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 harness

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.

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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 recruiting

Links to resources mentioned in the episode:

Austin on X: @tedescau

Dan's January essay on OpenAI's catch-up problem: every.to/chain-of-thought/openai-has-some-catching-up-to-do

Every's vibe check on GPT-5.5: every.to/vibe-check/gpt-5-5

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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 EVERY

Timestamps
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.dev

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 joy

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

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Visit 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

If you found this episode interesting, please like, subscribe, comment, and share! 

To hear more from Dan Shipper:
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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 tools
17:45 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

To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 

Download Grammarly for FREE at grammarly.com

Timestamps 
Introduction: 00:01:39
What's gotten easier—and what hasn't—about building products in the age of AI: 00:02:33
Why vibe coding creates "indoor trees": 00:05:00
How rewrites have become a normal part of the development process: 00:09:00
What "agent native" product design means: 00:11:39
How Mike's labs team is structured and the cofounder model: 00:24:27
The best signal for a product bet is someone with "break through walls" conviction: 00:29:33
Navigating enterprise customers while keeping pace with rapid AI change: 00:38:51
OpenClaw, personal agents, and the product question defining 2026: 00:40:54

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

To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 

Timestamps
0:01 – Introduction and Kate's early career as a literary agent
4:45 – From book publishing to tech: Medium, WeWork, and Stripe Press
12:00 – How Kate joined Every and what made the role click
27:00 – What it's like to be a knowledge worker at the frontier of AI
31:00 – The “aha” moment: using AI to manage hundreds of applicants
36:24 – How Every's editorial team uses AI to enforce standards and train taste
45:06 – Publishing two reviews of major model releases on the same day
51:39 – What automating copy editing requires

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

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.

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Get 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 Proof
00:07:24 — From Mac app to collaborative web editor
00:09:00 — What makes Proof “agent native”
00:14:30 — Live demo: watching an agent join and write inside a shared document
00:20:51 — How Austin uses Proof for creative writing and food journalism
00:24:30 — The challenge of multiple agents editing one document simultaneously
00:26:48 — When AI-written docs are better read by agents than by humans
00:29:30 — Brandon’s agent-to-agent collaboration loop
00:37:09 — Proof as a lightweight scratchpad vs. existing tools like Notion and GitHub
00:42:18 — Why Proof is open source and what that means for builders

Links to resources mentioned in the episode:
Proof Editor:⁠ https://proofeditor.ai⁠
Proof GitHub repo (open source):⁠ https://github.com/EveryInc/proof⁠
Every's compound engineering plugin:⁠ https://github.com/EveryInc/compound-engineering-plugin⁠

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!
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Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper

Intent is what comes after your IDE. Try it yourself: augmentcode.com/intent

Head to granola.ai/every to get 3 months free.

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Timestamps
00:00:00 — Introduction and how Sam and Dan's paths first crossed
00:01:40 — 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 CEO
00:07:30 — How specialization across the founding journey creates advantages
00:10:40 — Building AI-durable businesses versus AI-native ones
00:16:10 — How an AI agent transformed their customer discovery process
00:19:30 — Where synthetic customer calls completely fail
00:29:30 — Deploying AI inside established companies
00:32:30 — Why newer projects see huge gains from AI while mature companies see 10 percent
00:37:00 — A preview of what's next for Bolton and Watt

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?

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To hear more from Dan Shipper:

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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/intent

Head to granola.ai/every to get 3 months free.

Timestamps:  

00:00:00 – Start 
00:01:30 – Introduction
00:04:08 – A typical day inside Alpha High School
00:06:54 – Why Alpha replaced teachers with “guides” focused on motivating students
00:12:09 – Why Mathew doesn’t use AI to cheat, even though he could
00:19:51 – Do ambitious teenagers care about going to college?
00:25:12 – Mathew’s take on how Gen Z thinks about AI
00:27:52 – How Mathew thinks about the effects of social media
00:31:29 – Gen Z’s relationship with books and reading
00:38:57 – Mathew ranks ChatGPT, Claude, Gemini, and Grok
00:47:12 – Why Mathew is building Berry, an AI stuffed animal for teen mental health

Links to resources mentioned in the episode:

• Alex Mathew: Alex Mathew (@alxmthew)
• More about Berry: https://berryplush.com/, Berry (@berryaiplushies) 

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! 

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Head to granola.ai/every and get 3 months free with the code EVERY.

Timestamps:  

00:00:00 - Start

00:01:27 - 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 

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! 

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Move fast, don’t break things

Most 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 - Introduction
00:11:51 - Designing an AI browser that’s intuitive to use
00:15:24 - How the web changes if agents do most of the browsing
00:25:06 - Why traditional websites will not become obsolete
00:29:00 - A browser that stays out of the way versus one that shows you around
00:39:51 - How the team uses Codex to build Atlas
00:44:47 - The craft of coding with AI tools
00:52:33 - Why Goodger and Fisher care so much about browsers

Links to resources mentioned in the episode:

• Ben Goodger: Ben Goodger (@bengoodger) 
• Darin Fisher: Darin Fisher (@darinwf) 

OpenAI’s browser, Atlas: Introducing ChatGPT Atlas 

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?

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To hear more from Dan Shipper:

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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 - Introduction

00:01:30 - Why successful AI adoption requires coordinated, top-down effort

00:07:05 - How a private equity firm reduced investment memo creation from weeks to 30 minutes

00:13:30 - The benefits of connecting AI to proprietary context

00:15:20 - The plan-delegate-assess-compound framework for engineering teams

00:17:55 - How non-technical team members are becoming vibe coding addicts

00:20:50 - Building Claudie: an AI project manager from scratch

00:23:00 - Why creative exploration time outside the 9-to-5 is essential

00:27:50 - Live demo: How Claudie automates client onboarding and tracking

00:38:40 - The human side of AI: spending less time in spreadsheets, more time with people

Links 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-consulting

Every’s compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin 

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?

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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 - Start

00:01:07 - Introduction

00:02:48 - Why Opus 4.5 feels like the iPhone moment for vibe coding

00:08:31 - Why designers have a unique advantage with AI
00:14:10 - How Wilkinson built a custom email client with Claude Code

00:18:13 - An AI trained on your relationship that predicts your fights

00:30:40 - Using AI meeting notes to make your life better
00:35:11 - Don't inject your opinion into prompts

00:40:21 - Wilkinson’s Claude Code tips and workflows

00:47:59 - Your personal stylist is a prompt away

00:53:17 - How AI is changing the way Wilkinson invests in software

Links 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 Stick
• Every’s compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin

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/subscribe
• Follow him on X: https://twitter.com/danshipper

Timestamps:

00:00:00 - Start

00:00:36 - Introduction

00:01:49 - Why you need a dedicated AI learning app

00:04:32 - The process of learning is more passive than you might think

00:10:21 - Live demo of Oboe to create a course about philosopher Ludwig Wittgenstein

00:16:52 - Learning works best when it comes in many formats

00:28:21 - Where AI agents currently fall short in the learning experience

00:34:10 - The importance of making learning feel accessible

00:35:56 - How Zicherman uses Oboe to learn quantum physics

00:40:54 - How embeddings spaces remind Dan of quantum mechanics

Links to resources mentioned in the episode:

• Nir Zicherman: @NirZicherman
• Learn something new with Oboe: https://oboe.com/

In this live Vibe Check, Dan Shipper and Kieran Klaassen explore the new interface together, testing what works (and what doesn't) in real time. Anthropic’s Felix Rieseberg joins midway through to explain the philosophy behind Cowork's design: why it separates "Tasks" from "Chats," how the queue system lets you send messages while the agent is working, and what "agent-native" architecture means in practice. They also dig into Skills—Claude's prompt system that lets you customize how it works—and the Chrome connector for browser automation.

This is a raw, unfiltered first look at what might be the future of how knowledge workers interact with AI: async workflows instead of turn-by-turn chat.

If you found this episode interesting, please like, subscribe, comment, and share!

Want even more?

Check out Dan's guide to building agent-native applications: https://every.to/guides/agent-native
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper

00:01:00 - What is Claude Cowork
00:02:36 - First demo: competitor analysis
00:03:33 - Email drafting that sounds like me
00:06:18 - Calendar audit running for an hour
00:07:39 - Book taxonomy demo
00:08:42 - PostHog analytics via Chrome browsing
00:14:36 - Chat vs Code vs Cowork: when to use what
00:31:06 - Felix from Anthropic joins
00:36:39 - Why they built it in a week and a half
00:37:57 - Design decision: why a separate tab
00:43:57 - Skills as the primary hackable surface
00:49:36 - Agent-native architecture principles
00:56:57 - The origin story of skills at Anthropic
01:03:00 - Our final rating

In his third appearance on AI & I, Hoffman lays out his predictions for where AI will go in the 12 months ahead. He talks to Dan Shipper about how agents will break out of coding into other domains and who’s winning the coding agent race. They also get into how Hoffman defines artificial general intelligence, the way he believes enterprises will use AI, and why public debate on AI might turn more negative, even as the technology becomes more empowering for individuals.

Hoffman’s other bets on the future include cofounding AI drug discovery startup Manas AI, investing at venture capital firm Greylock Partners, writing books, and hosting the Masters of Scale podcast. He’s also an investor at Every.

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
  
  

Timestamps:

00:00:00 - Start

00:00:52 - Introduction

00:02:20 - The future of work is an entrepreneurial mindset

00:05:22 - Creation is addictive (and that’s okay)

00:09:22 - Why discourse around AI might get uglier this year

00:17:03 - AI agents will break out of coding in 2026

00:24:18 - What makes Anthropic’s Opus 4.5 such a good model

00:28:46 - Who will win the agentic coding race

00:36:13 - Why enterprise AI will finally land this year

00:43:16 - How Hoffman defines AGI

00:55:33 - The most underrated category to watch in AI right now

Links to resources mentioned in the episode:

• Reid Hoffman: Reid Hoffman (@reidhoffman)
• The AI drug discovery startup Hoffman cofounded: Manas 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/subscribe 

Follow him on X: https://twitter.com/danshipper 

Timestamps:  
00:00:00 — Start
00:01:05 — Introduction
00:01:34 — Reflections on Every’s growth over the past year
00:09:38 — What changes when a company grows from 20 to 50 people
00:11:55 — How agent-native architecture will change software in 2026
00:17:13 — Why designers are slated to become power users of AI
00:23:24 — The new kind of software engineer who will direct AI agents
00:33:42 — Why the next wave of AI training will focus on autonomy

Reid is a founder, investor, podcaster, and author. But before he did any of these things, he studied philosophy—and it changed the way he thinks.

Studying philosophy trains you to think deeply about truth, human nature, and the meaning of life. It helps you see the big picture and reason through complex problems—invaluable skills for founders grappling with existential questions about their business.

I usually bring guests onto my podcast to discuss the actionable ways in which people have incorporated ChatGPT into their lives. But this episode is different. 

I sat down with Reid to tackle a deeper question: How is AI changing what it means to be human? 

It was honestly one of the most meaningful shows I’ve recorded yet. We dive into:
- How philosophy prepares you to be a better founder
- The importance of interdisciplinary thinking
- Essentialism v. nominalism in the context of AI
- How language models are evolving to be more “essentialist”
- The co-evolution of humans and technology

 Reid also shares actionable uses of ChatGPT for people who want to think more clearly, like:
- Input your argument and ask ChatGPT for alternative perspectives
- Generate custom explanations of complex ideas
- Leverage ChatGPT as an on-demand research assistant

This episode is a must-watch for anyone curious about some of the bigger questions prompted by the rapid development of 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/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 framer.com, and use code DAN to get your first month of Pro on the house!

Timestamps:
00:00:00 - START 
00:04:35 - Why philosophy will make you a better founder
00:08:22 - The fundamental problem with “trolley problems”
00:14:27 - How AI is changing the essentialism v. nominalism debate
00:29:33 - Why embeddings align with nominalism
00:34:26 - How LLMs are being trained to reason better
00:44:52 - How technology changes the way we see ourselves and the world around us
00:46:24 - Why most psychology literature is wrong
00:52:46 - Why philosophers didn’t come up with AI
00:56:30 - How to use ChatGPT to be more philosophically inclined

Links to resources mentioned in the episode:
Reid Hoffman: https://twitter.com/reidhoffman
The podcasts that Reid hosts: Possible (possible.fm) and Masters of Scale (https://mastersofscale.com/)
Reid’s book: Impromptu https://www.impromptubook.com/
The book Reid recommends if you want to be more philosophically inclined: Gödel, Escher, Bach https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
Reid’s article in the Atlantic: "Technology Makes Us More Human" https://www.theatlantic.com/ideas/archive/2023/01/chatgpt-ai-technology-techo-humanism-reid-hoffman/672872/
The book about why psychology literature is wrong: The WEIRDest People in the World by Joseph Henrich https://www.amazon.com/WEIRDest-People-World-Psychologically-Particularly/dp/0374173222
The book about how culture is driving human evolution: The Secrets of Our Success by Joseph Henrich https://press.princeton.edu/books/paperback/9780691178431/the-secret-of-our-success

Jhanas are meditative bliss states and they traditionally require thousands of hours of practice. But Stephen Zerfas and his team at Jhourney are changing that—creating retreats where most participants hit a Jhana in their first week.

Dan Shipper went to one of their retreats earlier this year, and it was by far the best he’s been to. So we had Stephen on AI & I to show us how he gets into a Jhana and what the future of super wellbeing might look like.

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

Timestamps:

Introduction: 00:00:56

A primer on Jhana meditation: 00:01:18

Zerfas guides himself towards Jhana: 00:05:47

Why Jhana is about resting into what already exists: 00:36:04

Approaching meditation with play and curiosity: 00:39:30

The potential pitfalls of Jhana meditation: 00:45:04

How to hack your personality through memory reconsolidation: 00:48:21

Why Jhana won't let you numb yourself to real problems: 00:53:10

How Jhana meditation has changed Zerfas: 00:55:36

How Jhourney is using AI to make Jhanas more accessible: 01:09:41

Links to resources mentioned in the episode:

• Stephen Zerfas: Stephen Zerfas
• Jhourney: https://www.jhourney.io/
• The Donella Meadows book Zerfas refers to: Thinking in Systems: A Primer

This includes Seinfeld episodes, YouTube videos about lemurs, eight hours of snoring per night—and even conversations with me, all condensed into MP3 and text files that are used to train the AI. Since FetusGPT is learning English from such a narrow, idiosyncratic slice of the world, it mostly babbles right now, and if she swears, it picks that up too.

FetusGPT is one zany example of how Siskind uses humor to make a bigger point: AI is what we make of it. It’s an approach that feeds through her comedy writing and work as the founder of science and technology communications agency Hello SciCom.

We had Siskind on AI & I to talk about how she uses AI in her creative process as a comedian, and the unexpected support it's become, both practical and emotional, as she navigates pregnancy.

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

Pitch is the AI presentation platform that helps professionals collaborate on, create, and deliver winning slide decks — all while staying on brand: https://pitch.com/use-cases/ai-presentation-maker/?utm_medium=paid-influencer&utm_campaign=every 

Timestamps:

00:00:00 - Start

00:01:54 - Introduction

00:02:03 - How Siskind is running an experiment between her unborn child and an LLM

00:07:34 - A demo of Siskind’s FetusGPT

00:15:16 - Siskind’s pick for the funniest LLM

00:17:12 - How Siskind uses AI in her comedy writing

00:24:41 - Dan and Siskind use ChatGPT to write a joke together live on the show

00:37:21 - Why AI is useful even when you don’t use its output directly

00:44:15 - How Siskind used a ChatGPT project to biohack her energy levels

00:57:09 - A question we fundamentally couldn’t have asked in pre-ChatGPT times

01:05:29 - How ChatGPT is a source of emotional support for Siskind in pregnancy

Links to resources mentioned in the episode:

Sarah Rose Siskind: https://sarahrosesiskind.com/

Siskind’s agency Hello SciCom: https://www.hellosci.com/

Siskind’s book recommendations: I Forced a Bot to Write This Book, The Let Them Theory, Artificial Intelligence: An Illustrated History

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/danshipper⁠

Head to ⁠ai.studio/build⁠ to create your first app
Ready to build a site that looks hand-coded—without hiring a developer? Launch your site for free at ⁠Framer.com⁠, and use code DAN to get your first month of Pro on the house!

Timestamps:
00:00:00 - Start
00:01:57 - Introduction
00:03:28 - How Claude Opus 4.5 made the future feel abruptly close
00:08:12 - The design principles that make Claude Code a powerful coding tool
00:10:57 - How Ford uses Claude Code to build real software
00:20:12 - Why collapsing job titles and roles can feel overwhelming
00:22:56 - Ford’s take on using LLMs to write
00:24:09 - A metaphor for weathering existential moments of change
00:25:45 - What GLP-1s taught Ford about how people adapt to big shifts
00:49:36 - Why you should care what your LLM was trained on
00:52:15 - Ford prompts Claude Code to forecast the future of the consulting industry
00:59:18 - Recognize when an LLM is reflecting your assumptions back to you
01:12:39 - How large enterprises might adopt AI

Links to resources mentioned in the episode:
Paul Ford: ⁠Paul Ford⁠Ford’s company Aboard: ⁠https://aboard.com/⁠The piece Ford wrote for Bloomberg in 2015: ⁠What Is Code?⁠Alan Kay’s concept of a personal computer for children: ⁠https://en.wikipedia.org/wiki/Dynabook⁠

That’s exactly what The Browser Company did with Arc.

Originally recorded in July before The Browser Company’s acquisition by software giant Atlassian earlier this year, we’re republishing this episode because its lessons are truly timeless. Today, the team continues to operate independently under Atlassian’s umbrella.

The internet backlash when the company killed Arc in May 2025 was intense, but cofounders Josh Miller and Hursh Agrawal saw that AI was about to make the web something you talk to, not just click into. The best home for that assistant was the thing that's already between you and the internet—the browser. And they realized they couldn’t just duct-tape it on to Arc.

One year of heads-down work later, the team launched Dia in beta, and people are raving about it. Dia is a sleek, fast, browser with AI at its core—it gets better with every tab you open, becoming more and more helpful with time.

And even though it’s still early, Josh and Hursh’s big pivot looks like one for the ages.

In this episode of AI & I, Josh and Hursh spoke for the first time in a full-length podcast about their pivot from Arc to Dia. We talked through their decision-making process, the very public backlash the company faced, and the grit it took to stay the course.

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

Timestamps:
00:00:00 - Start
00:00:48 - Introduction
00:02:22 - The story of how Dan might've been the CEO of The Browser Company
00:09:40 - The moment Josh and Hursh knew they had to walk away from Arc
00:16:59 - How to handle the weight of the unknown in a pivot
00:23:24 - The prototype-driven culture that kept The Browser Company alive
00:25:06 - Why having a product loved by millions of users isn't enough
00:32:12 - The architectural decisions underlying how Dia was built
00:46:04 - How Dia almost shipped without its best feature
00:50:45 - The best ways people are using Dia in the wild
01:07:27 - How Josh and Hursh think about competing with incumbents
01:17:13 - How romanticism informs the product decisions behind Dia

Links to resources mentioned in the episode:
Hursh Agrawal: @hursh
Josh Miller: @joshm
More about Dia: https://www.diabrowser.com/
Writer and investor M.G. Siegler’s essay about the AI browser wars: https://spyglass.org/ai-browser-wars/

Note: This episode is a rerun from our archives.

Two engineers at Every shipped six features, five bug fixes, and three infrastructure updates in one week—and they did it by designing workflows with AI agents, where each task makes the next one easier, faster, and more reliable.

In this episode of AI & I, Dan Shipper interviewed the pair—Kieran Klaassen, general manager of Cora, our inbox management tool, and Cora engineer Nityesh Agarwal—about how they’re compounding their engineering with AI. They walk Dan through their workflow in Anthropic’s agentic coding tool, Claude Code, and the mental models they’ve developed for making AI agents truly useful. Kieran, our resident AI-agent aficionado, also ranked all the AI coding assistants he’s used.

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 ai.studio/build to create your first app.

Pitch is the AI presentation platform that helps professionals collaborate on, create, and deliver winning slide decks — all while staying on brand: https://pitch.com/use-cases/ai-presentation-maker/?utm_medium=paid-influencer&utm_campaign=every 

Timestamps:
Episode start: 00:00:00
Introduction: 00:01:16
Why Kieran believes agents are turning a corner: 00:03:18
Why Claude Code stands out from other agents: 00:06:36
What makes agentic coding different from using tools like Cursor: 00:11:58
The Cora team’s workflow to turn tasks into momentum: 00:15:20
How to build a prompt that turns ideas into plans: 00:23:07
The new mental models for this age of software engineering: 00:34:00
Why traditional tests and evals still matter: 00:39:13
Kieran ranks all the AI coding agents he’s used: 00:42:00

Links to resources mentioned in the episode:
Try Cora, our AI email assistant: https://cora.computer/

Kieran Klaassen: @kieranklaassen
Nityesh Agarwal: @nityeshaga
The book that helps Nityesh form mental models to work with AI agents: High Output Management

After helping launch Barkbox and Ro Health through his incubator Prehype, Henrik is distilling everything he knows into Audos, a platform that helps you use AI agents to turn your idea into a profitable, lasting company.

We had him on AI & I to talk about “portfolio entrepreneurship”—a new breed of entrepreneurship shepherded in by AI, where founders build families of products around the same customer, instead of one moonshot idea. It’s a philosophy we hold close to our hearts at Every.

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 ai.studio/build to create your first app.

Ready to build a site that looks hand-coded—without hiring a developer? Launch your site for free at https://www.framer.com/, and use code DAN to get your first month of Pro on the house!

Pitch is the AI presentation platform that helps professionals collaborate on, create, and deliver winning slide decks — all while staying on brand: https://pitch.com/use-cases/ai-presentation-maker/?utm_medium=paid-influencer&utm_campaign=every !

Timestamps:

00:01:33 - Introduction

00:02:50 - Dan and Henrik on the new breed of entrepreneurship that AI makes possible

00:11:08 - Why Henrik believes the future belongs to a million $1M companies

00:16:14 - How to build “relationship capital” with your customers

00:21:35 - Why “customer-founder fit” shapes lasting companies

00:23:01 - Everything Henrik learned about himself from a decade of building companies

00:31:44 - How Henrik finds focus and meaning in the daily chaos

00:34:17 - How Henrik is parenting two kids in the age of AI

00:50:33 - The way AI can fix what social media broke

00:56:59 - What happens when AI agents become part of how we tell stories

Links to resources mentioned in the episode:

• Henrik Werdelin: https://hellohenrik.com/
• Try Audos: https://www.audos.com/
• Henrik’s new book: https://www.amazon.com/Me-My-Customer-AI-Entrepeneurship/dp/B0FCSQ1C7H

Instead, he cares most about making great products—like ⁠Basecamp⁠, ⁠HEY⁠, and ⁠Ruby on Rails⁠—products that are centered around a single, coherent idea. These products are complete wholes, where each piece matters—like a Frank Lloyd Wright house or a vintage car.

But how do you create products like that?

In this conversation, we talk to Jason about what two decades of building 37signals has been like—and how to build products that have soul.

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

Listen to Working Smarter wherever you get your podcasts, or visit ⁠workingsmarter.ai⁠. 

Timestamps:

00:00:00 - Start

00:00:32 - Introduction

00:02:06 - What architecture, watches, and cars teach us about software

00:10:54 - How Jason thinks AI plays into product-building

00:20:58 - How developers at 37signals use AI

00:25:47 - Jason’s biggest realization after 26 years of running 37signals

00:29:58 - Where Jason thinks luck shaped his career

00:32:41 - What Jason would do if he were graduated into the AI boom

00:37:22 - Dan asks for advice on running a non-traditional company like Every

00:46:39 - Why staying true to yourself is the only way to build something lasting

00:49:38 - Wholeness as the north star for building products—and companies

Links to resources mentioned in the episode:

• Jason Fried: ⁠Jason Fried (@jasonfried)⁠, ⁠Jason Fried⁠
• More about 37Signals: ⁠37signals⁠
• The book about architecture by Christopher Alexander: ⁠The Timeless Way of Building⁠

At Dreamforce 2025, Every CEO Dan Shipper sat down with Silvio Savarese, chief AI scientist at Salesforce, to discuss how one of the world’s largest software companies is shaping the future of AI for the enterprise.

Together, Dan and Savarese explore how his team at Salesforce develops AI solutions that now power more than 13,000 businesses—including OpenAI, Dell, and FedEx—helping them become truly Agentic Enterprises that operate with greater scale, speed, and precision. Examples include a large language model built for Salesforce developers years before ChatGPT’s release, and Agentforce, the company’s agentic layer that enables a hybrid future of work where humans and AI agents collaborate to achieve more than either could alone.

They also discuss how Agentforce gives enterprises a deeply unified AI platform that connects their data with agent functionality—making it both powerful and practical. The conversation touches on how Salesforce builds trust with enterprise customers amid the jagged frontier of AI by ensuring consistency in results, while continuing to push the boundaries of what agents can do autonomously. Savarese shares how enterprise-grade simulation environments help them strike that balance, and reflects on how AI agents will ultimately transform how businesses and individuals alike get things done.

@Salesforce #SalesforcePartner #DF25

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

Timestamps:
00:00 – Start
01:16 – Inside Salesforce’s early AI innovations
02:50 – How Agentforce works and what it can do
07:03 – The real challenges of deploying AI at scale
08:57 – Why Salesforce builds simulation environments for AI
12:35 – The future of agents and enterprise AI

They now ship to codebases they barely know, each new feature makes the next easier to build, and even non-technical teammates confidently use the terminal.

To explore how this happened, AI & I host Dan Shipper invited Claude Code’s creators—Cat Wu (@_catwu) and Boris Cherny (@bcherny) from Anthropic AI—to discuss what they’ve learned from building one of the most beloved AI engineering tools in the world.

This episode is a must-watch for anyone—technical or not—who wants to understand how to use Claude Code like the people who built it.

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

Build your first AI-powered app at [ai.studio/build](http://ai.studio/build).

Timestamps: 

00:00:00 - Start

00:01:26 - Introduction

00:02:25 - Claude Code’s origin story

00:07:03 - How Anthropic dogfoods Claude Code

00:14:06 - Boris and Cat’s favorite slash commands

00:15:49 - How Boris uses Claude Code to plan feature development

00:21:53 - Everything Anthropic has learned about using sub-agents well

00:26:16 - Use Claude Code to turn past code into leverage

00:33:14 - The product decisions for building an agent that’s simple and powerful

00:36:38 - Making Claude Code accessible to the non-technical user

00:45:12 - The next form factor for coding with AI

Links to resources mentioned in the episode: 
- Cat Wu: https://x.com/_catwu 
- Boris Cherny: https://x.com/bcherny
- Claude Code: https://www.claude.com/product/claude-code

Danny Aziz wrestled with this question while building the new version of Spiral, an AI writing partner informed by our editorial taste at Every that launched yesterday. 

The result is a product—and a philosophy—built by the ultimate craftsman who believes you can lean into AI without blunting your edge with slop. We had Danny on AI & I to talk about using AI without losing your craft. We get into the hidden alpha in AI tools that slow you down, how to code with AI without losing your craft, and everything Danny learned about cajoling AI to write well.

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 Framer.com, and use code DAN to get your first month of Pro on the house. 

Timestamps: 

00:00:00 – Start

00:01:00 – Introduction
00:05:26 – How Danny used Spiral to prepare for this podcast
00:08:29 – Why slowing down makes AI writing better
00:13:42 – The agents working under the hood for Spiral
00:14:46 – How Spiral helps you explore the canvas of possibilities
00:24:41 – Why Danny pivoted away from the old version of Spiral
00:31:51 – How to use AI without losing your craft
00:34:55 – Danny’s workflow for building Spiral as a solo engineer
00:40:39 – Code with AI while staying in control
00:45:26 – What Danny learned about getting AI to write well
00:47:52 – How Danny used DSPy to give AI taste
00:56:16 – Dan v. AI Dan: Can the machine match the man?

Links to resources mentioned in the episode:

• Danny Aziz: Danny Aziz (@DannyAziz97) / X 
• Give Spiral a go: Spiral 
• The article Every published about DSPy: I’ve Stopped Writing Prompts—DSPy Does It Better 

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

Timestamps

00:00:00 - Start

00:01:48 - Introduction

00:04:14 - Why evals and benchmarks are broken

00:07:13 - The sneakiest LLMs in the market

00:13:00 - A competition that turns prompting into a sport

00:15:49 - Building a business around using games to make AI better

00:22:39 - Can language models learn how to be funny

00:25:31 - Why games are a great way to evaluate and train new models

00:26:58 - What child psychology tells us about games and AI

00:30:10 - Using games to unlock continual learning in AI

00:36:42 - Why Alex cares deeply about games

00:44:37 - Where Alex sees the most promise in AI

00:50:54 - Rethinking how young people start their careers in the age of AI

Links to resources mentioned in the episode:

• Alex Duffy: alex duffy (@alxai_)
• Good Start Labs: https://goodstartlabs.com/, good start (@goodstartlabs)
• The book Alex is reading about the importance of games: Playing with Reality: How Games Shape Our World
• The book Dan recommends by the psychoanalyst D.W. Winnicott: Playing and Reality

Aaron’s been the CEO of Box for 20 years–long enough to see a few tech revolutions up close—and taking the company AI-first gave him a glimpse of what the next one means for us. We get into why jobs aren’t going away, the new shape of work, and what it takes to build an AI-first company from the inside.

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

Meet NotebookLM, the AI research tool and thinking partner that can analyze your sources, turn complexity into clarity and transform your content: https://notebooklm.google.com/

Timestamps:

00:00:00 - Start

00:01:30 – Introduction

00:02:36 – Why AI won’t take your job

00:06:42 – Jevons Paradox and the future of work

00:10:40 – How Aaron’s experience with the cloud era shapes his view of AI

00:19:44 – Why every knowledge worker is becoming a manager of AI agents

00:25:21 – What Aaron’s learned from bringing AI into every corner of Box

00:33:57 – What’s overhyped in AI today

00:43:31 – How Aaron balances everyday execution with innovation

Links to resources mentioned in the episode:

• Aaron Levie: Aaron Levie (@levie)
• Box: https://www.box.com/
• Dan’s essay on the shift toward the allocation economy: "The Knowledge Economy Is Over. Welcome to the Allocation Economy"
• Dwarkesh’s podcast with Richard Sutton: https://www.dwarkesh.com/p/richard-sutton

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.

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 Framer.com, and use code DAN to get your first month of Pro on the house.

Timestamps:

00:00:00 - Start
00:01:14 - Introduction
00:02:54 - Why Alex likes running barefoot
00:05:09 - APIs and MCP, the connectors of the new internet
00:10:53 - Why MCP servers are hard to get right
00:20:07 - Design principles for reliable MCP servers
00:23:50 - Scaling MCP servers for large APIs
00:25:14 - Using MCP for business ops at Stainless
00:28:12 - Building a company brain with Claude Code
00:33:59 - Where MCP goes from here
00:41:10 - Alex’s take on the security model for MCP

Links to resources mentioned in the episode:

• Alex Rattray: Alex Rattray (@RattrayAlex), Alex Rattray 
• Stainless: https://www.stainless.com/

Scott Wu, Cognition’s cofounder and CEO, has a front-row seat to what comes next. In this episode of AI & I, we talk with Wu about why the fundamentals of computer science still matter in an AI-first world, the direction he sees for the short- and long-term future of programming, and why he believes we may already be living with AGI.

Timestamps: 
00:00:00 – Start
00:02:02 – Introduction
00:02:32 – Why Scott thinks AGI is here
00:09:27 – Scott’s personal journey as a founder
00:16:55 – Why the fundamentals of computer science still matter
00:22:30 – How the future of programming will evolve
00:26:50 – A new workflow for the AI-first software engineer
00:29:33 – How Devin stacks up against Claude Code
00:40:05 – Reinforcement learning to build better coding agents
00:50:05 – What excites Scott about AI beyond Cognition

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 

Links to resources mentioned in the episode:

• Scott Wu: Scott Wu (@ScottWu46) 
• Learn more about Cognition: https://cognition.ai/ 

Try the world’s first autonomous coding agent: https://devin.ai/ 

Most apps launch first and then try to find users. Monologue, Naveen’s AI voice dictation app that came out of beta yesterday, did the opposite. It built a following of thousands of users during its incubation period at Every—many of them switching over from venture capital-backed competitors—all while the app barely had a landing page.

The growth has continued in the 24 hours since launch, with an average of 1 million words being transcribed weekly, and in this episode of AI & I, we sit down with Naveen to talk about his journey as the single engineer behind a viral app. We get into the false starts and side projects that taught Naveen how to ship fast, the brutal feedback that kept Monologue honest, why Every decided to build in a crowded category, and the AI coding tools that let one developer do the work of a team.

Get free early access to Amazon's Alexa Plus: https://www.amazon.com/dp/B0DCCNHWV5?ref_=aucc_us_dis_everyalexa_q3_25

Timestamps:

00:01:27 – Introduction

00:03:51 – A live demo of Monologue

00:06:27 – Hard lessons from Naveen’s years in the wilderness

00:12:29 – Building a muscle to ship fast

00:21:11 – The spark that became Monologue

00:26:09 – Dogfooding your way to a killer feature

00:29:45 – Why the harshest product feedback is the most valuable

00:31:47 – Every’s strategy for launching an app in a crowded space

00:40:08 – Giving Monologue the Every “smell”

00:45:09 – Naveen’s one-person AI stack to build beautiful apps

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

Links to resources mentioned in the episode:
https://www.monologue.to/

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! 

Start building in Google AI Studio at ai.dev. 

Ready to build a site that looks hand-coded—without hiring a developer? Launch your site for free at Framer.com, and use code DAN to get your first month of Pro on the house. 

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 

Timestamps: 

00:01:19 - Introduction

00:04:28 - How you can do deep work on your phone

00:06:14 - Why Noah thinks Grok has the best voice AI

00:11:39 - The nuts and bolts of Noah’s Claude Code-Obsidian setup

00:23:59 - Using an agent in Claude Code as a “thinking partner”

00:35:07 - Noah’s Thomas’ English Muffin theory of AI

00:44:04 - The white space still left to explore in AI

00:50:41 - How Noah is preparing his kids for AI

01:01:54 - How he brought his Claude Code setup to mobile

Links 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: ⁠Simple Sabotage Field Manual⁠ 
• The apps Noah used to set up Claude Code on his phone: ⁠Termius⁠, ⁠Tailscale⁠

Mid-sentence, he transformed from a startup founder in a black t-shirt to a wizard with light shooting from his hands. Then, he was in a white-walled game universe, and when he picked up the tissue box on his table, it morphed into a gun which he could shoot by moving his arm.

He did it with one of his products, ⁠Mirage⁠: It takes any live video feed (like Dean on the pod) and instantly renders each frame into a new style of your choosing—40 milliseconds from input to output.

Dean is the co-founder and CEO of the creators of ⁠Decart⁠ which makes Mirage. They recently raised $100 million at a $3.1 billion valuation to build a new era of real-time generative AI experiences like this.

Realtime generative video models are going to change video games forever, and Dean is on the forefront: imagine creating endless variations on existing titles, like GTA-V with a frigid winter filter, or taking a bare-bones vibe-coded prototype and using Mirage to texture it. 

But games are just the beginning, Dean sees Mirage as opening the door to a new medium, a new experience created by AI. 

In this episode, we take a look at how Mirage works under the hood, and what the Decart team learned about the future of software while wrestling with its toughest research problems. We also debate AGI—how close it really is, what counts as progress, and what kind of society it might create. This episode is a must watch for anyone interested in the future of gaming, creativity, or if you just want your mind blown by what’s already possible. 

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 

Timestamps: 

• Introduction: 00:00:47
• A demo of Mirage, the first real-time video-to-video model in the world: 00:02:38
• How Mirage can take your vibe-coded game to the next level: 00:06:22
• The new architecture of modern software: 00:08:45
• How Mirage works so blazingly fast: 00:16:34
• Inside Decart’s invention of a new “live stream diffusion” model: 00:20:33
• Solving the error accumulation problem for real-time video: 00:21:17
• How Dean thinks about inventing a new creative medium: 00:29:55
• Dean’s take on the post-AGI world: 00:39:43
• Why AI brings back the age of the generalist: 00:51:15

Links to resources mentioned in the episode:

• Dean Leitersdorf: @DLeitersdorf
• Decart: ⁠https://about.decart.ai/⁠ 
• Try Mirage and Delulu: ⁠https://mirage.decart.ai/⁠, ⁠https://play.google.com/store/apps/details?id=ai.decart.delulu⁠, ⁠https://apps.apple.com/il/app/delulu-by-decart/id6749955738⁠  
• More about Yan LeCun’s error accumulation problem: ⁠https://x.com/ylecun/status/1640123182983045120⁠ 

According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well.

Reid is the cofounder of LinkedIn, Inflection AI, and Manas AI; a partner at venture capital firm Greylock Partners; an early backer and board member of OpenAI; and an award-winning podcaster

We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways:
Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial
In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility
In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error
How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data
Why now may be finally be the era where quantified self tools become valuable
…and more, including everything in his new book Superagency, out this week.

It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with 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-.... 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:   / danshipper

Sponsor:
Attio is the AI-native CRM built for the next era of companies. With Attio, setup takes minutes. Connect your email and calendar, and it instantly builds a CRM that mirrors your business. Go to https://www.⁠⁠⁠⁠attio.com/every to get 15% off on your first year.

Timestamps:
00:00:00 — Episode Start
00:01:29 — Introduction
00:02:50 — Patterns in how we've historically adopted technology
00:07:02 — Why humans have typically been fearful of new technologies
00:13:25 — How Reid developed his own sense of agency
00:20:08 — The way Reid thinks about making investment decisions
00:22:00 — Attio: Go to⁠⁠⁠⁠ https://attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.
00:29:40 — AI as a "techno-humanist" compass
00:35:30 — How to prepare yourself for the way AI will change knowledge work
00:41:39 — Why equitable access to AI is important
00:45:15 — Reid's take on why private commons will be beneficial for society
00:47:23 — How AI is making Silicon Valley's conception of the "quantified self" a reality
00:52:14 — The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence
01:03:29 — Reid's new book, Superagency

Links to resources mentioned in the episode:
Reid Hoffman: @reidhoffman Superagency, Reid’s newest book: https://www.superagency.ai/

Claire Vo built ChatPRD—an on-demand chief product officer powered by AI. It’s now used by over 10,000 product managers and is pulling in six figures in revenue. 

The best part?

Claire has a demanding day job as the CPO at LaunchDarkly. So she built all of ChatPRD herself—over the weekend—with AI.

I sat down with Claire to talk about how ChatPRD works, how she built it as a side hustle using AI, and all of the ways she’s using AI tools to accelerate her work and life. 

We get into:

- How she used AI to build ChatPRD over Thanksgiving break
- The part of product management that Claire thinks AI will disrupt
- Why the PMs of tomorrow will be “proto-managers” who create prototypes rather than just specs
- How junior PMs can use AI to upskill faster
- The ways in which ChatPRD is baked into her own workflow
- How building ChatPRD is making Claire a better PM
- How Claire uses AI as a tech-forward parent

This is a must-watch for anyone interested in turning their side hustle into a thriving business or who works in product.

If you found this episode interesting, please like, subscribe, comment, and share! 

Thanks to Google and LTX Studio for sponsoring this episode!

The Gemini 2.5 family of models is now generally available. 2.5 Pro, the most advanced model, is great for reasoning over complex tasks; next up, 2.5 Flash finds the sweet spot between performance and price; and finally, 2.5 Flash Lite is ideal for low-latency, high-volume tasks. Start building in Google AI Studio at ⁠https://ai.dev/⁠

LTX Studio is helping storytellers go from concept to delivery in one seamless platform. Whether you're storyboarding your next film, prototyping ad concepts, or creating pixel-ready assets, LTX Studio allows you to fully realize your imaginations. Check them out here: ⁠https://tinyurl.com/2d5nx3ut⁠

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

Links to resources mentioned in the episode:
- Claire Vo: https://x.com/clairevo; @chiefproductofficer
- ChatPRD: https://www.chatprd.ai/; https://x.com/chatprd; https://www.linkedin.com/company/chatprd/; https://www.youtube.com/@ChatPRD    
- Some of the AI tools that Claire used to build ChatPRD: http://Clerk.dev; https://tiptap.dev/ 
- Greeking Out, the Greek mythology podcast that Claire’s son enjoys: https://www.nationalgeographic.com/podcasts/greeking-out

Guillermo Rauch is one of the most prolific coders of this generation. 

But he doesn’t think of himself as a coder anymore. 

Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. 

Guillermo is the founder and CEO of Vercel, the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent.

If you found this episode interesting, please like, subscribe, comment, and share! 

Sponsors:

LTX Studio is helping storytellers go from concept to delivery in one seamless platform. Whether you're storyboarding your next film, prototyping ad concepts, or creating pixel-ready assets, LTX Studio allows you to fully realize your imaginations. Check them out here: https://tinyurl.com/2d5nx3ut

Attio is the AI-native CRM built for the next era of companies. With Attio, setup takes minutes. Connect your email and calendar, and it instantly builds a CRM that mirrors your business. Go to https://www.⁠⁠⁠⁠attio.com/every to get 15% off on your first year.

Want even more?
Read Dan Shipper's essay on developing taste with AI: https://every.to/chain-of-thought/what-i-do-when-i-can-t-sleep

Try Cora to manage your email with AI: https://cora.computer
Try Spiral to repurpose content with AI: https://spiral.computer
Try Sparkle to organize your files with AI: https://makeitsparkle.co

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
 
Timestamps
00:00:00 - Episode start
00:01:33 - Introduction
00:03:18 - How to spot trends early
00:07:34 - Why you should be your own customer
00:14:55 - How to create an ecosystem of talent and ambition
00:17:29 - Why Guillermo doesn't identify as a coder
00:20:50 - AI is gearing us toward an allocation economy
00:28:34 - How Vercel's copilot compares with other coding agents
00:40:35 - Guillermo's advice on having better taste
00:42:46 - The future of AI agents is specialized
00:47:50 - How AI startups can compete with big tech

Links to resources mentioned in the episode:
Guillermo Rauch: @rauchg
Vercel: https://vercel.com/ 
Last week’s episode with Nabeel Hyatt: https://every.to/podcast/the-venture-capitalist-who-only-makes-two-bets-a-year
Dan’s essay about the allocation economy: https://every.to/chain-of-thought/the-knowledge-economy-is-over-welcome-to-the-allocation-economy

He’s using LLMs to enhance how he reads, learns, thinks, and conducts interviews. 

Dwarkesh is a podcaster who’s interviewed a wide range of people, like Mark Zuckerberg, Tony Blair, and Marc Andreesen. Before conducting each of these interviews, Dwarkesh learns as much as he can about his guest and their area of expertise—AI hardware, tense geopolitical crises, and the genetics of human origins, to name a few. 

The most important tool in his learning arsenal? AI—specifically Claude, Claude Projects, and a few custom tools he’s built to accelerate his workflow.

He does this by researching extensively, and as his knowledge grows, each piece of new information builds upon the last, making it easier and easier to grasp meaningful insights. 

In this interview, I turn the tables on him to understand how the prolific podcaster uses AI to become a smarter version of himself. We get into:

How he uses LLMs to remember everything
His podcast prep workflow with Claude to understand complex topics
Why it’s important to be an early adopter of technology
His taste in books and how he uses LLMs to learn from them
How he thinks about building a worldview 
His quick takes on the AI’s existential questions—AGI and P(doom)

We also use Claude live on the show to help Dwarkesh research for an upcoming podcast recording.

This is a must-watch for curious people who want to use AI to become smarter.

If you found this episode interesting, please like, subscribe, comment, and share! 

Sponsor: 
Gemini: Experience high quality AI video generation with Google's most capable video model: Veo 3. Try it in the Gemini app at gemini.google with a Google AI Pro plan or get the highest access with the Ultra plan.

Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-.... 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:   / danshipper   

Timestamps:
00:00:00 - Teaser
00:01:44 - Introduction 
00:05:37 - How Dwarkesh uses LLMs to remember everything 
00:11:50 - Dwarkesh's taste in books and how he uses AI to learn from them 
00:17:58 - Why it's important to be an early adopter of technology 
00:20:44 - How Dwarkesh uses Claude to understand complex concepts
00:26:36 - Dwarkesh on how you can compound your intelligence 
00:28:21 - Why Dwarkesh is on a quest to know everything 
00:39:19 - Dan and Dwarkesh prep for an upcoming interview 
01:04:14 - How Dwarkesh uses AI for post-production of his podcast 
01:08:51 - Rapid fire on AI's biggest questions—AGI and P(doom)

Links to resources mentioned in the episode:
Dwarkesh Patel:   / dwarkesh_sp  
Dwarkesh’s podcast and newsletter: https://www.dwarkeshpatel.com/; https://substack.com/@dwarkesh 
Dwarkesh’s interview with researcher Andy Matuschak on spaced repetition: https://www.dwarkeshpatel.com/p/andy-... 
The book about technology and society that both Dan and Dwarkesh are reading: Medieval Technology and Social Change
Dan’s interview with Reid Hoffman: https://every.to/chain-of-thought/rei... 
The book by Will Durant that inspires Dwarkesh: Fallen Leaves https://www.amazon.com/Fallen-Leaves-... 
One of the most interesting books Dwarkesh has read: The Great Divide https://www.amazon.com/Great-Divide-N...
Upcoming guests on Dwarkesh’s podcast: David Reich  https://reich.hms.harvard.edu/ and Daniel Yergin https://www.danielyergin.com/

The same-origin policy—a well-intentioned browser security rule from the 1990s—accidentally created Facebook, Google, and every data monopoly since. It locks your data in silos—and you stayed where your stuff already is. This dynamic created aggregators.

Alex Komoroske—who led Chrome's web platform team at Google and ran corporate strategy at Stripe—saw this pattern play out firsthand. And he's obsessed with the tiny decisions that will shape AI's next 30 years:

Whether AI keeps memory centrally or user-controlled?

Is AI free/ad-supported or user-paid?

Should AI be engagement-maximizing or intention-aligned?

How should we handle prompt injection in MCP and agentic systems?

Should AI be built with AOL-style aggregation or web-style openness?

This is a much-watch if you care about the future of AI and humanity.

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 

Sponsors: 
Google Gemini: Experience high quality AI video generation with Google's most capable video model: Veo 3. Try it in the Gemini app at gemini.google with a Google AI Pro plan or get the highest access with the Ultra plan.

Attio: Go to⁠⁠⁠⁠ https://attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

1. Introduction: 00:01:45
2. Why chatbots are a feature not a paradigm: 00:04:25
3. Toward AI that’s aligned with our intentions: 00:06:50
4. The four pillars of “intentional technology”: 00:11:54
5. The type of structures in which intentional technology can thrive: 00:14:16
6. Why ChatGPT is the AOL of the AI era: 00:18:26
7. Why AI needs to break out of the silos of the early internet: 00:25:55
8. Alex’s personal journey into systems-thinking: 00:41:53
9. How LLMs can encode what we know but can’t explain: 00:48:15
10. Can LLMs solve the coordination problem inside organizations: 00:54:35
11. The under-discussed risk of prompt injection: 01:01:39

Links to resources mentioned in the episode:

• Alex Komoroske: @komorama
• Common Tools: https://common.tools/ 
• The public Google document with Alex’s raw ideas and thoughts: Bits and Bobs
• A couple of Alex’s favorite books: Why Information Grows by Cesar Hidalgo and The Origin of Wealth by Eric Beinhocker

That’s exactly what The Browser Company did with Arc.

The internet backlash was intense, but cofounders Josh Miller and Hursh Agrawal saw that AI was about to make the web something you talk to, not just click into. The best home for that assistant was the thing that's already between you and the internet—the browser. And they realized they couldn’t just duct-tape it on to Arc.

One year of heads-down work later, the team launched Dia in beta, and people are raving about it. Dia is a sleek, fast, browser with AI at its core—it gets better with every tab you open, becoming more and more helpful with time. 

And even though it’s still early, Josh and Hursh’s big pivot looks like one for the ages.

This week on AI & I, Josh and Hursh joined me for their first full-length podcast about their pivot from Arc to Dia. We talk through their decision-making process, the very public backlash the company faced, and the grit it took to stay the course. 

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 

Sponsor:
Attio: Go to⁠⁠⁠⁠ https://attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

1. Introduction: 00:01:13
2. The story of how Dan might’ve been the CEO of The Browser Company: 00:02:47
3. The moment Josh and Hursh knew they had to walk away from Arc: 00:09:42
4. How to handle the weight of the unknown in a pivot: 00:17:08
5. The prototype-driven culture that kept The Browser Company alive: 00:23:31
6. Why having a product loved by millions of users isn’t enough :00:25:42
7. The architectural decisions underlying how Dia was built: 00:33:29
8. How Dia almost shipped without its best feature: 00:47:12
9. The best ways people are using Dia in the wild: 00:51:18
10. How Josh and Hursh think about competing with incumbents: 01:07:55
11. How romanticism informs the product decisions behind Dia: 01:17:04

Links to resources mentioned in the episode:

• Hursh Agrawal: @hursh
• Josh Miller: @joshm
• More about Dia: https://www.diabrowser.com/ 
• Writer and investor M.G. Siegler’s essay about the AI browser wars: https://spyglass.org/ai-browser-wars/ 

Cora is the AI chief of staff we built for your email at Every. It’s been in private beta for the last 6 months and currently manages email for 2,500 beta users—and today we’re making it available for anyone to use. Start your free 7-day trial by going to: https://cora.computer/

Cora is the $150K executive assistant that costs $15/month. Or $20/month if you want an Every subscription, too. This is what that actually means:

• Cora understands what’s important to you, screens your inbox, and only lets the most relevant emails through. 
• The rest of your emails are summarized in a beautifully designed brief that’s sent to you twice a day.
• If it has enough context, Cora drafts replies for you in your voice.
• You can talk to Cora like you would your chief of staff—you can give it special instructions on how you want certain emails handled, ask it to summarize things, and even give you an opinion on complex decisions.

In this episode of AI & I, I sat down with the team behind Cora—⁠Brandon Gell⁠, head of the product studio; ⁠Kieran Klaassen⁠, Cora’s general manager; and ⁠Nityesh Agarwal⁠, engineer at Cora—for a closer look at how it all came together. We talk about:

• The story of the first time Brandon, Kieran, and I used Cora, while sipping wine at the Every retreat in Nice. 
• The evolution of Cora’s categorization system, from a 4-hour vibe-coded prototype to a multi-faceted product with thousands of happy users.
• The features on Cora’s roadmap we’re most excited about: a unified brief across different email accounts, an iOS app, and an even more powerful assistant.

This is a must-watch if you’re curious about what it feels like to give Cora your inbox, and take back your life. Go to https://cora.computer/ to start your 7-day free trial now.

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.

Sponsor: Experience high quality AI video generation with Google's most capable video model: Veo 3. Try it in the Gemini app at gemini.google with a Google AI Pro plan or get the highest access with the Ultra plan.

To hear more from Dan Shipper:

• Subscribe to Every: ⁠https://every.to/subscribe⁠ 
• Follow him on X: ⁠https://twitter.com/danshipper⁠ 

Timestamps:

1. Introduction: 00:01:40
2. Three ways Cora transforms your inbox (and your day): 00:04:21
3. A live walkthrough of Cora’s features: 00:05:09
4. The inside story of the first time Kieran, Brandon, and Dan used Cora: 00:12:13
5. Train Cora like you would a trusted chief of staff: 00:16:30
6. The AI tools that blew our minds while building Cora: 00:27:25
7. How we build workflows that compound with AI at Every: 00:30:34
8. The dream features that we’d like to put on Cora’s roadmap: 00:42:36

Links to resources mentioned in the episode:

• Try Cora now with a 7-day free trial: cora.computer 
• The episode about how Kieran and Nityesh use Claude Code to build Cora: ⁠"How Two Engineers Ship Like a Team of 15 With AI Agents"⁠ 

From inside OpenAI, he witnesses the full spectrum of human emotion that comes with bringing something new into the world—the exhilaration, the terror, the weight of it all. He feels these emotions, too: He believes AI will eventually replace what he does as a coach.

But instead of fixating on that fear, Hudson is asking a deeper question: Who is he becoming in the meantime? He believes that moments like this—when we can feel the ground quiver—can be powerful catalysts for transformation, but only if we’re willing to face the uncertainty they bring.

In this episode of AI & I, Dan Shipper sits down with Hudson to talk about how he’s answering that question. They get into what happens when the thing you’ve built your life around might disappear, how to find who you are beneath your professional identity, and why Hudson believes intention is the key to growing with 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/subscribe 
• Follow him on X: https://twitter.com/danshipper

Sponsors: 

Google Gemin: Experience high quality AI video generation with Google's most capable video model: Veo 3. Try it in the Gemini app at ⁠gemini.google⁠ with a Google AI Pro plan or get the highest access with the Ultra plan.

Attio: Go to⁠⁠⁠⁠ ⁠https://attio.com/every⁠⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

1. Introduction: 00:01:49
2. What it feels like inside the room where AGI is being built: 00:03:14
3. The most important question to ask yourself as AGI approaches: 00:08:15
4. The importance of sitting with uncertainty: 00:17:49
5. How Joe is preparing his daughters for a post-AGI world: 21:11:04
6. How we think, feel, and react; the three layers of human awareness: 27:25:01
7. Staying grounded while coaching the people shaping our future: 35:34:04
8. Why Joe doesn’t take things personally—even when the stakes are high: 42:44:03

Links to resources mentioned in the episode:

• Joe Hudson: @FU_joehudson; 
• Learn more about the coaching and workshops that Joe runs: Art of Accomplishment

Two engineers at Every shipped six features, five bug fixes, and three infrastructure updates in one week—and they did it by designing workflows with AI agents, where each task makes the next one easier, faster, and more reliable.

In this episode of AI & I, Dan Shipper interviewed the pair—Kieran Klaassen, general manager of Cora, our inbox management tool, and Cora engineer Nityesh Agarwal—about how they’re compounding their engineering with AI. They walk Dan through their workflow in Anthropic’s agentic coding tool, Claude Code, and the mental models they’ve developed for making AI agents truly useful. Kieran, our resident AI-agent aficionado, also ranked all the AI coding assistants he’s used.

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   

Sponsors:
Microsoft Teams
Want seamless collaboration without the cost? Microsoft Teams offers a robust free plan for individuals that delivers unlimited chat, 60-minute video meetings, and file sharing—all within one intuitive workspace that keeps your projects moving forward. Head to ⁠https://aka.ms/every⁠ to use Teams for free, and experience effortless collaboration, today.

Attio: Go to⁠⁠⁠⁠ https://www.⁠⁠⁠⁠attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

1. Introduction: 00:01:16
2. Why Kieran believes agents are turning a corner: 00:03:18
3. Why Claude Code stands out from other agents: 00:06:36
4. What makes agentic coding different from using tools like Cursor: 00:11:58
5. The Cora team’s workflow to turn tasks into momentum: 00:15:20
6. How to build a prompt that turns ideas into plans: 00:23:07
7. The new mental models for this age of software engineering: 00:34:00
8. Why traditional tests and evals still matter: 00:39:13
9. Kieran ranks all the AI coding agents he’s used: 00:42:00

Links to resources mentioned in the episode:

• Try Cora, our AI email assistant: https://cora.computer/  
• Kieran Klaassen: @kieranklaassen
• Nityesh Agarwal: @nityeshaga
• The book that helps Nityesh form mental models to work with AI agents: High Output Management
• A guide to Anthropic’s prompt improver: https://www.anthropic.com/news/prompt-improver 

In the rush to create scalable treatments, disorders like depression and OCD are squeezed into diagnostic checklists—from which the complexity of the human mind invariably leaks out. The field of psychiatry is broken, and I spoke to someone on the inside about how AI can help fix it .

⁠Awais Aftab⁠ has been questioning psychiatry’s rigid categories from inside the field. He’s a clinical assistant professor at ⁠Case Western Reserve University⁠, editor of ⁠Conversations in Critical Psychiatry⁠—an Oxford University Press volume that tackles philosophical and critical perspectives in psychiatry—and author of the Substack newsletter ⁠Psychiatry at the Margins⁠. We get into how AI is transforming psychiatry by embracing the complexity of human minds instead of flattening it.

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 

Sponsor: Microsoft Teams
Want seamless collaboration without the cost? Microsoft Teams offers a robust free plan for individuals that delivers unlimited chat, 60-minute video meetings, and file sharing—all within one intuitive workspace that keeps your projects moving forward. Head to ⁠https://aka.ms/every⁠ to use Teams for free, and experience effortless collaboration, today.

Timestamps:

1. Introduction: 00:01:20
2. The case Awais makes for pluralistic thinking in psychiatry: 00:03:38
3. A pragmatic approach to mental healthcare: 00:15:30
4. Awais’s take on why my OCD diagnosis took 10 years: 00:19:04
5. Why psychiatry is stuck where machine learning was decades ago: 00:24:19
6. Why psychiatry’s focus should shift from explanations to predictions: 00:31:05
7. How Awais thinks AI is already changing the psychiatric profession: 00:39:19

Links to resources mentioned in the episode:

• Awais Aftab: @awaisaftab, ⁠awais aftab⁠ 
• Awais’s Substack: ⁠Psychiatry at the Margins⁠
• The book Awais edited: ⁠Conversations in Critical Psychiatry⁠

Last week at Microsoft Build, I interviewed the CEO of GitHub Thomas Dohmke to find out. 

I wanted to know: Is their huge existing user base a blessing or a curse? And will their latest launch—an autonomous coding agent built into GitHub—let them retake the lead? Watch this episode of AI & I to find out

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 

Sponsor:
Attio: Go to⁠⁠⁠⁠ https://www.⁠⁠⁠⁠attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

00:00:38 - Introduction  

00:07:40 - Copilot’s place in the AI coding agent race  

00:10:42 - Inside the product decisions behind Copilot’s new agent  

00:16:18 - How Dohmke thinks about shaping Copilot’s personality  

00:20:29 - How GitHub supports both AI-native developers and legacy enterprise users  

00:26:57 - Dohmke’s predictions for the future of software development  

With 41 years of programming behind him, Kevin has lived through nearly every big shift in modern software development. Here’s his clear-eyed take on what’s changing with AI, and how we can navigate what’s next:

• The real breakthrough for the agentic web is better plumbing. Kevin thinks agents won’t be useful until they can take action on your behalf by using tools and fetching data. To do this, agents need access across your systems—and Microsoft’s answer is adopting Model Context Protocol, or “MCP,” that allows an agent to access tools and fresh data beyond its knowledge base, as their standard protocol for agents to move through contexts and get things done.
• How the agentic web echoes the early internet. Just as protocols like HTTP and HTML gave the web a shared language, Kevin believes the  agentic web needs its own infrastructure—the first glimpses of this include MCP (the HTTP of agents) and NLWeb, Microsoft’s push to make websites legible to agents (similar to what HTML did for browsers).
• Open ecosystems can coexist with strong security systems. Kevin argues that the “tradeoff” between ecosystems that allow “permissionless” innovation and robust security is a false dichotomy. With AI agents that understand your personal risk preferences—and know your communication habits across email, text, and other channels—they could detect when something suspicious is happening and act on your behalf. 
• The craftsman’s dilemma in the age of agents. Kevin is a lifelong maker—of software, ceramics, even handmade bags—and he cares deeply about how things are made. Because this can feel at odds with coding with AI agents, Kevin’s approach is to notice where the process matters most to him, and where it's okay to optimize for outcomes. After four decades of seeing breakthrough technologies, his advice is simple: be curious, try stuff, and use it if it works for you.
• The future of software engineering agents is plural. Kevin believes the future of software engineering agents will be diverse because developers who enjoy the freedom of playing with different tools is one of the most consistent patterns he’s seen in his decades in tech. What will drive this diversity, he says, is builders who deeply understand specific problems and tailor agents to solve them exceptionally well.
• How agentic workflows will evolve. Kevin sees a shift from short back-and-forth interactions with agents to longer, async feedback loops. As the agentic web matures and model reasoning improves, people will start handing off bigger, more ambitious tasks and letting agents run with them.

Timestamps:

1. Introduction: 00:01:44
2. The race to close the “capability overhang”: 00:02:49
3. How agents will evolve into practical, useful tools: 00:04:31
4. The role Kevin sees Microsoft playing in the agent ecosystem: 00:06:48
5. How robust security measures can coexist with open ecosystems: 00:12:05
6. Kevin's philosophy on being a craftsman in the age of agents: 00:15:39
7. How the landscape of software development agents will evolve: 00:20:52
8. The future of agentic workflows: 00:25:33

I invited Alexander Embiricos, a member of the OpenAI product staff responsible for Codex, to demo the agent live on a special edition of AI & I. We talk through:

- What Codex is and how it works. Codex’s UI allows developers to see the list of tasks the agent is working on, how many lines were changed for each, and the status of the PR. It’s built for the senior software engineer who wants to delegate and review tasks efficiently.
- How OpenAI is thinking about agents. Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. 
- Why an “abundance mindset” is best for interacting with agents. Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task, like a difficult bug—it’s worth it even if only one of them succeeds.
- OpenAI’s vision for the future of programming. In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding.

Timestamps:

1. Introduction: 00:00:52
2. The product decisions behind Codex’s interface: 00:01:40
3. How Codex works under the hood: 00:06:20
4. Why you need an abundance mindset to work well with agents: 00:14:06
5. Setting Codex to work on a real task in “Ask” mode: 00:16:28
6. How OpenAI is thinking about designing agents: 00:18:54
7. The future of programming is social: 00:31:16
8. Reviewing Codex’s work live: 00:37:21
9. How the landscape of agents will evolve: 00:39:41

It’s working: 75% of his 400-person team are using ChatGPT daily—and Walleye is well on its way to transforming into an AI-first juggernaut. They record every meeting, use LLMs to ingest and analyze earnings reports, and are building “The Borg”—a firmwide intelligence layer.

What’s surprising? Will isn’t some AI hype man: He’s the CEO, CIO, and managing partner of Walleye Capital, a multi-strategy hedge fund competing with firms like Citadel, Millenium, and Point72. He’s Princeton and Oxford educated, but he’s based in Minnesota, doesn’t have an X account, and rarely gives interviews.

In my experience, teams go as their CEO goes—and Will is the best example of a CEO going all in on AI that I’ve seen. "It would be irresponsible not to go after AI with maximum discipline and intensity," Will told me—and in this episode he lays out his exact playbook for doing it.

We get into:

• Why AI is essential operating leverage. At Walleye, using AI is treated like using email or Excel. Ignoring it means getting left behind—in an industry where information = money, every edge counts. England makes this not optional for anyone, backed by internal leaderboards and cash incentives.
• How Will uses AI for journaling and decision-making. Will journals every day using ChatGPT, which helps him with everything from decision-making at work to reflecting on his family life to tracking his workouts. 
• How Will pivoted his billion dollar firm. Will’s commitment to AI isn’t theoretical—he announced AI as the new standard for work at Walleye, and made avoiding it unacceptable. 
• How to lead during times of technological change. Will leads with an ethic of personal responsibility: "If we get disrupted by AI, that's on me.”
• Why students of history do better at handling the future. Will sees today like the 1860s–1910s era—when the Industrial Revolution introduced factories and railroads and the skills and roles needed inside of companies transformed quickly.
• How Will uses AI to write faster. Will uses ChatGPT to help him draft emails or memos that would have taken hours in 15 minutes. He bullets out of his thoughts and then uses LLMs to turn that into polished prose. Having AI handle the linguistic syntax gives him more time for conceptual thinking.

This is a must-watch for anyone who wants to lead a team through change with clarity and conviction.  

Sponsor:

Attio: Go to⁠⁠⁠⁠ https://www.⁠⁠⁠⁠attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

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 

Timestamps:

1. Introduction: 00:00:51
2. What pushed Will to go all in on AI: 00:03:25
3. Inside the ‘AI-first’ memo Will shared at Walleye: 00:14:08
4. Why you shouldn’t be afraid of using AI for work: 00:15:56
5. How Will uses LLMs to sharpen his thinking: 00:31:01
6. Walleye’s approach to using AI to reduce risk: 00:35:32
7. What history can teach us about leading through change: 00:39:10
8. Will’s first principles to making better decisions: 00:56:45
9. Why Will journals everyday—and how AI makes it easier: 00:58:58 

Links to resources mentioned in the episode:

• Will England: ⁠https://walleyecapital.com/bio/will-england⁠ 
• Walleye Capital: ⁠https://walleyecapital.com/⁠ 
• Work with Every’s consulting team: ⁠https://every.to/consulting⁠  
• Everything we’ve learned from consulting with clients like Walleye: ⁠"How We Built a 7-figure AI Consulting Business in Less Than a Year"⁠

Nadia is a writer and researcher of technology and culture. She published Working in Public, a book about the evolution of open-source development, with Stripe Press. Her latest book, Antimemetics, is about why some ideas don’t go viral even though they’re powerful. 

I had her on the show to talk about her experience with Jhana meditation and how it reshaped the way she thinks about being human in the age of AI. We get into:

• Jhana as a means to nurture profound joy and calm. Unlike many meditation practices that emphasize passive observation, Jhana is goal-oriented—practitioners proactively cultivate states of concentrated bliss. Apart from helping her regulate her emotions, it prompted Nadia to reexamine deep questions of our human existence. 
• Self-talk is not essential as it seems. Nadia describes how advanced meditation quieted her inner voice—challenging the idea that self-talk is core to being human.
• How years of cultural evolution have shaped our sense of self. According to Nadia, our modern conception of “self”  isn’t as timeless as we assume. She draws on psychologist Julian Jaynes’s theory that our inner dialogue—what we often equate with consciousness—only emerged in humans a few thousand years ago; a provocation to reconsider the benchmarks we use to assess the intelligence or sentience of LLMs.
• What it is like to experience a “cessation.” On her last meditation retreat, Nadia experiences a cessation where your consciousness abruptly winks out—like suddenly flipping a switch. Nadia described it as slipping into nothingness, then returning with the jarring realization that even your sense of self can vanish and reappear.
• Why she likes the unknowability of AI. The mechanics of exactly how LLMs predict their next token remain a mystery. Driven by thousands of subtle, context-dependent correlations, they’re too complex to distill into a simple explanation. Nadia finds joy in the unknowability of it all, seeing the ambiguity as an invitation to explore. 
• How she uses AI as a writing partner. Nadia believes the trope of the solitary, brooding writer is beginning to shift with the rise of LLMs. For her, ChatGPT has made writing feel less isolating. She turns to it at both ends of the process: to help make sense of early ideas, and later, to sharpen phrasing and land on just the right words.

This is a must-watch for anyone interested in consciousness, technology, and what it means to be human in an AI world.

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

Timestamps:

1. Introduction: 00:01:15
2. The beginning of Nadia’s journey with Jhana: 00:02:34
3. How Jhana is different from other meditation practices: 00:05:51 
4. Jhana reframed the way Nadia thinks about being human: 00:09:52
5. How Nadia integrates her experience with Jhana into her life: 00:14:16
6. Nadia describes her experience of the final stage of Jhana: 00:16:44
7. Why our modern sense of self isn’t as timeless as you might assume: 00:19:11
8. How new technologies can be a mirror to ourselves: 00:23:53
9. Nadia embraces the feeling of not knowing how AI precisely works: 00:33:55
10. How Nadia uses ChatGPT to make writing less isolating: 00:38:03

Links mentioned:

• Nadia Asparouhova: ⁠https://nadia.xyz/⁠ 
• Her deep dive on Jhana meditation: ⁠https://nadia.xyz/jhanas⁠ 
• Nadia’s book: ⁠Working in Public⁠, ⁠Antimemetics⁠

There’s no easy way to discover great prompts or share the ones that worked. As a venture partner at Benchmark, Sarah believes that the next wave of consumer AI will be built on this missing social layer—by product-driven founders who understand people, not just models. 

Sarah has seen this shift before. As one of Pinterest’s first product managers, she saw the company grow from a niche consumer tool to a beloved global community. On this episode of Every's podcast AI & I, we talk about how she’s applying the lessons she learned to AI—and what it takes to build a breakout consumer AI app today. 

We get into:

• Why product geniuses win as new tech matures. In the early days of a new technology, companies win by wrangling raw innovation into something usable. But as the infrastructure matures, Sarah says the edge shifts to product thinkers—founders who turn new capabilities into delightful user experiences.
• The future of prompting is social. When Sarah had to dig through Reddit to find a prompt to help her interpret her blood test results, she saw a gap: The best prompt creators are invisible. Sarah bets that a social AI product that makes them discoverable and followable would gain traction.
• Sarah’s method to spot exceptional founders. Sarah backs founders for whom building a company feels like a calling—or even an affliction. These are people who have fallen in love with the process and are obsessed with learning how to grow alongside their companies.
• How to tell if your startup really has network effects. Founders raising money love to say that their business has “network effects.” Sarah has learned to look for early signs they’re real—like traction in a small, white-hot segment of the market. If there’s no evidence the flywheel is already starting to spin, it’s probably not a network effect.
• How LLMs change the way the best VCs invest. Sarah thinks the future of venture will be shaped by how well VCs can turn the decisions they make into training data. After every pitch, she logs what she liked, what she didn’t, the deal terms, and her reasoning. Over time, she’s building a dataset of her own judgment—one an LLM could help her use to pressure-test decisions and avoid past mistakes.

This is a must-watch for if you’re building a consumer AI product and want to see ahead of the curve.

If you found this episode interesting, please like, subscribe, comment, and share! 

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Timestamps:

1. Introduction: 00:01:10
2. Why the future of consumer AI belongs to founders with product intuition: 00:02:26
3. What Sarah sees as ChatGPT’s biggest weakness: 00:11:09
4. How Sarah would design a consumer AI app with social DNA: 00:18:45
5. The kind of founders Sarah invests in: 00:25:04
6. How to know if your startup’s network-effects are real: 00:29:26
7. What’s catching Sarah’s eye beyond AI: 00:36:33
8. How AI will change the way top venture capitalists invest: 00:41:35

Links to resources mentioned in the episode:

• Sarah Tavel: @sarahtavel
• Sarah’s substack: ⁠https://www.sarahtavel.com/⁠ 
• Eugene Wei’s essay about Status-as-a-Service: ⁠https://www.eugenewei.com/blog/2019/2/19/status-as-a-service⁠ 
• The book Sarah talks about in the context of founders who become CEOs in pursuit of status: ⁠The Five Temptations of a CEO⁠

From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what’s next.

Today, as Wired's senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering. 

He’s a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on the show. We get into:

• How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn’t staying at the edge forever; it's returning with a story to tell.
• Why history is so important to help you understand the future To stay grounded while exploring what’s new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands.
• From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked ChatGPT to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself.
• What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we’re building intelligent machines without really understanding what intelligence is.
• Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn’t a single force, but a compound of many cognitive elements. He draws from Marvin Minsky’s “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today.
• Your competitive advantage is being yourself. Don’t aim to be the best—aim to be the only. Kevin realized that the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work.

This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired.

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 

Sponsors:
Vanta: Get $1,000 off Vanta at ⁠⁠⁠⁠https://www.vanta.com/every⁠⁠⁠⁠ and automate up to 90% of the work for SOC 2, ISO 27001, and more.

Attio: Go to⁠⁠ https://www.⁠⁠⁠⁠attio.com/every⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

1. Introduction: 00:00:50
2. Why Kevin and I love Annie Dillard: 00:01:10
3. Learn how to predict the future like Kevin: 00:12:50
4. What the history of electricity can teach us about AI: 00:16:08
5. How Kevin thinks about the nature of intelligence: 00:20:11
6. Kevin’s advice on discovering your competitive advantage: 00:27:21
7. The story of how Kevin assembled a bench of star writers for Wired: 00:31:07
8. How Kevin used ChatGPT to co-create a book: 00:36:17
9. Using AI as a mirror for your mind: 00:40:45
10. What Kevin learned from betting on VR in the 1980s: 00:45:16

Links to resources mentioned in the episode:

• Kevin Kelly: @kevin2kelly
• Kelly’s books: https://kk.org/books 
• Annie Dillard books that Kelly and Dan discuss: Pilgrim at Tinker Creek, Teaching a Stone to Talk, Holy the Firm, The Writing Life
• Dillard’s account of the total eclipse: "Total Eclipse"