AI & I

Afleveringen

• We Automated Everything With AI and Tripled Our Headcount 27.05.2026 41min

  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
• Inside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 Million 20.05.2026 51min

  If your MCP server has dozens of tools, it's probably built wrong. You need tools that are specific and clear for each use case—but you also can't have too many. This creates an almost impossible tradeoff that most companies don't know how to solve.That's why we interviewed Alex Rattray, the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. We get into MCP and the future of the AI-native internet. [Disclosure: Dan is a small investor in Stainless.]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/danshipperGet started with Braintrust at https://www.braintrust.dev/ Timestamps: 00:01:15 - Introduction 00:05:09 - APIs and MCP, the connectors of the new internet 00:11:00 - Why MCP exists 00:17:15 - Why MCP servers are hard to get right 00:20:24 - Design principles for reliable MCP servers 00:25:06 - Using MCP for business ops at Stainless 00:40:57 - Alex's take on the security model for MCP 00:44:42 - How one-off AI actions become permanent production softwareLinks to resources mentioned in the episode:Alex Rattray: Alex Rattray (@RattrayAlex), Alex RattrayStainless: https://www.stainless.com/Inside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 Million
• Claude Code Can Be Your Second Brain 13.05.2026 1u 10min

  From time to time, we will republish episodes that you might have missed. This episode originally aired in September 2025.Noah Brier uses Claude Code as his second brain—it’s the coolest notetaking setup we’ve ever seen.He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian vault. He does it all from his phone.We had him on the show to tell us exactly how he’s pulling this off. Dan and Noah get into:The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates.The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away.How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works.This episode is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain.If you found this episode interesting, please like, subscribe, comment, and share! Timestamps: 00:00:52 - Introduction 00:02:10 - How you can do deep work on your phone 00:05:30 - Why Noah thinks Grok has the best voice AI 00:11:11 - The nuts and bolts of Noah's Claude Code-Obsidian setup 00:26:05 - Using an agent in Claude Code as a "thinking partner" 00:30:23 - Noah's Thomas' English Muffin theory of AI 00:39:47 - The white space still left to explore in AI 00:48:44 - How Noah is preparing his kids for AI 01:00:06 - How he brought his Claude Code setup to mobileLinks to resources mentioned in the episode:Noah Brier: ⁠https://www.noahbrier.com/⁠, ⁠Noah Brier (@heyitsnoah) / X⁠Alephic, his AI strategy consultancy: ⁠alephic.com⁠ The conference he leads about marketing and AI: ⁠http://BRXND.AI⁠ A newsletter he writes about AI: ⁠newsletter.brxnd.ai⁠  The declassified relic from World War II they talk about: ⁠https://www.alephic.com/sabotageThe apps Noah used to set up Claude Code on his phone: ⁠Termius⁠, ⁠Tailscale⁠
• The Secrets of Claude's Platform From the Team Who Built It 08.05.2026 43min

  In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget.That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from @every, I talk with Angela Jiang (@angjiang), head of product for the Claude platform, and Katelyn Lesse (@katelyn_lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production.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/danshipperTimestamps:00:01:48 - How the Claude platform evolved from API to agents00:04:09 - The primitives that make up Claude Managed Agents00:10:37 - Why the harness and the model are becoming a single unit00:18:49 - The infrastructure wall that kills most agent projects in production00:24:49 - Why team agents need a different shape than individual productivity tools00:26:36 - How Anthropic's legal team uses an agent to review marketing copy00:34:24 - Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms00:35:50 - How to measure agent success with outcome and budget as the end state00:39:11 - What the platform looks like a year from now, when Claude writes its own harness
• Why We Switched From Claude Code to Codex 06.05.2026 58min

  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/subscribeFollow him on X: twitter.com/danshipperJoin the membership for Where You Live at joinbilt.com/danTimestamps 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 recruitingLinks to resources mentioned in the episode:Austin on X: @tedescauDan's January essay on OpenAI's catch-up problem: every.to/chain-of-thought/openai-has-some-catching-up-to-doEvery's vibe check on GPT-5.5: every.to/vibe-check/gpt-5-5
• How Stripe Is Building for an Agent-native World 29.04.2026 53min

  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/danshipperHead to http://granola.ai/every and get 3 months free with the code EVERYTimestamps00:00:45 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 purchasesLinks 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
• The AI Sandwich: Where Humans Excel in an AI World 22.04.2026 28min

  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 EVERYTo 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 episodeCompound 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-scratchTimestamps:   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
• The AI Model Built for What LLMs Can't Do 15.04.2026 53min

  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 EVERYTo hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: 00:00:51 - Introduction00:02:09 - Why correctness and verifiability matter in AI00:09:33 - What an energy-based model is00:14:21 - How EBMs construct energy landscapes to understand data00:19:00 - Why modeling intelligence through language alone is a flawed approach00:26:54 - What it means for a model to "understand" data00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code00:43:21 - Why LLM progress is plateauing00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap
• We Gave Every Employee an AI Agent. Here's What Happened. 08.04.2026 49min

  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
• If SaaS Is Dead, Linear Didn't Get the Memo 01.04.2026 52min

  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
• How to Build an Agent-native Product | Mike Krieger 25.03.2026 48min

  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
• How Every Builds a Writing Team in the Age of AI 18.03.2026 56min

  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/
• We Made a Document Editor Where Humans and AI Work Side by Side 11.03.2026 44min

  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
• Meet the Slowest Startup Incubator in the World—Pumping Out Billion-dollar Companies 04.03.2026 45min

  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
• Meet the Student With No Teachers, No Homework—Just AI 25.02.2026 53min

  Depending on whom you ask, AI is either the best or worst thing that can happen to the next generation. The arguments come from educators, venture capitalists, op-ed writers, and anxious parents—but rarely from the young people in question. On this episode of AI & I, Dan Shipper sat down with one: Alex Mathew, a 17-year-old high-school senior at Alpha High School in Austin, Texas. Alpha School, a rapidly expanding network of kindergarten through grade 12 private schools, is not without controversy. Inside Alpha High School, there are no traditional teachers, all academic content is delivered through an AI-powered platform, and the adults in the classroom, known as “guides,” focus solely on supporting the students emotionally and keeping them motivated to learn. The students have two- to three-hour learning blocks every morning and spend the rest of the day going deep on a project in an area they care about, spanning art, sport, life skills, and entrepreneurship.Mathew’s project is a startup called Berry, built around an AI stuffed animal designed to help teenagers with their mental health. His vision is for teens to talk to the plushie for five to 10 minutes a day and, in the process, learn to recognize and cope with their problems in the right way. In this episode, Dan and Mathew talk about what a day at Alpha High looks like, what keeps students from cheating when AI is everywhere, and how Generation Z—people born between 1997–2012—really feels about college, social media, and books.  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/subscribe Follow him on X: https://twitter.com/danshipper In a world of generic AI, don’t sound like everyone else. With Grammarly, you never will. Download Grammarly for free at Grammarly.com.Intent is what comes after your IDE. Try it yourself: augmentcode.com/intentHead to granola.ai/every to get 3 months free.Timestamps:  00:00:00 – Start 00:01:30 – Introduction00:04:08 – A typical day inside Alpha High School00:06:54 – Why Alpha replaced teachers with “guides” focused on motivating students00:12:09 – Why Mathew doesn’t use AI to cheat, even though he could00:19:51 – Do ambitious teenagers care about going to college?00:25:12 – Mathew’s take on how Gen Z thinks about AI00:27:52 – How Mathew thinks about the effects of social media00:31:29 – Gen Z’s relationship with books and reading00:38:57 – Mathew ranks ChatGPT, Claude, Gemini, and Grok00:47:12 – Why Mathew is building Berry, an AI stuffed animal for teen mental healthLinks to resources mentioned in the episode:Alex Mathew: Alex Mathew (@alxmthew)More about Berry: https://berryplush.com/, Berry (@berryaiplushies) 
• OpenAI's Codex: This Model Is So Fast It Changes How You Code 18.02.2026 46min

  OpenAI’s hottest app isn’t ChatGPT—it’s Codex.In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January, and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl.Dan Shipper talked to Thibault Sottiaux, head of Codex, and Andrew Ambrosino, a member of technical staff who built the Codex app, for Every’s AI & I about what OpenAI is building and how they’re using it internally.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/subscribe Follow him on X: https://twitter.com/danshipper Head to granola.ai/every and get 3 months free with the code EVERY.Timestamps:  00:00:00 - Start00:01:27 - Introduction 00:05:27 - OpenAI's evolving bet on its coding agent 00:09:42 - The choice to invest in a GUI (over a terminal) 00:20:38 - The AI workflows that the Codex team relies on to ship 00:26:45 - Teaching Codex how to read between the lines 00:28:45 - Building affordances for a lightening fast model 00:33:15 - Why speed is a dimension of intelligence 00:36:30 - Code review is the next bottleneck for coding agents 00:41:24 - How the Codex team positions against the competition Links to resources mentioned in the episode:Thibault Sottiaux: Tibo (@thsottiaux)Andrew Ambrosino: Andrew Ambrosino (@ajambrosino)Every’s vibe check on everything the Codex team launched: OpenAI's Codex App Gains Ground on Claude Code, GPT-5.3 Codex—The 10x Engineer, Now More Fun at Parties, AI as Fast as Your Train of Thought 
• Inside OpenAI’s Agentic Browser, Atlas 11.02.2026 55min

  The AI labs fighting for attention during the Super Bowl call to mind another iconic Super Bowl moment: Apple’s 1984 ad for the Macintosh, which promised that the personal computer would be a source of unbound wonder, freedom, and delight.They were right, but over time, the personal computer has also become cluttered with errands.These “computer errands”—downloading a W-2 when tax season rolls around, hunting for the right coupon code before checkout, or navigating the unholy labyrinth of the Amazon Web Services dashboard just to change one permission setting—have taken over our digital lives. Atlas, OpenAI’s agentic browser, sprang from the idea that AI should handle this tedium for you.In this week’s episode of AI & I, Dan Shipper sat down with two members of the Atlas team, Ben Goodger and Darin Fisher. Goodger is Atlas’s head of engineering, and Fisher is a member of the technical staff. Both are legends of the browser world. They’ve spent decades building the modern web, working together on Netscape, Firefox, and Chrome before arriving at Atlas. From that vantage point, they told Dan how they think browsing is about to change, why building a browser is harder than it looks, and what it’s like to create a new one with AI coding tools like Codex.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/subscribe Follow him on X: https://twitter.com/danshipper Move fast, don’t break thingsMost AI coding tools don’t know which line of code will actually break your system. Try Augment Code, which understands your entire codebase, including the repos, languages, and dependencies that actually runs your business, and use their playbook to learn more about their framework, checklists, and assessments. Ship 30% faster with 40% shorter merge times.[Playbook at ⁠augmentcode.com⁠]Timestamps:  00:01:57 - Introduction00:11:51 - Designing an AI browser that’s intuitive to use00:15:24 - How the web changes if agents do most of the browsing00:25:06 - Why traditional websites will not become obsolete00:29:00 - A browser that stays out of the way versus one that shows you around00:39:51 - How the team uses Codex to build Atlas00:44:47 - The craft of coding with AI tools00:52:33 - Why Goodger and Fisher care so much about browsersLinks to resources mentioned in the episode:Ben Goodger: Ben Goodger (@bengoodger) Darin Fisher: Darin Fisher (@darinwf) OpenAI’s browser, Atlas: Introducing ChatGPT Atlas 
• How We Built 'Claudie,' Our AI Project Manager (Full Walkthrough) 04.02.2026 47min

  A few weeks ago, Natalia Quintero wouldn’t have called herself technical. But since the beginning of January, she has woken up at 6 a.m. to vibe code with Claude. The AI project manager she built saved her 14 hours a week. Getting there meant scrapping the system three times and starting over. But the result handles everything from onboarding new clients to generating weekly updates across all projects.Natalia is the head of AI consulting at Every. As part of the role, she's spoken with over 100 organizations in the past year and worked with a select two dozen, including hedge funds, private equity firms, and Fortune 500 companies. She’s seen what separates companies thriving with AI from those floundering, and it comes down to patterns that have nothing to do with having the most resources or the fanciest tools.Dan Shipper had her on AI & I to share what she’s learned from this front-row seat to AI adoption. Quintero reveals how a private equity firm cut investment memo creation from three weeks to 30 minutes, why AI adoption needs to come from the top down, and what happened when she learned from her early morning experiments.She also explains why the companies going furthest with AI are the ones that give employees permission to fail—and how that counterintuitive approach is revolutionary.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/subscribe Follow him on X: https://twitter.com/danshipper 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.Timestamps:  00:00:00 - Introduction00:01:30 - Why successful AI adoption requires coordinated, top-down effort00:07:05 - How a private equity firm reduced investment memo creation from weeks to 30 minutes00:13:30 - The benefits of connecting AI to proprietary context00:15:20 - The plan-delegate-assess-compound framework for engineering teams00:17:55 - How non-technical team members are becoming vibe coding addicts00:20:50 - Building Claudie: an AI project manager from scratch00:23:00 - Why creative exploration time outside the 9-to-5 is essential00:27:50 - Live demo: How Claudie automates client onboarding and tracking00:38:40 - The human side of AI: spending less time in spreadsheets, more time with peopleLinks to resources mentioned in the episode:Natalia Quintero: Natalia Quintero (@NataliaZarina)What Natalia learned from working with companies on AI adoption: https://every.to/on-every/the-next-chapter-of-every-consultingEvery’s compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin 
• How Andrew Wilkinson Uses Opus 4.5 in His Work and Life 21.01.2026 1u 2min

  Entrepreneur Andrew Wilkinson used to sleep nine hours a night. Now he wakes up at 4 a.m. and goes straight to work—because he can’t wait to keep building with Anthropic’s latest model, Opus 4.5.Two years ago, Wilkinson was obsessed with vibe coding on AI software development platform Replit. It was thrilling to describe something in plain English and watch an app appear, less thrilling when the apps were always broken in some way, often full of maddening bugs. So he set his app creation ambitions aside until technology caught up with them.Then, a few weeks ago, he started playing with Claude Code and Opus 4.5. It felt, he says, like having a “$100,000-a-month payroll of engineers” working for him around the clock.Wilkinson is the cofounder of Tiny, a company that buys profitable businesses and holds them for the long term. The Tiny portfolio includes the AeroPress coffee maker and Dribbble, a platform where designers can share their work and find jobs. Dan Shipper had him on AI & I to talk about the automations Wilkinson has built for his work and personal life, including an AI relationship counselor, a custom email client, and a system that texts him outfit recommendations each morning. Wilkinson revealed how all of this individual exploration has changed the way he thinks about buying software companies at Tiny.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/danshipperReady 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!Timestamps:00:00:00 - Start00:01:07 - Introduction00:02:48 - Why Opus 4.5 feels like the iPhone moment for vibe coding00:08:31 - Why designers have a unique advantage with AI00:14:10 - How Wilkinson built a custom email client with Claude Code00:18:13 - An AI trained on your relationship that predicts your fights00:30:40 - Using AI meeting notes to make your life better00:35:11 - Don't inject your opinion into prompts00:40:21 - Wilkinson’s Claude Code tips and workflows00:47:59 - Your personal stylist is a prompt away00:53:17 - How AI is changing the way Wilkinson invests in softwareLinks to resources mentioned in the episode:Andrew Wilkinson: Andrew Wilkinson (@awilkinson)The book Wilkinson references in his prompts, when writing copy with AI: Made to StickEvery’s compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin
• Why Your AI Learning Projects Keep Fizzling Out 14.01.2026 55min

  LLMs have made it absurdly easy to go deep on almost any topic. So why haven’t we all used ChatGPT to earn college degrees we wished we had majored in or pursued a niche interest, like learning how to name the trees in our neighborhood? I know I’m not the only one to feel guilty for well-intentioned attempts at autodidactism that inevitably peter out.Entrepreneur Nir Zicherman has a reason for this disconnect: LLMs can answer most of your questions, but they won’t notice when you’re lost or pull you back in when your motivation starts to fade.As the CEO and cofounder of Oboe, a platform that generates personalized courses about everything from the history of snowboarding to JavaScript fundamentals using AI, Zicherman has thought deeply about why the ability to access information does not automatically lead to understanding a concept. In this episode of AI & I, he talks to Dan Shipper about everything he’s learned about learning with LLMs.They get into Zicherman’s counterintuitive belief that learning is a more passive process than you’d think, the biggest blocker for most people who want to learn something new, and where AI agents currently fall short in providing a meaningful learning experience.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/danshipperTimestamps:00:00:00 - Start00:00:36 - Introduction00:01:49 - Why you need a dedicated AI learning app00:04:32 - The process of learning is more passive than you might think00:10:21 - Live demo of Oboe to create a course about philosopher Ludwig Wittgenstein00:16:52 - Learning works best when it comes in many formats00:28:21 - Where AI agents currently fall short in the learning experience00:34:10 - The importance of making learning feel accessible00:35:56 - How Zicherman uses Oboe to learn quantum physics00:40:54 - How embeddings spaces remind Dan of quantum mechanicsLinks to resources mentioned in the episode:Nir Zicherman: @NirZichermanLearn something new with Oboe: https://oboe.com/