INTHEBLACK June 2026 - Magazine - Page 51
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AI-generated content has become
very convincing at passing as
human, but it still carries some
telltale markers. Once professionals
know what to look for, fixing it is
quick and the difference is obvious.
Words Megan Breen
GENERATIVE ARTIFICIAL INTELLIGENCE (AI)
content has improved remarkably since the
first platforms appeared a few years ago.
Those early versions produced flowery writing
with little structure and obvious repetition,
while today’s systems generate far more
coherent and natural-sounding prose. However,
underlying issues, such as generic phrasing
and a lack of specificity, still remain.
The growing sophistication of AI content
has also made detection far less reliable.
When a data scientist fed the US Declaration
of Independence into an AI-detection tool,
it said 97 per cent of the document was written
with AI. It sounds absurd, but the reason is simple:
AI has gotten so good at mimicking human prose
that detection tools do not always keep up.
“AI has been trained on the formulas of good
writing such as the rule of three, juxtaposition
and formal cadence,” says Ralph Grayden,
an AI writing specialist and co-founder of
Antelope Media. “It almost writes too well
and is very formulaic.”
Even so, AI-generated writing is not entirely
beyond detection. When a book publisher
recently pulled a novel from shelves after
suspected AI use, it was not a detection tool
that raised the alarm. Readers pointed to flat
emotional tone, repetitive sentence structures
and heavy use of lists of three.
The problem is not that AI writes badly. It is
that it has common, recognisable characteristics.
The key is learning to recognise these patterns,
correct them and add unique information.
HOW AI IS TRAINED
Dr Kavita Ganesan, founder and chief
AI strategist and architect at Opinosis Analytics,
argues that these systems have absorbed
enough books, reports and web content to
mistake formulaic language for good writing.
“The knowledge embedded in them
is like a PhD in English and other fields
such as a PhD in physics, computer
science and political science, combined.
The problem is, they express it by
always reaching for the most obvious
word,” she says.
And it is getting worse. AI
models are increasingly training
on their own output rather than
original human prose, creating
a feedback loop that makes the
writing more generic over time.
In fact, Ganesan argues that
some newer models sound
more generic and have less
personality than the older models.
“There is no critical thinking in
AI-generated content,” she says. “The models
generating output are not really thinking, they
are just stating (or rather, generating) what is
most plausible given what you have asked for.
It is a useful first draft if you are already
a subject matter expert on the topic.”
WHAT TO LOOK FOR
At its worst, AI writing a few years ago
might have sounded like this: “As we stand
at the crossroads of a transformative moment
in human history, organisations that fail
to leverage synergistic opportunities risk
being left behind in an increasingly
competitive landscape”. While that might
be grammatically fine, is it something that
a real person would say?
Today it is more subtle. “Most people
generate something, read it back and think
it sounds okay — because it does, technically,”
says Grayden. “Spotting the problem requires
a specific lens, and developing that lens is
where most people get stuck.”
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