Afleveringen
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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/
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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.
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Zijn er afleveringen die ontbreken?
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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
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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
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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 -
The "SaaSpocalypse"—the panic that AI will make software-as-a-service obsolete—hasn't rattled Figma’s Matt Colyer. As the company’s director of product management for developers, he's been building his own agents for two years and is buying more software services than ever.
In addition to making the case that AI is a “goldmine” for SaaS companies, Colyer talked with Dan Shipper for AI & I about why great design requires a diamond-shaped process: First you diverge, generating as many ideas as possible, then you converge around the best ones. Chat is linear, which makes it good for iterating on one design but bad at generating lots of options. Figma's new on-canvas agent is a first attempt at fixing that.
They also get into why AI design tools need to break free of the text box, how Figma's MCP server is closing the loop between code and design, and why "review" has become the biggest bottleneck in AI-assisted product work.If you found this episode interesting, please like, subscribe, comment, and share!
Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipper
To hear more from Dan Shipper:Timestamps:
1:03 - Introduction2:15 - Why the SaaSpocalypse narrative has it backwards5:27 - Matt’s email agent origin story13:21 - Divergent vs. convergent design thinking17:39 - Figma’s MCP server19:45 - Why design agents need personalization22:09 - Every problem is a context problem25:12 - Apple and Google as the reigning kings of context28:18 - Why review is the new bottleneckLinks to resources mentioned in the episode:
Matt Colyer on X: https://x.com/mcolyerFigma: https://figma.comFigma MCP server: https://www.figma.com/blog/introducing-figma-mcp-server/ -
Dan Shipper runs one of the most AI-native companies today. Every has agents embedded in nearly every workflow—“if you swing a stick in our Slack, you're as likely to hit a human as an agent,” he says. And yet the company has grown from four people to 30 since GPT-3 came out, and is still hiring.
Why does Dan believe there's more human work to do than ever?
In a format flip for AI & I, Every's COO Brandon Gell turns the tables and interviews Dan about his latest essay, “After Automation”—an 8,000-word argument for why rising automation doesn't eliminate demand for human work, it increases it. The thesis: AI makes yesterday's expert competence cheap and widely available, which floods every field with output that's close but not quite right—and that creates more demand for the humans who can take it the rest of the way.
Dan talked with Brandon about the paradox at the heart of agent-native work: The more AI can do, the more humans are needed to direct it, refine its output, and decide what matters next.
If you found this episode interesting, please like, subscribe, comment, and share!
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Links to resources mentioned in the episode:
“After Automation” by Dan Shipper: https://every.to/chain-of-thought/after-automation
Brandon Gell on Every: https://every.to/@brandon_5263
Join the membership for where you live at joinbilt.com/dan
Timestamps:
00:00:51 Introduction
00:05:51 The AI paradox: more automation, more human work
00:10:00 How AI makes yesterday's expert competence cheap
00:18:00 AI can act autonomously but it does not have agency
00:20:39 Why Dan is all in on AGI
00:21:57 AI layoffs are a lie
00:25:42 Ride the models and you'll be fine
00:35:30 How to use AI as a long-form features editor
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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/
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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
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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
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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
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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
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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/danshipperTimestamps
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 requiresLinks to resources mentioned in the episode:
Proof: https://www.proofeditor.ai/ -
Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipper
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:
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 buildersLinks 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
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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
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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)
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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
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