The Every Podcast
- Épisodes
- 136
- Parution
- hebdomadaire
- Dernier épisode
- 11 Nov 2026
À propos du podcast
The Every Podcast is Every's flagship show. Co-hosts Dan Shipper and Natalia Quintero talk with founders, researchers, writers, and operators about what they're building and how they use AI in their own work. The show also takes you behind the scenes at Every. We share how our team is using and exploring AI, including what we're trying, what's working, and what we're learning along the way. Formerly known as AI & I. Read more at every.to.
Épisodes 136
A New Vision for Human Work After Automation
Thesis: 2027 is made possible by our founding partners, Lovable and Notion.Lovable is the fastest way to turn ideas into software people love: https://link.lovable.dev/everyNotion is the collaborative AI …
Notes de l’épisode
Thesis: 2027 is made possible by our founding partners, Lovable and Notion.
Lovable is the fastest way to turn ideas into software people love: https://link.lovable.dev/every
Notion is the collaborative AI workspace where teams and agents work side by side: https://www.notion.com/?utm_source=youtube&utm_campaign=thesis-2027&utm_content=every-261111-video
What does great human work look like after automation?
It’s a question we try to answer every day at Every, and the one we built our conference, Thesis, around. In his closing keynote, Dan Shipper argues that as AI makes intelligence cheap and widely available, what matters most is human perspective. It’s your “non-consensus” point of view, plus your responsibility for a particular corner of the world, that helps you spot the insight no one else would.
On this week’s episode of The Every Podcast, we’re sharing Dan’s full talk at Thesis: 2027. Dan explains why benchmark charts make the future of work look bleaker than it is, why picking the right idea still takes a human, and how we should really measure AI models.
Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign= (https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode)thesistalkepisode
Links to resources mentioned in the episode:
- Thesis: https://every.to/thesis-2027
- Dan Shipper: https://x.com/danshipper
- OpenAI’s GDPval benchmark: https://openai.com/index/gdpval/
Timestamps:
0:00 Introduction
1:51 Work after automation
5:00 1 person vs 5 jobs
10:18 Why perspective matters
17:55 Better AI, more valuable humansThe Inside Story of the 50 Who Built Modern AI With Kevin Roose
How did the race to build AGI really unfold, and who made the decisions that shaped it?Tech journalist Kevin Roose interviewed 150 people at the center of the AI industry to find out. The result is The AGI …
Notes de l’épisode
How did the race to build AGI really unfold, and who made the decisions that shaped it?
Tech journalist Kevin Roose interviewed 150 people at the center of the AI industry to find out. The result is The AGI Chronicles, a riveting account of how modern AI was built and the choices that will determine what comes next.
On this week’s episode of The Every Podcast, Natalia Quintero sits down with Roose to talk about how he got a secretive industry to open up, how he did (and didn’t) use AI in his book writing process, and why he sees his job as explaining the “very confusing and insular” world of Silicon Valley to everyone else.
Links to resources mentioned in the episode:
- Follow Kevin Roose: https://x.com/kevinroose
- Get Kevin Roose’s book, The AGI Chronicles: https://theagichronicles.com/
- Machine Gods: https://www.npr.org/podcasts/g-s1-143588/machine-gods
- “The Big Blob of Compute Hypothesis” excerpt on Kevin’s Substack: https://kevinroose.substack.com/p/the-essay-that-started-the-ai-race
- Profiles of the Future by Arthur C. Clarke: https://www.amazon.com/Profiles-Future-Inquiry-Limits-Possible/dp/0575402776
- The Making of the Atomic Bomb by Richard Rhodes: https://www.amazon.com/Making-Atomic-Bomb-Richard-Rhodes/dp/1451677618
- NotebookLM: https://notebook.google/
Timestamps:
0:00 Introduction
2:23 Are we doomed? Roose on AI risk and the case for mandatory safety testing
7:13 Why Roose raced to write the history of AGI before the records disappeared
10:45 How Roose got 150 sources in a secretive industry to open up
13:49 Dario Amodei’s “Big Blob of Compute” memo
19:12 How Roose’s mother’s book on the survivors of the atomic bomb influenced his work
21:40 The moment Roose first “felt the AGI”
23:24 How a “council of Claudes” fact-checked Roose’s book
31:59 Launching Machine Gods and reinventing video journalism
41:41 Weightlifting versus forklifting: How Roose decides what to hand off to AICan You Use AI To Make Good Art? — With Fred Graver
Subscribe to Every (free for 14 days): https://every.to/youtube-every-offerCan AI create good art? Fred Graver thinks the answer depends on you. The Emmy Award–winning writer, producer, and founder of AI …
Notes de l’épisode
Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer
Can AI create good art?
Fred Graver thinks the answer depends on you. The Emmy Award–winning writer, producer, and founder of AI Writer’s Studio says AI can help with many parts of the creative process—but it can’t give you something to say. Without your own point of view, AI will only make your work more average.
On this week’s episode of The Every Podcast, Natalia Quintero sits down with Graver. They discuss why every writer should have a ‘who I am as a writer’ document, the cast of AI collaborators he’s built into his own writers’ room, and the human quirks that make characters endearing—the part he insists AI can’t supply.
Get Fred’s Skill: https://every.to/context-window/every-model-has-a-six-month-shelf-life-now
Links to resources mentioned in the episode:
• Follow Every: https://x.com/every
• Follow Natalia Quintero: https://x.com/NataliaZarina
• Follow Fred Graver: https://x.com/fredgraver
• AI Writers Studio: https://aiwritersstudio.substack.com/
• Compound Writing plugin: https://every.to/guides/compound-writing
• Compound Engineering plugin: https://every.to/guides/compound-engineering
• Thesis conference: https://every.to/thesis-2027
• Follow Katie Parrott: https://x.com/kplikethebird
• Follow Kieran Klaassen: https://x.com/kieranklaassen
• Follow Dan Shipper: https://x.com/danshipperTimestamps:
0:00 Introduction
2:22 Fred’s path from National Lampoon and Letterman to Twitter and Microsoft
7:47 The “I want in” instinct behind every career pivot
12:19 How Best Week Ever taught Fred to build with the audience
13:26 Using AI as a virtual writers’ room, not a ghostwriter
16:07 Codifying industry expertise and studio culture into an AI core
20:32 The three phases of making entertainment with AI
26:27 Why you should brain-dump your idea before opening AI
38:47 Fred’s thesis: Success is knowing what the audience wants before it does
43:44 Fred’s day-to-day workflow across Claude, ChatGPT, and NotebookLMHow a Frontier Team Can Help Your Company Keep Up With AI
Avec Katie Parrott, Mike Taylor, Jannik Jung, Jack Cheng, Arielle Shipper, et Kieran Klaassen
Check out Gemini Audio: https://goo.gle/Every-Gemini-AudioEvery time a new AI model drops, companies face the same dilemma: Figure out what’s new and how to use it, or focus on getting work done.Every solves …
Notes de l’épisode
Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio
Every time a new AI model drops, companies face the same dilemma: Figure out what’s new and how to use it, or focus on getting work done.
Every solves this dilemma with a frontier team: a small group that tests new models and turns useful experiments into tools and workflows for the rest of the company.
On this week’s episode of The Every Podcast, Natalia Quintero sits down with all seven members of Every’s frontier team to ask what they’re building—from a tabless browser that helps protect your attention to a personal agent orchestrator called Minga—and what they want to try next.
Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode
Links to resources mentioned in the episode:
- Follow Every: https://x.com/every
- Compound Writing plugin: https://every.to/guides/compound-writing
- Every’s vibe checks: https://every.to/vibe-check
- Follow Dan Shipper: https://x.com/danshipper
- Follow Natalia Quintero: https://x.com/NataliaZarina
- Follow Mike Taylor: https://x.com/hammer_mt
- Follow Jack Cheng: https://x.com/jackcheng
- Follow Arielle Shipper: https://x.com/ArielleShipper
- Follow Katie Parrott: https://x.com/kplikethebird
- Follow Jannik Jung: https://x.com/JannikJung
0:00 Introduction: Why every company needs a frontier team
2:42 Why the best frontier teams have pirates and architects
4:57 How Head of Evals Mike Taylor builds personal benchmarks
6:46 How Opus edited an explainer video using Slack messages and meeting notes
10:20 Working with AI means protecting other people’s attention
12:26 Why a frontier team needs a “wrangler” like Arielle Shipper
18:58 Why senior editor Jack Cheng built a tabless browser
24:37 How resident writer Katie Parrott teaches her AI writing plugin who the reader is
30:51 Inside engineer Jannik Jung’s personal AI orchestrator
35:41 Compound engineer Kieran Klaassen uses AI to build power toolsHow Hugging Face’s Investor Spots Winners
Avec Matt Hartman
Check out Gemini Audio: https://goo.gle/Every-Gemini-AudioBet on founders who understand the technology, because their first idea probably won’t be the one that sticks.That’s the view of Matt Hartman, founder …
Notes de l’épisode
Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio
Bet on founders who understand the technology, because their first idea probably won’t be the one that sticks.
That’s the view of Matt Hartman, founder and managing partner of Factorial Capital. In 2016, as a partner at Betaworks, Hartman was part of the team that wrote the first check into Hugging Face, which was then a chatbot app for teenagers. Today it’s the largest hub for open-source AI, and Nvidia has agreed to buy it for about $13 billion.
On this week’s episode of The Every Podcast, Hartman tells Natalia Quintero how backing Hugging Face shaped his investment thesis, how Factorial grew into a “family tree” of technical founders who help him scout deals, and why vibe-coding his own app gave him a new respect for SaaS.
Subscribe to Every (free for 14 days): https://every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=matthartmanepisode
Links to resources mentioned in the episode:
Factorial Capital: https://factorialcap.com
Ghost Pepper, Matt's open-source voice-to-text app: https://github.com/matthartman/ghost-pepper
Superpowers, Jesse Vincent's skill for Claude Code: https://github.com/obra/superpowers
Hugging Face: https://huggingface.co
Follow Every: https://x.com/every
Follow Natalia Quintero: https://x.com/NataliaZarina
Follow Matt Hartman: https://x.com/MattHartman
0:00 Introduction
5:06 How Hugging Face went from a teen chatbot to the home of open-source AI
9:52 The three decisions that set Hugging Face on its path
12:49 Hartman’s approach to finding the newly technically possible
18:10 Why Hartman backs technical founders
21:10 How Factorial’s portfolio founders source its next deals
27:09 Why faster iteration makes product insight the most valuable skill
34:28 What Hartman learned building Ghost Pepper, an open-source voice-to-text app
40:02 Hartman’s current theses, from non-language models to full-stack AI apps
44:14 Why the best founders right now are experimentingWhy Every Traded Personal Agents for One Company Agent
Subscribe to Every (free for 14 days): every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode On this week's episode of The Every Podcast, Dan Shipper sits down with …
Notes de l’épisode
Subscribe to Every (free for 14 days): every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode
On this week's episode of The Every Podcast, Dan Shipper sits down with Willie Williams, Every's head of platform and one of the driving forces behind the Every Agent, which launched this week. They discuss how Every went from everyone running their own OpenClaw to relying on one shared company agent—and where they think personal agents belong in everyday life.
Links to resources mentioned in the episode:
- Willie Williams on X: https://x.com/bigwilliestyle
- The Every Agent: https://every.to/agent?utm_source=podcast&utm_campaign=podcast&utm_content=1006companyagent
- OpenClaw: https://openclaw.ai
- Tend, Dan's open-source feed app: https://every.to/tend?utm_source=podcast&utm_campaign=podcast&utm_content=1006companyagent
- Every’s Vibe Check on Dots: https://every.to/vibe-check/vibe-check-dots-always-on-agents-in-chatgpt?utm_source=podcast&utm_campaign=podcast&utm_content=1006companyagent
- Dot from OpenAI: https://openai.com/index/introducing-dots/
0:00 Introduction
5:58 How OpenClaw spread through Every
11:13 Why everyone's personal agents eventually died
14:51 The difference between building for builders and building for operators
16:20 Why work will run on company agents while personal agents will stay personal
19:56 How engineering management has changed in the age of AI
21:43 Using an AI-powered feed to see everything happening across a product launch
23:53 Why Willie's token usage fell off a cliff
29:05 The benefit of having separate AI coaches for each part of your life
31:45 How Every is building its agent to surface information unprompted
How Sam Altman Uses Dots to Take Back His Time
Avec Sam Altman
Sam Altman's Dot, OpenAI's new always-on agent, gave him back the most productive part of his day.The OpenAI CEO would lose his early mornings—his window of "maximum creativity"—to whatever fire needed putting …
Notes de l’épisode
Sam Altman's Dot, OpenAI's new always-on agent, gave him back the most productive part of his day.
The OpenAI CEO would lose his early mornings—his window of "maximum creativity"—to whatever fire needed putting out. Now, his Dot triages those issues and flags only what's truly urgent. It's also gotten him building again: In spare moments, he gives his Dot ideas for a feature, and his Dot builds five or six versions for him to review.
On the first episode of The Every Podcast (formerly AI & I), Dan Shipper interviews Altman at OpenAI's DevDay. They discuss how Altman uses his Dot to protect his time, why he only prompts on Ultrafast, and his belief that AI will spark a new Renaissance.
If you found this episode interesting, please like, subscribe, comment, and share!
Go to https://attio.com/every and get 15% off your first year.
To hear more from Dan Shipper:
• Subscribe to Every: https://every.to/subscribe
• Follow him on X: https://twitter.com/danshipper
Chapters:
00:00:00 Introduction
00:00:55 Why AI hasn’t replaced developers
00:04:45 OpenAI’s platform bet on the rise of small-team companies
00:05:45 Why the AI era is more “Renaissance” than “Industrial Revolution”
00:07:40 How Sam’s Dot, OpenAI’s always-on AI agent, gave him back his mornings
00:12:00 Sam’s process for building new features with his Dot
00:16:00 Inside OpenAI’s “live documents” that agents and humans can build together
00:20:55 What bring-your-own-subscription changes for app developers
00:24:00 Why “OpenAI just killed X startup” never comes true
00:27:55 Sam’s life on Ultrafast and the push to make speed as cheap as intelligence
Links to resources mentioned in the episode:
• Sam Altman on X: https://x.com/sama
• Codex: https://openai.com/codex
• Space: https://help.openai.com/en/articles/20001549-getting-started-with-space-in-chatgpt
• Every's Vibe Check on Dots: https://every.to/vibe-check/vibe-check-dots-always-on-agents-in-chatgpt?utm_source=podcast
• Every's Vibe Check on OpenAI DevDay 2026: https://every.to/vibe-check/vibe-check-openai-devday-2026?utm_source=podcast
How a Professional Writer Writes With AI
Two years ago, Katie Parrott was laid off from a crypto firm and couldn’t afford a career coach—so she turned to a $20-a-month ChatGPT subscription instead.The Every staff writer’s habit of feeding AI good …
Notes de l’épisode
Two years ago, Katie Parrott was laid off from a crypto firm and couldn’t afford a career coach—so she turned to a $20-a-month ChatGPT subscription instead.
The Every staff writer’s habit of feeding AI good context eventually became compound writing, a codified system for brainstorming, drafting, and editing with AI. Today, that system is a plugin any writer can use.
On this week’s AI & I, Natalia Quintero talks with Katie about turning good ingredients into good writing, borrowing the taste of writers she admires, and why AI helped her fall back in love with the page.
If you found this episode interesting, please like, subscribe, comment, and share!
To hear more from Natalia Quintero:Subscribe to Every: https://every.to/subscribe
Follow her on X: @NataliaZarinaGo to https://attio.com/every and get 15% off your first year.
Timestamps:
0:00 Start
0:49 Introduction
1:21 How Katie went from being laid off to using ChatGPT as a career coach
8:06 Turning AI into a “content agency of one”
10:59 Building the context files and style guides that make AI outputs useful
14:15 What makes a Claude project actually work
21:18 How AI helped with a mental health crisis and “computer errands”
27:03 Katie’s career coach, now a fully autonomous Codex project
32:27 What compounding means, and building the Compound Writing plugin
38:00 Borrowing Vonnegut, Hitchcock, and Sorkin as AI editing skills
44:49 Katie’s thesis: education and access will matter more than everLinks to resources mentioned in the episode:
Katie Parrott on X: @kplikethebird
Compound writing plugin: https://github.com/EveryInc/compound-writing
Compound engineering plugin (Kieran Klassen): github.com/EveryInc/compound-engineering-plugin
Parrott’s companion piece on compound writing: https://every.to/guides/compound-writing
Parrott, “I Hired ChatGPT as My Career Coach”: every.to/working-overtime/i-hired-chatgpt-as-my-career-coach
Parrott, “AI Turned Me Into a Content Agency of One”: every.to/working-overtime/ai-turned-me-into-a-content-agency-of-oneA $10B Hedge Fund’s AI Playbook (Best of the Pod)
Will England is the CEO of Walleye Capital, a hedge fund managing nearly $10 billion in assets. An engineer by training with a math background from Oxford, he has spent his career at the intersection of …
Notes de l’épisode
Will England is the CEO of Walleye Capital, a hedge fund managing nearly $10 billion in assets. An engineer by training with a math background from Oxford, he has spent his career at the intersection of machines and markets—and has made AI fluency mandatory for all 400 employees.
England believes refusing to use AI is like refusing to use the internet in 1995 because it wasn’t perfect. His use of AI is public and effusive, including in a firm-wide email that opened with “I used ChatGPT to write this email. You should be using it, too, and be proud of it.” AI informs how Walleye drafts memos and selects stocks.
On Every’s AI & I, Dan Shipper spoke with England about why he’s betting his entire organization on AI, why “results are what matter” more than blood, sweat, and tears, and what the American frontier can teach us about leading through technological change.
Like, subscribe, comment, and share!
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow Dan Shipper on X: https://twitter.com/danshipperGo to https://attio.com/every and get 15% off your first year.
Timestamps:
0:00 Start
0:51 Introduction
3:25 What pushed Will to go all in on AI
15:15 Inside the ‘AI-first’ memo Will shared at Walleye
17:02 Why you shouldn’t be afraid of using AI for work
31:25 How Will uses LLMs to sharpen his thinking
35:57 Walleye’s approach to using AI to reduce risk
39:35 What history can teach us about leading through change
57:10 Will’s first principles for making better decisions
59:23 Why Will journals every day—and how AI makes it easierLinks to resources mentioned in the episode:
Will England/Walleye Capital: https://walleyecapital.com/bio/will-england
Every’s AI tools—Monologue, Cora, Spiral, and Sparkle: https://every.to/studio
Every’s AI consulting: https://every.to/consultingThe AI Alien Companion App That's Bringing In $4M a Year (Best of the Pod)
LLMs are a new medium for storytelling.That’s according to the creators of Portola, the company behind Tolan: an embodied AI companion that lives on its own planet and chats to you with a distinct personality …
Notes de l’épisode
LLMs are a new medium for storytelling.
That’s according to the creators of Portola, the company behind Tolan: an embodied AI companion that lives on its own planet and chats to you with a distinct personality. In 2025, Portola's founder and CEO Quinten Farmer and Head of Story Eliot Peper joined Dan Shipper to explain how they’re building this new medium from scratch. Their aim is to help users go from overwhelmed to grounded through conversations with Tolan that feel personal and spontaneous, not scripted.
On this week’s AI & I, Dan revisits his conversation with Quinten and Eliot. They discuss why response time is everything for voice-based AI interfaces, how Portola designs AI personalities users will click with, and why character-driven AI could become a new computing interface.
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/danshipperGo to https://attio.com/every and get 15% off your first year.
Timestamps:
00:01:30 - Introduction
00:04:07 - Talking to the Portola CEO's Tolan, Clarence
00:09:11 - How Portola went from building software for kids to AI companions
00:23:40 - Why response time is everything for voice-based AI interfaces
00:29:54 - Tolans don't use scripted prompts—they're taught to improvise
00:37:23 - How to know which AI personalities your users will click with
00:42:27 - Developing the character traits of an AI companion
00:49:48 - What does it mean to build technology that makes us flourish
01:01:10 - How Portola evaluates whether Tolans are resonating with users
01:11:01 - Inside Portola's viral growth strategyMicrosoft’s Vision for an Internet Made for Agents With CTO Kevin Scott (Best of the Pod)
In 2025, Kevin Scott bet that the agentic web would be the next big thing in AI.The Microsoft CTO argued that for agents to be genuinely useful, they'd need to be able to take action on our behalf—which would …
Notes de l’épisode
In 2025, Kevin Scott bet that the agentic web would be the next big thing in AI.
The Microsoft CTO argued that for agents to be genuinely useful, they'd need to be able to take action on our behalf—which would mean giving them access to the same sprawl of tools, data, and systems that make up the internet. Today, that bet is starting to pay off, as the foundational infrastructure for the agentic web is now being built.On this week's AI & I, Dan Shipper revisits his conversation with Kevin. They discuss Microsoft's role in the agentic web, why openness doesn't have to come at the expense of security, and why programmers should stay curious about new tools rather than resist them on principle.
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/danshipperGo to https://attio.com/every and get 15% off your first year.
Timestamps:
0:00 Start
1:44 Introduction
2:49 The race to close the "capability overhang"
4:31 How agents will evolve into practical, useful tools
6:48 The role Kevin sees Microsoft playing in the agent ecosystem
12:05 How robust security measures can coexist with open ecosystems
15:39 Kevin's philosophy on being a craftsman in the age of agents
20:52 How the landscape of software development agents will evolve
25:33 The future of agentic workflowsLinks to resources mentioned in the episode:
Kevin Scott on X: https://twitter.com/kevin_scott
Model Context Protocol (MCP): https://modelcontextprotocol.io
NLWeb: https://github.com/microsoft/NLWeb
GitHub Copilot: https://github.com/features/copilotWhy the Next Hit AI Product Will Be Social Why the Next Hit AI Product Will Be Social (Best of the Pod)
Most consumer AI so far has been single-player: you and a chatbot, alone.Benchmark partner Sarah Tavel, one of Pinterest's first 30 employees, is betting that's about to change. She's looking for a product …
Notes de l’épisode
Most consumer AI so far has been single-player: you and a chatbot, alone.
Benchmark partner Sarah Tavel, one of Pinterest's first 30 employees, is betting that's about to change. She's looking for a product genius who can build an AI product with social DNA: status, network effects, and multiplayer dynamics. That'll enable users of ChatGPT and other models to learn from how others use AI and level up.
On this week’s AI & I, Dan Shipper revisits his conversation with Sarah. They talk about why technical founders dominate the early days of a platform shift while product-minded founders win later, what ChatGPT is still missing, and what separates a founder's real network effect from a slide with a flywheel diagram.
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
Timestamps for YouTube:
0:00 Start
1:10 Introduction
2:26 Why the future of consumer AI belongs to founders with product intuition
11:09 What Sarah sees as ChatGPT's biggest weakness
18:45 How Sarah would design a consumer AI app with social DNA
24:10 The kind of founders Sarah invests in
28:33 How to know if your startup's network effects are real
35:40 What's catching Sarah's eye beyond AI
40:41 How AI will change the way top venture capitalists invest
Links to resources mentioned in the episode:
Sarah Tavel on X: https://x.com/sarahtavel
Benchmark: https://benchmark.com
Agentio (marketplace for YouTube creators and brands): https://agentio.com/
Chainalysis: https://chainalysis.com
The Five Temptations of a CEO by Patrick Lencioni: https://www.amazon.com/dp/B007BZBRB8
Thinking in Bets by Annie Duke: https://www.amazon.com/dp/B0HBBW23PM
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 …
Notes de l’épisode
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/subscribe
Follow 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 Kelly
16:10 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/kevin2kelly
The Inevitable by Kevin Kelly: https://www.amazon.com/Inevitable-Understanding-Technological-Forces-Future/dp/0525428089
Pilgrim at Tinker Creek by Annie Dillard: https://www.amazon.com/Pilgrim-Tinker-Harper-Perennial-Classics/dp/0061233323
1,000 True Fans by Kevin Kelly: https://www.amazon.com/1000-True-Fans-Kellys-Simple-ebook/dp/B01N9P9O4G
Full episode transcript: https://every.to/podcast/transcript-be243312-ea22-4193-8c56-b9cd45a79a87How 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 …
Notes de l’épisode
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/subscribe
- Follow him on X: https://twitter.com/danshipper
Timestamps 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 Design
34:51 Tips on What to Build First
43:46 What's Next for All Access
Links to resources mentioned in the episode:- Brandon Gell on X: https://x.com/bran_don_gell
- Yash Poojary on X: https://x.com/poojary_yash
- Austin Tedesco on X: https://x.com/tedescau?lang=en
- Douglas Brundage on X: https://x.com/DABrundage
- Introducing Every All Access: https://every.to/on-every/introducing-every-all-access
- Get the Builder Pack: every.to/builder-pack
Go to https://attio.com/every and get 15% off your first year.
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 …
Notes de l’épisode
“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/subscribe
Follow him on X: https://twitter.com/danshipperTimestamps:
00:00:59 Introduction
00:01:57 Why starting a company feels like a knife fight
00:04:33 Granola's counterintuitive view on competition
00:10:44 Dan's "pirate and architect" framework for structuring early-stage product teams
00:13:09 How Granola's "shaping" and "validation" phases work for building new features
00:18:17 Why Dan lives almost entirely inside Codex
00:24:40 The case for "Codex-native apps"
00:35:37 Granola's "handrail" philosophy
00:38:12 Why Granola is betting on owning meeting-adjacent context instead of competing as a general agent
00:44:19 What a transcript alone can never captureEpisode resources:
Chris Pedregal on X: https://twitter.com/cjpedregal
Granola on X: https://twitter.com/meetgranola
Granola: https://granola.ai
Granola 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.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 …
Notes de l’épisode
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/subscribe
Follow him on X: https://twitter.com/danshipperTimestamps:
0:00 Introduction
3:51 Rebuilding Quicken and Campaign Monitor with AI
6:24 Building The Good Place, a private Twitter alternative for Craig’s members
10:39 Why we’re entering a “golden age of tool building”
12:17 Why AI could help writers build audiences
17:35 Using AI to build a newsletter archive and a searchable board-meeting Q&A library
27:58 Creating a technology-free buffer to protect deep thinking
30:31 Why Craig is resisting the temptation to “mainline” AI for ten hours a day
39:44 Why anthropomorphizing AI is “psychotic,” and why Apple got Siri right
47:42 Being adopted, and making peace with humanity’s fragile place in an AI future
Go 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.com
Roden (Craig’s monthly newsletter): https://craigmod.com/roden/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 …
Notes de l’épisode
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/subscribe
Follow him on X: https://twitter.com/danshipperTimestamps:
00:01:05 Introduction
00:02:35 How Natalia manages Claudie, the consulting team's AI project manager
00:04:55 Why the consulting team still pays for SaaS products
00:11:47 Codex as a game changer
00:14:55 Building personalized learning guides and illustrated explainers with AI
00:21:40 Inside Natalia's AI-powered email triage system
00:26:44 The shift from knowledge work as sculpting to knowledge work as gardening
00:28:57 Using Codex to one-shot a custom CRM
00:33:16 Using Codex to build an app that coordinates her father's medical care
Links to resources mentioned in the episode:
Natalia Quintero on X: https://x.com/NataliaZarina
Asana (project management): https://asana.com
Every Consulting: https://every.to/consulting
Go to attio.com/every and get 15% off your first year.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 …
Notes de l’épisode
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/dan
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipperTimestamps:
00:00:54 Introduction
00:01:49 Surge as a "school for AGI"
00:04:46 What AI's capacity for novel mathematics says about human achievement
00:07:29 Motivation in an era when AI can do everything
00:14:34 The trap of optimizing AI models for engagement
00:29:34 Training using datasets versus training using environments
00:35:09 The value of personal data
00:39:40 Why models are bad at writing
00:42:00 Chen's AGI timelineLinks to resources mentioned in the episode:
Edwin Chen on X: https://x.com/echen
Surge: https://surgehq.ai
Riemann-bench (research-level math benchmark): https://surgehq.ai/leaderboards/riemann-bench
Hemingway-bench (creative writing benchmark): https://surgehq.ai/leaderboards/hemingway-bench
Talkie-1930 (language model trained on pre-1930 text): https://huggingface.co/talkie-lm/talkie-1930-13b-it
Ted 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=youtube
Follow Every: https://x.com/every
Follow Dan Shipper: https://x.com/danshipperGitHub’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 …
Notes de l’épisode
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/subscribe
Follow him on X: https://x.com/hammer_mt
Timestamps for YouTube:
00:00:52: Introduction
00:03:27: The agentic PR flood
00:04:33: GitHub's approach to helping open-source maintainers manage the surge
00:06:15: What 14 billion commits means for code quality
00:08:03: Moving from per-seat licensing to usage-based pricing
00:09:45: Kyle's dual role as GitHub COO and Microsoft's chief marketing officer for developers
00:13:03: Developer choice as competitive moat
00:14:57: How to balance dogfooding your own tools with staying honest about the competition
00:19:45: Hill climbing, frontier tuning, and solving the model-routing problem
00:24:45: Kyle's agentic communication hack
Links to resources mentioned in the episode:
Kyle Daigle on X: https://x.com/kdaigle
Mike Taylor on Every: https://every.to/@mike_2114
Mike’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-one
GitHub Copilot: https://github.com/features/copilotHow 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 …
Notes de l’épisode
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/subscribe
Follow him on X: https://twitter.com/danshipper
Get started with Braintrust at https://www.braintrust.dev/Timestamps:
0:03 Introduction
1:48 How Fable completely reshaped Mike's workflow
4:48 When to use Sonnet versus Fable
10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture
15:00 The cost to build has collapsed
19:03 Is software engineering over?
21:48 How Anthropic's engineering teams work today
38:39 The mechanics of verification
44:39 What people should use the model to build
47:24 Dynamic workflowsLinks to resources mentioned in the episode:
Mike Krieger on X: https://x.com/mikeyk
Anthropic Labs: https://www.anthropic.com
Claude Code: https://claude.ai/code
Every: https://every.toThe SaaS Apocalypse Is a Goldmine With Figma’s Matt Colyer
The "SaaSpocalypse"—the panic that AI will make software-as-a-service obsolete—hasn't rattled Figma’s Matt Colyer. As the company’s director of product management for developers, he's been building his own …
Notes de l’épisode
The "SaaSpocalypse"—the panic that AI will make software-as-a-service obsolete—hasn't rattled Figma’s Matt Colyer. As the company’s director of product management for developers, he's been building his own agents for two years and is buying more software services than ever.
In addition to making the case that AI is a “goldmine” for SaaS companies, Colyer talked with Dan Shipper for AI & I about why great design requires a diamond-shaped process: First you diverge, generating as many ideas as possible, then you converge around the best ones. Chat is linear, which makes it good for iterating on one design but bad at generating lots of options. Figma's new on-canvas agent is a first attempt at fixing that.
They also get into why AI design tools need to break free of the text box, how Figma's MCP server is closing the loop between code and design, and why "review" has become the biggest bottleneck in AI-assisted product work.
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
Timestamps:
- 1:03 - Introduction
- 2:15 - Why the SaaSpocalypse narrative has it backwards
- 5:27 - Matt’s email agent origin story
- 13:21 - Divergent vs. convergent design thinking
- 17:39 - Figma’s MCP server
- 19:45 - Why design agents need personalization
- 22:09 - Every problem is a context problem
- 25:12 - Apple and Google as the reigning kings of context
- 28:18 - Why review is the new bottleneck
Links to resources mentioned in the episode:
- Matt Colyer on X: https://x.com/mcolyer
- Figma: https://figma.com
- Figma MCP server: https://www.figma.com/blog/introducing-figma-mcp-server/
We Automated Everything With AI and Tripled Our Headcount
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 …
Notes de l’épisode
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/subscribe
Follow 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-automation
Brandon Gell on Every: https://every.to/@brandon_5263
Join the membership for where you live at joinbilt.com/dan
Timestamps:
00:00:51 Introduction
00:05:51 The AI paradox: more automation, more human work
00:10:00 How AI makes yesterday's expert competence cheap
00:18:00 AI can act autonomously but it does not have agency
00:20:39 Why Dan is all in on AGI
00:21:57 AI layoffs are a lie
00:25:42 Ride the models and you'll be fine
00:35:30 How to use AI as a long-form features editorInside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 Million
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 …
Notes de l’épisode
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/subscribe
Follow 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 Rattray
Stainless: https://www.stainless.com/Inside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 MillionClaude Code Can Be Your Second Brain
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 …
Notes de l’épisode
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) / X
Alephic, 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/sabotage
The apps Noah used to set up Claude Code on his phone: Termius, TailscaleThe Secrets of Claude's Platform From the Team Who Built It
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 …
Notes de l’épisode
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/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
00:01:48 - How the Claude platform evolved from API to agents
00:04:09 - The primitives that make up Claude Managed Agents
00:10:37 - Why the harness and the model are becoming a single unit
00:18:49 - The infrastructure wall that kills most agent projects in production
00:24:49 - Why team agents need a different shape than individual productivity tools
00:26:36 - How Anthropic's legal team uses an agent to review marketing copy
00:34:24 - Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms
00:35:50 - How to measure agent success with outcome and budget as the end state
00:39:11 - What the platform looks like a year from now, when Claude writes its own harnessWhy 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 …
Notes de l’épisode
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 Introduction
00:00:57 How Codex went from a tool for senior engineers to a daily driver for knowledge work
00:02:42 How Claude Code proved that a great coding agent works for any knowledge work
00:07:24 Austin's switch to Codex
00:13:48 How Austin set up Codex with folders, keys, and reviewer agents
00:18:24 Using Codex to brainstorm automations across Gmail, Slack, and Notion
00:22:42 How Austin manages the human review step when Codex is drafting communications
00:28:54 Using Codex to build specialized agents inspired by product executive Claire Vo
00:31:09 Synthesizing meeting transcripts and Slack threads into a go-to-market plan
00:40:15 Building a live KPI tracker in Notion that agents can read
00:44:54 Using Codex for recruitingLinks 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
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 …
Notes de l’épisode
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/subscribe
Follow him on X: https://twitter.com/danshipper
Head to http://granola.ai/every and get 3 months free with the code EVERYTimestamps
00:00:45 Introduction
00:01:27 New rules for an agent-driven economy
00:03:57 Compute theft is the new payment fraud
00:10:00 How Stripe expanded fraud detection from checkout to the full customer lifecycle
00:19:48 Why AI companies are scaling way faster than top SaaS companies
00:23:27 Outcome-based billing is replacing seat-based pricing
00:29:57 Where AI spending is coming from
00:36:45 How the developer experience changes when agents are the builders
00:41:00 The agentic commerce spectrum, from assisted buying to autonomous purchasing
00:51:06 Meet Link, a consumer wallet for delegated agent purchases
Links to resources mentioned in the episode:
Emily Glassberg Sands on X: https://x.com/emilygsands
Stripe: https://stripe.com
Stripe Radar: https://stripe.com/radar
Stripe Link: https://link.com
Lovable: https://lovable.devThe 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 …
Notes de l’épisode
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-plugin
- Compound engineering guide: https://every.to/source-code/compound-engineering-the-definitive-guide
- Compound 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 joyThe 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 …
Notes de l’épisode
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
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 …
Notes de l’épisode
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/danshipperVisit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.
Timestamps:
00:00 Introduction
00:02:21 How Brandon built Zosia, an AI agent to run his household
00:07:09 Brandon's aha moment re: using agents for work
00:09:39 What happened when everyone on the team got their own agent
00:12:42 How agents take on their owners' personalities, and why that matters inside an org
00:23:51 Why it's important for agents to do work in public
00:30:51 What we're still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem
00:40:45 How we built Plus One, our hosted OpenClaw product
00:47:27 The cultural shift required to make agents work at scale
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