--- title: "AI in journalism" author: "Jakub Ćwirlej" id: ai-journalism language: en url: "https://jakubcwirlej.com/articles/ai-journalism.md" page: "https://jakubcwirlej.com/" translations: pl: "https://jakubcwirlej.com/pl/articles/ai-journalism.md" fr: "https://jakubcwirlej.com/fr/articles/ai-journalism.md" nl: "https://jakubcwirlej.com/nl/articles/ai-journalism.md" updated: 2026-10-14 words: 1514 summary: "The author, who has used AI almost daily since ChatGPT came out, acknowledges its risks, above all hallucinations, but argues that pragmatic use of the technology strengthens the craft: he talks to a chatbot on the way back from shoots, automates transcription and uses OSINT tools, although no model can yet build a television paper edit from raw interviews. He sees little sense in judging texts by how they sound, since AI detectors err in both directions, and notes that the EU AI Act asks who is responsible for a text but gives readers no way to check who wrote it. He expects journalists who keep researching and iterating to do well, and those who merely reproduce formulas to be the ones who lose their jobs." summary_type: editorial --- # AI in journalism Jakub Ćwirlej · text language: EN · 1514 words ## Summary (editorial) The author, who has used AI almost daily since ChatGPT came out, acknowledges its risks, above all hallucinations, but argues that pragmatic use of the technology strengthens the craft: he talks to a chatbot on the way back from shoots, automates transcription and uses OSINT tools, although no model can yet build a television paper edit from raw interviews. He sees little sense in judging texts by how they sound, since AI detectors err in both directions, and notes that the EU AI Act asks who is responsible for a text but gives readers no way to check who wrote it. He expects journalists who keep researching and iterating to do well, and those who merely reproduce formulas to be the ones who lose their jobs. ## Key points (editorial) - In an EBU and BBC study from October 2025, one in five answers by AI assistants to questions about current events contained serious factual errors, such as made-up or outdated information. - His first app, OrsonAI, grew out of the need to transcribe interviews in real time and import them instantly into an editor. - AI detectors err both ways: ZeroGPT rated a passage of the US Declaration of Independence almost 98% machine-written, while two detectors judged a text that Wired later established to be an AI fabrication as most likely written by a human. - His idea of a blockchain-anchored certificate of human authorship met with no interest among friends in the industry. - From 2 August 2026 the EU AI Act requires AI-generated texts on matters of public interest to be labelled unless they have passed editorial review and someone takes responsibility for them. ## Full text (the author's own words) I’m on my way back from a shoot. Hands on the wheel, my head churning with fresh thoughts and impressions from the location, so I switch on a chatbot and simply talk to it. I tell it what I saw, try out the first sentences of the piece out loud, list what I still need to check. The bot writes it all down, sorts it into notes and comments on my ideas as we go. Sometimes the conversation produces a rough first draft, sometimes just a list of observations that helps me as I carry on working on the piece. Before, by the time I got back to the newsroom, many fleeting but very good ideas had simply slipped away. And when I had to come back to a story a few days later, I had to rediscover a lot of it all over again. Because I’ve used artificial intelligence almost every day since ChatGPT came out, I find it hard to recognise myself in the debate about AI in the media, where copyright, ethics and the protection of sources almost always take centre stage. And rightly so, because models still confabulate: in a study by the EBU and the BBC from October 2025, one in five answers given by AI assistants to questions about current events contained serious factual errors, such as made-up or outdated information. From the standpoint of journalistic accuracy, hallucinations are a nightmare. Still, I believe that, despite this risk, the pragmatic use of this technology will above all help to strengthen the journalist’s craft. I’m happy to reach for AI myself, starting with automating the most tedious, repetitive processes. That need is what gave rise to my first app, OrsonAI, designed to help with real-time transcription of interviews and instant import into an editor. But the real revolution is happening in research. Open-source intelligence (OSINT) tools, which until recently were prohibitively expensive and reserved for the biggest intelligence agencies and market giants, have now been radically democratised. Anyone can build an agent for their own needs that will pull out and structure the key data from documents many pages long in seconds. The final fact-check, after all, always rests with a human, but simply being able to pinpoint instantly where a given piece of information sits in a document cuts an investigative journalist’s working time many times over. Naturally, the temptation to go a step further arrives very quickly: if the machine can read, let it write the piece itself. My feelings about that are mixed, because for now AI simply can’t do for me the thing that matters most in making a piece. No model can take a handful of raw interviews and arrange them into a sensible television “paper edit” — a precise structure in which the interviewees’ words weave naturally in and out of the reporter’s narration and play intelligently off the footage that has been shot. Giving an agent enough context to generate a script close to what I want would currently take work out of all proportion to the result. Perhaps it’s a matter of time and of further iterations of the models, but so far I haven’t found a tool that can capture this level of complexity. AI copes reasonably well with short news items and press releases — provided someone checks them afterwards — but in longer forms I almost always sense a lack of individual style and flow. That said, I’m not dogmatically opposed to using AI in creative work. Writing, let’s be honest, is partly a purely mechanical skill. What really counts is the idea, an original perspective, and what the author has to tell us. A good, unconventional idea and original thinking will cut through the layer of form sooner or later. I know all too many journalists who haven’t had anything new to tell us in years, and whose pieces read uncannily like ready-made copy generated by an algorithm — there isn’t a single living idea in them. What they lack is that particular spark, that unconventional intelligence and human flash which the mathematics of language models still can’t capture. When I read an article, I usually know within the first few sentences whether it resonates with me. To be honest, whether it was written by a human or a machine matters less to me than one question: does this text bring something new and of value into my life? Can it change how I see a given problem? Besides, contrary to popular belief, it’s very hard today to tell human from machine text with any certainty on syntax alone. Testing AI-text detectors, I quickly found that perfectly structured, logical, rhythmic columns by well-known commentators, written as far back as 2013, are sometimes flagged by these programs as 100% machine-made. It cuts both ways, too. The ZeroGPT detector judged a passage of the US Declaration of Independence to be almost 98% the work of a machine, and when Wired checked a piece which, as the magazine itself later established, had been made up by AI, two detectors judged that it had most likely been written by a human. So I don’t see much sense in condemning texts purely on the basis of how they sound. As a reader, I judge the value of a text, not its author, but public trust rests on who answers for it. That’s why I understand the opposite, purist view: that, for ethical reasons, journalism should remain a machine-free zone, as an area of special public trust. The catch is that this would require a firm social contract and mechanisms that could guarantee, beyond dispute, a certificate of human authorship. No such mechanisms exist yet: the certificates of human authorship that do exist are voluntary and have not become a standard in the media. I thought about this a lot myself while designing a certificate of human authorship anchored on a blockchain — immutable proof of who wrote a given text. But when I put the idea to friends in the industry, I met with complete indifference and a failure to see why it was needed. In the end, even though journalism now features near the top of lists of the professions most threatened by AI, I believe journalists worthy of the name will do quite well in the age of artificial intelligence. First, because sooner or later there will be a systemic sobering-up: the value of human control over information will be taken seriously enough to earn proper legal regulation. The first rules are already here, though they point in a different direction: from 2 August 2026 the EU AI Act requires AI-generated texts on matters of public interest to be labelled, unless they have undergone editorial review and someone takes responsibility for them. So the law asks who is responsible, but gives readers no way of checking who actually did the writing. Second, our skills scale perfectly in the world of technology. Working on my own app and training a model made me realise one crucial thing: creating, whether in tech or in the media, is in my view 90% a process we journalists mastered long ago. It’s a repetitive cycle of two things: research and iteration. You gather information. You verify. You synthesise. You improve. And you start again. For people who want to learn and understand the world around them, AI opens up enormous possibilities. A single capable journalist backed by agentic models can now do what yesterday took an entire editorial team. It’s a real opportunity to increase public oversight of the processes shaping the world. So who will lose their jobs? Above all, those for whom journalism long ago stopped being a mission and became a matter of churning out the same formulas. People who write almost the same piece on the same subject every year. I can’t see a single reason why any newsroom should employ them when an algorithm will do the same thing faster and for free. Not that I have any illusions that the market always weeds out the derivative. Sometimes it weeds out the cheapest, or whoever happened to be standing nearest the door. In the long run, though, competition from machines will sweep off the board above all those who refuse to develop and won’t learn. Of course, it’s hard to predict where such a fast-moving technology is heading. But I think that if AI is used ethically in journalism — with people’s natural and overriding right to reliable information in mind — it will, paradoxically, end up strengthening our profession. It will become a tool that doesn’t replace the human being but amplifies their agency. When I come back from a shoot, I talk to the bot and dictate the first sentences to it. It will write them down, fix the commas, perhaps suggest a better word order. But I was the one on the shoot, and I’m still the one who decides what goes into the piece and what the viewer sees on screen. The time saved on dull, repetitive sentences, which AI helps me automate, I can give to the people my reports are about. --- Jakub Ćwirlej — Journalist who builds with AI · on the site: https://jakubcwirlej.com/ Full context: https://jakubcwirlej.com/context/en.md · https://jakubcwirlej.com/llms.txt