AI & I

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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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
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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.
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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.
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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.
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To hear more from Dan Shipper:
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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

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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