Reddit Users Discover Six AI Writing Giveaways

As AI-written text blooms across timelines, inboxes, and comment sections alike, Reddit users have been sharing notes on the subtle tics that give away machine writing: disciplined references to self-improvement techniques; a hint of artful disorganization.

Their judgment is not about single “gotcha” words but patterns — stylistic grooves large language models can fall into because they were trained to sound confident, helpful, and hyper-readable at all costs.

These tells from the community aren’t necessarily foolproof. OpenAI killed off an AI text classifier for being too inaccurate, and the Stanford HAI AI Index writes that most detectors continue to be brittle and easy to bypass. Yet the probability that a bot did the typing rises fast when you have several of those signs, Redditors say.

“And honestly” as a rhetorical speed bump in posts

Redditors often refer to the construction that launches a new sentence with an airy pivot: “And honestly?” or “And truthfully?” followed by a tidy conclusion. Example: “The policy is ambitious. And honestly? It could reshape the market.”

Humans deploy this for effect; models rely on it to approximate intimacy and emphasis. The tic is just the product of being trained in reinforcement to be charming — a conversational varnish that sounds more like a script than actual thought.

Declaring “No Fluff” and Throwing in Fluff

Another fan-flagged tell (what the hell is a “tell?” — it’s not philosophy!) that I’m imploring for is the sign of brevity when you hype up your post by saying “no fluff” right before giving a huge explanation. You see it all the time in listicles, advice threads, and product rundowns: “Here’s the playbook — no fluff.” Then several paragraphs of on-ramps and disclaimers, combined with the hoary old generic tips.

This contradiction is illustrative of the sort of prompt I encounter a lot, where users request succinct answers and models respond as though they are sales copy (yes, there is some kind of inverse effect there that occurs because the ability to be “helpful” puts a lower bound on length). Detail is not the issue with Redditors; a promise and a lack of delivery are.

Machine-gunned short sentences in succession

Look out for clusters of commodities, cut into staccato lines meant to whip up drama: “You tried. You learned. You improved. That matters.” One or two lines like that can pop. Ten in a row feel engineered.

Models picked up the cadence from social captioning and self-help prose that is rewarded for its punchy rhythm.

The overuse, Redditors complain, makes the tone of the writing itself poor. “I am quite annoyed about this,” RoachPuppy poses a humorous scenario: “You’re reading an article or blog post that you find interesting and the author has decided to use those fragment sentences that irritate me.” MiniMonk says they bug him/her or her/him. Annoyance 7 on GFP’s list: fragments where it’s not needed. “Posts obvious, listen,” Pinfastic contends. “It would appear something was cut,” FortuneCooke says. “Not text messaging. So many difficult to read,” writes DiRtYSCaNe77. “Overdone. Where do you— you damn sure not some big writer. Articles sometimes drive me mad,” KevinsRevenge signs the death certificate with a dispersal curse: “Thirteen up for twelve down phrases.”

Contrast framing: it’s not X, it’s Y pattern

Cousin to staccato is repetitive contrast framing: “It’s not a setback. It’s a setup.” or “Not noise, but signal.” It’s a formula that plays catchily in small doses but tends to clunk when stretched across the page.

Why it works: There are always contrasting opposites in sample data for motivational posts and ad copy. The structure is one that models can reproduce, and it telegraphs confidence even when specifics are scarce.

Over-signposting every turn in routine writing

Redditors also decry heavy “signposting” — the use of connective phrases piled up like traffic cones: “First, second, finally”; “in contrast”; “key takeaway”; “here’s what matters.” These guide readers through esoteric logic in expert writing. In outputs of AI systems, they occur even for straight lines.

The impulse makes sense. Models are positively rewarded for clarity and tend to scaffold prose with transitions by default. When signposts appear every few sentences, readers sniff automation rather than authorial voice.

Engagement prompts without engagement or replies

“Curious what others think.” That is the line Redditors continue to see at the end of dodgy posts, particularly those on LinkedIn and niche subreddits. It’s not the question itself that’s the tell — after all, lots of humans solicit feedback — it’s the pattern: accounts that ask for discussion and never engage with responses.

Its learning mirrors social growth playbooks that models will absorb from examples in training data. Gartner has forecasted that by 2025, 30% of large business-to-consumer companies will use synthetic-generated responses in their outbound marketing messages, which corresponds to increased use of engagement hooks and pablum.

The bigger picture: why these tells surface

Under the hood, these models use statistical techniques to predict the next token most likely to come in a sequence. That nudges them toward high-frequency phrases, recognizable transitions, and safe, upbeat framing — the linguistic mean of the internet. The result is “competent sameness,” which has been polished until it gleams like nothing else, and feels almost inappropriately generic.

Detection remains hard. The Stanford HAI AI Index also highlights how detectors create false positives, particularly among non-native English writers and the stilted prose of students. OpenAI has investigated watermarking and provenance signals, but dependable, cross-platform work is not yet in evidence.

How Redditors Cross-Check Posts They Suspect Are Written by AI

Users recommend looking for groups of tells instead of one. Then pressure-test material: get details, facts and figures, or credible sources. While human writers can draw on lived experiences, iterate on feedback, or stay in character over the course of a series of tweets, AI may generalize or repeat the prompt.

The takeaway from Reddit’s experiment in crowd forensics: none of these signals on its own is proof positive of authorship, but all together form a workable early-warning system. If a post has the tone of a motivational poster sewn to a style guide, there’s a good chance it wasn’t even written by the model — that permission-granting pocket patch isn’t coming off.