These words scream bot.
The complete 2026 list of AI words and phrases that instantly flag your text as machine-generated. Find out what to write instead.
You're reading an article. Maybe someone sent it to you. Maybe you found it through search. The intro is smooth, the structure is logical, the sentences flow well. Then somewhere around paragraph three you feel it. That specific flatness. The writing is technically fine but somehow empty, like food that looks right but has no flavor.
Then you hit a sentence like "In today's fast-paced digital landscape, it is worth noting that leveraging the right tools can seamlessly transform your workflow." And the moment you read that, you know. A language model wrote this. No human being writes that sentence.
Here's what's uncomfortable: you've probably written sentences like that too. When you use ChatGPT or Claude to help draft something and you don't clean it up properly, those phrases sneak in. They're baked into the model's default output. Anyone paying attention will spot them immediately.
Human readers notice them first. A detector might give you a score. A reader just stops trusting you. That's worse.
This article gives you the complete list. Not just the obvious stuff like "delve" and "leverage," but the structural patterns, the transition habits, the sycophantic openers, the hedge clusters. By the time you finish, you will never look at AI output — including your own — the same way.
Before you start cutting words, understand why they're there. This isn't random. There's a specific mechanical reason every major language model reaches for the same vocabulary.
Language models are trained on enormous amounts of text from the internet: books, articles, academic papers, forum posts, corporate communications, Wikipedia. That corpus has a heavy bias toward formal written English — business reports, how-to guides, listicles, press releases. The writing that shows up most frequently in training data is not great writing. It's competent, forgettable, corporate writing.
When a model learns from that data, it learns that certain phrases appear very frequently in "good" professional writing. "It is worth noting" appears constantly in academic papers. "Furthermore" appears constantly in formal essays. "Leverage" appears constantly in business writing. The model learns these as high-probability tokens — the words it confidently predicts will belong in the output.
Then RLHF — reinforcement learning from human feedback — makes it worse. Human raters gave higher scores to responses that seemed thorough, balanced, and professional. Raters rewarded completeness, hedging, and politeness. That's exactly the writing pattern that now screams AI.
Understanding RLHF alignment, 2024The model isn't being sycophantic because it's trying to manipulate you. It's sycophantic because the humans who trained it gave higher scores to sycophantic outputs. You trained it to talk this way.
The practical result is massive vocabulary convergence. GPT-4, Claude, Gemini, Llama, Mistral — they all reach for the same words because they were trained on overlapping datasets using similar alignment. Same hedge phrases, same transitions, same qualifiers, same structure.
If you take an AI paragraph and swap synonyms, you're still leaving the underlying structure intact. The sentence architecture, the transition logic, the hedge placement, the summary reflex at the end — all of that survives a surface-level rewrite. Detection tools and human readers pattern-match at a structural level, not just a word level.
These are the words that appear where a real action verb should go. They sound like they mean something. They don't. Every one of them is a placeholder for the specific thing you actually want to say.
The word that broke the internet. "Delve" appears in AI text at a frequency that has no parallel in human writing. It's slightly archaic, slightly formal, and slightly awkward. AI loves it because it appeared frequently in academic text and signals "thorough investigation" without committing to anything specific. Replace it with a word that names the actual action: look at, break down, walk through, pull apart, get into.
Business English's favorite fake verb. "Leverage your skills" just means "use your skills." Every time this word appears, there's a more precise verb waiting to replace it. Use: use, apply, build on, work with, rely on, tap into.
Same problem as leverage, but worse because it's longer for no reason. "Utilize" just means "use." There is no context where "utilize" adds precision over "use." Every time you see "utilize," replace it with "use" and the sentence gets better.
These three appear constantly in AI writing about relationships, growth, and culture. "Foster a culture of innovation." "Cultivate meaningful connections." They're used so interchangeably and so generically that they mean nothing. Replace with the actual mechanism: build, create, develop, invest in, show up for, pay attention to.
"Harness the power of AI." "Harness data to drive decisions." Dramatic without being specific. What does it actually mean to harness something? The vagueness is the tell.
Ask yourself: what is the specific physical or mechanical action being described? If you can't answer, the verb is hollow. Replace it with one that names the actual thing happening.
These are the phrases AI models use to signal that one thought is connected to another. They're not wrong, exactly. They're just mechanical — the connective tissue of writing trained on formal essays rather than how humans actually string ideas together.
No human under 80 who is not writing a legal brief uses the word "furthermore" in conversation or casual writing. It's a formal essay connector that AI uses constantly because formal essay structure is deeply baked into training data. Replace with: "Also," "And," "On top of that," or just start the new sentence without a connector.
Same category. These words exist, but their frequency in AI output is wildly disproportionate to how often actual humans use them. "Nevertheless, the results were significant." That sentence was written by a language model 90% of the time.
Almost a joke at this point. It appears when a model wants to introduce a caveat but doesn't know how to weave it into the surrounding prose. A flag planted in the text saying "here is a parenthetical thought I couldn't integrate organically." The fix: just say the thing. "Note that X." Or better, restructure so the information sits in the sentence where it belongs.
AI loves summarizing. The problem: conclusion summaries are dead weight in most online writing. Your reader just read the article. If your writing is good, the conclusion should do something new, not repeat what already happened.
These are the adjectives that seem to add weight but actually subtract meaning. They appear in AI text because they signal quality without committing to any specific claim that could be challenged.
"A comprehensive guide." "A robust framework." "A holistic approach." The word does no work. Either your piece actually covers everything (in which case the comprehensiveness will be self-evident), or it doesn't (in which case calling it comprehensive is misleading). Cut these entirely or replace with the specific quality: complete, reliable, tested, integrated, end-to-end.
Nothing is seamless. Every integration has friction. "Seamless" is aspirational language from marketing copy that AI absorbed and now deploys everywhere. Humans who have actually used things and experienced friction write differently: "it took me about ten minutes to set up" or "works without touching the settings." That's real.
Marketing adjectives that signal importance without specifying it. If something is actually new, name what specifically changed.
These words came from business strategy consulting, leaked into corporate communications, got absorbed into training data, and now AI regurgitates them constantly. They don't mean anything anymore.
Synonymous with "I'm trying to sound professional": synergy, ecosystem, streamline, optimize, maximize, empower, transform, elevate.
Replace every one with what specifically happens. "Optimize your strategy" becomes "cut three steps from your strategy." "Maximize results" becomes "convert 14% more leads." The specificity is the fix.
Hedging is what AI does when it wants to say something without being wrong. The verbal equivalent of a lawyer qualifying every statement. Humans hedge too, but not with this density or this predictability.
These are trained deeply through chat interfaces. Human raters preferred responses that acknowledged the question before answering it. In isolation, they seem polite. In writing, they're immediate tells.
Cut completely: "Great question!", "That's a fantastic point", "Certainly!", "Of course!", "Absolutely!", "This is a great opportunity to...".
No editor should let these survive into published content.
This is the category most people miss. You can strip every bad word from an AI piece and still have something that reads as AI because the structure itself is a tell.
AI converts every possible topic into a numbered list. When an entire article is lists, it reads as AI because real writing mixes formats. Some ideas need narrative. Some need comparison. Some need a single sentence.
AI text has perfect parallel structure almost all the time. Every item in a list starts with the same grammatical form. Every sentence in a series has the same structure. Human writing is messier.
AI structures paragraphs and sections the same way: introduction of concept, elaboration, summary. It also structures entire articles the same way: intro explains what you're about to read, body covers the points, conclusion recaps the body. Real articles don't always work this way.
AI generates headers like "Understanding the Basics of X" or "How to Improve Your Y" because these are the patterns it learned from how-to content. Human writers give sections more interesting, opinionated, or surprising headers.
You can replace every banned word and still have structurally AI-looking content. The structure has to change too, not just the vocabulary.
Every word and phrase on this list has been documented in AI output across multiple models, appears at rates far above what you'd see in authentic human writing, and will flag your text to both detectors and human readers.
50 AI Tells: Words to Cut and Human Alternatives
| AI Tell | Category | Human Alternative |
|---|---|---|
| Delve | Hollow action verb | look at, get into, break down |
| Leverage (non-financial) | Hollow action verb | use, apply, build on |
| Utilize | Hollow action verb | use |
| Foster | Hollow action verb | build, create, develop |
| Cultivate | Hollow action verb | grow, develop, invest in |
| Harness | Hollow action verb | use, direct, tap into |
| Nurture | Hollow action verb | support, invest in, pay attention to |
| Furthermore | Fake transition | Also, And, On top of that |
| Moreover | Fake transition | And, On top of that |
| Nevertheless | Fake transition | Still, But, Even so |
| In conclusion | Fake transition | Cut it, or reframe the final point |
| To summarize | Fake transition | Cut it — your reader already read it |
| It is worth noting that | Fake transition | Note that X (or just say X) |
| In today's landscape | Fake context phrase | Right now, These days, Currently |
| In today's world | Fake context phrase | Cut it, start with the actual point |
| Comprehensive | Empty qualifier | Complete, thorough (or just cut it) |
| Robust | Empty qualifier | reliable, detailed, thorough, tested |
| Holistic | Empty qualifier | full, integrated, end-to-end |
| Seamless / Seamlessly | Empty qualifier | easy, instant (or be specific) |
| Cutting-edge | Empty qualifier | new, current (name the advance) |
| Groundbreaking | Empty qualifier | new, first, the first to... |
| Revolutionary | Empty qualifier | name what specifically changed |
| Game-changer | Empty qualifier | name the specific impact |
| Synergy | Corporate buzzword | these reinforce each other |
| Ecosystem | Corporate buzzword | network, industry, community |
| Streamline | Corporate buzzword | simplify, speed up, cut steps |
| Optimize | Corporate buzzword | name what specifically improves |
| Maximize | Corporate buzzword | name the outcome and tradeoff |
| Empower | Corporate buzzword | let, help, give people the ability to |
| Transform | Corporate buzzword | name what specifically changes |
| Elevate | Corporate buzzword | improve, strengthen, raise |
| Pain points | Buzzword | problems, frustrations, what's broken |
| Actionable | Buzzword | concrete, specific, practical, usable |
| Takeaways | Buzzword | the main thing is, what this means is |
| Best practices | Buzzword | what works, how to do it right |
| It is important to note that | Hedge phrase | Cut it, say the thing directly |
| It goes without saying | Hedge phrase | Cut it entirely |
| Having said that | Hedge phrase | But, Still, Though |
| At the end of the day | Hedge phrase | Ultimately, What matters is |
| Moving / Going forward | Hedge phrase | From here, Starting now, Next |
| Great question! | Sycophantic opener | Cut it, just answer |
| That's a fantastic point | Sycophantic opener | Cut it, respond directly |
| Certainly! | Sycophantic opener | Cut it |
| Of course! | Sycophantic opener | Cut it |
| Absolutely! | Sycophantic opener | Cut it |
| In today's fast-paced | Opening cliche | Start with the actual problem |
| Deep dive | Meta phrase | look at, examine, cover |
| Unpack | Meta phrase | explain, break down, cover |
| Touch base / Circle back | Corporate phrase | talk, follow up, check in |
| Shed light on | Meta phrase | explain, show, reveal, clarify |
Knowing the list is one thing. Seeing the transformation makes the pattern recognition click. Study the differences — not just word swaps, but structural changes, specificity increases, the shift from announcement to demonstration.
Typical AI output, 2026In today's rapidly evolving landscape, it is worth noting that businesses must leverage comprehensive strategies to foster meaningful growth. Furthermore, cultivating robust relationships with stakeholders will seamlessly align organizational synergies toward maximizing overall performance.
Nine tells in two sentences. Human rewrite: "Businesses that grow fast share one trait: they know exactly who they're building for and they talk to those people constantly. That's it. No framework required." Shorter, more specific, takes a position.
Typical SaaS marketing copy, AI-generatedOur groundbreaking platform seamlessly integrates with your existing workflow, providing a robust and comprehensive solution for optimizing your content strategy. By harnessing the power of AI, users can leverage cutting-edge features to maximize their output and elevate their brand presence.
Eleven tells in two sentences. Human rewrite: "You paste text, you get human-sounding text back. It takes thirty seconds. No learning curve, no settings, and it actually works on GPTZero." Specifics. Time claim. Named tools.
AI-generated academic proseIt is important to note that the comprehensive analysis of socioeconomic factors reveals several noteworthy findings. Furthermore, it goes without saying that researchers must take a holistic approach when examining these complex interrelationships. Having said that, the robust methodology employed in this study provides valuable insights.
Seven tells in three sentences. Human rewrite: "The data shows two things working against each other: income gaps widen when schooling quality drops, but the relationship reverses when controlling for neighborhood stability. That's not obvious. Most prior studies missed it because they looked at income and education separately." Specific. Names a finding. Takes a stance.
In every example, the human rewrite is shorter. This is almost always the case. AI writes around its points. Humans make their points. Cutting tells almost always cuts word count 20–40% while increasing information density.
Most people make the same handful of mistakes. They solve the surface problem and leave the underlying structure intact.
You swap every "leverage" for "use," every "furthermore" for "also." Then you paste it into a detector and it still flags AI. Why? You only changed word-level tells. The structural tells — the three-part paragraph sandwich, the parallel list structure, the summary conclusion, the headers that describe rather than argue — are still there.
This is subtle. You replace "leverage" with "utilize." "Comprehensive" with "all-encompassing." You haven't fixed anything. Those replacements are on the same list of AI-favored vocabulary. Ask yourself: would I say this in a text message? If not, it's probably still in the AI register.
The fastest, cheapest, and most underused editing technique. Every sentence that makes you stumble, that feels awkward to say, that sounds like you're presenting in a boardroom when you'd normally just be talking — flag it. AI text is often grammatically perfect but tonally wrong.
AI openings and closings are the most formulaic parts. The opening sets up the topic ("In this guide, we will explore..."). The closing summarizes ("In conclusion, we have examined..."). Those are the highest-tell sections and they're the first and last thing your reader sees.
Some people treat clean-up as a game against the detector. The goal becomes "get below 20% AI probability" rather than "make this sound human." These goals are not identical. Detectors can be fooled in ways that make writing worse. The right goal is genuine quality.
Cleaning AI tells takes multiple passes. First pass catches obvious words. Second catches structural issues. Third catches tonal problems. Fourth is the read-aloud test. Most people do one pass and declare it done.
The deepest fix is not vocabulary or structure. It's specificity. AI writing is generic by design. Human writing is specific: specific time, specific person, specific context, specific number, specific failure mode. The specificity has to come from you. That's the part no tool can do.
Not vague advice about "sounding more human." The mechanical steps you follow every time.
Run a baseline detection scan
Before you edit anything, paste into a detector and get your baseline. Note which specific sentences are flagged as high-confidence AI. Run at least two detectors — one statistical model like GPTZero and one stylometric tool. The sentences flagged by both are your hardest problems.
Do the vocabulary sweep with the 50-word list
Go through and highlight every word or phrase from the table above. Don't replace anything yet, just mark them all. Getting the full picture prevents the "swap one for another" mistake. Heavy clustering tells you where the AI is most dense.
Replace hollow action verbs with specific ones
For each hollow verb, ask: what is the specific action being described? "Leverage your data" becomes "sort your data," "pull the data and compare it," "filter the data by X." If you can't answer the specificity question, the sentence itself needs to be reconsidered, not just the word.
Cut or replace fake transitions
For every "furthermore," "moreover," "nevertheless" — delete it and read the sentence without it. In most cases it works fine. If not, use a short natural connector: "And," "But," "Still," "Plus." If the transition requires a formal connector to make sense, the paragraph order is probably wrong.
Gut the opening and closing paragraphs
These are almost always the worst sections. Delete the first paragraph if it starts by explaining what the article will cover. Start with the second paragraph instead. Delete any concluding paragraph that starts with "In conclusion" or "To summarize." Replace with one specific, concrete thing the reader should do next.
Add one specific detail per major section
For each H2, identify the most generic AI-averaged claim. Replace it with a specific example, number, name, or failure mode. "Companies that invest in content marketing see higher returns" becomes "Backlinko's study of 912 million blog posts found 94% receive zero links." Cite a study, reference a product, name a real person.
Do the structure variation pass
Read through looking for structural monotony. All paragraphs the same length? Throw in a two-sentence paragraph. All headers in the same format? Turn one declarative header into a question. All sentences subject-verb-object? Start one with the object. This breaks the statistical signature detectors look for.
Read the entire piece out loud
Not optional. Read at speaking pace — the pace you'd use telling a smart friend about this topic over coffee. Every sentence that makes you pause or feels like a formal presentation, rewrite it in the voice you'd use in conversation. This catches tonal problems no word list will find.
Run the final detection scan
Paste back into your detection tools and compare to baseline. If specific sentences are still flagged, rewrite them from scratch without looking at the AI original. Start from the idea you want to express and write it fresh. A blank-page rewrite is sometimes faster than editing AI-structured sentences.
Get a second reader
If stakes are high, have someone who doesn't know the source read it. Ask them to flag anything that "doesn't sound like you." Human pattern recognition for AI writing is more sensitive than most tools right now. A reader told to look for AI tells will find things that passed all your checks.
If you want to skip the manual process, HumanLike.pro handles the structural and vocabulary transformation automatically. Paste your AI-assisted text, select your tone, the tool rewrites it to remove the statistical signature detection tools pick up. Worth noting: no automated tool replaces the specificity you add yourself. HumanLike gets you to a clean base. You add the real details.
The most widely known AI detection tool. Uses perplexity and burstiness scores. Good for a quantitative baseline and identifying highest-confidence AI sentences. Weakness: confused by complex vocabulary or highly technical content, and sometimes flags academic writing as AI. Use as one signal, not the only one.
Better for longer content and catching paraphrased AI. More expensive than GPTZero but gives more granular sentence-level flags. Also runs plagiarism detection alongside AI detection.
Control-F is underrated. Paste the 50-word list into a note, use your browser's find to check each one. Takes five minutes, costs nothing. Won't catch structural tells but finds every vocabulary tell instantly.
Any basic TTS — macOS (Cmd-Opt-S), Windows (Ctrl-Win-Enter) — will read your text aloud. Listen at 1.25×. The unnatural sentences become immediately obvious when heard rather than read.
Manual editing
Automated tools
Cutting AI tells is a two-layer job: vocabulary at the word level, structure at the document level. Detectors catch both. Humans catch the absence of genuine thought. The fix is specificity — real examples, real numbers, real opinions that cost you something. No tool can add that. That part is yours.
Paste your writing into HumanLike and see exactly which AI tells are still in there — before anyone else does.
Writing about AI humanization, detection accuracy, content strategy, and the future of human-AI collaboration at HumanLike.
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