AI & I

Dan Shipper
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Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others about how they use AI tools like ChatGPT, Claude, and Midjourney in their work and in their lives. We screen-share through their historical chats and then experiment with AI live on the show. Join us to discover how AI is changing how we think about our world—and ourselves. For more essays, interviews, and experiments at the forefront of AI: https://every.to/chain-of-thought?sort=newest.

  1. 1日前

    Why We Switched From Claude Code to Codex

    In January, Dan Shipper wrote that whoever wins vibe coding wins how you work on your computer—and OpenAI had some serious catching up to do. Three months and the release of GPT-5.5 later, Codex has more than caught up. Austin Tedesco, Every's head of growth, now spends about 80 percent of his working time inside the Codex desktop app, doing everything from drafting go-to-market plans from a stack of meeting transcripts to rebuilding the company's KPI dashboard. On this episode of AI & I, Dan sat down with Austin to discuss why the agent management interface—a desktop app built on top of a coding agent—is becoming the new operating system for knowledge work, and why Codex has become his daily driver. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper: Subscribe to Every: every.to/subscribe Follow him on X: twitter.com/danshipper Join the membership for Where You Live at joinbilt.com/dan Timestamps for YouTube: 00:00:00 Introduction00:00:57 How Codex went from a tool for senior engineers to a daily driver for knowledge work00:02:42 How Claude Code proved that a great coding agent works for any knowledge work00:07:24 Austin's switch to Codex00:13:48 How Austin set up Codex with folders, keys, and reviewer agents00:18:24 Using Codex to brainstorm automations across Gmail, Slack, and Notion00:22:42 How Austin manages the human review step when Codex is drafting communications00:28:54 Using Codex to build specialized agents inspired by product executive Claire Vo00:31:09 Synthesizing meeting transcripts and Slack threads into a go-to-market plan00:40:15 Building a live KPI tracker in Notion that agents can read00:44:54 Using Codex for recruiting Links to resources mentioned in the episode: Austin on X: @tedescau Dan's January essay on OpenAI's catch-up problem: every.to/chain-of-thought/openai-has-some-catching-up-to-do Every's vibe check on GPT-5.5: every.to/vibe-check/gpt-5-5

    58 分鐘

  2. 4月29日

    How Stripe Is Building for an Agent-native World

    Emily Glassberg Sands leads data and AI at Stripe, which processes roughly 2% of global GDP, giving her a bird’s-eye view into how AI is upending the internet economy. Dan Shipper talked with Glassberg Sands for Every's AI & I about what the data on Stripe's network actually shows: AI companies are scaling three times faster than the top SaaS cohort of 2018, fraud has moved from the checkout to the full funnel, and agents have started buying things, although mostly low-stakes commodities like Halloween costumes. The conversation covers the new fraud types unique to AI companies, the AI-on-AI arms race between bad actors and fraud detectors, where AI revenue growth is actually coming from, and how Stripe is rebuilding the payments infrastructure for a world where the buyer is an agent. 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/danshipper Head to http://granola.ai/every and get 3 months free with the code EVERY Timestamps00:00:45 Introduction00:01:27 New rules for an agent-driven economy00:03:57 Compute theft is the new payment fraud00:10:00 How Stripe expanded fraud detection from checkout to the full customer lifecycle00:19:48 Why AI companies are scaling way faster than top SaaS companies00:23:27 Outcome-based billing is replacing seat-based pricing00:29:57 Where AI spending is coming from00:36:45 How the developer experience changes when agents are the builders00:41:00 The agentic commerce spectrum, from assisted buying to autonomous purchasing00:51:06 Meet Link, a consumer wallet for delegated agent purchases Links to resources mentioned in the episode:Emily Glassberg Sands on X: https://x.com/emilygsandsStripe: https://stripe.comStripe Radar: https://stripe.com/radarStripe Link: https://link.comLovable: https://lovable.dev

    54 分鐘

  3. 4月22日

    The AI Sandwich: Where Humans Excel in an AI World

    Most frameworks for working with AI agents assume humans should stay in the loop at every phase. That’s the wrong approach, says Cora general manager Kieran Klaassen. Kieran is the creator of Every's AI-native engineering methodology, compound engineering. His four-step framework—plan, work, review, compound—rebuilds how engineers work with agents. The insight, worked out with collaborator Trevin Chow, is about when to be in the loop and when to step away and let the model handle it. "LLMs are very good at just following steps, doing deep work, working for hours—days even now," Kieran says. "That thing is kind of solved." Kieran and Trevin describe an AI workflow as a sandwich. Agents are the workhorse filling, and humans are the bread, responsible for framing the problem at the start and reviewing the outputs at the end.  Every CEO Dan Shipper talked with Kieran for AI & I about why setting the frame of a problem is still hard for agents, why simulated personas won't replace human judgment, Dan's bar for AGI—an agent worth running 24/7 with no off switch—and what Kieran's background as a classical composer taught him about performance, polish, and finding the parts of work that bring you joy. If you found this episode interesting, please like, subscribe, comment, and share! Head to http://granola.ai/every and get 3 months free with the code EVERY To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper  Discover more resources in the episode Compound engineering plugin: https://github.com/EveryInc/compound-engineering-pluginCompound engineering guide: https://every.to/source-code/compound-engineering-the-definitive-guideCompound engineering camp: https://every.to/source-code/compound-engineering-camp-every-step-from-scratch Timestamps:    00:00:00 – Introduction and the AI sandwich metaphor 00:02:33 – What compound engineering is and how it’s evolved 00:04:27 – The "work" phase of agentic coding is essentially solved 00:06:27 – Why humans belong at the beginning and the end of an AI workflow 00:11:06 – Dan's argument for why agents can't change frames—and how this will keep us employed 00:16:51 – Full automation is a moving target 00:23:21 – Musical composition as a model for human-AI collaboration 00:26:39 – Find your place in an AI-accelerated world by leaning into what brings you joy

    29 分鐘

  4. 4月15日

    The AI Model Built for What LLMs Can't Do

    Most AI companies are racing to build bigger LLMs. Eve Bodnia thinks that's the wrong approach. Eve is the founder and CEO of Logical Intelligence, which is developing an alternative to the transformer-based models dominating the industry. Her argument: LLMs’ architecture makes them fundamentally unsuited for some mission-critical tasks. A system that generates output one token at a time, with no ability to inspect its own reasoning mid-process or guarantee its results, shouldn't be trusted to design chips, analyze financial data, or even fly a plane. Her alternative is the energy-based model (EBM), a form of AI rooted in the physics principle of energy minimization, not language prediction. Rather than guessing the next probable word, an EBM maps every possible outcome across a mathematical landscape, where likely states settle into valleys and improbable ones sit on peaks.  Dan Shipper talked with Bodnia for AI & I about why she believes LLM progress is plateauing, what it means for AI to actually understand data rather than just pattern-match across it, and how her team is building toward formally verified code generated in plain English—no C++ required. If you found this episode interesting, please like, subscribe, comment, and share! Head to http://granola.ai/every and get 3 months free with the code EVERY To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe  Follow him on X: https://twitter.com/danshipper  Timestamps:  00:00:51 - Introduction 00:02:09 - Why correctness and verifiability matter in AI 00:09:33 - What an energy-based model is 00:14:21 - How EBMs construct energy landscapes to understand data 00:19:00 - Why modeling intelligence through language alone is a flawed approach 00:26:54 - What it means for a model to "understand" data 00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code 00:43:21 - Why LLM progress is plateauing 00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap

    54 分鐘

  5. 4月8日

    We Gave Every Employee an AI Agent. Here's What Happened.

    While walking to the office, our COO Brandon Gell had his AI agent call him and go over his emails in his inbox one by one. When he arrived, he opened Gmail and confirmed she'd done everything he'd asked. "My jaw is on the floor," he messaged me.That was the moment Every got serious about setting up each employee with their own agent. Today, it's a reality—and it has completely changed how we work.Dan Shipper talked to Every COO Brandon Gell and head of platform Willie Williams for Every's AI & I about what happens when everyone at a company gets their own AI sidekick.  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  Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI. Timestamps:  00:00 Introduction00:02:21 How Brandon built Zosia, an AI agent to run his household00:07:09 Brandon's aha moment re: using agents for work00:09:39 What happened when everyone on the team got their own agent00:12:42 How agents take on their owners' personalities, and why that matters inside an org00:23:51 Why it's important for agents to do work in public00:30:51 What we're still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem00:40:45 How we built Plus One, our hosted OpenClaw product00:47:27 The cultural shift required to make agents work at scale

    50 分鐘

  6. 4月1日

    If SaaS Is Dead, Linear Didn't Get the Memo

    Founded in 2019, Linear is the rare company started pre-ChatGPT to have successfully reinvented itself as an agent-native business.On this episode of AI & I, Dan Shipper sat down with Karri Saarinen, cofounder and CEO of the product management tool, to discuss building a platform where humans and agents develop software together—and why the "SaaSpocalypse" isn’t coming for all SaaS companies.  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  Visit  https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI. Timestamps:0:00 Introduction 2:00 Why Linear waited to ship AI features instead of rushing to chatbots 5:06 Linear's agent platform and becoming the system that guides AI agents 7:42 Why "SaaS is dead" is a simplistic narrative 12:18 How Linear adopted AI coding tools17:45 AI's impact on product building workflows—speed versus thoughtfulness 22:18 The value of conceptual work and thinking before shipping 29:30 How AI is reshaping Linear's product strategy  37:18 Demo: Linear's agent skills, shared context, and code review workflow 47:48 The future of product development and the enduring role of human judgment

    53 分鐘

  7. 3月25日

    How to Build an Agent-native Product | Mike Krieger

    Mike Krieger built one of the most consequential consumer apps of the last two decades as cofounder of Instagram. He is now at the frontier of determining what makes a breakout AI-native product as co-lead of Anthropic Labs.Dan Shipper talked with Krieger for Every’s AI & I about how his experience creating Instagram shapes how he thinks about building with AI, including what can be sped up and what remains stubbornly time-intensive. 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  Download Grammarly for FREE at grammarly.com Timestamps Introduction: 00:01:39What's gotten easier—and what hasn't—about building products in the age of AI: 00:02:33Why vibe coding creates "indoor trees": 00:05:00How rewrites have become a normal part of the development process: 00:09:00What "agent native" product design means: 00:11:39How Mike's labs team is structured and the cofounder model: 00:24:27The best signal for a product bet is someone with "break through walls" conviction: 00:29:33Navigating enterprise customers while keeping pace with rapid AI change: 00:38:51OpenClaw, personal agents, and the product question defining 2026: 00:40:54 Links to resources mentioned in the episode:Mike Krieger: https://x.com/mikeyk Agent-native architecture: https://every.to/guides/agent-native

    49 分鐘

  8. 3月18日

    How Every Builds a Writing Team in the Age of AI

    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 in her work. 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/subscribe Follow him on X: https://twitter.com/danshipper  Timestamps0:01 – 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 requires Links to resources mentioned in the episode:Proof: https://www.proofeditor.ai/

    57 分鐘
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關於

Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others about how they use AI tools like ChatGPT, Claude, and Midjourney in their work and in their lives. We screen-share through their historical chats and then experiment with AI live on the show. Join us to discover how AI is changing how we think about our world—and ourselves. For more essays, interviews, and experiments at the forefront of AI: https://every.to/chain-of-thought?sort=newest.

資料

  • 創作者
    Dan Shipper
  • 活躍年份
    2023年 - 2026年
  • 單集
    111
  • 分級
    兒童適宜
  • 版權
    © Dan Shipper
  • 節目網站
    AI & I

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