AI Words to Avoid in Cold Emails: The Full List We Strip

There is a specific list of AI words to avoid in cold emails, and most are not exotic. They are the words a general-purpose model reaches for when it has nothing particular to say: delve, synergy, leverage, seamless, game-changer, and the opener that died a decade ago, "I hope this email finds you well."

Recipients notice. Hunter's 2026 survey of decision makers found that 69% are bothered by AI-written emails, and a widely read Hacker News thread from July 2026 put it more bluntly: AI-written outreach is "instantly obvious" and "a bit annoying."

Key takeaways

• 69% of decision makers say AI-written emails bother them, according to Hunter's 2026 State of Email Outreach. Overselling (65%) and irrelevance (61%) are the top complaints.
• The tells cluster in three places: vocabulary (delve, tapestry, cutting-edge), rhythm (long dashes, triads, "not X but Y"), and openers ("I came across your profile").
• Gong's word-level data shows a measurable cost: "thoughts" cuts meeting bookings by 20%, ROI language by 15%, guilt phrasing by 14%.
• Length is a tell too. Gong found the best replies come from emails of 100 words or fewer; Hunter puts the sweet spot at 61 to 80 words.
• Stripping words is the floor, not the fix. Hunter found bodies with two custom attributes replied at 5.6% against 3.6% for non-personalized copy, so research comes before writing.
• We strip these by default rather than leaving it to a checklist, because a filter also runs on the email you forgot to check.

Do AI-written cold emails work? What 69% of decision makers say

They can, but they start from a deficit. Hunter's State of Email Outreach 2026, pairing 31 million sent emails with a decision-maker survey, found that 69% of respondents are bothered by AI-written emails. The two biggest complaints were not about technology: 65% cited overselling, 61% irrelevance. 28% said they never receive a valuable cold email.

The qualitative evidence matches. In a 302-point Hacker News thread on cold email from July 2026, recipients called AI-written messages "instantly obvious" and said "the majority of us do not differentiate between cold email and spam." The same thread describes what works: "short and informal," and "genuine ones I'll always respond to."

The objection is not that a model helped write the email. It is that the reader can tell nobody thought about them before sending it. Every item below transmits exactly that.

The full list: AI words to avoid in cold emails

Our writing filter strips these from every draft before it reaches the review queue. The right-hand column matters more than the left: deleting a banned word and replacing it with another vague word buys nothing.

The published lists overlap heavily with ours. AiSDR's rundown of words that flag AI copy names delve, tapestry, "in today's ever-evolving world," cutting-edge, multifaceted, testament, revolutionary and "Certainly!" The convergence is the point: if every published list carries the same vocabulary, recipients have seen those words many times over.

The clearest community evidence is a r/coldemail post titled "11 banned phrases lifted our cold email reply rate from 1.8% to 4.5%", reporting the removal of cliches such as "I hope this finds you well." That is one operator's self-reported result, not a study: informative in direction, unproven in size. The thread's diagnosis travels better anyway: a general-purpose model's default voice is the average of every cold email in its training data, which is exactly the voice you do not want.

Punctuation and structure tells

Vocabulary is the easy half. Structure gives an email away even when the words are clean.

The long dash. Heavy em dash use in short business emails has become one of the most discussed tells, fairly or not. Our filter converts them to commas or periods.

The rule of three. "Faster, cheaper, and more reliable." Models love triads. A human writing quickly manages one adjective.

"Not X, but Y." The antithesis construction ("it's not about volume, it's about relevance") is one of the most recognizable rhythm tells in generated drafts.

The closing summary. A 70-word email that restates its own point at the end was assembled, not written.

Perfectly parallel bullets. Three bullets of identical length and grammar are a formatting fingerprint. Real bullets are lopsided.

Header residue. Drafts sometimes carry the scaffolding of the tool that produced them, such as leftover subject labels. Our filter removes that fingerprint before approval.

Openers that mark an email as automated

The first sentence carries most of the automation signal. Gong's study of executive buyers found executives spend under three seconds deciding whether to open and no more than nine seconds reading: roughly one sentence of attention.

Three openers to retire:

1. "I came across your profile." So did the software. It describes your process, not their situation.
2. "At [Company], we help B2B teams..." A company description in sentence one is an About page in an inbox, and a staple of generated openings.
3. Flattery with no object. "I've been really impressed by what you're building" is unfalsifiable, and unfalsifiable praise reads as a mail merge.

Surface personalization does not fix this. As a commenter put it in an r/coldemail thread on volume versus personalization, "surface personalization is a custom first line pulled from a LinkedIn bio. It doesn't signal you understand the business." The better rule is from the same thread: "treat research as a prerequisite to writing, not as a variable to inject into a template."

Before and after: rewrites that pass the filter

Each rewrite deletes the sender-centric clause and replaces it with something only true of that reader. That is why the after column is shorter: the filler was carrying no information.

Word-level data: "thoughts," ROI language and guilt

Some phrases carry a measured cost. Gong's cold email analysis, updated March 2026 and drawn from millions of cold emails, found:

• The word "thoughts" cuts meeting bookings by 20%.
• ROI language decreases success by 15%.
• Guilt phrasing such as "I never heard back" cuts meetings by 14%.
• Interest-based asks ("open to learning more?") outperform specific meeting requests.
• "Hope all is well" correlates with 24% more meetings, the one finding that cuts against received wisdom. Correlation is not causation, and a short pleasantry is not the full "I hope this email finds you well" formula, but the goal is a human register, not a sterile one.

Gong's 28 million email study adds the structural limits: pitching cuts reply rates by as much as 57%, the best replies come from emails of 100 words or fewer in three or four sentences, and buzzwords or numbers in subject lines reduce opens by up to 17.9%. The 30MPC and Gong report on 85 million emails is stricter on subjects: under four words, lowercase, phrased like an internal email.

Why we strip AI tells by default

A banned-words list only works when someone applies it, and the email nobody proofreads is the one that goes out at volume.

So the filter is not optional in SDRclose. Every draft passes through it before reaching the review queue: the vocabulary above is stripped, long dashes converted, tool fingerprint headers removed. Writing controls live on the workspace (tone, call to action, subject style, personalization depth, custom instructions), so the register is set once rather than negotiated email by email. Cold first touches also stay light on links and never propose meeting times, because an opener that behaves like a booking flow reads as automated whatever its vocabulary.

The review queue is the second layer. Nothing sends without approval unless a sequence is put on autopilot, so a person sees the words first. The layering is the point, and we make the full case in AI SDR that actually works.

How to write cold emails that do not sound like AI

The vocabulary list is the last step, not the first.

1. Research before you write. Smart research reads the prospect and their company first and stores the notes on the lead record: a summary, facts with sources, a recent win, a company pain, a recommended angle. Writing then draws on something true.
2. Open with the observation, not yourself. Sentence one should be a fact about them that they would recognize as accurate.
3. Keep it to 61 to 80 words. Hunter's best-performing range, inside Gong's 100-word ceiling and three-to-four-sentence shape.
4. Ask for interest, not a slot. Gong found interest-based asks beat specific meeting requests; a first email asking for 30 minutes reads as a script.
5. Use two real attributes. Hunter found bodies with two custom attributes replied at 5.6% against 3.6% for non-personalized copy. Two is enough. Five is a mail merge showing its work.
6. Read it aloud. Anything you would not say in a hallway goes.

Then check the list. If a draft still has a banned word after those six steps, the problem is the offer, not the vocabulary. More on where the numbers land in our reply rate benchmarks.

The shortest version

Delete every word that could appear in anyone's email to anyone. What remains is either specific to this reader or not worth sending. That one test catches most of the list above, and an inbox applies it in three seconds.

If you would rather have the filter run on every draft automatically, with research attached before the writing starts, start a free 7-day trial.