We Automated Everything With AI and Tripled Our Headcount (41 min)

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• Release date: 2026-05-27
• Listen on Spotify: Open episode
• Episode description:

  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/subscribeFollow him on X: https://twitter.com/danshipperLinks to resources mentioned in the episode:“After Automation” by Dan Shipper: https://every.to/chain-of-thought/after-automationBrandon Gell on Every: https://every.to/@brandon_5263Join the membership for where you live at joinbilt.com/danTimestamps:00:00:51 Introduction00:05:51 The AI paradox: more automation, more human work00:10:00 How AI makes yesterday's expert competence cheap00:18:00 AI can act autonomously but it does not have agency00:20:39 Why Dan is all in on AGI00:21:57 AI layoffs are a lie00:25:42 Ride the models and you'll be fine00:35:30 How to use AI as a long-form features editor

Summary

• 🤖 Automation creates more human work: AI makes expert competence cheap, flooding the zone with near-perfect outputs that require human experts to refine and shepherd, paradoxically increasing demand for human labor.
• 🧑‍💻 Humans remain essential for direction: Even advanced AI lacks self-motivated agency; it always looks back to humans for what to do next, keeping people in the loop for setting goals and making value judgments.
• 📉 Skepticism toward AI-driven layoffs: Many layoffs blamed on AI are actually due to poor management or strategic shifts; the narrative is often self-serving and oversimplifies the complex impact of AI on jobs.
• 🔄 Recursive change requires human adaptation: AI rapidly alters what is valuable, forcing humans to continuously update priorities and decide what matters, a role that cannot be automated away.
• 🚀 Riding the models leads to opportunity: Those who actively learn and use new AI models will find more fulfilling work and greater ability to lead ambitious lives, as AI amplifies human potential rather than replacing it.

Insights

• Why does more automation actually lead to more human work?
  • Time: 0:00 – 10:55
  • Answer: The podcast explains that AI makes yesterday’s expert competence cheap, flooding the zone with close-but-not-quite-right outputs. This increases demand for experts to shepherd, refine, and build systems around that work, paradoxically creating more human tasks.
• What is the ‘sandwich’ of AI work and why does it keep humans essential?
  • Time: 5:28 – 9:35
  • Answer: The speakers describe a workflow where AI produces a draft, a human expert refines it, and then the AI executes again. This sandwich structure means humans are needed to set direction and ensure quality, preventing full automation.
• How does the ‘tide rising’ metaphor explain the changing value of expertise?
• What does the ‘Achilles and the tortoise’ paradox reveal about AI’s limitations?
• Why is the ‘agent’ label misleading when it comes to AI autonomy?
• Why might customer service automation fail even when technically feasible?
• How does the ‘recursive’ nature of AI change what matters for humans?
• Why should we be skeptical of CEOs who blame AI for mass layoffs?
• What does the ‘last job you’ll ever have’ idea suggest about future compensation?
• How can writers use AI to articulate inarticulable ideas?

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