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

Kate Lee has spent her career working with words—first as a literary agent, then in roles at Medium, WeWork, and Stripe. As Every’s editor in chief, she’s been the quiet force behind the newsletter for more than three years.

Lately, something has shifted in Kate’s work. After years of watching her colleague Dan Shipper evangelize AI from the front lines, Katie has started rewiring how she works and is integrating more and more AI tools into her workflow.

We had Kate on to talk about her career path from book deals to tech startups, what it really means to run a newsletter as a small team in the age of AI, and what she thinks the bottleneck to automating copyediting is. Plus: the story of pulling off reviews of two major model releases in 24 hours, and how she’s using her AI-powered browser to help her hire.

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

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/

Every has unveiled a new product, built by CEO Dan Shipper. It's called Proof, a free, open-source, live collaborative document editor built for humans and AI agents to work in together. 
Proof started as a Mac app designed to show the provenance of AI-written text—purple for AI, green for human. But when Shipper rebuilt it as a web app with real-time collaboration, something clicked. Suddenly, everyone at Every was using it for everything from planning docs, to creative writing and even daily to-do lists. The team realized they needed a lightweight space where their OpenClaw agents and humans could co-author documents and leave comments. 
In this special episode, Shipper is joined by Every chief operating officer Brandon Gell, Cora general manager Kieran Klaassen, and head of growth Austin Tedesco to demo Proof live and share how it's changed the way they work. Brandon walks through a loop where his Codex agent writes a plan, Dan's personal Claw R2-C2 reviews it, and the humans just steer. Austin explains how he uses Proof to write a weekly food newsletter, texting ideas to his Claw on runs and watching an outline take shape. And Kieran makes the case that Proof's power is its lightness—just a link you can hand to any agent or colleague.
The conversation covers what "agent native" means in practice, why AX (agent experience) matters as much as UX (user experience), what happens when 10 agents edit one document at the same time, and why some writing is now better read by an AI than a human.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:

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

Get started building today at 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

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)

OpenAI’s hottest app isn’t ChatGPT—it’s Codex.

In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January, and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl.

Dan Shipper talked to Thibault Sottiaux, head of Codex, and Andrew Ambrosino, a member of technical staff who built the Codex app, for Every’s AI & I about what OpenAI is building and how they’re using it internally.

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

Want even more?

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

To hear more from Dan Shipper:

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

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

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

Timestamps:  

00:00:00 - 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

The AI labs fighting for attention during the Super Bowl call to mind another iconic Super Bowl moment: Apple’s 1984 ad for the Macintosh, which promised that the personal computer would be a source of unbound wonder, freedom, and delight.

They were right, but over time, the personal computer has also become cluttered with errands.

These “computer errands”—downloading a W-2 when tax season rolls around, hunting for the right coupon code before checkout, or navigating the unholy labyrinth of the Amazon Web Services dashboard just to change one permission setting—have taken over our digital lives. Atlas, OpenAI’s agentic browser, sprang from the idea that AI should handle this tedium for you.

In this week’s episode of AI & I, Dan Shipper sat down with two members of the Atlas team, Ben Goodger and Darin Fisher. Goodger is Atlas’s head of engineering, and Fisher is a member of the technical staff. Both are legends of the browser world. They’ve spent decades building the modern web, working together on Netscape, Firefox, and Chrome before arriving at Atlas. From that vantage point, they told Dan how they think browsing is about to change, why building a browser is harder than it looks, and what it’s like to create a new one with AI coding tools like Codex.

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

Want even more?

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

To hear more from Dan Shipper:

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

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

Move fast, don’t break 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 https://www.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

A few weeks ago, Natalia Quintero wouldn’t have called herself technical. But since the beginning of January, she has woken up at 6 a.m. to vibe code with Claude. The AI project manager she built saved her 14 hours a week. 

Getting there meant scrapping the system three times and starting over. But the result handles everything from onboarding new clients to generating weekly updates across all projects.

Natalia is the head of AI consulting at Every. As part of the role, she's spoken with over 100 organizations in the past year and worked with a select two dozen, including hedge funds, private equity firms, and Fortune 500 companies. She’s seen what separates companies thriving with AI from those floundering, and it comes down to patterns that have nothing to do with having the most resources or the fanciest tools.

Dan Shipper had her on AI & I to share what she’s learned from this front-row seat to AI adoption. Quintero reveals how a private equity firm cut investment memo creation from three weeks to 30 minutes, why AI adoption needs to come from the top down, and what happened when she learned from her early morning experiments.

She also explains why the companies going furthest with AI are the ones that give employees permission to fail—and how that counterintuitive approach is revolutionary.

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

Want even more?

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

To hear more from Dan Shipper:

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

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

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

Timestamps:  

00:00:00 - 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

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/