Will England is the CEO of Walleye Capital, a hedge fund managing nearly $10 billion in assets. An engineer by training with a math background from Oxford, he has spent his career at the intersection of machines and markets—and has made AI fluency mandatory for all 400 employees.

England believes refusing to use AI is like refusing to use the internet in 1995 because it wasn’t perfect. His use of AI is public and effusive, including in a firm-wide email that opened with “I used ChatGPT to write this email. You should be using it, too, and be proud of it.” AI informs how Walleye drafts memos and selects stocks.

On Every’s AI & I, Dan Shipper spoke with England about why he’s betting his entire organization on AI, why “results are what matter” more than blood, sweat, and tears, and what the American frontier can teach us about leading through technological change.

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Timestamps:
0:00 Start
0:51 Introduction
3:25 What pushed Will to go all in on AI
15:15 Inside the ‘AI-first’ memo Will shared at Walleye
17:02 Why you shouldn’t be afraid of using AI for work
31:25 How Will uses LLMs to sharpen his thinking
35:57 Walleye’s approach to using AI to reduce risk
39:35 What history can teach us about leading through change
57:10 Will’s first principles for making better decisions
59:23 Why Will journals every day—and how AI makes it easier

Links to resources mentioned in the episode:
Will England/Walleye Capital: https://walleyecapital.com/bio/will-england
Every’s AI tools—Monologue, Cora, Spiral, and Sparkle: https://every.to/studio
Every’s AI consulting: https://every.to/consulting

LLMs are a new medium for storytelling.

That’s according to the creators of Portola, the company behind Tolan: an embodied AI companion that lives on its own planet and chats to you with a distinct personality. In 2025, Portola's founder and CEO Quinten Farmer and Head of Story Eliot Peper joined Dan Shipper to explain how they’re building this new medium from scratch. Their aim is to help users go from overwhelmed to grounded through conversations with Tolan that feel personal and spontaneous, not scripted. 

On this week’s AI & I, Dan revisits his conversation with Quinten and Eliot. They discuss why response time is everything for voice-based AI interfaces, how Portola designs AI personalities users will click with, and why character-driven AI could become a new computing interface. 
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

Go to https://attio.com/every and get 15% off your first year.

Timestamps:
 00:01:30 - Introduction
 00:04:07 - Talking to the Portola CEO's Tolan, Clarence
 00:09:11 - How Portola went from building software for kids to AI companions
 00:23:40 - Why response time is everything for voice-based AI interfaces
 00:29:54 - Tolans don't use scripted prompts—they're taught to improvise
 00:37:23 - How to know which AI personalities your users will click with
 00:42:27 - Developing the character traits of an AI companion
 00:49:48 - What does it mean to build technology that makes us flourish
 01:01:10 - How Portola evaluates whether Tolans are resonating with users
 01:11:01 - Inside Portola's viral growth strategy

In 2025, Kevin Scott bet that the agentic web would be the next big thing in AI.
The Microsoft CTO argued that for agents to be genuinely useful, they'd need to be able to take action on our behalf—which would mean giving them access to the same sprawl of tools, data, and systems that make up the internet. Today, that bet is starting to pay off, as the foundational infrastructure for the agentic web is now being built.

On this week's AI & I, Dan Shipper revisits his conversation with Kevin. They discuss Microsoft's role in the agentic web, why openness doesn't have to come at the expense of security, and why programmers should stay curious about new tools rather than resist them on principle.

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

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Timestamps: 
0:00 Start
1:44 Introduction
2:49 The race to close the "capability overhang"
4:31 How agents will evolve into practical, useful tools
6:48 The role Kevin sees Microsoft playing in the agent ecosystem
12:05 How robust security measures can coexist with open ecosystems
15:39 Kevin's philosophy on being a craftsman in the age of agents
20:52 How the landscape of software development agents will evolve
25:33 The future of agentic workflows

Links to resources mentioned in the episode:
Kevin Scott on X: https://twitter.com/kevin_scott
Model Context Protocol (MCP): https://modelcontextprotocol.io
NLWeb: https://github.com/microsoft/NLWeb
GitHub Copilot: https://github.com/features/copilot

Most consumer AI so far has been single-player: you and a chatbot, alone.

Benchmark partner Sarah Tavel, one of Pinterest's first 30 employees, is betting that's about to change. She's looking for a product genius who can build an AI product with social DNA: status, network effects, and multiplayer dynamics. That'll enable users of ChatGPT and other models to learn from how others use AI and level up.

On this week’s AI & I, Dan Shipper revisits his conversation with Sarah. They talk about why technical founders dominate the early days of a platform shift while product-minded founders win later, what ChatGPT is still missing, and what separates a founder's real network effect from a slide with a flywheel diagram.

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 for YouTube:

0:00 Start

1:10 Introduction

2:26 Why the future of consumer AI belongs to founders with product intuition

11:09 What Sarah sees as ChatGPT's biggest weakness

18:45 How Sarah would design a consumer AI app with social DNA

24:10 The kind of founders Sarah invests in

28:33 How to know if your startup's network effects are real

35:40 What's catching Sarah's eye beyond AI

40:41 How AI will change the way top venture capitalists invest

Links to resources mentioned in the episode:

Sarah Tavel on X: https://x.com/sarahtavel

Benchmark: https://benchmark.com

Agentio (marketplace for YouTube creators and brands): https://agentio.com/ 

Chainalysis: https://chainalysis.com

The Five Temptations of a CEO by Patrick Lencioni: https://www.amazon.com/dp/B007BZBRB8

Thinking in Bets by Annie Duke: https://www.amazon.com/dp/B0HBBW23PM

Kevin Kelly has spent over 30 years experiencing the edge of new technology: from the earliest days of the internet to the first years of Burning Man. But he’s always treated the frontier as a place to visit, not somewhere to live. It’s partially how he’s been able to stay grounded through tech’s various hype cycles.
As founding executive editor of Wired and author of The Inevitable, Kelly spends as much time analyzing the latest in AI as he does reading about significant moments in history. It’s a discipline he traces back to his work with the Long Now Foundation, which he cofounded to encourage long-term thinking, reaching from the last 10,000 years to the next. 
On this week’s AI & I, Dan Shipper revisits his conversation with Kelly. They get into why historians can be the best futurists, and how our bid to understand what intelligence is has parallels with early scientists' attempts to figure out electricity. 
Kelly also describes the joy he found in creating an AI-generated saga featuring Leonardo Da Vinci, Christopher Columbus, and Martin Luther—one that will only ever be read and enjoyed by him.

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 for YouTube:
0:00 Start 
0:50 Introduction 
1:10 Why Dan and Kelly love Annie Dillard 
12:52 How to predict the future like Kelly
16:10 What the history of electricity can teach us about AI 
20:13 How Kelly thinks about the nature of intelligence 
25:44 Kelly's advice on discovering your competitive advantage 
29:33 How Kelly assembled a bench of star writers for Wired 
34:43 How Kelly used ChatGPT to co-create a book 
39:12 Using AI as a mirror for your mind 
43:43 What Kelly learned from betting on VR in the 1980s

Links to resources mentioned in the episode:
Kevin Kelly on X: https://twitter.com/kevin2kelly
The Inevitable by Kevin Kelly: https://www.amazon.com/Inevitable-Understanding-Technological-Forces-Future/dp/0525428089
Pilgrim at Tinker Creek by Annie Dillard: https://www.amazon.com/Pilgrim-Tinker-Harper-Perennial-Classics/dp/0061233323
1,000 True Fans by Kevin Kelly: https://www.amazon.com/1000-True-Fans-Kellys-Simple-ebook/dp/B01N9P9O4G
Full episode transcript: https://every.to/podcast/transcript-be243312-ea22-4193-8c56-b9cd45a79a87

Yash Poojary, a growth engineer at Every, dropped an idea for a campaign in Slack at 7 p.m. Instead of building it himself, Every’s head of growth Austin Tedesco took a screenshot of the Slack thread, dropped it into Codex, typed "Can you do this?", and went to the gym.

By the time he got back, Codex had built four audience segments, drafted emails for each one, and pulled a social image that had worked before. It took Austin 10 minutes to make some tweaks and schedule the whole thing to send the next morning. Within a few hours, it generated more than $25,000 in revenue. 

That story came out of the launch week for All Access, Every’s new $625-a-year membership built around the Builder Pack. It includes $7,000 in credits and free usage from ten of the AI products Every uses every day, including Claude Max, Codex, Cursor Pro+, PostHog, Notion, Framer, Render, and Flora.

On this episode of AI & I, four of Every's own builders—COO Brandon Gell, head of marketing Douglas Brundage, as well as Yash and Austin—sit down to show how they use AI, breaking down their personal stacks and giving insight into their own strategies and mindset for building.

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 for YouTube:

0:00 Intro 

0:35 All Access Explained 

3:01 Yash's Tech Stack and How He's Automating Testing Pipelines 

8:02 The Idea to Execution Loop 

10:25 How an Agent Turned an Idea into $25K 

17:50 The AI Sandwich Workflow 

22:03 Making AI Tools Accessible to Solo Builders 

28:50 Douglas on Brand and Design

34:51 Tips on What to Build First 

43:46 What's Next for All Access

Links to resources mentioned in the episode:

• Brandon Gell on X: https://x.com/bran_don_gell
• Yash Poojary on X: https://x.com/poojary_yash
• Austin Tedesco on X: https://x.com/tedescau?lang=en
• Douglas Brundage on X: https://x.com/DABrundage
• Introducing Every All Access: https://every.to/on-every/introducing-every-all-access
• Get the Builder Pack: every.to/builder-pack 

Go to https://attio.com/every and get 15% off your first year.

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

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/

Go to https://attio.com/every and get 15% off your first year.

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.

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:
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

Go to https://attio.com/every and get 15% off your first year.

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

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