Conversations about artificial intelligence (AI) in journalism tend to revolve around the same two fears: that AI will either save journalism or destroy it.
But both narratives may be far too simplistic.
AI is often presented today as a force that will fundamentally transform the media industry. In reality, journalism had already spent years adapting itself to the logic of algorithms and social media long before generative AI entered the newsroom.
In that sense, many of the myths surrounding AI say more about the current condition of journalism than about the technology itself.
Myth 1: AI will destroy journalism
This is perhaps the most emotional fear surrounding AI.
It is built on the assumption that, before AI, journalism existed as an entirely human space, guided primarily by editorial judgement and journalistic values.
But the reality is far more complicated.
For years, journalism has operated under the pressures of speed, optimisation, and platform visibility. Newsrooms learned to write for search engines, tailor headlines to algorithms and produce content at a pace often dictated less by reporting rhythms than by the demands of social media.
Seen from this perspective, AI is not a radical break from the past so much as an extension of an already existing media system.
Yes, AI can transcribe interviews, summarise documents, process data, and even generate basic news copy. But journalism has never been simply about producing text.
Investigative reporting, building trust with sources, understanding political and
cultural context and making editorial judgments remain deeply human forms of work. Not because machines “lack emotions”, but because journalism is not only about information. It is also about responsibility.
Myth 2: AI will solve the media’s financial crisis
One of the most common claims about AI is that it will make journalism more “efficient”.
Newsrooms are promised tools that can produce more content with fewer resources, accelerate workflows, and even help address the industry’s long-running financial problems.
But this raises an important question: if audiences are no longer consuming news the way they once did, what is the point of simply producing more content?
The crisis facing journalism is no longer just about the quantity of content. The deeper problem is that the forms of journalism requiring the most time and resources have become the hardest to sustain financially.
And perhaps the greatest risk of the AI era is not simply job displacement, but a shift in editorial priorities themselves.
When speed and output become the dominant measures of value, the first thing pushed aside is often the journalism that requires time, verification, depth and sustained reporting.
Myth 3: AI is neutral
AI systems are often described as objective tools that merely “analyse data”. But data is never neutral.
AI learns from already existing information ecosystems. If those ecosystems are shaped by political bias, cultural imbalance or linguistic inequality, those same distortions are likely to be reproduced by machine systems.
This is particularly dangerous for smaller languages and smaller media markets.
Armenian-language content remains comparatively limited and unevenly digitised within global information flows. As a result, smaller languages and local stories risk becoming even less visible in AI-driven environments.
Myth 4: Journalists simply need to “adapt”
In recent years, the language of “adaptation” has become almost unquestionable within the media industry. Newsrooms are constantly told they must adapt to new technologies and new algorithms.
But adapt to whose rules?
The rhetoric of technological adaptation is often presented as an inevitable process, when in reality it reshapes the balance of power between journalism and technology companies.
Journalism has already gone through this once before.
During the rise of social media, news organisations were repeatedly told that if they optimised correctly, they could “beat the algorithm”. Instead, many became increasingly dependent on platforms capable of changing the rules at any moment.
Today, the same logic is returning through AI.
If “adaptation” means constantly reshaping journalism around systems it does not control, then this is not simply professional flexibility. It is a form of dependency.
Responding to technological change is necessary. But journalism cannot endlessly adapt itself to systems whose priorities are fundamentally different from its own.
Myth 5: AI can replace journalists
Almost every discussion about AI eventually arrives at the same question: will it replace journalists?
But perhaps that is not the most important question. A more urgent question is what gets lost when journalism begins to be measured primarily through the metrics of speed and scale.
At that point, the very qualities that distinguish journalism from mere content production begin to erode: scepticism, verification, contextual reporting and the willingness to ask difficult questions of power.
And perhaps the greatest danger is not that machines will begin writing articles, but that journalism itself may begin to lose its own standards.
