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/subscribe
Follow him on X: https://twitter.com/danshipper
Get started with Braintrust at https://www.braintrust.dev/ 

Timestamps:
0:03 Introduction
1:48 How Fable completely reshaped Mike's workflow
4:48 When to use Sonnet versus Fable
10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture
15:00 The cost to build has collapsed
19:03 Is software engineering over?
21:48 How Anthropic's engineering teams work today
38:39 The mechanics of verification
44:39 What people should use the model to build
47:24 Dynamic workflows

Links to resources mentioned in the episode:
Mike Krieger on X: https://x.com/mikeyk
Anthropic Labs: https://www.anthropic.com
Claude Code: https://claude.ai/code
Every: https://every.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!
To hear more from Dan Shipper:

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

Timestamps:

• 1:03 - Introduction
• 2:15 - Why the SaaSpocalypse narrative has it backwards
• 5:27 - Matt’s email agent origin story
• 13:21 - Divergent vs. convergent design thinking
• 17:39 - Figma’s MCP server
• 19:45 - Why design agents need personalization
• 22:09 - Every problem is a context problem
• 25:12 - Apple and Google as the reigning kings of context
• 28:18 - Why review is the new bottleneck

Links to resources mentioned in the episode:

• Matt Colyer on X: https://x.com/mcolyer
• Figma: https://figma.com
• Figma MCP server: https://www.figma.com/blog/introducing-figma-mcp-server/

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

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/Inside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 Million

From time to time, we will republish episodes that you might have missed. This episode originally aired in September 2025.

Noah Brier uses Claude Code as his second brain—it’s the coolest notetaking setup we’ve ever seen.
He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian vault. He does it all from his phone.

We had him on the show to tell us exactly how he’s pulling this off. 

Dan and Noah get into:

The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates.

The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away.

How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works.

This episode is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain.

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

Timestamps: 

00:00:52 - Introduction 
00:02:10 - How you can do deep work on your phone 
00:05:30 - Why Noah thinks Grok has the best voice AI 
00:11:11 - The nuts and bolts of Noah's Claude Code-Obsidian setup 
00:26:05 - Using an agent in Claude Code as a "thinking partner"
 00:30:23 - Noah's Thomas' English Muffin theory of AI 
00:39:47 - The white space still left to explore in AI 
00:48:44 - How Noah is preparing his kids for AI 
01:00:06 - How he brought his Claude Code setup to mobile

Links to resources mentioned in the episode:
Noah Brier: ⁠https://www.noahbrier.com/⁠, ⁠Noah Brier (@heyitsnoah) / X⁠
Alephic, his AI strategy consultancy: ⁠alephic.com⁠ 
The conference he leads about marketing and AI: ⁠http://BRXND.AI⁠ 
A newsletter he writes about AI: ⁠newsletter.brxnd.ai⁠  
The declassified relic from World War II they talk about: ⁠https://www.alephic.com/sabotage
The apps Noah used to set up Claude Code on his phone: ⁠Termius⁠, ⁠Tailscale⁠

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