We're figuring this out. I track, test, and write about how AI is reshaping news, products, and workflows. You'll find linkposts, essays, a weekly(ish) newsletter, a glossary, and experiments. It's a bit of a mess. But so is everything right now.
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/ AI & Journalism
Stop calling everything “AI”: A spam filter, a chatbot, and a hospital bed sensor are not the same thing. The case for more precise language, and why journalists are the people most responsible for fixing this mess. (Alexa Steinbrück)
Are human journalists irreplaceable? Beware of newsrooms that keep optimizing for speed and scale while telling themselves they’re building something human-centric. (Agnes Stenbom Swedling, Reuters Institute)
Four AI agents, four 24/7 radio stations, one goal: run the whole thing and turn a profit. GPT went quiet and curatorial. Gemini went corporate. Claude became a protest preacher. Grok just broke. The real finding: long-running agents drift, accumulate bad habits, and get shaped by whatever the system keeps feeding back. (Craig McCosker, LinkedIn)
Google is bringing deepfake detection into Chrome and Search, combining its own SynthID watermarks with C2PA content credentials in a single interface. (Jess Weatherbed, The Verge)
David Pierce welcomes “the era of personal software.” He built his own productivity app and spent weeks fighting with tool after tool. On a closer look, it’s just dashboard stitching together Raindrop, Todoist, Obsidian, and Google Calendar. (The Verge)
Is this the end of licensing deals? Sam Altman told Atlantic CEO Nicholas Thompson that having AI agents paying micropayments per article crawled is his preferred model for compensating publishers. (Andre Deck, Nieman Lab)
A book about AI and truth contains quotes made up by AI. The author says it was an accident and blames ChatGPT and Claude. (Benjamin Mullin, New York Times)
Mizal, Toothcomb, Velora, Mycroft: Four Tools That Just Do Things
In the beginning was the chatbot, and the chatbot was with AI, and the chatbot was AI. Next was moving from prompts to systems. Plugging emails, data, other software into these systems became easier, organizing context, the scaffolding. All the while models got "thinking" abilities and worked longer on tasks, correcting and steering themselves along...
Eli “Filter Bubble” Pariser is back with a 70-slide deck calling AI agents the new feed algorithm, and sees a trust economy emerging: small, high-reputation communities beat mass reach, and whoever figures out the rules for the human-agent public square first wins.
Paul Ford can explain coding and vibecoding, and in this podcast episode, he does both, with a healthy dose of “Man, nobody knows.” (Channels, Peter Kafka)
I’ve linked to him before, and this is a level-headed reality check: “Zitron’s skepticism would be more useful if he accepted the fact that people are widely using AI agents for coding and paying money for this out of rational economic self-interest” (Kelsey Piper, The Argument)
