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.

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Timestamps for YouTube:

00:00:52: Introduction

00:03:27: The agentic PR flood

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

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

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

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

00:13:03: Developer choice as competitive moat

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

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

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

Links to resources mentioned in the episode:

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

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

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

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

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 The way he’d been thinking about productivity, strategy, and time management was out of date.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

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.

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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.

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To hear more from Dan Shipper:

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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.

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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/

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

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

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

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

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

To hear more from Dan Shipper:

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

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

Timestamps:

00:00:51 - Introduction

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

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

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

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

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

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

00:43:21 - Why LLM progress is plateauing

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

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

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

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

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

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

Timestamps:

00:00 Introduction

00:02:21 How Brandon built Zosia, an AI agent to run his household

00:07:09 Brandon's aha moment re: using agents for work

00:09:39 What happened when everyone on the team got their own agent

00:12:42 How agents take on their owners' personalities, and why that matters inside an org

00:23:51 Why it's important for agents to do work in public

00:30:51 What we're still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem

00:40:45 How we built Plus One, our hosted OpenClaw product

00:47:27 The cultural shift required to make agents work at scale

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

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

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

To hear more from Dan Shipper:

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

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

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

Timestamps:

0:00 Introduction

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

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

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

12:18 How Linear adopted AI coding tools

17:45 AI's impact on product building workflows—speed versus thoughtfulness

22:18 The value of conceptual work and thinking before shipping

29:30 How AI is reshaping Linear's product strategy

37:18 Demo: Linear's agent skills, shared context, and code review workflow

47:48 The future of product development and the enduring role of human judgment

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

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

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

To hear more from Dan Shipper:

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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.

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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.
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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:

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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