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AI and I: Daily Summaries Delivered
by Dan Shipper
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.
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- Today53 min
Best of the Pod: Wired's Kevin Kelly on Why AI Is a 50-year Overnight Success
Kevin Kelly has spent over 30 years experiencing the edge of new technology: from the earliest days of the internet to the first years of Burning Man. But he’s always treated the frontier as a place to visit, not somewhere to live. It’s partially how he’s been able to stay grounded through tech’s various hype cycles.As founding executive editor of Wired and author of The Inevitable, Kelly spends as much time analyzing the latest in AI as he does reading about significant moments in history. It’s a discipline he traces back to his work with the Long Now Foundation, which he cofounded to encourage long-term thinking, reaching from the last 10,000 years to the next. On this week’s AI & I, Dan Shipper revisits his conversation with Kelly. They get into why historians can be the best futurists, and how our bid to understand what intelligence is has parallels with early scientists' attempts to figure out electricity. Kelly also describes the joy he found in creating an AI-generated saga featuring Leonardo Da Vinci, Christopher Columbus, and Martin Luther—one that will only ever be read and enjoyed by him.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 for YouTube:0:00 Start 0:50 Introduction 1:10 Why Dan and Kelly love Annie Dillard 12:52 How to predict the future like Kelly16:10 What the history of electricity can teach us about AI 20:13 How Kelly thinks about the nature of intelligence 25:44 Kelly's advice on discovering your competitive advantage 29:33 How Kelly assembled a bench of star writers for Wired 34:43 How Kelly used ChatGPT to co-create a book 39:12 Using AI as a mirror for your mind 43:43 What Kelly learned from betting on VR in the 1980sLinks to resources mentioned in the episode:Kevin Kelly on X: https://twitter.com/kevin2kellyThe Inevitable by Kevin Kelly: https://www.amazon.com/Inevitable-Understanding-Technological-Forces-Future/dp/0525428089Pilgrim at Tinker Creek by Annie Dillard: https://www.amazon.com/Pilgrim-Tinker-Harper-Perennial-Classics/dp/00612333231,000 True Fans by Kevin Kelly: https://www.amazon.com/1000-True-Fans-Kellys-Simple-ebook/dp/B01N9P9O4GFull episode transcript: https://every.to/podcast/transcript-be243312-ea22-4193-8c56-b9cd45a79a87
- 1 weeks ago46 min
How Every's Team Used AI to Ship Its Biggest Launch Ever
Yash Poojary, a growth engineer at Every, dropped an idea for a campaign in Slack at 7 p.m. Instead of building it himself, Every’s head of growth Austin Tedesco took a screenshot of the Slack thread, dropped it into Codex, typed "Can you do this?", and went to the gym.By the time he got back, Codex had built four audience segments, drafted emails for each one, and pulled a social image that had worked before. It took Austin 10 minutes to make some tweaks and schedule the whole thing to send the next morning. Within a few hours, it generated more than $25,000 in revenue. That story came out of the launch week for All Access, Every’s new $625-a-year membership built around the Builder Pack. It includes $7,000 in credits and free usage from ten of the AI products Every uses every day, including Claude Max, Codex, Cursor Pro+, PostHog, Notion, Framer, Render, and Flora.On this episode of AI & I, four of Every's own builders—COO Brandon Gell, head of marketing Douglas Brundage, as well as Yash and Austin—sit down to show how they use AI, breaking down their personal stacks and giving insight into their own strategies and mindset for building.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 for YouTube:0:00 Intro 0:35 All Access Explained 3:01 Yash's Tech Stack and How He's Automating Testing Pipelines 8:02 The Idea to Execution Loop 10:25 How an Agent Turned an Idea into $25K 17:50 The AI Sandwich Workflow 22:03 Making AI Tools Accessible to Solo Builders 28:50 Douglas on Brand and Design34:51 Tips on What to Build First 43:46 What's Next for All AccessLinks to resources mentioned in the episode:Brandon Gell on X: https://x.com/bran_don_gellYash Poojary on X: https://x.com/poojary_yashAustin Tedesco on X: https://x.com/tedescau?lang=enDouglas Brundage on X: https://x.com/DABrundageIntroducing Every All Access: https://every.to/on-every/introducing-every-all-accessGet the Builder Pack: every.to/builder-pack Go to https://attio.com/every and get 15% off your first year.
- 2 weeks ago59 min
The Founder of a $1.5B AI Company on What Comes After the First Wave of AI Apps
“Running a startup is a knife fight whether things are going well or not,” says Chris Pedregal, cofounder and CEO of Granola. Granola recently raised a $125 million series C round at a $1.5 billion valuation on the strength of its AI meeting notetaker.That valuation hasn’t made Pedregal complacent. Granola built its name as the first to make good AI meeting notes, but Notion, OpenAI, and Zoom have all since released their own versions. Pedregal isn’t rattled—he never thought meeting notes were the real prize. The bigger fight, he says, is over “what interface we use for work, and what work looks like in an AI-native world.”That’s why Granola is betting on owning the entire meeting workflow: preparing people for a call, helping them act on it afterward, and making that context available to whatever agent—Claude, Codex, or anything else—people bring to the table. Over the next few months, the company plans to push hard on its API and MCP to make that possible.Dan Shipper talked with Pedregal for AI & I about why Granola pre-generates millions of meeting briefs, most of which go unopened, what “bring your own agent” software could look like, and why Pedregal still thinks “easy come, easy go” about Granola’s own success.If you found this episode interesting, please like, subscribe, comment, and share.More from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps:00:00:59 Introduction00:01:57 Why starting a company feels like a knife fight00:04:33 Granola's counterintuitive view on competition00:10:44 Dan's "pirate and architect" framework for structuring early-stage product teams00:13:09 How Granola's "shaping" and "validation" phases work for building new features00:18:17 Why Dan lives almost entirely inside Codex00:24:40 The case for "Codex-native apps"00:35:37 Granola's "handrail" philosophy00:38:12 Why Granola is betting on owning meeting-adjacent context instead of competing as a general agent00:44:19 What a transcript alone can never captureEpisode resources:Chris Pedregal on X: https://twitter.com/cjpedregalGranola on X: https://twitter.com/meetgranolaGranola: https://granola.aiGranola hits $1.5B valuation (TechCrunch): https://techcrunch.com/2026/03/25/granola-raises-125m-hits-1-5b-valuation-as-it-expands-from-meeting-notetaker-to-enterprise-ai-app/Go to https://attio.com/every and get 15% off your first year.
- 3 weeks ago53 min
How a Writer Uses AI Without Losing His Voice
Craig Mod used to pay Campaign Monitor roughly $7,000 a year to send his newsletters. After rebuilding the tool himself with AI, his bill is closer to $150. It’s the kind of thing that convinces him we’re about to enter a “golden age of tool building”—one where anyone can build tools specifically suited to their needs, instead of settling for software from incumbents that are slow to innovate.Mod is the writer and photographer behind the newsletters Roden and Ridgeline and books like Things Become Other Things and Kissa by Kissa—as well as a lifelong technologist. He’s rebuilt the tax software Quicken, created a private alternative for Twitter for his members which he calls The Good Place, and used AI to build an archive for his pop-up newsletters. But while Mod is an advocate of using AI to build, he draws the line at using it to write.Mod talks to Dan Shipper about using AI as a research assistant, why he keeps a tech-free zone in the mornings for deep thinking, and why he’s resisting the pull of the “mainlining” AI era.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:0:00 Introduction3:51 Rebuilding Quicken and Campaign Monitor with AI6:24 Building The Good Place, a private Twitter alternative for Craig’s members10:39 Why we’re entering a “golden age of tool building”12:17 Why AI could help writers build audiences17:35 Using AI to build a newsletter archive and a searchable board-meeting Q&A library27:58 Creating a technology-free buffer to protect deep thinking30:31 Why Craig is resisting the temptation to “mainline” AI for ten hours a day39:44 Why anthropomorphizing AI is “psychotic,” and why Apple got Siri right47:42 Being adopted, and making peace with humanity’s fragile place in an AI futureGo to https://attio.com/every and get 15% off your first year.Links to resources mentioned in the episode:Craig Mod’s website: https://craigmod.comRoden (Craig’s monthly newsletter): https://craigmod.com/roden/
- 4 weeks ago41 min
The AI Workflows Behind Every's Consulting Team
Natalia Quintero joined Every as head of consulting with a mandate to bring AI into the workflows of executives at hedge funds, private equity firms, and tech companies. She is also a recent Codex convert—someone who spent months resisting the tool before Dan Shipper’s daily pestering finally got her to try it.Natalia encountered Codex as a non-technical builder who had learned to navigate file systems and folder structures in Claude Code through sheer effort. She’s now used Codex to do everything from automate her CRM setup to build a portal to manage her father’s medical care.Dan talked with Natalia for AI & I about what it looks like to go from non-technical to building software with Codex, why Every still uses software-as-a-service products from Attio and Asana instead of vibe coding their own tools, and where she thinks AI agents like Every’s internal Claudie employee require human managers.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:00:01:05 Introduction00:02:35 How Natalia manages Claudie, the consulting team's AI project manager00:04:55 Why the consulting team still pays for SaaS products00:11:47 Codex as a game changer00:14:55 Building personalized learning guides and illustrated explainers with AI00:21:40 Inside Natalia's AI-powered email triage system00:26:44 The shift from knowledge work as sculpting to knowledge work as gardening00:28:57 Using Codex to one-shot a custom CRM00:33:16 Using Codex to build an app that coordinates her father's medical careLinks to resources mentioned in the episode:Natalia Quintero on X: https://x.com/NataliaZarinaAsana (project management): https://asana.comEvery Consulting: https://every.to/consultingGo to attio.com/every and get 15% off your first year.
- Jun 24, 202643 min
Building a School Where AI Models Learn About Humanity
If scaling laws hold—and Surge AI CEO Edwin Chen believes they do—we’re hurtling toward a future where there’s nothing humans can do that AI can’t do better. When OpenAI’s models disproved an open conjecture posed by mathematician Paul Erdős using novel algebraic geometry techniques, Fields medalist Timothy Gowers felt the shift acutely. He initially thought the model had proved an upper bound, and braced himself: that would mean it was “all over for mathematicians very soon.” When he realized it had only found a counterexample, he was relieved—it bought him another year or two before the thing he’s devoted his life to becomes something AI does better.As founder and CEO of the company behind the data environments and evals the major model companies use to train their models, Chen has a unique perspective on how quickly AI models are absorbing tasks we used to think of as uniquely human.Dan Shipper talked with Chen for AI & I about what the act of creating or building means when AI can do it better—and whether an answer to that question already exists within science fiction.If you found this episode interesting, please like, subscribe, comment, and share!Join the membership for Where You Live at https://www.joinbilt.com/danTo hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps:00:00:54 Introduction00:01:49 Surge as a "school for AGI"00:04:46 What AI's capacity for novel mathematics says about human achievement00:07:29 Motivation in an era when AI can do everything00:14:34 The trap of optimizing AI models for engagement00:29:34 Training using datasets versus training using environments00:35:09 The value of personal data00:39:40 Why models are bad at writing00:42:00 Chen's AGI timelineLinks to resources mentioned in the episode:Edwin Chen on X: https://x.com/echenSurge: https://surgehq.aiRiemann-bench (research-level math benchmark): https://surgehq.ai/leaderboards/riemann-benchHemingway-bench (creative writing benchmark): https://surgehq.ai/leaderboards/hemingway-benchTalkie-1930 (language model trained on pre-1930 text): https://huggingface.co/talkie-lm/talkie-1930-13b-itTed Chiang, “What’s Expected of Us”: https://www.nature.com/articles/436150aEvery is the most AI-native startup on the internet. Through ideas, software and education, subscribers get the tools to work at the frontier of AI. Start your free trial today: https://every.to/subscribe?utm_source=youtubeFollow Every: https://x.com/everyFollow Dan Shipper: https://x.com/danshipper
- Jun 17, 202628 min
GitHub’s COO Explains Why AI Hasn’t Replaced Developers
Last year, there were 1 billion commits on GitHub. This year, Kyle Daigle expects that number to exceed 14 billion, a two-component explosion caused by more humans—and their agents—issuing pull requests. In March alone, 17 million pull requests on GitHub were created by agents.Daigle is the COO of GitHub and Microsoft’s chief marketing officer for developer products. He’s been at GitHub for 13 years, and is paying close attention to how AI is expanding the platform’s user base. Along with agents, legal, sales, and marketing professionals are building apps with the GitHub Copilot app. The line between developer and non-developer is disappearing.On this episode of AI & I, guest host Mike Taylor sat down with Daigle at Microsoft Build to discuss how GitHub is building infrastructure for an agent-native world: agentic code review, model routers that automatically select the right model for the task, and a philosophy that the most durable advantage in this market is developer choice.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?To hear more from Mike Taylor:Subscribe to Every: https://every.to/subscribeFollow him on X: https://x.com/hammer_mtTimestamps for YouTube:00:00:52: Introduction00:03:27: The agentic PR flood00:04:33: GitHub's approach to helping open-source maintainers manage the surge00:06:15: What 14 billion commits means for code quality00:08:03: Moving from per-seat licensing to usage-based pricing00:09:45: Kyle's dual role as GitHub COO and Microsoft's chief marketing officer for developers00:13:03: Developer choice as competitive moat00:14:57: How to balance dogfooding your own tools with staying honest about the competition00:19:45: Hill climbing, frontier tuning, and solving the model-routing problem00:24:45: Kyle's agentic communication hackLinks to resources mentioned in the episode:Kyle Daigle on X: https://x.com/kdaigleMike Taylor on Every: https://every.to/@mike_2114Mike’s piece on building an AI version of Kyle Daigle: https://every.to/also-true-for-humans/i-interviewed-an-ai-version-of-github-s-coo-then-spoke-to-the-real-oneGitHub Copilot: https://github.com/features/copilot
- Jun 10, 202652 min
How Anthropic Uses Claude Fable 5 With Mike Krieger
Mike Krieger built one of the most consequential consumer apps of the last two decades as the cofounder of Instagram. He is now at the frontier of AI-native product development as head of Anthropic Labs, the team responsible for figuring out what the most capable AI models can do in the hands of real builders.When Krieger first got access to Fable 5 months before its public release, it was exciting and disorienting. “I feel like a total newbie again,” he remembers telling his team. The way he’d been thinking about productivity, strategy, and time management was out of date. The model had outpaced his workflows.Dan Shipper talked with Krieger for AI & I about what it looks like to build with a model as capable as Fable 5, including the new rhythms, challenges, and possibilities it reveals.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:0:03 Introduction1:48 How Fable completely reshaped Mike's workflow4:48 When to use Sonnet versus Fable10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture15:00 The cost to build has collapsed19:03 Is software engineering over?21:48 How Anthropic's engineering teams work today38:39 The mechanics of verification44:39 What people should use the model to build47:24 Dynamic workflowsLinks to resources mentioned in the episode:Mike Krieger on X: https://x.com/mikeykAnthropic Labs: https://www.anthropic.comClaude Code: https://claude.ai/codeEvery: https://every.toTimestamps:0:03 Introduction1:48 How Fable completely reshaped Mike's workflow4:48 When to use Sonnet vs. Fable10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture15:00 The cost to build has collapsed19:03 Is software engineering over?21:48 How Anthropic's engineering teams work today38:39 The mechanics of verification44:39 What people should use the model to build47:24 Dynamic workflowsLinks to resources mentioned in the episode:Mike Krieger on X: https://x.com/mikeykAnthropic Labs: https://www.anthropic.comClaude Code: https://claude.ai/codeEvery: https://every.to
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About AI and I
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.
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