What Happened

Every few months, the internet invents new theories about how to spot AI writing—certain words, certain tones, certain habits. And every few months, those theories fall apart because the models evolve, the prompts change, the patterns shift.

But as TechCrunch reports, Wikipedia’s editors have been quietly doing the hard work since 2023.

The article highlights a community effort called Project AI Cleanup, where Wikipedia volunteers review massive volumes of suspected AI-generated edits every day. With millions of edits flowing in, the platform has become an unexpected laboratory for understanding what AI writing actually looks like in the wild.

Over time, editors assembled a detailed, evidence-backed guide titled “Signs of AI writing.”
(Source: Techcrunch, Nov 2025)

A poet on X first pointed attention to the document, but the real story is the quality of the guide itself.

Key facts from the piece:

• The guide does not rely on automated detection tools (Wikipedia says they’re basically useless).

• Instead, it focuses on linguistic patterns, tone, and structural habits common in AI-generated text.

• AI writing tends to emphasize “why a subject is important” using vague, generic phrases.

• Models over-index on present participle clauses like “emphasizing the significance” or “reflecting the continued relevance.”

• AI submissions often highlight minor media mentions to artificially inflate notability.

• LLMs overuse “marketing language” — scenic, breathtaking, clean, modern — phrasing more befitting an advertisement than an encyclopedia.

The guide is not a theoretical document.
It’s a field manual built from observing thousands of real examples.

And that makes it useful far beyond Wikipedia.

Why This Matters

Something important is happening beneath the surface of this story.

We’re entering a world where:

• Millions of people publish with AI

• Hundreds of millions consume AI-written content

• Organizations worry about credibility

• Creators blend AI with human work

• Educators and editors struggle to assess authenticity

The TechCrunch piece makes a subtle point:
As models get more sophisticated, spotting AI writing becomes less about a single giveaway and more about understanding how models think.

Wikipedia’s guide works because it focuses on the underlying training biases of LLMs—not momentary quirks.

For example:

When a model tries to make a topic seem notable, it leans on generic framing: “a pivotal moment,” “a broader cultural movement,” “emphasizing the significance…” These are not content errors. They’re statistical tendencies, baked into how LLMs learn from the internet.

Marketing language creeps in because the web is full of marketing.
Hazy present participles appear because models are trained on writing that leans toward explanation, not precision.

This is why Wikipedia’s approach matters.
It shows that AI writing can be subtle, polished, even elegant—but still structurally different from how humans write when they’re not trying to impress anyone.

The Bigger Shift

The presence of a community-built guide signals a deeper shift in how we treat writing itself.

For years, the conversation around “AI detection” has been dominated by tools promising certainty:

“This is 92% AI.”
“This paragraph is 74% machine-written.”

The community already knows these tools fail—both false positives and false negatives. TechCrunch underscores that Wikipedia editors explicitly avoid them.

Instead, we’re moving toward a world where interpretation matters more than detection.

The question is no longer “did AI write this line?”
It’s:

• Does this text sound anchored in real sources?

• Is it specific instead of trying to sound important?

• Is it rooted in facts rather than vibes?

• Does the tone match the context?

• Is the structure human or machine-ish?

This is a subtle but profound shift.

We’re not detecting machines.
We’re evaluating writing.

Creators, editors, engineers, product builders — all of us are going to need this skill.

Wikipedia’s guide is simply the first version of a literacy method millions will adopt.

A Builder’s View

If you’re building tools in writing, editing, education, plagiarism detection, moderation, or even AI-assisted creation, this guide is a signal.

A few things stand out.

AI writing now has patterns that humans are learning to recognize.
The more the public learns these patterns, the more creators will need to justify, annotate or contextualize AI usage.

Detection is shifting from “tools” to “reading practice.”
This is crucial: the market for “AI detection software” is likely to shrink as literacy increases.

Creators will need to explain their process.
Clients, employers and audiences may expect transparency: what was written by the person vs. what was machine-assisted.

Engineers will need to rethink UX around AI writing.
If AI output is detectable through tone or structure, users may want features that help them correct or humanize it.

Editors will adjust their standards.
Wikipedia’s guide may become the baseline for “natural human tone” vs. “machine-leaning tone.”

For indie-builders, it means opportunities:

• Tools that highlight AI-like phrasing for revision

• Writing experiences that blend AI drafting with human refining

• Quality filters for publishing platforms

• Educational tools that teach AI literacy

• Content workflows that combine AI speed with human nuance

The future of writing is not AI vs. humans.
It’s AI + humans who know how to avoid sounding like a machine.

Where the Opportunity Opens

This is one of those subtle stories that unlocks bigger second-order questions.

If Wikipedia can reliably spot AI writing, then:

• Content moderation workflows change

• Editorial pipelines evolve

• Credibility tools mature

• AI-guided writing assistants become more “self-aware”

• Anti-AI phrasing tools become a category

• Professional writing shifts toward authenticity, specificity, and voice

And for creators, a new skill emerges:

The ability to write in a way that feels unmistakably human.

Not poetic.
Not theatrical.
Not exaggerated.

Just… grounded.

The kind of writing that doesn’t try too hard.

Ironically, that’s something AI still struggles to do consistently.

The Deeper Pattern

The TechCrunch article ends with an interesting reflection:
Even as models evolve, many of their habits will remain detectable because they come from how LLMs are trained.

You can disguise surface-level details.
You can rewrite structure.
You can prompt for tone.

But models are trained on the broad sweep of the internet.
And the internet overwhelmingly rewards:

• Generic importance

• Dramatic framing

• Vague positivity

• Subtle exaggeration

• Marketing phrasing

Humans, on the other hand, communicate with context.
We skip what doesn’t matter.
We don’t try to impress with every sentence.
We write unevenly, because real thoughts are uneven.

These distinctions don’t vanish with larger models.

If anything, as AI becomes more fluent, detecting these deeper patterns may become easier—not harder.

Wikipedia’s guide marks the beginning of a new literacy:
not how to catch the machine, but how to understand the writing.

Closing Reflection

Sometimes the most impactful AI stories are not new models or features—they’re documents written quietly by volunteers.

Wikipedia’s “Signs of AI writing” guide is one of those.

It shows us that AI writing can be polished, structured and articulate—and still feel slightly off.
Not wrong.
Not bad.
Just… not human.

As creators, engineers, editors and founders, the question we face is simple:

How do we build, write and teach in a world where AI can write everything—but humans still prefer what feels alive?

The answer starts with understanding the differences.
Wikipedia just gave us a very good place to begin.