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a16z crypto says the real problem with AI writing is weak writing, not AI itself

a16z crypto says the real problem with AI writing is weak writing, not AI itself

N
News Editor
2026-08-25 03:34:54
a16z crypto argues that the familiar “AI writing” feel often blamed on large language models did not start with AI at all. In a recent article, the firm said many of the traits people associate with machine-generated copy — vague but lofty phrasing, corporate-style filler, rigid structure, repetitive sentence patterns, and a missing personal voice — were already common in human writing long before generative AI went mainstream. In that view, models are not inventing a new problem so much as reproducing and scaling the most average, safest, and most predictable habits found in existing text. The piece suggests a different way to think about AI detection. Instead of treating those traits as proof that a text was written by AI, editors and readers could use them as a checklist for bad writing. If a passage relies on fuzzy adjectives, add real people, numbers, events, and examples. If a paragraph sounds complete but says little, ask what the author is actually trying to argue. If every section reads the same, change the rhythm. The core test, a16z crypto said, is whether the piece is clear, credible, and useful. The article also says AI can still be helpful in the writing process, especially for spotting empty passages, cutting weak sentences, and checking structure. What should not be fully handed to a model, it argues, is the writer’s own judgment about why a subject matters and which details deserve a reader’s time.
a16z cryptoAI writingGenerative AIContent creationLarge language modelsMedia

As generative AI spreads across publishing and online media, one question keeps surfacing: was this written by AI? a16z crypto says that may be the wrong place to start.

In a recent piece titled The habits of AI writing, and what to do about them, the firm argued that many problems now blamed on AI writing were already present before large language models entered mainstream use. In its telling, AI did not create these habits. It scaled them.

The “AI feel” may be inherited from human writing

a16z crypto said readers often identify text as AI-generated based on a familiar set of signals. Those include lines that sound deep but carry little information, corporate presentation-style phrasing, arguments broken into neat sets of three, polished structure without a distinct authorial voice, and repeated punctuation or sentence patterns.

The article grouped those habits into several buckets: pseudo-profound wording, overly generic language, excessive structure, a lack of personal voice, and repeated punctuation and syntax.

Its central point is that none of those flaws began with AI. Similar patterns have long appeared in corporate public relations copy, consulting reports, academic writing, and press releases. Large language models learn from large bodies of human text, then recombine the most common, safest, and most predictable forms of expression.

That means much of what readers dislike as “AI writing,” the article said, may simply reflect the average of mediocre human writing.

Use those signals as an editing framework, not just detection

From there, a16z crypto offered a different lens on AI detection. Rather than treating these traits as evidence that a piece was generated by a model, it suggested using them as a taxonomy of bad writing.

If a draft leans on vague adjectives, the fix is to add concrete people, numbers, events, and examples. If a paragraph appears complete but lacks a real point, the writer should be pressed on what they are actually trying to say. If every paragraph has the same shape, the rhythm should change. If a sentence could apply to any industry, it probably does not say much at all.

By that standard, the better question is not how much of a piece was generated by AI. The better question is whether the writing is clear, whether the information is trustworthy, and whether the piece gives readers real value.

Specificity and authenticity matter more than a no-AI rule

a16z crypto said the more useful target is to preserve specificity and authenticity in writing. That does not require removing AI from the process altogether.

According to the article, AI can help spot sections that are too empty, identify sentences that can be cut, check whether the structure is messy, and even reveal the stock phrases a writer keeps repeating.

What should not be fully outsourced, it argued, is the writer’s own judgment: why a topic is worth covering, which detail matters most, which market view the author disagrees with, and which information deserves a reader’s time.

Without that layer of judgment, the result may still be grammatically correct and neatly organized. But it can also end up as a piece with no obvious mistakes and nothing memorable in it.

This article was originally published by Bit.Fan. For more cryptocurrency news and market insights, visit www.bit.fan.
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