want AI disclosures when it’s used in writing, but 42% of readers trust the article less when they see one.
Managing perception
So where does that leave us? AI watermarking may help a little. Anthropic recently introduced an AI “fingerprint” into Claude’s text outputs, adding a potentially more reliable tool than standard detection since it creates a true provenance signal. But even that isn’t perfect: Editing and paraphrasing can obfuscate or erase it. And, honestly, shouldn’t it? If you’re editing or paraphrasing, you’re by definition applying human judgment to the copy, which was supposed to be the point.
Ultimately, editors in newsrooms and on comms teams need to shift their perspective toward managing credibility, not simply AI use. And that comes down to perception, something that shifts all the time as models improve, AI tools become more common, and the public becomes increasingly AI-savvy as both readers and users. The only reliable defense is writing well enough that nobody thinks to ask.
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