Show Notes
Dan Shipper runs one of the most AI-native companies today. Every has agents embedded in nearly every workflow—“if you swing a stick in our Slack, you're as likely to hit a human as an agent,” he says. And yet the company has grown from four people to 30 since GPT-3 came out, and is still hiring. Why does Dan believe there's more human work to do than ever? In a format flip for AI & I, Every's COO Brandon Gell turns the tables and interviews Dan about his latest essay, “After Automation”—an 8,000-word argument for why rising automation doesn't eliminate demand for human work, it increases it. The thesis: AI makes yesterday's expert competence cheap and widely available, which floods every field with output that's close but not quite right—and that creates more demand for the humans who can take it the rest of the way. Dan talked with Brandon about the paradox at the heart of agent-native work: The more AI can do, the more humans are needed to direct it, refine its output, and decide what matters next. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Links to resources mentioned in the episode: “After Automation” by Dan Shipper: https://every.to/chain-of-thought/after-automation Brandon Gell on Every: https://every.to/@brandon_5263 Join the membership for where you live at joinbilt.com/dan Timestamps: 00:00:51 Introduction 00:05:51 The AI paradox: more automation, more human work 00:10:00 How AI makes yesterday's expert competence cheap 00:18:00 AI can act autonomously but it does not have agency 00:20:39 Why Dan is all in on AGI 00:21:57 AI layoffs are a lie 00:25:42 Ride the models and you'll be fine 00:35:30 How to use AI as a long-form features editor
More from The Every Podcast
Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio Every time a new AI model drops, companies face the same dilemma: Figure out what’s new and how to use it, or focus on getting work done. Every solves this dilemma with a frontier team: a small group that tests new models and turns useful experiments into tools and workflows for the rest of the company. On this week’s episode of The Every Podcast, Natalia Quintero sits down with all seven members of Every’s frontier team to ask what they’re building—from a tabless browser that helps protect your attention to a personal agent orchestrator called Minga—and what they want to try next. Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode Links to resources mentioned in the episode: Follow Every: https://x.com/every Compound Writing plugin: https://every.to/guides/compound-writing Every’s vibe checks: https://every.to/vibe-check Follow Dan Shipper: https://x.com/danshipper Follow Natalia Quintero: https://x.com/NataliaZarina Follow Mike Taylor: https://x.com/hammer_mt Follow Jack Cheng: https://x.com/jackcheng Follow Arielle Shipper: https://x.com/ArielleShipper Follow Katie Parrott: https://x.com/kplikethebird Follow Jannik Jung: https://x.com/JannikJung 0:00 Introduction: Why every company needs a frontier team 2:42 Why the best frontier teams have pirates and architects 4:57 How Head of Evals Mike Taylor builds personal benchmarks 6:46 How Opus edited an explainer video using Slack messages and meeting notes 10:20 Working with AI means protecting other people’s attention 12:26 Why a frontier team needs a “wrangler” like Arielle Shipper 18:58 Why senior editor Jack Cheng built a tabless browser 24:37 How resident writer Katie Parrott teaches her AI writing plugin who the reader is 30:51 Inside engineer Jannik Jung’s personal AI orchestrator 35:41 Compound engineer Kieran Klaassen uses AI to build power tools
Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio Bet on founders who understand the technology, because their first idea probably won’t be the one that sticks. That’s the view of Matt Hartman, founder and managing partner of Factorial Capital. In 2016, as a partner at Betaworks, Hartman was part of the team that wrote the first check into Hugging Face, which was then a chatbot app for teenagers. Today it’s the largest hub for open-source AI, and Nvidia has agreed to buy it for about $13 billion. On this week’s episode of The Every Podcast, Hartman tells Natalia Quintero how backing Hugging Face shaped his investment thesis, how Factorial grew into a “family tree” of technical founders who help him scout deals, and why vibe-coding his own app gave him a new respect for SaaS. Subscribe to Every (free for 14 days): https://every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=matthartmanepisode Links to resources mentioned in the episode: Factorial Capital: https://factorialcap.com Ghost Pepper, Matt's open-source voice-to-text app: https://github.com/matthartman/ghost-pepper Superpowers, Jesse Vincent's skill for Claude Code: https://github.com/obra/superpowers Hugging Face: https://huggingface.co Follow Every: https://x.com/every Follow Natalia Quintero: https://x.com/NataliaZarina Follow Matt Hartman: https://x.com/MattHartman 0:00 Introduction 5:06 How Hugging Face went from a teen chatbot to the home of open-source AI 9:52 The three decisions that set Hugging Face on its path 12:49 Hartman’s approach to finding the newly technically possible 18:10 Why Hartman backs technical founders 21:10 How Factorial’s portfolio founders source its next deals 27:09 Why faster iteration makes product insight the most valuable skill 34:28 What Hartman learned building Ghost Pepper, an open-source voice-to-text app 40:02 Hartman’s current theses, from non-language models to full-stack AI apps 44:14 Why the best founders right now are experimenting
Subscribe to Every (free for 14 days): every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode On this week's episode of The Every Podcast, Dan Shipper sits down with Willie Williams, Every's head of platform and one of the driving forces behind the Every Agent, which launched this week. They discuss how Every went from everyone running their own OpenClaw to relying on one shared company agent—and where they think personal agents belong in everyday life. Links to resources mentioned in the episode: Willie Williams on X: https://x.com/bigwilliestyle The Every Agent: https://every.to/agent?utm_source=podcast&utm_campaign=podcast&utm_content=1006companyagent OpenClaw: https://openclaw.ai Tend, Dan's open-source feed app: https://every.to/tend?utm_source=podcast&utm_campaign=podcast&utm_content=1006companyagent Every’s Vibe Check on Dots: https://every.to/vibe-check/vibe-check-dots-always-on-agents-in-chatgpt?utm_source=podcast&utm_campaign=podcast&utm_content=1006companyagent Dot from OpenAI: https://openai.com/index/introducing-dots/ 0:00 Introduction 5:58 How OpenClaw spread through Every 11:13 Why everyone's personal agents eventually died 14:51 The difference between building for builders and building for operators 16:20 Why work will run on company agents while personal agents will stay personal 19:56 How engineering management has changed in the age of AI 21:43 Using an AI-powered feed to see everything happening across a product launch 23:53 Why Willie's token usage fell off a cliff 29:05 The benefit of having separate AI coaches for each part of your life 31:45 How Every is building its agent to surface information unprompted
Sam Altman's Dot, OpenAI's new always-on agent, gave him back the most productive part of his day.
Two years ago, Katie Parrott was laid off from a crypto firm and couldn’t afford a career coach—so she turned to a $20-a-month ChatGPT subscription instead. The Every staff writer’s habit of feeding AI good context eventually became compound writing, a codified system for brainstorming, drafting, and editing with AI. Today, that system is a plugin any writer can use. On this week’s AI & I, Natalia Quintero talks with Katie about turning good ingredients into good writing, borrowing the taste of writers she admires, and why AI helped her fall back in love with the page. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Natalia Quintero: Subscribe to Every: https://every.to/subscribe Follow her on X: @NataliaZarina Go to https://attio.com/every and get 15% off your first year. Timestamps: 0:00 Start 0:49 Introduction 1:21 How Katie went from being laid off to using ChatGPT as a career coach 8:06 Turning AI into a “content agency of one” 10:59 Building the context files and style guides that make AI outputs useful 14:15 What makes a Claude project actually work 21:18 How AI helped with a mental health crisis and “computer errands” 27:03 Katie’s career coach, now a fully autonomous Codex project 32:27 What compounding means, and building the Compound Writing plugin 38:00 Borrowing Vonnegut, Hitchcock, and Sorkin as AI editing skills 44:49 Katie’s thesis: education and access will matter more than ever Links to resources mentioned in the episode: Katie Parrott on X: @kplikethebird Compound writing plugin: https://github.com/EveryInc/compound-writing Compound engineering plugin (Kieran Klassen): github.com/EveryInc/compound-engineering-plugin Parrott’s companion piece on compound writing: https://every.to/guides/compound-writing Parrott, “I Hired ChatGPT as My Career Coach”: every.to/working-overtime/i-hired-chatgpt-as-my-career-coach Parrott, “AI Turned Me Into a Content Agency of One”: every.to/working-overtime/ai-turned-me-into-a-content-agency-of-one