Editing AI Writing
 Editing AI WritingPlus a new episode of the AI & I podcast with Every editor in chief Kate Lee.png) Was this newsletter forwarded to you? Sign up to get it in your inbox. 'AI & I': How Every builds a writing team in the age of AIToday, we're releasing a new episode of our podcast AI & I. Dan Shipper sits down with Every's editor in chief, Kate Lee, to discuss how she views AI as an editorial leader and how she uses it daily. Kate's career has spanned a stint as a New Yorker-featured literary agent to roles at Medium, WeWork, and Stripe. Contrary to his "early adopter" persona, Dan classifies Kate as a "pragmatic knowledge worker," someone open to AI, but who isn't going to immediately change her workflow unless a tool makes her life better. Watch on X or YouTube, or listen on Spotify or Apple Podcasts to learn what tools have convinced Kate. You can also read the transcript. Here are the highlights:
Miss an episode? Catch up on Dan's recent conversations with LinkedIn cofounder Reid Hoffman; the team that built Claude Code, Cat Wu and Boris Cherny; Vercel cofounder Guillermo Rauch; podcaster Dwarkesh Patel; and others, and learn how they use AI to think, create, and relate. The machine translation problemA friend recently translated a healthcare app into French. "It was the most painful work I've ever done," she told me. Instead of asking for a full translation, the app's creator gave her a machine-translated version to correct, arguing that it would cost less money. Given how poor the writing was, it would have been far quicker—and better—to translate the app from scratch. I've felt the same way when I edit writing clearly generated with AI. Just like my translator friend, I often feel as if it would be easier to write it from scratch rather than trying to save it with an edit. Besides the tells (staccato, lists of three), AI-generated text is flimsy. Poke it just a bit—what do you really mean?—and it falls down. It is the opposite of what I call "bulletproof writing," a style that was drilled into me as a financial reporter at the Wall Street Journal. Each word printed was scrutinized by tough editors and even tougher readers—so they had to be intentional. This could be avoided, or at least mitigated. Our staff writer Katie Parrott recently shared her process for using AI to write, and the most striking thing was how much work she does before drafting. She fed Claude examples of her writing, had it interview her about her preferences, and produced a style guide that lives inside a dedicated project. She treats the whole thing like a bonsai garden—she prunes old examples, adds new ones, and reruns the analysis. With this upfront investment, Claude has her DNA when she sits down to write. As someone who often edits Katie, I can tell the difference. The writing feels like her. Kate, our editor in chief, has also codified Every's style guide in a Claude project that everyone can use, as she talks about in this week's podcast. Reading matters, too. It teaches you what good writing is—something Katie also believes. So before you summarize an article with ChatGPT, think again. What do you miss when you skip the actual text? Study the structure, the argument. Steal it. Writing is still hard. Don't let AI make you think it's easy.—Eleanor Warnock Log onWe host camps and workshops on topics like compound engineering and writing with AI to share the knowledge we've acquired from training teams at companies like the New York Times and leading hedge funds, and by learning and playing with AI every day ourselves. Upcoming camps
Recordings you may have missed
Straight from Slack If you needed another reminder of the power of model context protocol (MCP), here it is. We've always had the product analytics platform PostHog installed, but linking it to AI like Claude through an MCP has given the team even deeper insights on which to base product decisions by asking simple questions in plain English. That data sourcing and interrogation would previously have taken hours. For example, PostHog data showed us that 33 percent of buyers of the lifetime plan of our file organization software, Sparkle—which costs $279—come through ChatGPT in the last 30 days. (The monthly plan is $15.) The PostHog data fed through the MCP also helped the team notice that existing customers were pausing lifetime plans and then restarting them, suggesting that they were researching and considering the product. "The PostHog MCP data flow is now effortless. It helps us to make decisions and cross-check them before we take them live," says Sparkle general manager Yash Poojary. "You now have a top-tier product manager available to you. You just need to know which question to ask them." The new version of Sparkle launches on April 14 and will allow users to customize and create their own folder structure. Existing users will be upgraded automatically. The latest version is available already. To read more essays like this, subscribe to Every, and follow us on X at @every and on LinkedIn. We build AI tools for readers like you. Write brilliantly with Spiral. Organize files automatically with Sparkle. Deliver yourself from email with Cora. Dictate effortlessly with Monologue. We also do AI training, adoption, and innovation for companies. Work with us to bring AI into your organization. For sponsorship opportunities, reach out to sponsorships@every.to. What did you think of this post?Get More Out Of Your SubscriptionTry our AI tools for ultimate productivity  Front-row access to the future of AI  In-depth reviews of new models on release day Playbooks and guides for putting AI to work Prompts and use cases for builders  Bundle of AI software  Sparkle: Organize your Mac with AI  Cora: The most human way to do email  Spiral: Repurpose your content endlessly  Monologue: Effortless voice dictation for your Mac |
