Why So Many Press Releases Now Sound Alike, and What Makes One Read Human
Key Points
- According to the head of e-releases, about 97% of press releases sent through a newswire earn no media coverage, even when senders paid $1,700 or more.
- When Thy Art Is Murder announced its album Godlike, Metal-Temple ran the band’s release word for word and Everblack published identical text on 9 May 2023.
- AEO Content judges an article by its most machine-like passage, so 144 of 146 reviewed articles were called AI although 56 held a passage that read human.
AI-drafted releases on PR Newswire and Business Wire sound alike because one model default fills one template for every sender. Reading human means that the release carries facts only the company holds.
According to the head of e-releases, those two wires form “largely a duopoly” in the US. Synonym swaps leave the template intact. The fix is company records, a named customer and a dated result in the slots the template leaves empty.
Drafting is no longer the hard part of a press release. Software writes the frame well; the idea inside it still has to come from the company.
According to the head of e-releases, a distribution service that sends about 10,000 releases a year, AI “is very good at writing the press release,” but not at the ideas behind it. The Duct Tape Marketing host who ran that May 2026 interview gave the reason: the software has “read all the bad press releases.”
The sameness has two layers. One is old. In my team’s audits, marketing copy written before ChatGPT already read like model output, and releases are built so outlets can paste them word for word. The other layer is new: one default voice now fills the same template for every sender. My team scores a piece by its most machine-like passage, so one stock paragraph can set the verdict for all of it.
The three-input test that follows separates the model’s default, the prompt and the company’s own facts, and only the last belongs to the sender alone.
About 97% of press releases sent through a newswire earn no media coverage, even when the sender paid $1,700 or more to reach the whole country.
According to the head of e-releases, interviewed on the Duct Tape Marketing podcast in May 2026, the releases worth studying are the 3% that do get picked up. Syndicated copies do not count.
Even real pickup is often a copy. When Thy Art Is Murder announced its album Godlike, Metal-Temple ran the band’s release word for word, and Everblack published identical text on 9 May 2023. Wall of Sound’s journalist, as a commenter in a music-marketing forum noted, “did some work and wrote a little” before quoting the band.
The release was built to be pasted. It was. AI drafting now produces that copy-ready shape for anyone who asks, so I would put one question to every sender: what can this release carry that no other company holds?
Why does AI drafting push every press release toward the same voice?
Generators start from a mass-appeal default, fill one release template and receive thin inputs, so different companies get near-identical copy unless someone supplies facts the model cannot guess.
In 2024, one standalone generator sold press releases at $15 each, while a communication agency’s finance director put the usual copywriter fee at $150 to $250 per release. At that gap, drafting became the cheapest line in the budget. Volume followed.
I read any draft through what I call the three-input test. Every sentence in a generated release comes from one of three places:
- The model’s default style, which is the same for every customer.
- The prompt, which varies less than teams expect, because most of them ask for the same thing: an announcement.
- Facts only the company holds, such as names, dates, figures and what actually happened.
When the first two inputs carry the draft, the output converges. According to a commenter in an April 2026 r/generativeAI thread, generic prompts on base models produce “obvious patterns” because models are set by default to “a mass appeal style of writing.”
The common assumption is that sharper prompting fixes this. Not necessarily. According to a June 2025 r/PublicRelations thread, a public affairs practitioner whose prompts named the release’s goal, the reporter’s beat and the keywords still got drafts that came out “too flowery” and lacking cohesion. Another member found that asking the model to strip adjectives often returned a slightly reworded sentence carrying the same number of adjectives. A long-time LLM user in that thread put the consequence plainly: “If you use ChatGPT to write press releases, your releases will be like everyone else’s.”
The template does the rest. A professional release writer who tested a wire service’s free generator in 2023 watched it lift phrases from a two-sentence input, drop them into a standard release frame and attach a partner quote from a CEO the software invented. He conceded that human release writers work from much the same frame. A 2024 practitioner added that corporate releases were already thick with jargon, and the models are trained on that jargon.
Taken together, the 5 sources behind this section describe one mechanism: shared defaults, shared templates, thin inputs. Those defaults are also measurable. AEO Content’s detector, by the company’s own calibration, is tuned on more than 10,000 verified human documents with a 0.5% false-positive cap in every writing register, not only on average. Holding the cap per register means formal copy gets judged against formal human writing.
In practice, a longer prompt buys polish. Difference has to come from the third input. Which leaves the harder question for anyone sending a release this week: can a reader, or a detector, actually tell who wrote it?
Can anyone tell an AI-written press release from a human one?
Sounding generic and being AI-written are separate things: only 4 of 2,868 pre-2021 Medium posts were misread as AI, and the misses were neutral institutional prose.
If AI releases share a fingerprint, spotting them should be easy. It isn’t. The press release register looked machine-like before the machines arrived.
In AEO Content’s own evaluation of its detector, 4 of 2,868 pre-2021 Medium posts (0.14%) were called AI. What the four shared was register: the calm, impersonal, institutional prose that most corporate announcements are built from, whether a person or a model produced the draft.
My team saw the same drift from the other direction. In an audit of one payments company’s blog, 7 of 50 posts published between January and October 2022 already read like GPT-3 or Jasper output. That result is why the team trusts a human label only on text dated 2021 or earlier. Marketing copy had found the generator sound on its own.
The clearest case for keeping two questions apart came from a style comparison. A human author’s posts and a sports equipment site’s AI-drafted guides landed close together on my team’s style scale, 57 versus 68, while origin detection separated them sharply. Style asks how generic a text reads. Origin asks who wrote it.
| Reading | Question it answers | What moves it |
|---|---|---|
| Origin | Did a person or a model produce the text? | The drafting process itself |
| Style | How generic does the text read? | Register, stock phrasing and how much specific detail the text carries |
Readers do no better than a blended score would. According to an April 2026 r/generativeAI thread, one commenter observed that people asked to tell the two apart “usually misidentify human content as AI generated,” a skepticism the commenter tied to life “in a new AI world.” Another ran the same text through “a few different AI detectors” and got a different answer from each.
The implication is uncomfortable for PR teams. A release written entirely by a person can still read as generated. Authorship alone settles nothing. So the useful question shifts from who typed the words to what the words actually contain.
What makes a press release read like a person wrote it?
Company-only facts change how copy reads: in articles built on a client’s own sales records, record-heavy sections read as AI 37% of the time, against 71% for the rest.
Style polish cannot fix a register that was generic to begin with. The answer has to come from material only the issuing organization could supply.
My team’s review of those sales-record articles puts that in numbers. Within the same articles, the sections carrying the client’s own records read human far more often than the sections written around them, 37% against 71% on the AI side of the ledger. The records gave those passages details no model could have predicted. Same article, same topic, different inputs.
The argument itself is old. According to Patrick Armitage, writing in MarTech in 2016, press releases are “canned statements” that have been edited “to suck the personality out of the company and the life out of the product.” His model of the alternative was restaurateur Nick Kokonas, who published real business data from Alinea, Next and the Aviary on replacing reservations with tickets. No competitor could have published that data.
The same logic now carries more weight. According to Amanda Natividad, writing in her newsletter The Menu in June 2026, “When everyone is generating, the un-generatable becomes the premium ingredient.” Her advice to companies is direct: “Stop using your experts as editors. Start using them as sources.”
| Release element | Template version | First-hand version |
|---|---|---|
| Executive quote | A stock line about being pleased to announce | A named person explaining a decision in their own terms |
| Proof | Adjectives such as leading or innovative | The company’s own figure, with its period and method |
| Context | General talk of growing demand | What changed, when, and for which customers |
| Outcome | Promised benefits | A result that already happened, with a date |
Specifics only work when they carry their method. In 2023, a survey research firm that had analyzed 3,106 newsmaker survey releases issued between 2013 and 2023 said reporters are trained to ask how many people were surveyed, from which regions, how they were interviewed and whether the data were weighted. A number without those answers reads as marketing, whoever drafted it.
What this tells us is practical. The person holding the records matters more than the person polishing sentences. In practice, every release needs at least one fact a competitor could not publish. Natividad’s shorthand for the job runs to three words: “Capture; don’t manufacture.”
What will decide whether a press release stands out in the next 12 to 24 months?
What will matter most is source material. As drafting gets cheaper, releases built on a company’s own figures, people and dates will stand apart, and templated ones will blur together.
| Prediction | Weak signal | Why it matters | Source |
|---|---|---|---|
| Generated drafts become the default, and templated releases converge further. | One custom GPT had written “tens of thousands of press releases,” and a PR database vendor had built a similar tool into its product. | Once anyone can produce the standard frame, the frame says nothing about the sender. | r/PublicRelations thread, December 2024 |
| Teams reverse the workflow: a person drafts from first-hand material, and the model reviews. | In 2025, a heavy model user wrote the human draft first and used the model only for review and brainstorming. | Source material is cheap to capture. One rule of thumb in a June 2026 newsletter holds that filming one hour-long internal talk yields “weeks of content.” | PR practitioners’ forum, June 2025 |
| Coverage in credible outlets outweighs syndication counts. | A marketing podcast host said in May 2026 that earned media is “probably going to become more important than it maybe ever was.” | AI answer engines lean on “credible sources,” the newswire operator in the same interview said, and a copied release on an unknown subdomain is not one. | Marketing podcast interview, May 2026 |
A brief aside on budgets. In May 2026, a small-business newswire operator put a 600-word national release on the two major US wires at around $1,800. Saving on the words while paying that for the send is a trade I find hard to defend.
Contrary to what many PR teams assume, a warmer tone will not carry a release through this period. In 2025, a PR agency owner put the working rule plainly: “stop pitching what happened, and start pitching what changed in the world because of it.” Get that wrong, the same owner warned, and “no clean sentence will save you.”
One human-sounding paragraph does not rescue a release. Of 146 articles my team reviewed, 144 were still called AI overall, although 56 held at least one passage that read human.
The verdict follows the most machine-like passage. A release template supplies plenty of those, because releases are built so outlets can paste them whole. The register is older than the models: one payments company’s blog read like machine output before ChatGPT launched.
According to the head of e-releases, one Waste News article prompted an Australian city to contact a waste and recycling plant builder, which was under contract for two facilities there within 6 months. The implication is that one story an editor chose to write can be worth more than a national send. Their working rule is the one I would adopt: “I never let AI decide what to write on.” Start from the company’s own figure, name or date, and hand the model only the frame.
Written by
Alex Shortov
CTO, AEO Content
Full-stack engineer and content infrastructure architect with 20 years of building enterprise systems.
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Frequently Asked Questions: What do PR teams ask about AI-drafted press releases?
Most questions are about workflow: where the model helps, where company facts must lead, and what counts as coverage after a release goes out.
Should a PR team stop using AI to draft releases?
No, but the order matters. In 2025, one PR agency owner said the agency used AI for structure and rewrote 60 to 70% of the output by hand. I would keep the model on the frame and the company on the facts.
Do syndication pickups count as media coverage?
Not as earned media. According to the head of e-releases, feeds charging $49 or $119 copy a release onto many sites, where a search for the company usually will not surface it. Syndication here means paid replication of the release text, and no journalist wrote anything.
Why do journalists ignore so many releases?
Volume without substance. In a 2024 thread among PR practitioners, a working journalist said many senders used cheap generators because most releases were “next to useless for earned media.” A writer of hundreds of releases a year was blunter: “AI writes boring, generic releases that do not get attention.”
Disclaimer: This article is for informational purposes only. It discusses press release writing trends and general communication practices. The views and examples presented are for educational purposes and should not be considered professional advice.