AI and I

Will England is the CEO of Walleye Capital, a hedge fund managing nearly $10 billion in assets. An engineer by training with a math background from Oxford, he has spent his career at the intersection of machines and markets—and has made AI fluency mandatory for all 400 employees.

England believes refusing to use AI is like refusing to use the internet in 1995 because it wasn’t perfect. His use of AI is public and effusive, including in a firm-wide email that opened with “I used ChatGPT to write this email. You should be using it, too, and be proud of it.” AI informs how Walleye drafts memos and selects stocks.

On Every’s AI & I, Dan Shipper spoke with England about why he’s betting his entire organization on AI, why “results are what matter” more than blood, sweat, and tears, and what the American frontier can teach us about leading through technological change.

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Go to https://attio.com/every and get 15% off your first year.

Timestamps:

0:00 Start

0:51 Introduction

3:25 What pushed Will to go all in on AI

15:15 Inside the ‘AI-first’ memo Will shared at Walleye

17:02 Why you shouldn’t be afraid of using AI for work

31:25 How Will uses LLMs to sharpen his thinking

35:57 Walleye’s approach to using AI to reduce risk

39:35 What history can teach us about leading through change

57:10 Will’s first principles for making better decisions

59:23 Why Will journals every day—and how AI makes it easier

Links to resources mentioned in the episode:

Will England/Walleye Capital: https://walleyecapital.com/bio/will-england

Every’s AI tools—Monologue, Cora, Spiral, and Sparkle: https://every.to/studio

Every’s AI consulting: https://every.to/consulting

LLMs are a new medium for storytelling.That’s according to the creators of Portola, the company behind Tolan: an embodied AI companion that lives on its own planet and chats to you with a distinct personality. In 2025, Portola's founder and CEO Quinten Farmer and Head of Story Eliot Peper joined Dan Shipper to explain how they’re building this new medium from scratch. Their aim is to help users go from overwhelmed to grounded through conversations with Tolan that feel personal and spontaneous, not scripted. On this week’s AI & I, Dan revisits his conversation with Quinten and Eliot. They discuss why response time is everything for voice-based AI interfaces, how Portola designs AI personalities users will click with, and why character-driven AI could become a new computing interface. If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperGo to https://attio.com/every and get 15% off your first year.Timestamps: 00:01:30 - Introduction 00:04:07 - Talking to the Portola CEO's Tolan, Clarence 00:09:11 - How Portola went from building software for kids to AI companions 00:23:40 - Why response time is everything for voice-based AI interfaces 00:29:54 - Tolans don't use scripted prompts—they're taught to improvise 00:37:23 - How to know which AI personalities your users will click with 00:42:27 - Developing the character traits of an AI companion 00:49:48 - What does it mean to build technology that makes us flourish 01:01:10 - How Portola evaluates whether Tolans are resonating with users 01:11:01 - Inside Portola's viral growth strategy

In 2025, Kevin Scott bet that the agentic web would be the next big thing in AI.

The Microsoft CTO argued that for agents to be genuinely useful, they'd need to be able to take action on our behalf—which would mean giving them access to the same sprawl of tools, data, and systems that make up the internet. Today, that bet is starting to pay off, as the foundational infrastructure for the agentic web is now being built.

On this week's AI & I, Dan Shipper revisits his conversation with Kevin. They discuss Microsoft's role in the agentic web, why openness doesn't have to come at the expense of security, and why programmers should stay curious about new tools rather than resist them on principle.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Go to https://attio.com/every and get 15% off your first year.

Timestamps:

0:00 Start

1:44 Introduction

2:49 The race to close the "capability overhang"

4:31 How agents will evolve into practical, useful tools

6:48 The role Kevin sees Microsoft playing in the agent ecosystem

12:05 How robust security measures can coexist with open ecosystems

15:39 Kevin's philosophy on being a craftsman in the age of agents

20:52 How the landscape of software development agents will evolve

25:33 The future of agentic workflows

Links to resources mentioned in the episode:

Kevin Scott on X: https://twitter.com/kevin_scott

Model Context Protocol (MCP): https://modelcontextprotocol.io

NLWeb: https://github.com/microsoft/NLWeb

GitHub Copilot: https://github.com/features/copilot

Most consumer AI so far has been single-player: you and a chatbot, alone.

Benchmark partner Sarah Tavel, one of Pinterest's first 30 employees, is betting that's about to change. She's looking for a product genius who can build an AI product with social DNA: status, network effects, and multiplayer dynamics. That'll enable users of ChatGPT and other models to learn from how others use AI and level up.

On this week’s AI & I, Dan Shipper revisits his conversation with Sarah. They talk about why technical founders dominate the early days of a platform shift while product-minded founders win later, what ChatGPT is still missing, and what separates a founder's real network effect from a slide with a flywheel diagram.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps for YouTube:

0:00 Start

1:10 Introduction

2:26 Why the future of consumer AI belongs to founders with product intuition

11:09 What Sarah sees as ChatGPT's biggest weakness

18:45 How Sarah would design a consumer AI app with social DNA

24:10 The kind of founders Sarah invests in

28:33 How to know if your startup's network effects are real

35:40 What's catching Sarah's eye beyond AI

40:41 How AI will change the way top venture capitalists invest

Links to resources mentioned in the episode:

Sarah Tavel on X: https://x.com/sarahtavel

Benchmark: https://benchmark.com

Agentio (marketplace for YouTube creators and brands): https://agentio.com/

Chainalysis: https://chainalysis.com

The Five Temptations of a CEO by Patrick Lencioni: https://www.amazon.com/dp/B007BZBRB8

Thinking in Bets by Annie Duke: https://www.amazon.com/dp/B0HBBW23PM

Kevin Kelly has spent over 30 years experiencing the edge of new technology: from the earliest days of the internet to the first years of Burning Man. But he’s always treated the frontier as a place to visit, not somewhere to live. It’s partially how he’s been able to stay grounded through tech’s various hype cycles.

As founding executive editor of Wired and author of The Inevitable, Kelly spends as much time analyzing the latest in AI as he does reading about significant moments in history. It’s a discipline he traces back to his work with the Long Now Foundation, which he cofounded to encourage long-term thinking, reaching from the last 10,000 years to the next.

On this week’s AI & I, Dan Shipper revisits his conversation with Kelly. They get into why historians can be the best futurists, and how our bid to understand what intelligence is has parallels with early scientists' attempts to figure out electricity.

Kelly also describes the joy he found in creating an AI-generated saga featuring Leonardo Da Vinci, Christopher Columbus, and Martin Luther—one that will only ever be read and enjoyed by him.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps for YouTube:

0:00 Start

0:50 Introduction

1:10 Why Dan and Kelly love Annie Dillard

12:52 How to predict the future like Kelly

16:10 What the history of electricity can teach us about AI

20:13 How Kelly thinks about the nature of intelligence

25:44 Kelly's advice on discovering your competitive advantage

29:33 How Kelly assembled a bench of star writers for Wired

34:43 How Kelly used ChatGPT to co-create a book

39:12 Using AI as a mirror for your mind

43:43 What Kelly learned from betting on VR in the 1980s

Links to resources mentioned in the episode:

Kevin Kelly on X: https://twitter.com/kevin2kelly

The Inevitable by Kevin Kelly: https://www.amazon.com/Inevitable-Understanding-Technological-Forces-Future/dp/0525428089

Pilgrim at Tinker Creek by Annie Dillard: https://www.amazon.com/Pilgrim-Tinker-Harper-Perennial-Classics/dp/0061233323

1,000 True Fans by Kevin Kelly: https://www.amazon.com/1000-True-Fans-Kellys-Simple-ebook/dp/B01N9P9O4G

Full episode transcript: https://every.to/podcast/transcript-be243312-ea22-4193-8c56-b9cd45a79a87

Yash Poojary, a growth engineer at Every, dropped an idea for a campaign in Slack at 7 p.m. Instead of building it himself, Every’s head of growth Austin Tedesco took a screenshot of the Slack thread, dropped it into Codex, typed "Can you do this?", and went to the gym.

By the time he got back, Codex had built four audience segments, drafted emails for each one, and pulled a social image that had worked before. It took Austin 10 minutes to make some tweaks and schedule the whole thing to send the next morning. Within a few hours, it generated more than $25,000 in revenue.

That story came out of the launch week for All Access, Every’s new $625-a-year membership built around the Builder Pack. It includes $7,000 in credits and free usage from ten of the AI products Every uses every day, including Claude Max, Codex, Cursor Pro+, PostHog, Notion, Framer, Render, and Flora.

On this episode of AI & I, four of Every's own builders—COO Brandon Gell, head of marketing Douglas Brundage, as well as Yash and Austin—sit down to show how they use AI, breaking down their personal stacks and giving insight into their own strategies and mindset for building.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps for YouTube:

0:00 Intro

0:35 All Access Explained

3:01 Yash's Tech Stack and How He's Automating Testing Pipelines

8:02 The Idea to Execution Loop

10:25 How an Agent Turned an Idea into $25K

17:50 The AI Sandwich Workflow

22:03 Making AI Tools Accessible to Solo Builders

28:50 Douglas on Brand and Design

34:51 Tips on What to Build First

43:46 What's Next for All Access

Links to resources mentioned in the episode:

Brandon Gell on X: https://x.com/bran_don_gell

Yash Poojary on X: https://x.com/poojary_yash

Austin Tedesco on X: https://x.com/tedescau?lang=en

Douglas Brundage on X: https://x.com/DABrundage

Introducing Every All Access: https://every.to/on-every/introducing-every-all-access

Get the Builder Pack: every.to/builder-pack

Go to https://attio.com/every and get 15% off your first year.

“Running a startup is a knife fight whether things are going well or not,” says Chris Pedregal, cofounder and CEO of Granola. Granola recently raised a $125 million series C round at a $1.5 billion valuation on the strength of its AI meeting notetaker.

That valuation hasn’t made Pedregal complacent. Granola built its name as the first to make good AI meeting notes, but Notion, OpenAI, and Zoom have all since released their own versions. Pedregal isn’t rattled—he never thought meeting notes were the real prize. The bigger fight, he says, is over “what interface we use for work, and what work looks like in an AI-native world.”

That’s why Granola is betting on owning the entire meeting workflow: preparing people for a call, helping them act on it afterward, and making that context available to whatever agent—Claude, Codex, or anything else—people bring to the table. Over the next few months, the company plans to push hard on its API and MCP to make that possible.

Dan Shipper talked with Pedregal for AI & I about why Granola pre-generates millions of meeting briefs, most of which go unopened, what “bring your own agent” software could look like, and why Pedregal still thinks “easy come, easy go” about Granola’s own success.

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

More from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps:

00:00:59 Introduction

00:01:57 Why starting a company feels like a knife fight

00:04:33 Granola's counterintuitive view on competition

00:10:44 Dan's "pirate and architect" framework for structuring early-stage product teams

00:13:09 How Granola's "shaping" and "validation" phases work for building new features

00:18:17 Why Dan lives almost entirely inside Codex

00:24:40 The case for "Codex-native apps"

00:35:37 Granola's "handrail" philosophy

00:38:12 Why Granola is betting on owning meeting-adjacent context instead of competing as a general agent

00:44:19 What a transcript alone can never capture

Episode resources:

Chris Pedregal on X: https://twitter.com/cjpedregal

Granola on X: https://twitter.com/meetgranola

Granola: https://granola.ai

Granola hits $1.5B valuation (TechCrunch): https://techcrunch.com/2026/03/25/granola-raises-125m-hits-1-5b-valuation-as-it-expands-from-meeting-notetaker-to-enterprise-ai-app/

Go to https://attio.com/every and get 15% off your first year.

Craig Mod used to pay Campaign Monitor roughly $7,000 a year to send his newsletters. After rebuilding the tool himself with AI, his bill is closer to $150. It’s the kind of thing that convinces him we’re about to enter a “golden age of tool building”—one where anyone can build tools specifically suited to their needs, instead of settling for software from incumbents that are slow to innovate.

Mod is the writer and photographer behind the newsletters Roden and Ridgeline and books like Things Become Other Things and Kissa by Kissa—as well as a lifelong technologist. He’s rebuilt the tax software Quicken, created a private alternative for Twitter for his members which he calls The Good Place, and used AI to build an archive for his pop-up newsletters. But while Mod is an advocate of using AI to build, he draws the line at using it to write.

Mod talks to Dan Shipper about using AI as a research assistant, why he keeps a tech-free zone in the mornings for deep thinking, and why he’s resisting the pull of the “mainlining” AI era.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps:

0:00 Introduction

3:51 Rebuilding Quicken and Campaign Monitor with AI

6:24 Building The Good Place, a private Twitter alternative for Craig’s members

10:39 Why we’re entering a “golden age of tool building”

12:17 Why AI could help writers build audiences

17:35 Using AI to build a newsletter archive and a searchable board-meeting Q&A library

27:58 Creating a technology-free buffer to protect deep thinking

30:31 Why Craig is resisting the temptation to “mainline” AI for ten hours a day

39:44 Why anthropomorphizing AI is “psychotic,” and why Apple got Siri right

47:42 Being adopted, and making peace with humanity’s fragile place in an AI future

Go to https://attio.com/every and get 15% off your first year.

Links to resources mentioned in the episode:

Craig Mod’s website: https://craigmod.com

Roden (Craig’s monthly newsletter): https://craigmod.com/roden/

Natalia Quintero joined Every as head of consulting with a mandate to bring AI into the workflows of executives at hedge funds, private equity firms, and tech companies. She is also a recent Codex convert—someone who spent months resisting the tool before Dan Shipper’s daily pestering finally got her to try it.

Natalia encountered Codex as a non-technical builder who had learned to navigate file systems and folder structures in Claude Code through sheer effort. She’s now used Codex to do everything from automate her CRM setup to build a portal to manage her father’s medical care.

Dan talked with Natalia for AI & I about what it looks like to go from non-technical to building software with Codex, why Every still uses software-as-a-service products from Attio and Asana instead of vibe coding their own tools, and where she thinks AI agents like Every’s internal Claudie employee require human managers.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps:

00:01:05 Introduction

00:02:35 How Natalia manages Claudie, the consulting team's AI project manager

00:04:55 Why the consulting team still pays for SaaS products

00:11:47 Codex as a game changer

00:14:55 Building personalized learning guides and illustrated explainers with AI

00:21:40 Inside Natalia's AI-powered email triage system

00:26:44 The shift from knowledge work as sculpting to knowledge work as gardening

00:28:57 Using Codex to one-shot a custom CRM

00:33:16 Using Codex to build an app that coordinates her father's medical care

Links to resources mentioned in the episode:

Natalia Quintero on X: https://x.com/NataliaZarina

Asana (project management): https://asana.com

Every Consulting: https://every.to/consulting

Go to attio.com/every and get 15% off your first year.

If scaling laws hold—and Surge AI CEO Edwin Chen believes they do—we’re hurtling toward a future where there’s nothing humans can do that AI can’t do better. When OpenAI’s models disproved an open conjecture posed by mathematician Paul Erdős using novel algebraic geometry techniques, Fields medalist Timothy Gowers felt the shift acutely. He initially thought the model had proved an upper bound, and braced himself: that would mean it was “all over for mathematicians very soon.” When he realized it had only found a counterexample, he was relieved—it bought him another year or two before the thing he’s devoted his life to becomes something AI does better.

As founder and CEO of the company behind the data environments and evals the major model companies use to train their models, Chen has a unique perspective on how quickly AI models are absorbing tasks we used to think of as uniquely human.

Dan Shipper talked with Chen for AI & I about what the act of creating or building means when AI can do it better—and whether an answer to that question already exists within science fiction.

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

Join the membership for Where You Live at ⁠https://www.joinbilt.com/dan

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps:

00:00:54 Introduction

00:01:49 Surge as a "school for AGI"

00:04:46 What AI's capacity for novel mathematics says about human achievement

00:07:29 Motivation in an era when AI can do everything

00:14:34 The trap of optimizing AI models for engagement

00:29:34 Training using datasets versus training using environments

00:35:09 The value of personal data

00:39:40 Why models are bad at writing

00:42:00 Chen's AGI timeline

Links to resources mentioned in the episode:

Edwin Chen on X: https://x.com/echen

Surge: https://surgehq.ai

Riemann-bench (research-level math benchmark): https://surgehq.ai/leaderboards/riemann-bench

Hemingway-bench (creative writing benchmark): https://surgehq.ai/leaderboards/hemingway-bench

Talkie-1930 (language model trained on pre-1930 text): https://huggingface.co/talkie-lm/talkie-1930-13b-it

Ted Chiang, “What’s Expected of Us”: https://www.nature.com/articles/436150a

Every is the most AI-native startup on the internet. Through ideas, software and education, subscribers get the tools to work at the frontier of AI. Start your free trial today: https://every.to/subscribe?utm_source=youtube

Follow Every: https://x.com/every

Follow Dan Shipper: https://x.com/danshipper

Last year, there were 1 billion commits on GitHub. This year, Kyle Daigle expects that number to exceed 14 billion, a two-component explosion caused by more humans—and their agents—issuing pull requests. In March alone, 17 million pull requests on GitHub were created by agents.

Daigle is the COO of GitHub and Microsoft’s chief marketing officer for developer products. He’s been at GitHub for 13 years, and is paying close attention to how AI is expanding the platform’s user base. Along with agents, legal, sales, and marketing professionals are building apps with the GitHub Copilot app. The line between developer and non-developer is disappearing.

On this episode of AI & I, guest host Mike Taylor sat down with Daigle at Microsoft Build to discuss how GitHub is building infrastructure for an agent-native world: agentic code review, model routers that automatically select the right model for the task, and a philosophy that the most durable advantage in this market is developer choice.

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

Want even more?

To hear more from Mike Taylor:

Subscribe to Every: https://every.to/subscribe

Follow him on X: https://x.com/hammer_mt

Timestamps for YouTube:

00:00:52: Introduction

00:03:27: The agentic PR flood

00:04:33: GitHub's approach to helping open-source maintainers manage the surge

00:06:15: What 14 billion commits means for code quality

00:08:03: Moving from per-seat licensing to usage-based pricing

00:09:45: Kyle's dual role as GitHub COO and Microsoft's chief marketing officer for developers

00:13:03: Developer choice as competitive moat

00:14:57: How to balance dogfooding your own tools with staying honest about the competition

00:19:45: Hill climbing, frontier tuning, and solving the model-routing problem

00:24:45: Kyle's agentic communication hack

Links to resources mentioned in the episode:

Kyle Daigle on X: https://x.com/kdaigle

Mike Taylor on Every: https://every.to/@mike_2114

Mike’s piece on building an AI version of Kyle Daigle: https://every.to/also-true-for-humans/i-interviewed-an-ai-version-of-github-s-coo-then-spoke-to-the-real-one

GitHub Copilot: https://github.com/features/copilot

Mike Krieger built one of the most consequential consumer apps of the last two decades as the cofounder of Instagram. He is now at the frontier of AI-native product development as head of Anthropic Labs, the team responsible for figuring out what the most capable AI models can do in the hands of real builders.When Krieger first got access to Fable 5 months before its public release, it was exciting and disorienting. “I feel like a total newbie again,” he remembers telling his team. The way he’d been thinking about productivity, strategy, and time management was out of date. The model had outpaced his workflows.Dan Shipper talked with Krieger for AI & I about what it looks like to build with a model as capable as Fable 5, including the new rhythms, challenges, and possibilities it reveals.If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperGet started with Braintrust at https://www.braintrust.dev/ Timestamps:0:03 Introduction1:48 How Fable completely reshaped Mike's workflow4:48 When to use Sonnet versus Fable10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture15:00 The cost to build has collapsed19:03 Is software engineering over?21:48 How Anthropic's engineering teams work today38:39 The mechanics of verification44:39 What people should use the model to build47:24 Dynamic workflowsLinks to resources mentioned in the episode:Mike Krieger on X: https://x.com/mikeykAnthropic Labs: https://www.anthropic.comClaude Code: https://claude.ai/codeEvery: https://every.to
Timestamps:0:03 Introduction1:48 How Fable completely reshaped Mike's workflow4:48 When to use Sonnet vs. Fable10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture15:00 The cost to build has collapsed19:03 Is software engineering over?21:48 How Anthropic's engineering teams work today38:39 The mechanics of verification44:39 What people should use the model to build47:24 Dynamic workflowsLinks to resources mentioned in the episode:Mike Krieger on X: https://x.com/mikeykAnthropic Labs: https://www.anthropic.comClaude Code: https://claude.ai/codeEvery: https://every.to

If your MCP server has dozens of tools, it's probably built wrong. You need tools that are specific and clear for each use case—but you also can't have too many. This creates an almost impossible tradeoff that most companies don't know how to solve.

That's why we interviewed Alex Rattray, the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. We get into MCP and the future of the AI-native internet.

[Disclosure: Dan is a small investor in Stainless.]

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Get started with Braintrust at https://www.braintrust.dev/

Timestamps:

00:01:15 - Introduction

00:05:09 - APIs and MCP, the connectors of the new internet

00:11:00 - Why MCP exists

00:17:15 - Why MCP servers are hard to get right

00:20:24 - Design principles for reliable MCP servers

00:25:06 - Using MCP for business ops at Stainless

00:40:57 - Alex's take on the security model for MCP

00:44:42 - How one-off AI actions become permanent production software

Links to resources mentioned in the episode:

Alex Rattray: Alex Rattray (@RattrayAlex), Alex Rattray

Stainless: https://www.stainless.com/

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget.

That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7.

On this week’s AI & I from @every, I talk with Angela Jiang (@angjiang), head of product for the Claude platform, and Katelyn Lesse (@katelyn_lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps:

00:01:48 - How the Claude platform evolved from API to agents

00:04:09 - The primitives that make up Claude Managed Agents

00:10:37 - Why the harness and the model are becoming a single unit

00:18:49 - The infrastructure wall that kills most agent projects in production

00:24:49 - Why team agents need a different shape than individual productivity tools

00:26:36 - How Anthropic's legal team uses an agent to review marketing copy

00:34:24 - Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms

00:35:50 - How to measure agent success with outcome and budget as the end state

00:39:11 - What the platform looks like a year from now, when Claude writes its own harness

In January, Dan Shipper wrote that whoever wins vibe coding wins how you work on your computer—and OpenAI had some serious catching up to do.

Three months and the release of GPT-5.5 later, Codex has more than caught up. Austin Tedesco, Every's head of growth, now spends about 80 percent of his working time inside the Codex desktop app, doing everything from drafting go-to-market plans from a stack of meeting transcripts to rebuilding the company's KPI dashboard.

On this episode of AI & I, Dan sat down with Austin to discuss why the agent management interface—a desktop app built on top of a coding agent—is becoming the new operating system for knowledge work, and why Codex has become his daily driver.

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

To hear more from Dan Shipper:

Subscribe to Every: every.to/subscribe

Follow him on X: twitter.com/danshipper

Join the membership for Where You Live at joinbilt.com/dan

Timestamps for YouTube:

00:00:00 Introduction
00:00:57 How Codex went from a tool for senior engineers to a daily driver for knowledge work
00:02:42 How Claude Code proved that a great coding agent works for any knowledge work
00:07:24 Austin's switch to Codex
00:13:48 How Austin set up Codex with folders, keys, and reviewer agents
00:18:24 Using Codex to brainstorm automations across Gmail, Slack, and Notion
00:22:42 How Austin manages the human review step when Codex is drafting communications
00:28:54 Using Codex to build specialized agents inspired by product executive Claire Vo
00:31:09 Synthesizing meeting transcripts and Slack threads into a go-to-market plan
00:40:15 Building a live KPI tracker in Notion that agents can read
00:44:54 Using Codex for 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

Emily Glassberg Sands leads data and AI at Stripe, which processes roughly 2% of global GDP, giving her a bird’s-eye view into how AI is upending the internet economy. Dan Shipper talked with Glassberg Sands for Every's AI & I about what the data on Stripe's network actually shows: AI companies are scaling three times faster than the top SaaS cohort of 2018, fraud has moved from the checkout to the full funnel, and agents have started buying things, although mostly low-stakes commodities like Halloween costumes. The conversation covers the new fraud types unique to AI companies, the AI-on-AI arms race between bad actors and fraud detectors, where AI revenue growth is actually coming from, and how Stripe is rebuilding the payments infrastructure for a world where the buyer is an agent.If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperHead to http://granola.ai/every and get 3 months free with the code EVERYTimestamps00:00:45 Introduction00:01:27 New rules for an agent-driven economy00:03:57 Compute theft is the new payment fraud00:10:00 How Stripe expanded fraud detection from checkout to the full customer lifecycle00:19:48 Why AI companies are scaling way faster than top SaaS companies00:23:27 Outcome-based billing is replacing seat-based pricing00:29:57 Where AI spending is coming from00:36:45 How the developer experience changes when agents are the builders00:41:00 The agentic commerce spectrum, from assisted buying to autonomous purchasing00:51:06 Meet Link, a consumer wallet for delegated agent purchasesLinks to resources mentioned in the episode:Emily Glassberg Sands on X: https://x.com/emilygsandsStripe: https://stripe.comStripe Radar: https://stripe.com/radarStripe Link: https://link.comLovable: https://lovable.dev

Most AI companies are racing to build bigger LLMs. Eve Bodnia thinks that's the wrong approach.

Eve is the founder and CEO of Logical Intelligence, which is developing an alternative to the transformer-based models dominating the industry. Her argument: LLMs’ architecture makes them fundamentally unsuited for some mission-critical tasks. A system that generates output one token at a time, with no ability to inspect its own reasoning mid-process or guarantee its results, shouldn't be trusted to design chips, analyze financial data, or even fly a plane. Her alternative is the energy-based model (EBM), a form of AI rooted in the physics principle of energy minimization, not language prediction. Rather than guessing the next probable word, an EBM maps every possible outcome across a mathematical landscape, where likely states settle into valleys and improbable ones sit on peaks.

Dan Shipper talked with Bodnia for AI & I about why she believes LLM progress is plateauing, what it means for AI to actually understand data rather than just pattern-match across it, and how her team is building toward formally verified code generated in plain English—no C++ required.

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

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Timestamps:

00:00:51 - Introduction

00:02:09 - Why correctness and verifiability matter in AI

00:09:33 - What an energy-based model is

00:14:21 - How EBMs construct energy landscapes to understand data

00:19:00 - Why modeling intelligence through language alone is a flawed approach

00:26:54 - What it means for a model to "understand" data

00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code

00:43:21 - Why LLM progress is plateauing

00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap

While walking to the office, our COO Brandon Gell had his AI agent call him and go over his emails in his inbox one by one. When he arrived, he opened Gmail and confirmed she'd done everything he'd asked. "My jaw is on the floor," he messaged me.

That was the moment Every got serious about setting up each employee with their own agent. Today, it's a reality—and it has completely changed how we work.

Dan Shipper talked to Every COO Brandon Gell and head of platform Willie Williams for Every's AI & I about what happens when everyone at a company gets their own AI sidekick.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.

Timestamps:

00:00 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

Founded in 2019, Linear is the rare company started pre-ChatGPT to have successfully reinvented itself as an agent-native business.

On this episode of AI & I, Dan Shipper sat down with Karri Saarinen, cofounder and CEO of the product management tool, to discuss building a platform where humans and agents develop software together—and why the "SaaSpocalypse" isn’t coming for all SaaS companies.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI.

Timestamps:

0:00 Introduction

2:00 Why Linear waited to ship AI features instead of rushing to chatbots

5:06 Linear's agent platform and becoming the system that guides AI agents

7:42 Why "SaaS is dead" is a simplistic narrative

12:18 How Linear adopted AI coding 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

Mike Krieger built one of the most consequential consumer apps of the last two decades as cofounder of Instagram. He is now at the frontier of determining what makes a breakout AI-native product as co-lead of Anthropic Labs.

Dan Shipper talked with Krieger for Every’s AI & I about how his experience creating Instagram shapes how he thinks about building with AI, including what can be sped up and what remains stubbornly time-intensive.

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

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

Download Grammarly for FREE at grammarly.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

Silicon Valley loves billion-dollar moonshots and AI darlings. Sam Gerstenzang and Dan Friedman are doing something different—they're starting medical spas and funeral homes.

On this episode of AI & I, Dan Shipper sat down with Gerstenzang and Friedman, partners at Boulton and Watt, which they call the "world's slowest startup incubator." Their model: Come up with an idea, achieve five or 10 million dollars in revenue themselves, then hand it off to a CEO who can take it to the next stage. They've used this playbook to build Moxie, a Series C company that helps nurses open their own medical spas, now with 600-plus customers and a 200-person team globally. Their second company, Meadow Memorials, is a contemporary funeral home with no physical real estate. It has become the largest provider of funeral services in California.

Both businesses launched right around the arrival of ChatGPT—and neither was built with AI in mind. So how are they thinking about AI inside companies where the core work isn't going to change? In this conversation, Gerstenzang and Friedman share how they built an AI agent called Matthew Bolton to power their customer discovery process, why synthetic customer calls completely failed for them, and why they believe you shouldn't give anyone credit for using AI.

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

Want even more?

Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/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.

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 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

Depending on whom you ask, AI is either the best or worst thing that can happen to the next generation. The arguments come from educators, venture capitalists, op-ed writers, and anxious parents—but rarely from the young people in question.

On this episode of AI & I, Dan Shipper sat down with one: Alex Mathew, a 17-year-old high-school senior at Alpha High School in Austin, Texas.

Alpha School, a rapidly expanding network of kindergarten through grade 12 private schools, is not without controversy. Inside Alpha High School, there are no traditional teachers, all academic content is delivered through an AI-powered platform, and the adults in the classroom, known as “guides,” focus solely on supporting the students emotionally and keeping them motivated to learn. The students have two- to three-hour learning blocks every morning and spend the rest of the day going deep on a project in an area they care about, spanning art, sport, life skills, and entrepreneurship.

Mathew’s project is a startup called Berry, built around an AI stuffed animal designed to help teenagers with their mental health. His vision is for teens to talk to the plushie for five to 10 minutes a day and, in the process, learn to recognize and cope with their problems in the right way. In this episode, Dan and Mathew talk about what a day at Alpha High looks like, what keeps students from cheating when AI is everywhere, and how Generation Z—people born between 1997–2012—really feels about college, social media, and books.

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

Want even more?

Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/subscribe

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

In a world of generic AI, don’t sound like everyone else. With Grammarly, you never will. Download Grammarly for free at Grammarly.com.

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

Entrepreneur Andrew Wilkinson used to sleep nine hours a night. Now he wakes up at 4 a.m. and goes straight to work—because he can’t wait to keep building with Anthropic’s latest model, Opus 4.5.

Two years ago, Wilkinson was obsessed with vibe coding on AI software development platform Replit. It was thrilling to describe something in plain English and watch an app appear, less thrilling when the apps were always broken in some way, often full of maddening bugs. So he set his app creation ambitions aside until technology caught up with them.

Then, a few weeks ago, he started playing with Claude Code and Opus 4.5. It felt, he says, like having a “$100,000-a-month payroll of engineers” working for him around the clock.

Wilkinson is the cofounder of Tiny, a company that buys profitable businesses and holds them for the long term. The Tiny portfolio includes the AeroPress coffee maker and Dribbble, a platform where designers can share their work and find jobs. Dan Shipper had him on AI & I to talk about the automations Wilkinson has built for his work and personal life, including an AI relationship counselor, a custom email client, and a system that texts him outfit recommendations each morning. Wilkinson revealed how all of this individual exploration has changed the way he thinks about buying software companies at Tiny.

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

Want even more?

Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/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

00:01:07 - 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-plugi

LLMs have made it absurdly easy to go deep on almost any topic. So why haven’t we all used ChatGPT to earn college degrees we wished we had majored in or pursued a niche interest, like learning how to name the trees in our neighborhood? I know I’m not the only one to feel guilty for well-intentioned attempts at autodidactism that inevitably peter out.

Entrepreneur Nir Zicherman has a reason for this disconnect: LLMs can answer most of your questions, but they won’t notice when you’re lost or pull you back in when your motivation starts to fade.

As the CEO and cofounder of Oboe, a platform that generates personalized courses about everything from the history of snowboarding to JavaScript fundamentals using AI, Zicherman has thought deeply about why the ability to access information does not automatically lead to understanding a concept. In this episode of AI & I, he talks to Dan Shipper about everything he’s learned about learning with LLMs.

They get into Zicherman’s counterintuitive belief that learning is a more passive process than you’d think, the biggest blocker for most people who want to learn something new, and where AI agents currently fall short in providing a meaningful learning experience.

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

Want even more?

Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.

To hear more from Dan Shipper:

Subscribe to Every: https://every.to/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/

Anthropic just dropped Claude Cowork—essentially Claude Code for everyone, not just engineers—and we got to chat about it with a product engineer at Anthropic who helped build it.

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

Tomorrow is the first day of 2026, and to give our listeners a view of the trends that’ll shape the year ahead, Dan Shipper had Every COO Brandon Gell on AI & I to discuss their predictions for what’s next. They discussed how software will be built, who will build it, and what it will take for truly autonomous AI agents to become a reality.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 — Start00:01:05 — Introduction00:01:34 — Reflections on Every’s growth over the past year00:09:38 — What changes when a company grows from 20 to 50 people00:11:55 — How agent-native architecture will change software in 202600:17:13 — Why designers are slated to become power users of AI00:23:24 — The new kind of software engineer who will direct AI agents00:33:42 — Why the next wave of AI training will focus on autonomy

Learn how to use philosophy to run your business more effectively.

Reid Hoffman thinks a masters in philosophy will help you run your business better than an MBA. 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

If you had millions of people using a product you spent years building, would you kill it?

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.

If you’re using AI to just write code, you’re missing out.

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

This episode contains sponsored content in partnership with Salesforce.

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

If your MCP server has dozens of tools, it’s probably built wrong.You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve.

That’s why we interviewed Alex Rattray, the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. We get into MCP and the future of the AI-native internet.

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/