AI and I

GitHub’s COO Explains Why AI Hasn’t Replaced Developers

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/subscribeFollow him on X: https://x.com/hammer_mtTimestamps for YouTube:00:00:52: Introduction00:03:27: The agentic PR flood00:04:33: GitHub's approach to helping open-source maintainers manage the surge00:06:15: What 14 billion commits means for code quality00:08:03: Moving from per-seat licensing to usage-based pricing00:09:45: Kyle's dual role as GitHub COO and Microsoft's chief marketing officer for developers00:13:03: Developer choice as competitive moat00:14:57: How to balance dogfooding your own tools with staying honest about the competition00:19:45: Hill climbing, frontier tuning, and solving the model-routing problem00:24:45: Kyle's agentic communication hackLinks to resources mentioned in the episode:Kyle Daigle on X: https://x.com/kdaigleMike Taylor on Every: https://every.to/@mike_2114Mike’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-oneGitHub Copilot: https://github.com/features/copilot

How Anthropic Uses Claude Fable 5 With Mike Krieger

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.toTimestamps: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 SaaS Apocalypse Is a Goldmine With Figma’s Matt Colyer

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/subscribeFollow him on X: https://twitter.com/danshipperTimestamps: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/

We Automated Everything With AI and Tripled Our Headcount

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/subscribeFollow him on X: https://twitter.com/danshipperLinks to resources mentioned in the episode:“After Automation” by Dan Shipper: https://every.to/chain-of-thought/after-automationBrandon Gell on Every: https://every.to/@brandon_5263Join the membership for where you live at joinbilt.com/danTimestamps:00:00:51 Introduction00:05:51 The AI paradox: more automation, more human work00:10:00 How AI makes yesterday's expert competence cheap00:18:00 AI can act autonomously but it does not have agency00:20:39 Why Dan is all in on AGI00:21:57 AI layoffs are a lie00:25:42 Ride the models and you'll be fine00:35:30 How to use AI as a long-form features editor

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

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/subscribeFollow him on X: https://twitter.com/danshipperGet 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 softwareLinks to resources mentioned in the episode:Alex Rattray: Alex Rattray (@RattrayAlex), Alex RattrayStainless: https://www.stainless.com/