AI & I

“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.
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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/

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

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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

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