Kate Lee on Taste, Hiring, and Running Editorial at Every (57 min)
ai-collaborations-with-creators ai-driven-innovation-economy ai-in-art-music-creation ai-in-everyday-life ai-in-pop-culture-media ai-in-workforce-disruption ai-literacy-public-awareness
- Release date: 2026-03-18
- Listen on Spotify: Open episode
- Episode description:
Kate Lee has spent her career working with words—first as a literary agent, then in roles at Medium, WeWork, and Stripe. As Every’s editor in chief, she’s been the quiet force behind the newsletter for more than three years.Lately, something has shifted in Kate’s work. After years of watching her colleague Dan Shipper evangelize AI from the front lines, Katie has started rewiring how she works and is integrating more and more AI tools into her workflow.We had Kate on to talk about her career path from book deals to tech startups, what it really means to run a newsletter as a small team in the age of AI, and what she thinks the bottleneck to automating copyediting is. Plus: the story of pulling off reviews of two major model releases in 24 hours, and how she’s using her AI-powered browser to help her hire.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps0:01 – Introduction and Kate's early career as a literary agent4:45 – From book publishing to tech: Medium, WeWork, and Stripe Press12:00 – How Kate joined Every and what made the role click27:00 – What it's like to be a knowledge worker at the frontier of AI31:00 – The “aha” moment: using AI to manage hundreds of applicants36:24 – How Every's editorial team uses AI to enforce standards and train taste45:06 – Publishing two reviews of major model releases on the same day51:39 – What automating copy editing requiresLinks to resources mentioned in the episode:Proof: https://www.proofeditor.ai/
Summary
- 📝 Custom AI Editors: Trained on style guides and successes, AI lifts editorial floors for consistency across teams, reducing manual fixes.
- 🤖 Agentic Ops Boost: AI browsers automate Notion hiring and settings, freeing knowledge workers from tedious admin.
- 💡 Aha Moments Drive Adoption: Practical wins in ops and reliability shift skeptics to AI enthusiasts.
- 🔄 Feedback Loops Train AI: Weekly reviews of hits/misses refine custom tools for tailored content suggestions.
- 🚀 Small Team Superpowers: AI enables rapid, multimedia breaking coverage rivaling big newsrooms.
Insights
Why do savvy knowledge workers adopt AI only after practical ‘aha’ moments?
Time: 29:40 – 30:46
Category: AI Literacy & Public Awareness, AI in Workforce DisruptionAnswer: Despite 3+ years of exposure, Kate resisted full AI integration for editing until recent agentic tools handled admin like Notion setups seamlessly, proving they saved time without creating more work. This reflects a shift from skepticism to enthusiasm when AI matches human reliability in real workflows. (Start at 29:40)
What administrative tasks can AI agents automate to free up knowledge workers?
Time: 31:01 – 34:27
Category: AI in Everyday Life, AI-Driven Innovation EconomyAnswer: Kate used AI browsers like Atlas to manage Notion for job postings, applicant filtering, and evaluation against criteria, saving hours on manual sifting during hiring without an HR team. This enabled parallel handling of core duties, highlighting AI’s role in operational efficiency for startups. (Start at 31:01)
How can AI enforce consistent editorial standards across diverse writers and editors?
Time: 36:37 – 38:32
Category: AI in Everyday Life, AI in Workforce DisruptionAnswer: Kate implemented a custom AI editor trained on a 400-rule style guide and past successful content, requiring writers to run drafts through it before final review, lifting the baseline quality and reducing variability from freelancers. This approach ensures non-generic, company-specific feedback that writers must consider, acting as a consistent first-pass editor. (Start at 36:37)
What new skills define editorial leadership in AI-augmented newsrooms?
Time: 39:03 – 42:11
Category: AI in Workforce Disruption, AI-Driven Innovation EconomyAnswer: Modern editors must articulate standards for AI training, structure style guides for model ingestion, enforce tool usage, and iterate via trial-and-error while fostering team-wide AI literacy. This enables small teams to produce daily high-quality newsletters with interactive elements like vibe checks. (Start at 39:03)
How do small media teams train AI on proprietary data for better content creation?
Time: 42:24 – 44:08
Category: AI Collaborations with Creators, AI in Art & Music CreationAnswer: Every’s team feeds weekly performance feedback on headlines, decks, and leads into Claude projects, creating tailored suggestions based on what works, not generic advice. Writers and editors must engage with these recommendations, treating AI as a trained collaborator to elevate output. (Start at 42:24)
How is AI enabling small teams to rival larger media operations?
Time: 48:31 – 50:30
Category: AI-Driven Innovation Economy, AI in Pop Culture & MediaAnswer: Recent model advances allow Every’s tiny team to sprint on breaking ‘vibe checks’ for dual model releases, creating interactive sites in 24 hours via AI-summarized Discord, vibe-coding, and cross-functional input. This step-change boosts output, multimedia, and speed without proportional headcount. (Start at 48:31)