On March 17th I posted Indicators of AI, a infographic set onto instagram and am now sharing it here :)
Notes on the Indicators Featured:
I shared this information because as AI writing becomes more and more streamlined into our daily lives we are endangering our own linguistics by passively consuiming these writing styles frequently. Think of how quickly new slang is adopted.
Another note is yes, these writing styles did exist before widespread use of AI but these writing devices were not as commonly used as they are being used today. In addition to that, AI’s tactics with these writing tropes are different than how professional writers use them. Ai is using these devices to create meaningless sentences filled with fluffy words that don’t entirely make sense together when you analyze them for logical reasoning.
These AI Indicators or rather tropes were identifyed by software engineer Ossama Chaib. The examples featured here were found on his website: www.tropes.fyi which is a resource dedicated to indifying these patterns with even more examples than I am sharing here.
In upcoming posts I will go over the importancy of AI literacy, common logical fallacies found in AI Writing, and maybe other tech stuff.
For now, I’m posting this on a readable platform as some have asked for a more accessible place to read this besides instagram.
This is my first post on substack!
Through sharing this informaiton I hope that we all can be more critical of what we’re reading and more intentional how we approach using the AIs.
Indicators of AI Writing
NEGATIVE PARALLELISM: SENTENCE STRUCTURE
The “It’s not X -- it’s Y” pattern, often with an em dash. Before LLMs, people simply did not write like this at scale. Includes the causal variant “not because X, but because Y” where every explanation is framed as a surprise reveal, the em-dash dismissal “X -- not Y”, and the cross-sentence reframe where the same noun is negated then repositioned: “The question isn’t X. The question is Y.”
examples:
“This isn’t just technical -- it’s a mindset change.”
“The question isn’t whether to optimize. The question is when to stop.”
“Half the bugs you chase aren’t in your code. They’re in your head.”
NOT X. NOT Y. JUST Z.: SENTENCE STRUCTURE
The dramatic countdown pattern. AI builds tension by negating two or more things before revealing the actual point. Creates a false sense of narrowing down to the truth.
examples:
“Not ambition. Not ego. Just a person who never learned to say enough.” “Not broken. Not behind - Just building something no one has a name for yet.”
“Not confidence. Not charm. Just the audacity of someone who was never told no.”“Not a pivot. Not a rebrand. The same product with a different story about why you should care this time.”
SHORT PUNCHY FRAGMENTS: PARAGRAPH STRUCTURE
Excessive use of very short sentences or sentence fragments as standalone paragraphs for manufactured emphasis. RLHF training has pushed models toward “writing for readability” aimed at the lowest common denominator: one thought per sentence, no mental state-keeping required. It’s an inhuman style. No real person writes first drafts this way because it doesn’t match how humans think or speak.
examples:
“He published this. Openly. In a book. As a priest.”
“The policy changed. No announcement. New terms. Same checkbox.”
“Sleep is not a reward. It’s not a treat. It’s not laziness.
It’s the whole thing.”
“The body kept score. Quietly. Accurately. Without judgment.”
GRANDIOSE STAKES INFLATION: TONE
Everything is the most important thing ever. AI inflates the stakes of every argument to world-historical significance. A blog post about API pricing becomes a meditation on the fate of civilization.
examples:
“This will fundamentally reshape how we think about everything.”
“will define the next era of computing”
“something entirely new”
“What you’re about to do has never been done by you. That matters more than you think.”
“This isn’t just a presentation. It’s the argument you’ve been building your whole career.”
“You are not running a race. You are setting the pace for everyone behind you.”
TAPESTRY AND LANDSCAPE: WORD CHOICE
Overuse of ornate or grandiose nouns where simpler words would do. “Tapestry” is used to describe anything interconnected. “Landscape” is used to describe any field or domain. Other offenders: “paradigm”, “synergy”, “ecosystem”, “framework”.
examples:
“The rich tapestry of human experience...”
“Navigating the complex landscape of modern AI...”
“The ever-evolving landscape of technology...”
“A tapestry woven from diverse threads...”
TRICOLON ABUSE: SENTENCE STRUCTURE
Overuse of the rule-of-three pattern, often extended to four or five. A single tricolon is elegant; three back-to-back tricolons are a pattern recognition failure.
examples:
“Products impress people; platforms empower them. Products solve problems; platforms create worlds. Products scale linearly; platforms scale exponentially.”
“One thread loose. One seam pulled. The whole thing undone.”
“Flour, water, salt, time. That’s it. That’s the whole secret.
“It bloomed, it browned, it dropped. Nobody was watching.”
ANAPHORA ABUSE: SENTENCE STRUCTURE
Repeating the same sentence opening multiple times in quick succession.
examples:
“They assume that users will pay... They assume that developers will build... They assume that ecosystems will emerge... They assume that...”
“They have built engines, but not vehicles. They have built power, but not leverage. They have built walls, but not doors.”
“It wasn’t the budget that failed. It wasn’t the timeline that failed. It wasn’t the team that failed. It was the premise.”
INVENTED CONCEPT LABELS: TONE
AI clusters invented compound labels that sound analytical without being grounded. It appends abstract problem-nouns (paradox, trap, creep, divide, vacuum, inversion) to domain words — “supervision paradox”, “acceleration trap”, “workload creep” — and uses them as if they’re established, rigorously defined terms. They function as rhetorical shorthand: name a thing, skip the argument. Multiple such labels in the same piece is a strong signal of AI slop.
examples:
“the supervision paradox”
“the acceleration trap”
“workload creep”
“the complexity divide”
“the identity inversion”
HERE’S THE KICKER: TONE
False suspense transitions that promise a revelation but deliver a point that did NOT need the buildup. The model uses these phrases to manufacture drama before an otherwise unremarkable observation.
examples:
“Here’s the kicker.”
“Here’s where it gets interesting.”
“Here’s what most people miss about this.”
“Here’s what most people miss”
THINK OF IT AS...: TONE
The patronizing analogy. AI constantly reaches for “Think of it as...” or “It’s like a...” to simplify concepts. The model defaults to teacher mode and assumes the reader needs a metaphor to understand anything. Often produces analogies that are less clear than the original concept.
examples:
“Think of it like a highway system for data.”
“Think of it as a Swiss Army knife for your workflow.”
“It’s like asking someone to buy a car they’re only allowed to sit in while it’s parked.”
THE TRUTH IS SIMPLE: TONE
Asserting that something is obvious, clear or simple instead of actually proving it. If you have to tell the reader your point is clear, it very likely isn’t. Also includes the dramatic reveal variant: “but none of them is the real story. The real story is...” -- claiming privileged insight while waving away everything before it.
examples:
“The reality is simpler and less flattering”
“The path forward is not complicated.”
...“but none of them is the real story. The real story is that...”
LISTICLE IN A TRENCH COAT: PARAGRAPH STRUCTURE
Numbered or labeled points dressed up as continuous prose. The model writes what is essentially a listicle but wraps each point in a paragraph that starts with “The first... The second... The third...” to disguise the format. Perhaps you told it to stop generating lists and it decided to do this instead... still very common.
examples:
“The first step is research. The second is drafting. The third is review.”
“The first reason is timing. The second is trust. The third is tone.”
“The first factor is price. The second is quality. The third is availability.”
SUPERFICIAL ANALYSES: SENTENCE STRUCTURE
Tacking a present participle (”-ing”) phrase onto the end of a sentence to inject shallow analysis that says nothing. The model attaches significance, legacy, or broader meaning to mundane facts using phrases like “highlighting its importance”, “reflecting broader trends”, or “contributing to the development of...”.
examples:
“contributing to the region’s rich cultural heritage”
“This etymology highlights the enduring legacy of the community’s resistance and the transformative power of unity in shaping its identity.”
“underscoring its role as a dynamic hub of activity and culture”
“showcasing how these dishes have integrated into the traditional diet alongside rice”
VAGUE ATTRIBUTIONS: TONE
Attributing claims to unnamed authorities instead of being specific. AI loves to invoke “experts”, “observers”, “industry reports”, and “several publications” without naming anyone. It also inflates the quantity of sources -- presenting what one person said as a widely held view, or writing “several publications have cited” when it means two. If you can’t name the expert, you don’t have a source.
examples:
“Experts argue that this approach has significant drawbacks.”
“Industry reports suggest that adoption is accelerating.”
“Observers have cited the initiative as a turning point.”
“Some critics argue that the methodology is flawed.”
FALSE RANGES: SENTENCE STRUCTURE
Using “from X to Y” constructions where X and Y aren’t on any real scale. In legitimate use, “from X to Y” implies a spectrum with a meaningful middle. AI uses it as a fancy way to list two loosely related things. “From innovation to cultural transformation” - what’s in between???? Nothing!
examples:
“From innovation to implementation to cultural transformation.”
“From the singularity of the Big Bang to the grand cosmic web.”
“From problem-solving and tool-making to scientific discovery, artistic expression, and technological innovation.”
“From fundamental physics to medicine and neuroscience.”
IT’S WORTH NOTING: SENTENCE STRUCTURE
Filler transitions that signal nothing. AI uses these phrases to introduce new points without actually connecting them to the previous argument. Also includes: “It bears mentioning”, “Importantly”, “Interestingly”, “Notably”.
examples:
“It’s worth noting that this approach has limitations.”
“Importantly, we must consider the broader implications.”
“Interestingly, this pattern repeats across industries.”
UNICODE DECORATION: FORMATTING
Use of unicode arrows (->), smart/curly quotes, and other special characters that can’t be easily typed on a standard keyboard. Real writers typing in a text editor produce straight quotes and -> or =>. Claude in particular loves the -> arrow.
examples:
“Input → Processing → Output”
“This leads to better outcomes → which means higher engagement”
“Smart quotes” instead of straight “quotes” that you’d actually type”
THE X? A Y: SENTENCE STRUCTURE
Self-posed rhetorical questions answered immediately in the next sentence or clause. The model asks a question nobody was asking, then answers it for dramatic effect. Thinks this is the epitome of great writing.
examples:
“The result? Devastating.”
“The worst part? Nobody saw it coming.”
“The scary part? This attack vector is perfect for developers.”
“The impact? Immediate.”
Sources:
All of the indicator descriptions were sourced from www.tropes.fyi - collected by developer Ossama Chahib
The exceptions for this is some examples I auto-generated with Claude and my personal writing for the introduction and caption.
Further reading:
The Register: Why AI writing is so generic — Semantic ablation
My Orginal Instagram Caption:
At least 50% of the captions and social media carousels I see lately contains AI generated writing.
As AI writing becomes more and more prevalent, I have noticed that people have started to write this way themselves….this is very dangerous. All the time I am reminded of the movie Idiocracy. Through consuming AI based writing we are allowing it to influence our thinking and linguistics. What saddens me about this is it seems content creators who use this tool are not thinking about their audiences at all.
AIs will inflate your idea and your ego…AI is a product and it wants you to continue to feed it prompts (this makes data collection particularly easy). It will tell you something is true because it can click that you want that thing to be true and it wants you to keep talking to it.
I’m sharing this information because I care. Since about early 2024 I have been keeping up to date on AI advancements and it is trickling into everything and I unfortunately don’t see it going away. Knowledge is power and is also protection.
As I stated in my last post caption..
I am not anti AI but I am anti the way we have been using it without any consideration for planet earth, our psyches, centuries of books and knowledge, and our communities.
Anywho, please share, with your fellow humans, we gotta combat the slop while we can ❤️👾