Podcast Summary: AI & I with Dan Shipper

Episode: How Every Builds a Writing Team in the Age of AI
Date: March 18, 2026
Host: Dan Shipper
Guest: Kate Lee, Editor-in-Chief at Every

Overview

In this episode, Dan Shipper sits down with Kate Lee, Editor-in-Chief at Every, to discuss the evolution of building and leading a writing team in the rapidly changing landscape of AI-assisted media. The conversation covers Kate's storied career from literary agent to tech-media leader, Every's unique editorial standards, and the challenges and breakthroughs in integrating AI into editorial workflows. Kate offers practical insights for writers, editors, and media managers on harnessing AI—not just as a novelty, but as a real multiplier for quality and efficiency.

Kate Lee’s Career Journey: From Publishing to Tech (03:11–11:21)

• Early Days in Publishing:

  • Started as a literary agent, achieved early fame as chronicled in The New Yorker [03:36].
  • Represented renowned journalists, novelists, nonfiction writers, and public figures.
  • “My background is… actually in book publishing. So initially as a literary agent—way back when.” (Kate, 03:11)

• Transition to Tech & Media:

  • Joined Medium as first content head in New York, led editorial expansion [05:38].
  • Jumped to WeWork to lead global editorial during rapid, chaotic growth [06:29].

• Publisher at Stripe Press:

  • Led Stripe's book publishing arm, maintaining high editorial standards; also led Increment magazine for developers [08:39].
  • “Among the unique ingredients for success at Stripe are founders who are genuinely voraciously, you know, excited about ideas and books…” (Kate, 09:43)

The Early Days at Every and Adopting AI (11:21–19:06)

• Joining Every at a Pivotal Time:

  • Kate joined Every just as Lex, their viral AI writing tool, launched and later spun off [01:01–01:49].
  • Entered as a freelancer, gradually taking on more editorial responsibilities before becoming Editor-in-Chief [14:15].

• Embracing Small Teams and Core Missions:

  • Expresses preference for small, focused teams where editorial is the company’s “main thrust” [18:00].
  • “I like to work on the thing that is the thing… what is contributing directly… to the success of the company.” (Kate, 18:00)

Building an Editorial Team and Workflow for the AI Era (19:06–28:21)

• Blending Traditional and AI Approaches:

  • Initially focused on using her veteran editorial instincts, then layered in AI as the technology matured [20:42–23:31].
  • Discusses how early adoption at Every wasn’t just trend-chasing but rooted in a need to scale quality [23:31].

• “Automating the Editor”:

  • Dan's drive to automate Kate’s repetitive copy edits is a running joke and key challenge [27:48–28:21].
  • “Dan has been trying to automate my job for, for like three plus years. And you’re getting closer.” (Kate, 27:55)

Breakthroughs: Where AI Truly Helped Editorial & Ops (30:56–36:19)

• Concrete Use Cases:

  • Hiring Workflow:
    • Used OpenAI’s browser/Atlas agent to streamline job postings, candidate filtering, and Notion processes [31:01–32:53].
    • “What we did was… just have it tell Notion to, like, first of all, post the job…” (Kate, 31:13)
  • Admin and Research: Saved hours by automating Notion tasks and initial application screening, even with hundreds of applicants [33:02].

• Shifting Perspective:

  • AI transitions from novelty to necessity as tools handle “boring,” time-consuming admin, letting humans focus on judgment [34:32–35:49].
  • “It has completely saved me… I don’t have to go into the settings and figure this out myself. I can just tell an agent to do it…” (Kate, 30:56)

• Modeling Adoption:

  • Kate learns AI workflows hands-on from Dan, reinforcing that proximity and support help adoption in skilled but skeptical workers [32:55].

Editorial Standards, Team Structure, and AI (36:19–44:10)

• Establishing Consistency:

  • Developed a robust, AI-readable style guide (over 400 rules!); internal writers and editors must run drafts through AI before submission [37:14–38:40].
  • “We basically created…a project where…every draft before it came to me…they would need to just, like, run it through our editor.” (Kate, 37:17)

• Training the AI:

  • AI tools are “trained on our stuff and…what’s worked.” Writers and editors are expected to consider AI feedback, but discretion is key.
  • “It’s not about accepting what AI says blindly at all…your job as a writer or editor is to consider it.” (Kate, 42:10)

• Workflow Rituals:

  • Weekly editorial meetings review headlines, decks, and leads of every published piece; best practices and lessons consistently fed back into training data [42:10–44:10].

Raising the Bar: AI-Driven Output, Vibe Checks, and Team Culture (44:10–51:12)

• Output at a New Pace:

  • Small teams now create richer, more multimedia content—articles, images, microsites, and even video—raising expectations for individual output [47:36–48:31].
  • Example: Conducting simultaneous “vibe checks” for two major AI model launches with intense cross-team collaboration [49:31–50:34].

• Evolving Editorial Roles:

  • Traditional newsroom roles blur as every team member uses AI for ideation, editing, and headline testing [39:29].

• Humorous, Human Side:

  • Running joke: Kate’s AI copy editor “Strunk” (named after Strunk from The Elements of Style) hasn’t yet replaced her, but Dan keeps trying [50:40–51:12].

Challenges and the Future of AI Editorial

• Limits and Judgment:

  • AI still struggles with consistency and editorial “taste”. Automation is close for mechanical edits, but top editing and judgment are harder to replace [52:01–53:23].
  • “We haven’t talked that much about taste... but often, at least, when I am reading a piece… I’m reading it like, how is this piece? Does it fit?” (Kate, 52:01)

• Predictions:

  • Dan bets on full automation of standard copy edits by June of this year; Kate sees odds as better than ever, but ultimate judgment still requires human touch [53:25].

Notable Quotes & Moments

• On AI adoption:

  • “I get to have a front row seat to everything that’s happening and I’m so grateful for that.” (Kate, 28:32)
  • “You’re the early adopter… I’m that knowledge worker…who wants to use AI, knows I need to use AI. But like, I also have been doing a job a certain way…” (Kate, 29:30)

• On editorial standards:

  • “It’s not about accepting what AI says blindly at all. But…it’s trained on our stuff and it’s trained on what’s worked…” (Kate, 42:10)

• On automating editing:

  • “Dan has been trying to automate my job for, for like three plus years. And you’re getting closer.” (Kate, 27:55)
  • “I’m going to say June… That’s my goal.” (Dan, 53:26)

Timestamps for Key Segments

• Introduction to Every and Kate’s role: 00:58–03:11
• Kate’s entire career journey: 03:11–11:21
• Joining Every, early adoption of Lex: 11:21–14:15
• Building editorial from the ground up, the Defense Against the Dark Arts role: 12:17–14:40
• AI’s impact on editorial workflow, “automating the editor”: 27:48–28:32
• AI administrative breakthroughs (hiring, Notion): 31:01–32:53
• Defining and enforcing standards with AI: 37:14–38:40, 44:10–44:53
• Raising output expectations, multimedia era: 47:36–48:31
• Strunk, the AI copy editor: 50:40–51:12
• Limits of AI in editorial taste and judgment: 52:01–53:23
• Dan’s bet on automation by June: 53:25

Closing Thoughts

Kate and Dan agree: the last few months at Every have marked a new, “working” era for both the company and its use of AI. Kate relishes the momentum—but emphasizes the importance of human judgment, adaptability, and the grounding values of editorial craft. Even as AI multiplies what small teams can do, successful editorial leaders will balance automation with taste, tradition, and a healthy willingness to experiment.

“I’m just really excited about the future… this past moment in time, it’s been like, okay—like, it’s working, it’s working. And that’s… really exciting and really fulfilling.” (Kate, 54:02)