AI & I

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

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.comTimestamps 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: 00:40:54Links to resources mentioned in the episode:Mike Krieger: https://x.com/mikeyk Agent-native architecture: https://every.to/guides/agent-native

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 requiresLinks to resources mentioned in the episode:Proof: https://www.proofeditor.ai/

Every has unveiled a new product, built by CEO Dan Shipper. It's called Proof, a free, open-source, live collaborative document editor built for humans and AI agents to work in together. Proof started as a Mac app designed to show the provenance of AI-written text—purple for AI, green for human. But when Shipper rebuilt it as a web app with real-time collaboration, something clicked. Suddenly, everyone at Every was using it for everything from planning docs, to creative writing and even daily to-do lists. The team realized they needed a lightweight space where their OpenClaw agents and humans could co-author documents and leave comments. In this special episode, Shipper is joined by Every chief operating officer Brandon Gell, Cora general manager Kieran Klaassen, and head of growth Austin Tedesco to demo Proof live and share how it's changed the way they work. Brandon walks through a loop where his Codex agent writes a plan, Dan's personal Claw R2-C2 reviews it, and the humans just steer. Austin explains how he uses Proof to write a weekly food newsletter, texting ideas to his Claw on runs and watching an outline take shape. And Kieran makes the case that Proof's power is its lightness—just a link you can hand to any agent or colleague.The conversation covers what "agent native" means in practice, why AX (agent experience) matters as much as UX (user experience), what happens when 10 agents edit one document at the same time, and why some writing is now better read by an AI than a human.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperGet started building today at http://framer.com/dan for 30% OFF a Framer Pro annual plan.Download Grammarly for free at Grammarly.com Timestamps 00:02:00 — Introduction and the origin story of Proof00:07:24 — From Mac app to collaborative web editor00:09:00 — What makes Proof “agent native”00:14:30 — Live demo: watching an agent join and write inside a shared document00:20:51 — How Austin uses Proof for creative writing and food journalism00:24:30 — The challenge of multiple agents editing one document simultaneously00:26:48 — When AI-written docs are better read by agents than by humans00:29:30 — Brandon’s agent-to-agent collaboration loop00:37:09 — Proof as a lightweight scratchpad vs. existing tools like Notion and GitHub00:42:18 — Why Proof is open source and what that means for buildersLinks to resources mentioned in the episode:Proof Editor:⁠ https://proofeditor.ai⁠Proof GitHub repo (open source):⁠ https://github.com/EveryInc/proof⁠Every's compound engineering plugin:⁠ https://github.com/EveryInc/compound-engineering-plugin⁠

Silicon Valley loves billion-dollar moonshots and AI darlings. Sam Gerstenzang and Dan Friedman are doing something different—they're starting medical spas and funeral homes.On this episode of AI & I, Dan Shipper sat down with Gerstenzang and Friedman, partners at Boulton and Watt, which they call the "world's slowest startup incubator." Their model: Come up with an idea, achieve five or 10 million dollars in revenue themselves, then hand it off to a CEO who can take it to the next stage. They've used this playbook to build Moxie, a Series C company that helps nurses open their own medical spas, now with 600-plus customers and a 200-person team globally. Their second company, Meadow Memorials, is a contemporary funeral home with no physical real estate. It has become the largest provider of funeral services in California.Both businesses launched right around the arrival of ChatGPT—and neither was built with AI in mind. So how are they thinking about AI inside companies where the core work isn't going to change? In this conversation, Gerstenzang and Friedman share how they built an AI agent called Matthew Bolton to power their customer discovery process, why synthetic customer calls completely failed for them, and why they believe you shouldn't give anyone credit for using AI.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperIntent is what comes after your IDE. Try it yourself: augmentcode.com/intentHead to granola.ai/every to get 3 months free.Ready to build a site that looks hand-coded—without hiring a developer? Launch your site for free at www.Framer.com, and use code DAN to get your first month of Pro on the house.Timestamps00:00:00 — Introduction and how Sam and Dan's paths first crossed00:01:40 — What it means to be “the world's slowest incubator”00:04:50 — Why Bolton and Watt runs companies to several million in revenue before handing off to a CEO00:07:30 — How specialization across the founding journey creates advantages00:10:40 — Building AI-durable businesses versus AI-native ones00:16:10 — How an AI agent transformed their customer discovery process00:19:30 — Where synthetic customer calls completely fail00:29:30 — Deploying AI inside established companies00:32:30 — Why newer projects see huge gains from AI while mature companies see 10 percent00:37:00 — A preview of what's next for Bolton and Watt