The Every Podcast

Thesis: 2027 is made possible by our founding partners, Lovable and Notion.
Lovable is the fastest way to turn ideas into software people love: https://link.lovable.dev/every
Notion is the collaborative AI workspace where teams and agents work side by side: https://www.notion.com/?utm_source=youtube&utm_campaign=thesis-2027&utm_content=every-261111-video
What does great human work look like after automation?
It’s a question we try to answer every day at Every, and the one we built our conference, Thesis, around. In his closing keynote, Dan Shipper argues that as AI makes intelligence cheap and widely available, what matters most is human perspective. It’s your “non-consensus” point of view, plus your responsibility for a particular corner of the world, that helps you spot the insight no one else would.
On this week’s episode of The Every Podcast, we’re sharing Dan’s full talk at Thesis: 2027. Dan explains why benchmark charts make the future of work look bleaker than it is, why picking the right idea still takes a human, and how we should really measure AI models.
Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign= (https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode)thesistalkepisode
Links to resources mentioned in the episode:
Thesis: https://every.to/thesis-2027
Dan Shipper: https://x.com/danshipper
OpenAI’s GDPval benchmark: https://openai.com/index/gdpval/
Timestamps:
0:00 Introduction
1:51 Work after automation
5:00 1 person vs 5 jobs
10:18 Why perspective matters
17:55 Better AI, more valuable humans

How did the race to build AGI really unfold, and who made the decisions that shaped it?
Tech journalist Kevin Roose interviewed 150 people at the center of the AI industry to find out. The result is The AGI Chronicles, a riveting account of  how modern AI was built and the choices that will determine what comes next. 
On this week’s episode of The Every Podcast, Natalia Quintero sits down with Roose to talk about how he got a secretive industry to open up, how he did (and didn’t) use AI in his book writing process, and why he sees his job as explaining the “very confusing and insular” world of Silicon Valley to everyone else.
Links to resources mentioned in the episode:
Follow Kevin Roose: https://x.com/kevinroose
Get Kevin Roose’s book, The AGI Chronicles: https://theagichronicles.com/
Machine Gods: https://www.npr.org/podcasts/g-s1-143588/machine-gods 
“The Big Blob of Compute Hypothesis” excerpt on Kevin’s Substack: https://kevinroose.substack.com/p/the-essay-that-started-the-ai-race 
Profiles of the Future by Arthur C. Clarke: https://www.amazon.com/Profiles-Future-Inquiry-Limits-Possible/dp/0575402776 
The Making of the Atomic Bomb by Richard Rhodes: https://www.amazon.com/Making-Atomic-Bomb-Richard-Rhodes/dp/1451677618 
NotebookLM: https://notebook.google/
Timestamps:
0:00 Introduction 
2:23 Are we doomed? Roose on AI risk and the case for mandatory safety testing
7:13 Why Roose raced to write the history of AGI before the records disappeared 
10:45 How Roose got 150 sources in a secretive industry to open up 
13:49 Dario Amodei’s “Big Blob of Compute” memo 
19:12 How Roose’s mother’s book on the survivors of the atomic bomb influenced his work
21:40 The moment Roose first “felt the AGI” 
23:24 How a “council of Claudes” fact-checked Roose’s book 
31:59 Launching Machine Gods and reinventing video journalism 
41:41 Weightlifting versus forklifting: How Roose decides what to hand off to AI

Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer
Can AI create good art? 
Fred Graver thinks the answer depends on you. The Emmy Award–winning writer, producer, and founder of AI Writer’s Studio says AI can help with many parts of the creative process—but it can’t give you something to say. Without your own point of view, AI will only make your work more average.
On this week’s episode of The Every Podcast, Natalia Quintero sits down with Graver. They discuss why every writer should have a ‘who I am as a writer’ document, the cast of AI collaborators he’s built into his own writers’ room, and the human quirks that make characters endearing—the part he insists AI can’t supply.
Get Fred’s Skill: https://every.to/context-window/every-model-has-a-six-month-shelf-life-now
Links to resources mentioned in the episode: 
• Follow Every: https://x.com/every 
• Follow Natalia Quintero: https://x.com/NataliaZarina 
• Follow Fred Graver: https://x.com/fredgraver
• AI Writers Studio: https://aiwritersstudio.substack.com/
• Compound Writing plugin: https://every.to/guides/compound-writing 
• Compound Engineering plugin: https://every.to/guides/compound-engineering
• Thesis conference: https://every.to/thesis-2027
• Follow Katie Parrott: https://x.com/kplikethebird 
• Follow Kieran Klaassen: https://x.com/kieranklaassen
• Follow Dan Shipper: https://x.com/danshipper
Timestamps:
0:00 Introduction
2:22 Fred’s path from National Lampoon and Letterman to Twitter and Microsoft
7:47 The “I want in” instinct behind every career pivot
12:19 How Best Week Ever taught Fred to build with the audience
13:26 Using AI as a virtual writers’ room, not a ghostwriter
16:07 Codifying industry expertise and studio culture into an AI core
20:32 The three phases of making entertainment with AI
26:27 Why you should brain-dump your idea before opening AI
38:47 Fred’s thesis: Success is knowing what the audience wants before it does
43:44 Fred’s day-to-day workflow across Claude, ChatGPT, and NotebookLM

Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio
Every time a new AI model drops, companies face the same dilemma: Figure out what’s new and how to use it, or focus on getting work done.
Every solves this dilemma with a frontier team: a small group that tests new models and turns useful experiments into tools and workflows for the rest of the company.
On this week’s episode of The Every Podcast, Natalia Quintero sits down with all seven members of Every’s frontier team to ask what they’re building—from a tabless browser that helps protect your attention to a personal agent orchestrator called Minga—and what they want to try next.
Subscribe to Every (free for 14 days): https://every.to/youtube-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=companyagentepisode
Links to resources mentioned in the episode:
Follow Every: https://x.com/every
Compound Writing plugin: https://every.to/guides/compound-writing
Every’s vibe checks: https://every.to/vibe-check
Follow Dan Shipper: https://x.com/danshipper
Follow Natalia Quintero: https://x.com/NataliaZarina
Follow Mike Taylor: https://x.com/hammer_mt
Follow Jack Cheng: https://x.com/jackcheng
Follow Arielle Shipper: https://x.com/ArielleShipper
Follow Katie Parrott: https://x.com/kplikethebird
Follow Jannik Jung: https://x.com/JannikJung
0:00 Introduction: Why every company needs a frontier team
2:42 Why the best frontier teams have pirates and architects 
4:57 How Head of Evals Mike Taylor builds personal benchmarks 
6:46 How Opus edited an explainer video using Slack messages and meeting notes 
10:20 Working with AI means protecting other people’s attention 
12:26 Why a frontier team needs a “wrangler” like Arielle Shipper 
18:58 Why senior editor Jack Cheng built a tabless browser 
24:37 How resident writer Katie Parrott teaches her AI writing plugin who the reader is
30:51 Inside engineer Jannik Jung’s personal AI orchestrator 
35:41 Compound engineer Kieran Klaassen uses AI to build power tools

Check out Gemini Audio: https://goo.gle/Every-Gemini-Audio
Bet on founders who understand the technology, because their first idea probably won’t be the one that sticks.
That’s the view of Matt Hartman, founder and managing partner of Factorial Capital. In 2016, as a partner at Betaworks, Hartman was part of the team that wrote the first check into Hugging Face, which was then a chatbot app for teenagers. Today it’s the largest hub for open-source AI, and Nvidia has agreed to buy it for about $13 billion.
On this week’s episode of The Every Podcast, Hartman tells Natalia Quintero how backing Hugging Face shaped his investment thesis, how Factorial grew into a “family tree” of technical founders who help him scout deals, and why vibe-coding his own app gave him a new respect for SaaS.
Subscribe to Every (free for 14 days): https://every.to/podcast-every-offer?utm_source=podcast&utm_medium=audio&utm_campaign=matthartmanepisode
Links to resources mentioned in the episode:
Factorial Capital: https://factorialcap.com
Ghost Pepper, Matt's open-source voice-to-text app: https://github.com/matthartman/ghost-pepper
Superpowers, Jesse Vincent's skill for Claude Code: https://github.com/obra/superpowers
Hugging Face: https://huggingface.co
Follow Every: https://x.com/every
Follow Natalia Quintero: https://x.com/NataliaZarina
Follow Matt Hartman: https://x.com/MattHartman
0:00 Introduction
5:06 How Hugging Face went from a teen chatbot to the home of open-source AI
9:52 The three decisions that set Hugging Face on its path
12:49 Hartman’s approach to finding the newly technically possible
18:10 Why Hartman backs technical founders
21:10 How Factorial’s portfolio founders source its next deals
27:09 Why faster iteration makes product insight the most valuable skill
34:28 What Hartman learned building Ghost Pepper, an open-source voice-to-text app
40:02 Hartman’s current theses, from non-language models to full-stack AI apps
44:14 Why the best founders right now are experimenting