Joe Hudson is a coach who works with the executives building AGI at OpenAI. 

From inside OpenAI, he witnesses the full spectrum of human emotion that comes with bringing something new into the world—the exhilaration, the terror, the weight of it all. He feels these emotions, too: He believes AI will eventually replace what he does as a coach.

But instead of fixating on that fear, Hudson is asking a deeper question: Who is he becoming in the meantime? He believes that moments like this—when we can feel the ground quiver—can be powerful catalysts for transformation, but only if we’re willing to face the uncertainty they bring.

In this episode of AI & I, Dan Shipper sits down with Hudson to talk about how he’s answering that question. They get into what happens when the thing you’ve built your life around might disappear, how to find who you are beneath your professional identity, and why Hudson believes intention is the key to growing with AI.

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

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To hear more from Dan Shipper:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper

Sponsors: 

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Timestamps:

1. Introduction: 00:01:49
2. What it feels like inside the room where AGI is being built: 00:03:14
3. The most important question to ask yourself as AGI approaches: 00:08:15
4. The importance of sitting with uncertainty: 00:17:49
5. How Joe is preparing his daughters for a post-AGI world: 21:11:04
6. How we think, feel, and react; the three layers of human awareness: 27:25:01
7. Staying grounded while coaching the people shaping our future: 35:34:04
8. Why Joe doesn’t take things personally—even when the stakes are high: 42:44:03

Links to resources mentioned in the episode:

• Joe Hudson: @FU_joehudson; 
• Learn more about the coaching and workshops that Joe runs: Art of Accomplishment

If you’re using AI to just write code, you’re missing out.

Two engineers at Every shipped six features, five bug fixes, and three infrastructure updates in one week—and they did it by designing workflows with AI agents, where each task makes the next one easier, faster, and more reliable.

In this episode of AI & I, Dan Shipper interviewed the pair—Kieran Klaassen, general manager of Cora, our inbox management tool, and Cora engineer Nityesh Agarwal—about how they’re compounding their engineering with AI. They walk Dan through their workflow in Anthropic’s agentic coding tool, Claude Code, and the mental models they’ve developed for making AI agents truly useful. Kieran, our resident AI-agent aficionado, also ranked all the AI coding assistants he’s used.

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper   

Sponsors:
Microsoft Teams
Want seamless collaboration without the cost? Microsoft Teams offers a robust free plan for individuals that delivers unlimited chat, 60-minute video meetings, and file sharing—all within one intuitive workspace that keeps your projects moving forward. Head to ⁠https://aka.ms/every⁠ to use Teams for free, and experience effortless collaboration, today.

Attio: Go to⁠⁠⁠⁠ https://www.⁠⁠⁠⁠attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

1. Introduction: 00:01:16
2. Why Kieran believes agents are turning a corner: 00:03:18
3. Why Claude Code stands out from other agents: 00:06:36
4. What makes agentic coding different from using tools like Cursor: 00:11:58
5. The Cora team’s workflow to turn tasks into momentum: 00:15:20
6. How to build a prompt that turns ideas into plans: 00:23:07
7. The new mental models for this age of software engineering: 00:34:00
8. Why traditional tests and evals still matter: 00:39:13
9. Kieran ranks all the AI coding agents he’s used: 00:42:00

Links to resources mentioned in the episode:

• Try Cora, our AI email assistant: https://cora.computer/  
• Kieran Klaassen: @kieranklaassen
• Nityesh Agarwal: @nityeshaga
• The book that helps Nityesh form mental models to work with AI agents: High Output Management
• A guide to Anthropic’s prompt improver: https://www.anthropic.com/news/prompt-improver 

With LTX Studio, you can bring your stories to life, complete with a cast, storyline, and settings, all according to your style and specifications. Check them out here: https://bit.ly/LTXStudioEvery

500K people are confiding in an AI alien—and it's on track to generate $4M this year.

It’s called a Tolan: an animated AI character that can talk to you like your best friend. The company behind it, Portola, has 4x’d their ARR in the last month from viral growth on TikTok and Instagram. 

Tolan isn’t just a hyper-growth startup—they’re also exploring AI as a completely new creative tool, and storytelling medium. Their goal is to help their users go from overwhelmed to grounded, and it’s working. 

Today, on AI & I, I sit down with two of the minds behind Tolans:

My good friend Quinten Farmer, Portola’s cofounder and CEO, and Eliot Peper, their head of story and a best-selling science fiction novelist. We get into:

• How to build AI personalities users love. During user onboarding, the team gathers information—through a light-touch personality quiz—and then uses frameworks like the Big Five and Myers-Briggs to shape a Tolan that mirrors the user; like an older sibling might. The aim is to create someone who feels familiar enough to be safe, but different enough to be interesting.
• Why AI characters are “improv actors”. Rather than scripting detailed prompts, the team trains Tolans to improvise—inspired by Keith Johnstone’s book Impro, where he talks about building strong narratives through free association and recombination.
• How “memory” is critical to developing compelling characters. Tolans develop their personalities through “situations”: small narrative setups (a memory, a joke, an embarrassing moment) the Tolan reacts to, remembers, and gradually weaves into its character; accumulating into something that feels like a real lived experience.
• Why response time is everything for voice AI interactions. A Tolan has at most two seconds to curate the right context about a user and deliver a reply that feels genuine—the team has found that even half a second slower can break the user’s immersive interaction with the AI.
• The future of AI as a totally new creative medium. New technologies bring about new formats and new mediums. AI creates the opportunity for creatives to tell completely new kinds of stories—if they’re brave enough to try it.
• “White mirror” technologies that make you feel more like yourself. Amid concerns that tech drives polarization and isolation, Tolan offers a counterexample: a tool designed to make the best of what humanity knows about being a flourishing individual available on demand. The company’s north star is helping users go from feeling overwhelmed to feeling grounded.

This is a must-watch for anyone exploring AI as a creative medium—or curious about the future of human-AI relationships.

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

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

Links to resources mentioned in the episode:

• Quinten Farmer: @quintendf
• Eliot Peper: @eliotpeper
• Make your own Tolan: https://www.tolans.com/ 
• Keith Johnston’s book about improvisation: Impro
• Stephen King’s book about writing: On Writing

This episode is sponsored by Vanta. Achieving SOC 2 compliance can help you win bigger deals, enter new markets, and deepen trust with your customers—but it can cost you real time and money. Vanta automates up to 90% of the work for SOC 2, ISO 27001, and more, getting you audit-ready in weeks instead of months and saving you up to 90% of associated costs—and Every listeners can get $1,000 off of Vanta at https://www.vanta.com/every.

As our creative lead, Lucas uses tools like native image gen in ChatGPT and Midjourney to generate the cover images you see every day. He also designs the interfaces for our products—Cora, Spiral, and Sparkle—and makes everything on our site feel as thoughtful and delightful as possible.

It was great to have him on the show to talk about how AI is changing his design process. We get into:

• Why Every’s aesthetic feels familiar and new at the same time. Every’s aesthetic plays with the tension between the old (like Greek statues and Baroque symbols) and the new (like saturated colors and modern motifs) to make the glamor of the past feel fresh.
• Art direction matters more than ever today. As AI makes it easier to generate images, Lucas says the real work of design is shifting toward art direction, specifically, curating an aesthetic that feels “organic;” on his X timeline that’s showing up as clouds, earthy landscapes, and textures.
• Reimagining what a website can be with AI. Lucas compares most websites to identical buildings—predictable, efficient, and forgettable—and wonders how AI can help us break that mold by designing experiences that prioritize serendipity over speed, and curiosity over control.
• Behind the scenes of Cora’s visual aesthetic. How Lucas designed the landing page and launch video for Cora by rooting it in the product’s philosophy: turning the inbox from a source of chaos into something that feels calm, thoughtful—like stepping into spring.
• The future of internet interfaces. Lucas believes the future of digital interfaces will be curated with the same care as a film set or ad campaign, where every detail is chosen with intention.

Lucas also walks us through how he created the headline image for Every’s consulting page—a human and robotic hand fist-bumping—using Midjourney to iterate from rough prompt to polished visual.

This is a must watch for anyone interested in the future of design and making the internet a little more beautiful every day.

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

1. Introduction: 00:01:41
2. How AI changed the course of Lucas’s career: 00:04:02
3. Why Every’s aesthetic feels both familiar and fresh: 00:08:00
4. Why Lucas thinks minimalism is overrated: 00:14:53
5. Art direction matters more than ever in the age of AI: 00:20:38 
6. How to reimagine what a website can be with AI: 00:23:42
7. Lucas’s process in Midjourney to generate cover images: 00:33:08
8. Midjourney v. image generation in ChatGPT: 00:42:30
9. Behind the scenes of Cora’s design language: 00:49:07
10. How AI is rewriting the role of a designer: 00:59:18

Links to resources mentioned in the episode:

• Lucas Crespo: @lucas__crespo
• The pieces Lucas has written for Every: “When An AI Tool Finally Gets You”, “A Definitive Guide to Using Midjourney” 
• Dan’s piece on the allocation economy: “The Knowledge Economy Is Over. Welcome to the Allocation Economy” 

AI forces us to reckon with what makes us human—a question caught between science and spirituality that MIT’s Dr. Alan Lightman is uniquely placed to explore.

Dr. Lightman is a physicist, bestselling novelist, and professor of the practice of humanities at MIT. As one of the first at MIT to hold a joint faculty position in both the sciences and the humanities, he’s at ease walking the line between the two disciplines.

I loved Dr. Lightman’s book Einstein’s Dreams, so I was psyched to have him on the show. We spent an hour talking about:

• Being a “spiritual materialist”: Dr. Lightman’s philosophy that knowing the scientific explanation for natural phenomena—like spiderwebs and lightning bolts—deepens our experience and feeling of wonder.
• The nature of consciousness: He believes that consciousness is a subjective experience emerging from the tangible activity of billions of neurons firing in our brains.
• AI isn’t conscious, even though it might appear to be: AI might display manifestations of consciousness—like the ability to plan for the future—but whether it has an inner experience in the truest sense is a fundamentally different question.
• Challenge your conceptions of what “natural” means: Dr. Lightman argues that since humans evolved through natural selection, everything our brains create—from eyeglasses and hearing aids to AI—can be considered “natural” as they are inevitable consequences of our naturally evolved intelligence
• AI that can do more than just data retrieval: Modern neural networks begin to approximate something resembling genuine thinking because the “digital neurons” process information in complex, non-linear ways.
• Evolution that blurs the lines between biology and technology: Dr. Lightman argues we’re driving our own evolution toward the “homo techno,” hybrid beings that merge human and machine; early examples include brain implants that enable paralyzed individuals to control robotic limbs.

Dr. Lightman also recently published a new book called The Miraculous From the Material, a collection of essays that combine scientific explanations of natural phenomena with his personal reflections on them. It has tons of striking pictures that you should check out.

This is a must watch for anyone interested in science, spirituality, and what it means to be human in the age of AI. 

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

1. Introduction: 00:01:18
2. Science can deepen your sense of the spiritual: 00:02:36
3. The nature of consciousness: 00:11:31
4. AI might appear to be conscious, but it isn’t: 00:13:11
5. Why AI can be considered to be “natural”: 00:19:50
6. AI shifts the focus of science from explanations to predictions: 00:30:40
7. How modern neural networks simulate thinking: 00:33:48
8. Lightman’s vision for how humans and machines will merge: 00:39:38 
9. Does AI know more about love than you?: 00:43:11
10. How technology is accelerating the pace of our lives: 00:49:18

Links to resources mentioned in the episode:

• Alan Lightman: https://cmsw.mit.edu/alan-lightman/ 
• Lightman’s books: The Miraculous From the Material, Einstein's Dreams
• His documentary: Searching: Our Quest for Meaning in the Age of Science

Walt Whitman’s poem: When I Heard the Learn’d Astronomer

Jonny Miller uploaded his entire life to ChatGPT to use it as the ultimate AI coach. 

He created what he calls a Codex Vitae—with core personality traits, values, goals, burnout signals and more to load into ChatGPT. It hyper-customizes his responses, to help him access deep meditation states, create custom supplementation plans, and do deep research on areas of brain and body that he finds interesting.  

Jonny runs a course on nervous system mastery, hosts a podcast, coaches founders and CEOs, and is building a wellness app—all using AI. As a long-time friend and writer for @every, I was psyched to have Jonny on AI & I to talk about how LLMs are expanding the breadth and depth of what he can do. We get into: 

• Energy as your greatest asset: Jonny’s philosophy around pursuing a non-traditional path—like us at Every—by fiercely protecting his energy and optimizing for “aliveness” instead of higher revenue figures.
• ChatGPT projects for everything: His use of projects in @ChatGPTapp to organize different areas of his life; for example, he uploads his meditation journal to a Jhana project and asks it for advice when he’s struggling with the practice on a particular day.
• Deep research in action: How he uses @OpenAI’s deep research to tackle practical questions about moving his family to Costa Rica, hilariously esoteric ones about whether there’s a connection between Pokémon and shamanism, and everything that lies in between.
• The rise of “centaur” teams: Jonny’s belief that @kevin2kelly’s prediction around “centaurs”—human + AI teams outperforming either human or AI working alone—is becoming our reality.

This is a must watch for anyone interested in using AI for personal development, coaching, or to build systems that can understand you. 

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

1. Introduction: 00:01:31
2. Dan and Jonny’s approach to running non-traditional businesses: 00:02:18
3. How Jonny uses ChatGPT to deepen his meditation practice: 00:12:04
4. Jonny uses AI to research a theory of how trauma is stored in our bodies: 00:25:44
5. Dan’s theory around how AI is changing science: 00:31:28
6. Jonny’s method to build personalized AI coaches: 00:32:35
7. How Jonny used OpenAI’s deep research to plan a move to Costa Rica: 00:47:07
8. Dan is developing an app that can predict his OCD symptoms: 00:52:50
9. AI makes the idea of a “quantified self” useful: 00:55:42
10. The future of human-AI coaching teams: 00:58:28

Links to resources mentioned in the episode:

• Jonny Miller: @jonnym1ller
• The nervous system mastery bootcamp: https://www.nsmastery.com/ 
• His podcast: Curious Humans with Jonny Miller 
• The nervous system regulation mobile app: Stateshift 
• Jonny’s method to build your AI coach: http://BuildyourAIcoach.com 
• More about Jhana: https://slatestarcodex.com/2017/09/20/meditative-states-as-mental-feedback-loops/ 
• Buster Benson’s Codex Vitate: https://2019.busterbenson.com/beliefs/
• The pieces Jonny has written for Every: “The Operating Manual for Your Nervous System,” “The Best Decision-Making Is Emotional,” “How to Pay Off Your Emotional Debt,” “The Art and Science of Interoception”  

I interviewed the Governor of New Jersey Phil Murphy on AI & I. 

We spent an hour talking about his vision for AI in government, economic development, and the regulatory challenges ahead. His approach is refreshingly pragmatic: 

• Spark real innovation at scale. Governor Murphy is laying the groundwork through an AI hub that pools the strengths of the government, academia (Princeton University), legacy tech (Microsoft), and next-gen players (CoreWeave). 
• Creating a place for the brightest minds to live and work. He’s making the Garden State irresistible for the best talent through walkable communities, legal recreational cannabis, and an angel investment tax credit.
• AI that augments teams, instead of replacing them. The Governor sees AI as an “accelerant” that enables teams to do more with the same number of employees. He’s walking the talk by training 61,000 NJ state employees in AI to automate busy work and free them to focus on strategic tasks.
• An integrated regulatory framework for AI. He believes that a technology as pervasive as AI should be regulated at a national level because the state-by-state approach could stifle innovation. 

Governor Phil Murphy is the first governor I’ve ever had on the show and I was honored he took the time to come on. I was also especially excited to do this because I grew up in New Jersey! This is a must watch for anyone interested in the intersection of AI and policy.

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

1. Introduction: 00:02:00
2. Why there should be a nation-wide framework to regulate AI: 00:04:31
3. How 61,000 state employees in New Jersey are adopting AI: 00:10:34
4. Why new tech is key to transforming government services: 00:12:20
5. The Governor is bringing startups back to New Jersey: 00:17:30
6. How to stimulate innovation at scale: 00:25:28
7. The Governor is making New Jersey a top choice for the best talent: 00:33:07
8. Balancing technological progress while ensuring the workforce isn’t left behind: 00:36:56
9. We’re moving toward an “allocation economy”: 00:41:39
10. The Governor’s take on international regulation of AI: 00:43:43

Links to resources mentioned in the episode:

• Governor Phil Murphy: @GovMurphy
• More about the New Jersey AI Hub: https://njaihub.org/ 

Steve Schlafman is using a $20 ChatGPT subscription to expand his consciousness.

He’s doing this through:

• Advanced dream work—Steve records himself talking about his dreams every morning, uploads the transcript to ChatGPT, and prompts the LLM to analyze it like a Jungian dream analyst would. The model pulls out archetypes and hidden emotions that he would’ve been oblivious to.
• Creating living records of meaningful experiences—Instead of losing key insights from therapy or coaching, Steve uses the LLM to highlight emotional patterns, pick out recurring symbols, and build a personal growth timeline.
• Leaning into voice interfaces—Diagnosed with ADD as a child, Steve often lost track of ideas because his brain processed information faster than he could type or write it out. AI voice interfaces free him to process information in a way that’s more natural to him.

Steve is a former VC-turned-executive coach and the founder of @downshift, the “decelerator” for founders and executives. If you think this episode is too “woo” for your liking, Steve argues that you might be over-indexing on just one way of experiencing the world. 

We see the world through four windows: thinking, sensing, feeling, and imagining—and according to him, the last two are often ignored. So if your rational mind has always run the show, Steve invites you to let your feelings and imagination take the lead.

This is a must watch for anyone interested in using AI to understand themselves better—and grow.  

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

1. Introduction: 00:01:07
2. The power of treating your startup as an evolving entity: 00:03:00
3. Building a business as a means of self-expression: 00:05:27
4. Prompting ChatGPT to do Jungian dream work: 00:17:45
5. Why you should listen to this episode, especially if it feels too “woo’” for you: 00:21:44
6. Visualizing Steve’s dream with ChatGPT: 00:36:31
7. Creating living records of meaning experiences with AI: 00:47:38
8. If you tend to think faster than you can type, lean into voice interfaces: 00:49:37
9. How Steve writes with AI: 00:52:13
10. How AI will disrupt traditional coaching and therapy: 00:54:03

Links to resources mentioned in the episode:

• Steve Schlafman: https://www.schlaf.co/ 
• Downshift, the “deaccelerator” that Steve founded: https://www.downshift.me/ 
• The book by Bill Plotkin that Steve talks about: Soulcraft 
• A piece Steve wrote for Every, a couple of years ago: “Why Is It So Hard to Change?” 

Our sponsor for this episode is Microsoft. Want seamless collaboration without the cost? Microsoft Teams offers a robust free plan for individuals that delivers unlimited chat, 60-minute video meetings, and file sharing—all within one intuitive workspace that keeps your projects moving forward. Head to https://aka.ms/every to use Teams for free, and experience effortless collaboration, today.

Mike Maples knows how AI startups can beat incumbents with billions of dollars. 

Mike—who wrote early checks to Twitter, Twitch, Okta, and Lyft, and now invests through Floodgate, the fund he cofounded—told me it's not about the smartest model, or raising the most money. 

Startups can win in AI with better strategy.

AI is changing the economics of startups—both how they’re started and how they’re funded. A new breed of companies is emerging, and I invited Mike on the show to talk about how they can best strategize. Last year, Mike co-authored a book called Pattern Breakers, which is essentially a guidebook to why there’s no guidebook to building companies. I really liked it, and my colleague Evan Armstrong reviewed it for Every, so I was glad to have him on. We talk about how shifts in technology create space for smaller players to compete—even with AI giants like OpenAI—and how to capitalize on them.

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Timestamps:

1. Introduction: 00:02:20
2. Innovate the business model, not just the product: 00:06:02
3. How startups can compete against the likes of OpenAI: 00:15:49
4. Mike’s take on DeepSeek: 00:19:34
5. Why the future has always belonged to the tinkerers: 00:21:44
6. How small teams today can make big money: 00:24:03
7. Find niches that incumbents can’t or don’t want to enter: 00:28:55
8. The qualities of the truly AI-native: 00:47:08
9. How AI changes the funding model for software companies: 00:53:46
10. Knowledge work is moving toward systems-level thinking: 00:58:23

Links to resources mentioned in the episode:

• Mike Maple: @m2jr
• The fund Mike confounded, Floodgate: @floodgatefund
• Evan’s piece reviewing Pattern Breakers: "A New Book of the Startup Bible" 
• Dan’s piece on the allocation economy: "The Knowledge Economy Is Over. Welcome to the Allocation Economy." 

Michael Taylor has perfected the art of getting AI to speak in tongues. He’s taught it to mimic the voices of your customers—so you can see how they would respond before you ship.

Michael is the creator of Rally, a market research tool that lets you simulate an audience of AI personas. He built a simulator that lets us A/B test Every’s headlines on an audience that mimics the real Hacker News audience. It’s become a part of my writing workflow, and I love it because you test your assumptions quickly, cheaply, and without any of the risks of putting something out into the world.

Besides Rally, Michael co-authored a book on prompt engineering for O’Reilly, and he writes a column for Every about managing AI tools like you would people. In a past life, he founded a growth marketing agency which he grew to 50 people and sold in 2020. One of the reasons I’m drawn to Michael’s work is because he has a tinkerer’s mindset. He’s always exploring the limits of what a new technology can do, and what he’s into today, everyone else will likely discover six months later. We spent an hour talking about using language models to judge your work, best practices for assessing an AI’s performance, and Michael’s flow inside Cursor. He also demos Rally live on the show, testing three different potential headlines for an Every article.

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

Want even more?

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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper

Timestamps:

1. Introduction: 00:01:32
2. AI can simulate human personalities with remarkable precision: 00:04:30
3. How Michael simulated a Hacker News audience: 00:08:15
4. Push AI to be a good judge of your work: 00:15:04
5. Best practices to run evals: 00:19:00
6. How AI compresses years of learning into shorter feedback loops: 00:23:01
7. Why prompt engineering is becoming increasingly important: 00:27:01
8. Adopting a new technology is about risk appetite: 00:44:49
9. Michael demos Rally, his market research tool: 00:47:10
10. The AI tools Michael uses to ship new features: 00:54:53

Links to resources mentioned in the episode:  

• Michael Taylor: @hammer_mt
• Join the waitlist for Rally, Michael’s synthetic market research tool: https://askrally.com/ 
• The book Michael co-authored on prompt engineering: Prompt Engineering for Generative AI 
• The column Michael writes for Every: Also True for Humans
• Michael’s article on personas of thought: "I Asked 100 AI Agents to Judge an Advertisement”
• Michael’s article on building a Hacker News simulator: "I Created a Hacker News Simulator to Reverse-engineer Virality” 

Nat Eliason’s career arc is borderline absurd—but it works. 

In the last five years, he ran an SEO agency, got into crypto, made $600,000 from a course on the note-taking tool Roam Research, flipped real estate in Austin for a 6x return, and published a book with Random House. He’s now writing a book of science fiction and running a viral course about building apps with AI.

I’ve known Nat for a long time, and I think he knows where the puck is headed better than anyone. He’ll see a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with crypto, Roam, real estate—and now AI. His app-building course has over 800 students and racked up $200,000 in pre-sales in one week.

Nat was one of the first guests I had on the podcast and I was delighted to have him on again. We spent an hour talking about how coding with AI is creating new behaviors in programming, Nat’s best practices for using the coding tool Cursor, and his take on the future of writing with AI. 

This episode is a must-watch for writers, creators, and anyone interested in the future of product building.

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

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

• Introduction: 00:01:45
• The origins of Nat’s viral course on building apps with AI: 00:10:15
• How coding with AI has evolved over the last two years: 00:17:16
• Nat creates an app using Composer, Cursor’s AI assistant: 00:20:52
• Tactical tips for coding with Cursor: 00:24:36
• How coding with AI is creating new behaviors in programming: 00:27:36
• What excites Nat the most about the future of AI: 00:31:11
• A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:37:28
• When does it make sense to build custom software: 00:43:22
• Nat’s take on the future of writing with AI: 00:47:48

Links to resources mentioned in the episode:  

• Nat Eliason: @nateliason
• Nat’s viral course about building apps with AI: Build Your Own Apps with AI
• The book Nat published about crypto: Crypto Confidential: Winning and Losing Millions in the New Frontier of Finance  
• Dan’s piece about how AI empowers creators: AI and the Age of the Individual 

Guillermo Rauch is one of the most prolific coders of this generation. 

But he doesn’t think of himself as a coder anymore. 

Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. 

Guillermo is the founder and CEO of Vercel, the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent.

Here are a few takeaways:

• One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day.
• He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO.
• He’s already seeing enterprises use Vercel’s AI coding copilot v0 to replace all of their programming—they just send v0 demos back and forth to iterate on new prototypes. 
• Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies.
• For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool.
• The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition.
• AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes.
• The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks.

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

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Links to resources mentioned in the episode:

Guillermo Rauch: @rauchg

Vercel: https://vercel.com/ 

Last week’s episode with Nabeel Hyatt: 🎧 The Venture Capitalist Who Finds the Best AI Products—Before They Win 

Dan’s essay about the allocation economy: The Knowledge Economy Is Over. Welcome to the Allocation Economy 

OCD treatment changed my life—but it took me a decade of chasing down wrong answers to be diagnosed. 

In the rush to create scalable treatments, disorders like depression and OCD are squeezed into diagnostic checklists—from which the complexity of the human mind invariably leaks out. The field of psychiatry is broken, and I spoke to someone on the inside about how AI can help fix it .

⁠Awais Aftab⁠ has been questioning psychiatry’s rigid categories from inside the field. He’s a clinical assistant professor at ⁠Case Western Reserve University⁠, editor of ⁠Conversations in Critical Psychiatry⁠—an Oxford University Press volume that tackles philosophical and critical perspectives in psychiatry—and author of the Substack newsletter ⁠Psychiatry at the Margins⁠. We get into how AI is transforming psychiatry by embracing the complexity of human minds instead of flattening it.

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

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• Subscribe to Every: ⁠https://every.to/subscribe⁠ 
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Sponsor: Microsoft Teams
Want seamless collaboration without the cost? Microsoft Teams offers a robust free plan for individuals that delivers unlimited chat, 60-minute video meetings, and file sharing—all within one intuitive workspace that keeps your projects moving forward. Head to ⁠https://aka.ms/every⁠ to use Teams for free, and experience effortless collaboration, today.

Timestamps:

1. Introduction: 00:01:20
2. The case Awais makes for pluralistic thinking in psychiatry: 00:03:38
3. A pragmatic approach to mental healthcare: 00:15:30
4. Awais’s take on why my OCD diagnosis took 10 years: 00:19:04
5. Why psychiatry is stuck where machine learning was decades ago: 00:24:19
6. Why psychiatry’s focus should shift from explanations to predictions: 00:31:05
7. How Awais thinks AI is already changing the psychiatric profession: 00:39:19

Links to resources mentioned in the episode:

• Awais Aftab: @awaisaftab, ⁠awais aftab⁠ 
• Awais’s Substack: ⁠Psychiatry at the Margins⁠
• The book Awais edited: ⁠Conversations in Critical Psychiatry⁠

AGI is coming. Reid Hoffman just wrote the book on how to prepare.

According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well.

In Superagency, his book that was released yesterday, Reid examines how we’ve historically adopted new technologies and focuses on AI’s potential to increase our agency—the ability to make decisions that affect outcomes. He wrote the book for two audiences: anyone who is curious, or even skeptical, about AI; and technologists who are building in AI, with the hope that they will think about human agency as a design principle for their products. As someone who straddles both worlds, I read the book and really liked it.

Beyond being a prolific author, Reid is the cofounder of LinkedIn, Inflection AI, and Manas AI; a partner at venture capital firm Greylock Partners; an early backer and board member of OpenAI; and an award-winning podcaster—and I was pleased to invite him onto AI & I again, this time in person. 

We recorded an hour-long conversation, going deep on:

• The notion of human agency, how our sense of agency shapes our response to new technologies, and its interplay with uncertainty
• Why Reid believes that private commons and equitable access to AI will be beneficial for society at large
• How the history of AI mirrors a philosophical shift in how we understand intelligence, from trying to program explicit rules about how thinking works, to building systems that learn patterns from data
• Reid’s take on how the next decade of AI will involve a play between rule-based systems and pattern-matching ones   

It’s a must-watch for anyone who wants to help build a more human future with AI. 

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

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Links to resources mentioned in the episode:

Reid Hoffman: @reidhoffman

Superagency, Reid’s newest book: https://www.superagency.ai/ 

As a general partner at Spark Capital, Nabeel Hyatt backs just one or two companies each year.  

But when he does invest, Nabeel picks winners. He was an early investor in Discord, video editor Descript, self-driving startup Cruise (acquired by General Motor for over $1 billion), and, recently, AI note-taking app Granola. 

Nabeel’s investment thesis is to look for products like the Japanese toilet. 

Don’t fret—Spark Capital hasn’t pivoted into the sanitaryware industry. Nabeel isn’t looking for startups that are disrupting plumbing. 

Rather, just like Japanese toilets, he’s looking for products that delight users with new experiences they didn’t know they wanted—and if his past investments are anything to go by, Nabeel has a good eye for that. 

On my recent trip to San Francisco, I sat down with Nabeel to talk about the qualities shared by remarkable products and the founders that build them, why he chooses not to invest in more than a couple of startups a year, and how he’s actually using AI in his daily life. Nabeel is one of my favorite people in AI, and this is one of my favorite recent conversations. It’s a must watch for founders who want to build useful AI products with soul. 

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

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Links to resources mentioned in the episode:

Nabeel Hyatt: @nabeel, https://nabeelhyatt.com/ 

Spark Capital: https://www.sparkcapital.com/ 

The piece Chris Pedregal wrote for Every: How to Build a Truly Useful AI Product 

Chris Pedregal on AI & I: 🎧 The Secret to Building Sticky AI Products 

The AI tools Nabeel talks about: Windsurf, Wordware 

Building an email client used to take many years and millions of dollars.

But Every’s Kieran Klaassen built Cora—a totally new way to manage your inbox with AI—in just 3 months. He even shipped the original MVP of the product in a single day—something that just wasn’t possible before the current state of generative AI. 

Now, there are almost 10,000 people on the waitlist for Cora, and we’re onboarding new users every single day. 

Every’s head of Studio Brandon Gell and I worked closely with Kieran as he built Cora, and to kick off my podcast, AI and I, in 2025, I invited both of them on the show to talk about it. We go behind the scenes, getting into how Kieran built the product with Cursor, o1, and o1 Pro, what we’re learning as we onboard new users every day, and the future of Cora and of Every as a multi-modal media company.

This is a must watch for anyone curious about our approach to building with AI at Every.

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

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Links to resources mentioned in the episode:

Sign up for the Cora waitlist: https://cora.computer/ 

Learn more about Cora: Introducing Cora: Manage Your Inbox With AI

Kieran Klaassen: @kieranklaassen

Brandon Gell: @bran_don_gell

AI is going to change science forever.

Small scale studies will give way to large scale open data gathering efforts. We’ll shift from seeking broad general theories to making contextual predictions in individual cases. The traditional research paper will change fundamentally.

That’s why I had Alice Albrecht on the show. Few people straddle the worlds of science and AI like she does: She holds a Ph.D. in cognitive neuroscience from Yale and is a machine learning researcher with almost a decade of experience. In 2021, she founded re:collect, an app that aimed to augment human intelligence with AI. It was acqui-hired by news curation app SmartNews in September of this year, and she is now the senior director of AI product. 

We discuss the contours of this new paradigm of science: the growing importance of data in scientific discovery, how AI makes N-of-1 studies imperative—when they’re normally seen as unscientific, the case for big tech to open-source their data for research, and the power of unbundling data from interpretations, in both science and media. Here is a link to the episode transcript. 

In January of this year, we published Alice’s thesis about how augmenting human intelligence with AI is more effective than attempting to achieve super intelligence through standalone AI systems, and in a happy coincidence, she’s our last podcast guest of 2024. Thank you for listening, and we’ll see you in the new year.

In the meantime, this is a must-watch for anyone interested in how AI is changing the future of scientific research.

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

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Links to resources mentioned in the episode:

Alice Albrecht: @AliceAlbrecht

The company that recently acquired Alice’s startup: SmartNews

The piece Alice wrote for Every about how AI can augment human intelligence: The Case for Cyborgs 

Every’s product incubations that we discuss in the context of how AI is changing media: Extendable Articles, TLDR 

Chris Pedregal knows how to build AI products that people love. 

Chris is the cofounder and CEO of Granola, an AI-powered notepad for meetings. We use it for many of our meetings at Every—the app listens in on the conversation, transcribes it in the background, and when the meeting ends, creates automated notes and a shareable transcript for anyone who missed it. If you take notes during the meeting, Granola polishes them to be more organized and complete. 

Granola is one of my favorite consumer AI products because it’s equal parts delightful and useful. It’s not a bland chat interface. It’s not an over-the-top demo that you wouldn’t use more than once. Granola is a product with “soul,” imbued with the team’s vision for how AI can work alongside you to turn discursive conversations into clear insights. And I’m not the only one who thinks so—since Granola’s launch in May 2024, its user base has grown by 5x, with around 5,000 weekly active users, and half the people who try the app still use it 10 weeks later for an average of six meetings a week. The company also recently raised a $20 million Series A.

Granola has the marks of being built by a thoughtful, intentional team, which is why I was excited to have Chris on the show. We spent an hour talking about Chris’s product development philosophy, the role of intuition in making products with “soul” and how he balances this with user feedback, how you can become a better product thinker, and the kinds of consumer AI startups Chris thinks will succeed. Here is a link to the episode transcript. 

This is a must-watch for anyone interested in building valuable, sticky AI products that users will love.

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

Want even more?

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Links to resources mentioned in the episode:

Chris Pedregal: @cjpedregal

Granola: http://Granola.ai, @meetgranola 

The piece Chris wrote for Every about building useful AI products: https://every.to/thesis/how-to-build-a-truly-useful-ai-product  

Here’s the most compelling benchmark of AI progress: A task that took 60 minutes a year ago now takes 60 seconds.

In January 2024, researcher Geoffrey Litt and I spent an hour coaxing ChatGPT to build a simple app on this podcast. Nearly 12 months later, Steve Krouse and I built the same app with one prompt in less than minute.

Steve is the cofounder and CEO of Val Town, a cloud-based platform for developers to write, share, and deploy code directly in the browser. In this episode, we used Townie, an AI assistant integrated into Val Town, to build an app that would keep track of time on the podcast, take notes, and generate more questions for the guest. 

Townie had generated the app even before Steve could finish describing it on the show, a mark of how much AI has evolved over the last year. As the founder of a growing startup, Steve tells me his contrarian take on why he isn’t focused on the needs of the non-technical AI programmer, betting instead on being the platform sophisticated developers turn to for backend infrastructure. He also tells me how he started programming and how it continues to shape his vision for Val Town. Here is a link to the episode transcript. (Disclosure: I’m a small investor in Val Town.)

This is a must-watch for founders building AI-powered developer tools, and anyone interested in the future of programming.

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

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Links to resources mentioned in the episode:

Steve Krouse: https://stevekrouse.com/, @stevekrouse 

Val Town: https://www.val.town/ 

Townie, the AI assistant integrated into Val Town: https://www.val.town/townie/signup?next=%2Ftownie 

Pieces on Val Town’s blog about how the team built Townie: How we built Townie—an app that generates fullstack apps, Building a code-writing robot and keeping it happy  

The book by Seymour Papert about how programming changes the way you think: Mindstorms: Children, Computers, and Powerful Ideas

Over the last few months at Every, we’ve launched two new AI products with tens of thousands of users, and we’ll release a third one before the end of the year. The weird thing is: We’re a media company with less than 10 full-time employees, and we’re mostly bootstrapped.

That’s not how things are supposed to work in startups.

When we were first starting Every Studio six months ago, we were told a million reasons why it wouldn’t work: divided focus, not enough money, and the biggest one—it would be too hard to find talented people to run the products we build.

Yesterday, we proved out one of the biggest risks to this strategy: We launched a brand-new version of our AI product Spiral with Danny Aziz as its general manager. 

Danny left a $200,000-a-year salary to come chase his dreams with us. So we decided to take this moment to pull back the curtain and ask, Why? Why did he join us? And why is the model we’ve built working so far? What have we learned about what happens when you mix media, software, and AI in a single organization?

That’s why I invited Danny and Brandon Gell, Every’s head of Studio, on the show. We get into the details of Every’s business model, how new technology and the right people can make the flywheel turn, the power of learning by doing and building from real needs, and where each of us sees ourselves one year from now. Here is a link to the episode transcript.

This is a must-watch for anyone who wants to build a business on their own terms, and have a lot of fun while doing it. 

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

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Links to resources mentioned in the episode:

Danny Aziz: @DannyAziz97

Brandon Gell: @bran_don_gell

Try Spiral here: https://spiral.computer/  

More about Every’s product incubation arm: https://every.to/p/introducing-every-studio 

Everyone told Vicente Silveira that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. 

Vicente is the cofounder and CEO of AI PDF, a tool to help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload documents to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Even when some of his competitors closed up shop, Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. 

Today, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it with a five-person team and a friends-and-family funding round.

I sat down with Vicente to understand, in granular detail, the success of AI PDF.

Vicente explains how staying small and specialized is a key strategic advantage for his business. We get into why lean startups are better positioned than companies like OpenAI and Anthropic to create cutting-edge solutions for users, the role early adopters of technology play in shaping the market for new products, Vicente’s candid take on raising capital as a growing startup, and his thoughts on the emerging role of AI managers who will be responsible for overseeing AI agents. We demo an agent integrated into AI PDF, prompting it to analyze a bunch of recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements—and even pit AI PDF against Perplexity live on the show. 

This is a must-watch for small teams building profitable companies at the bleeding edge of AI. 

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

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Links to resources mentioned in the episode:

Vicente Silveira: @vicentes

AI PDF: https://myaidrive.com/  

Dan’s piece on the allocation economy: https://every.to/chain-of-thought/the-knowledge-economy-is-over-welcome-to-the-allocation-economy    

Prompt engineering isn’t just about telling AI to solve your problems—it’s about knowing which ones to solve. 

Yet there’s a mismatch between the people who can identify the right problems—experts with deep domain knowledge—and the technical infrastructure required for developing and refining prompts. Jared Zoneraich, the cofounder and CEO of prompt engineering platform PromptLayer, is bridging the gap with a platform on which non-technical experts can manage, deploy, and evaluate prompts quickly.

The role of human prompt engineers, however, has been the topic of controversy, with some arguing that AI can optimize prompts better than us, while others suggest that more capable LLMs eliminate the need for meticulously crafted prompts altogether. I spent an hour talking to Jared about why he believes prompt engineering isn’t becoming obsolete. He also tells me everything he’s learned about writing a good prompt and what the future of AI tools looks like. Here is a link to the episode transcript.

This is a must-watch for prompt engineers, people interested in building with AI systems, or anyone who wants to generate predictably good responses from LLMs.

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

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Links to resources mentioned in the episode:

Jared Zoneraich: @imjaredz

PromptLayer: @promptlayer, https://www.promptlayer.com/

A couple of Steven Wolfram’s articles on ChatGPT: What Is ChatGPT Doing … and Why Does It Work?, ChatGPT Gets Its “Wolfram Superpowers”!   

Notion cofounder Simon Last told me everything he’s learned from integrating an AI application into a platform that has over 100 million users.

Simon likes to keep a low profile, even though he’s the driving force behind Notion AI, one of the most widely scaled AI applications in the world.

In this episode, we get into how AI changes the way he builds software since the days he cofounded Notion with Ivan Zhao in 2013. He talks about the challenges that arise because AI doesn’t follow the deterministic rules of traditional software, and how he designs evals to build AI systems that are reliable at scale. Simon tells me about the AI tools he uses to code and how he would think about rebuilding Notion from scratch with them. He also shares his thoughts on how the growing capabilities of AI are redefining human roles, and argues that we have the responsibility to shape technology to align with our collective vision of the future. 

This is a must-watch for anyone interested in building reliable AI products at scale.

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

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Links to resources mentioned in the episode:

Simon Last: @simonlast

Notion AI: https://www.notion.so/product/ai 

The AI code editor Simon uses: Cursor

OpenAI’s definition of AGI that Simon ascribes to: https://openai.com/charter/ 

GitHub Copilot has 15 million users—more than Cursor and Windsurf combined. So why does it feel like they're losing the AI coding race?

Last week at Microsoft Build, I interviewed the CEO of GitHub Thomas Dohmke to find out. 

I wanted to know: Is their huge existing user base a blessing or a curse? And will their latest launch—an autonomous coding agent built into GitHub—let them retake the lead? Watch this episode of AI & I to find out

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

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• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Sponsor:
Attio: Go to⁠⁠⁠⁠ https://www.⁠⁠⁠⁠attio.com/every⁠⁠⁠⁠⁠ and get 15% off your first year on your AI-powered CRM.

Timestamps:

00:00:38 - Introduction  

00:07:40 - Copilot’s place in the AI coding agent race  

00:10:42 - Inside the product decisions behind Copilot’s new agent  

00:16:18 - How Dohmke thinks about shaping Copilot’s personality  

00:20:29 - How GitHub supports both AI-native developers and legacy enterprise users  

00:26:57 - Dohmke’s predictions for the future of software development  

Union Square Ventures is building an AI operating system to support their investment team. 

But it’s not what you think: It’s a constellation of AI tools that captures and synthesizes the firm's collective wisdom. It’s evolving every day, and Matt Cynamon is the mad scientist in charge

Matt calls himself a “regular” at USV. In practice that means he’s responsible for running experiments with AI for the firm. As an inherently curious person with the professional obligation to tinker, he’s built a suite of tools for the firm, including: 

• The Librarian, a chatbot trained on around 15,000 articles from USV’s blog
• Portfolio Tracker, a GPT that analyzes the investments made by the firm
• Meeting Notes, a tool that makes it possible for team members to interact with meetings  

I sat down with Matt to talk about how AI is enabling him to bring his ideas to life as a generalist, get demos of the tools listed above, and exchange notes on all the other projects he has in the works at USV. We edit actionable insights extracted by an AI from meetings at USV and prepare them to be posted on the firm’s X handle live on the show. We even try out an art project at USV’s office called The Dream Machine, which generates art from conversations. Here’s a link to the episode transcript.   

This is a must-watch for anyone interested in riding the AI wave by learning how to ship useful products quickly.

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

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Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free.

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

TIMESTAMPS:

1. Introduction: 00:00:52
2. How Matt became in charge of everything AI at USV: 00:01:56
3. How AI empowers generalists to be creators: 00:06:22
4. The Librarian, a chatbot trained on everything USV has published: 00:10:41
5. Portfolio Tracker, an AI tool to track USV’s investments: 00:21:09
6. The AI projects that Matt has in the pipeline at USV: 00:27:21
7. Meeting Notes, USV’s AI note-taking tool: 00:34:33
8. Prompting AI to generate a post for USV’s X handle: 00:44:57 
9. Why it’s important to diversify ownership over data: 01:00:20
10. The Dream Machine, AI that generates images from conversations: 01:03:20

Links to resources mentioned in the episode:

Matt Cynamon: @mattcynamon

Union Square Ventures: @usv, https://www.usv.com/ 

More about the AI tools at USV: https://www.usv.com/people/the-librarian/, https://www.usv.com/writing/2024/02/ai-aesthetics/ 

The X post generated live on the show: https://x.com/usv/status/1847354782941663523  

Yohei Nakajima⁠ leads a double life. 

By day, he’s a general partner of a small venture firm, ⁠Untapped Capital⁠. 

By night, he’s one of the most prolific internet tinkerers in AI. (He also sometimes works on ⁠automating his job as a venture capitalist.)

He’s the creator of BabyAGI (@babyAGI_), the first open-source autonomous agent that went viral in March 2023. Yohei has since released seven iterations of BabyAGI, a coding agent called Ditto, a framework for building autonomous agents, and, most recently, BabyAGI 2o, a self-building autonomous agent.

I sat down with Yohei to talk about:

• What feeds Yohei’s drive to create new tools
• The evolution of BabyAGI into a more powerful version of itself 
• What Yohei learned about himself by tinkering on the internet
• Yohei’s personal philosophy about how the tools we build our extensions of ourselves
• Why founders in AI should think about their products from a modular lens, by addressing immediate problems while enabling growth in the future
• Yohei’s insight into a future where models will train themselves as you use them

We experiment with Ditto live on the show, using the tool to build a game of Snake and a handy scheduling app. Yohei also screenshares a demo of BabyAGI 2o in action.

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

Want even more?

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:

• Subscribe to Every: ⁠https://every.to/subscribe⁠ 
• Follow him on X: ⁠https://twitter.com/danshipper⁠ 

Timestamps:

1. Introduction: 00:00:59
2. BabyAGI and its evolution into a more powerful tool: 00:02:26
3. How better models are changing the way Yohei builds: 00:05:00
4. Using code building agent Ditto to build a game of Snake: 00:08:10
5. The ins and outs of how Ditto works: 00:13:24
6. How Yohei gets a lot done in little time: 00:19:21
7. Yohei’s personal philosophy around building AI tools: 00:21:50
8. How Yohei experiments with AI as a tech-forward parent: 00:33:13
9.  Demo of Yohei’s latest release, BabyAGI 2o: 00:39:29
10.  Yohei’s insights on the future of AI tooling: 00:51:24

Links to resources mentioned in the episode: 

• Yohei Nakajima: @yoheinakajima, ⁠http://yohei.me⁠ 
• Untapped Capital: @UntappedVC, ⁠https://www.untapped.vc/⁠ 
• My first interview with Yohei, around the time he released BabyAGI: ⁠https://every.to/chain-of-thought/this-vc-is-slowly-automating-their-job⁠ 
• The other AI tools Yohei has created: ⁠Ditto⁠, BabyAGI 2, BabyAGI 2o
• The tweet thread about AI bots being let loose on a Discord server: https://x.com/AISafetyMemes/status/1847312782049333701 

Simon Eskildsen is a learning machine. 

I first interviewed him in 2020 about how he leveled up from being an intern at Shopify to becoming the company’s director of production engineering by reading and applying insights from hundreds of books.

A lot has changed over the last four years. Language models have made it possible to access and contextualize information faster, easier, and more cheaply than ever before—and in this episode, Simon and I talk about how this changes the way he learns. 

Simon is now the cofounder and CEO of AI startup turbopuffer, which is building a search engine that makes vector search—an approach to information retrieval that uses machine learning to gather context—easy and affordable to run at scale.

We spent an hour talking about how he leverages LLMs’ contextual intelligence to supercharge his learning, such as helping him pick up new words as a non-native English speaker, do odd jobs to maintain his rural cabin in Quebec, and articulate technical concepts in legalese. As we talk, we screenshare through his Anki setup, including the flashcard template he finds most useful, and the custom AI commands he’s created in productivity software Raycast. Simon tells me about the clutch of AI tools he experiments with for journaling, writing, and coding, as well as his thoughts on how language models will fundamentally reshape the way we learn. Here’s a link to the transcript of this episode.  

This is a must-watch for note-taking aficionados and anyone who wants to supercharge their learning with AI.

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Simon Eskildsen: @Sirupsen

Simon’s startup, turbopuffer: turberpuffer.com, @turbopuffer

My first interview with Simon in 2020: https://every.to/superorganizers/how-to-build-a-learning-machine-299655 

The productivity tool through which Simon uses LLMs, Raycast: https://www.raycast.com/ 

The other AI tools that Simon is experimenting with: voice-to-text tool superwhisper, copilot for developers Supermaven, code editor Cursor

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Evan Armstrong: @itsurboyevan

The column Evan writes at Every: Napkin Math

Evan’s upcoming course about how to write with AI: https://www.writewithai.xyz/ 

The piece Dan wrote about using LLMs to articulate his taste: "What I Do When I Can’t Sleep"

Dan’s article about admitting that he wants to be a writer: "Admitting What Is Obvious"

The Browser Company isn’t just building a browser, they’re building a formidable brand—and they’re doing it with AI. 

The Browser Company has driven viral user growth, a $550 million valuation, and close to 100,000 YouTube subscribers. Its brand centers people, not products. It’s messy, authentic, and refreshing—and it seeps into everything the team does, from the job descriptions on their website to announcing new features through short films and giving keynote addresses in diners.  

I sat down with Nashilu Mouen-Makoua, the head of storytelling and a strategic advisor to the CEO at The Browser Company, to talk about how she’s weaving relatable stories around new technology.

We get into her philosophy around storytelling, including why she believes centering people is the key to building a memorable brand, The Browser Company’s focus on making technology accessible to a wide audience, and the brass tacks of how Nash’s team structures meetings to generate great ideas. Nash also tells me how she’s integrated LLMs into her workflow, to do deep research, get a gut check on a new article she’s written, and put the finishing touches on her words. 

As Nash explains that the best way to position a product is in response to contemporary social context, we screenshare through her conversations with ChatGPT about the socio-political climate in America preceding the release of a Tracy Chapman song. We also use the LLM to simulate a group of Arc users and interview one of these imaginary personas live on the show to gather preliminary customer insights.

This is a must-watch for people who want to use AI to tell compelling stories about what they’re building in tech.

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Nashilu Mouen-Makoua: https://twitter.com/lafillemouen

The Browser Company: https://thebrowser.company/, https://twitter.com/browsercompany

 Arc, the browser that reimagines the way we use the internet: https://arc.net/, https://twitter.com/arcinternet

Tracy Chapman’s song, Talkin’ About a Revolution: https://www.youtube.com/watch?v=Xv8FBjo1Y8I 

We’re building a mini-AI media and software empire at Every. 

Today on AI & I, Brandon Gell joins the show to turn the tables on me and act as podcast host to explore what we’re doing as a company, how we got here, and where we’re going.

Brandon is Every’s first entrepreneur in residence, and he was the perfect person to host, because he’s one of the key reasons for our recent acceleration.

Before joining Every, Brandon was the cofounder and CEO of Clyde, a startup that helped brands launch their own insurance and warranty programs, where he raised $50 million and led a team of 100 before selling it to global insurance tech company Cover Genius in early 2023.

In this episode, he interviews me about how I learned to code in middle school, how I built and sold my first startup coming out of college, and how it all led to Every.

We also talk about Brandon’s story. He joined Every just four months ago—and it feels like we’ve done the work of years since. We’ve launched two new AI products, an incredible amount of great writing, a new course, and more.

We get into my candid thoughts on entrepreneurship in the AI age—including why you should ship fast, and how not to be misled by metrics like TAM; how AI startups can find valuable niches—and live demos of our apps Spiral and Sparkle; Brandon’s hard-earned lessons from running a insuretech business for seven years; the confusing realities of being an exited founder, and how we navigated through those times; what brought Brandon to Every—including the email he sent me before joining; and Every’s master plan and what we hope to build over the next few months and years

This is a must-watch for anyone interested in building a calm, profitable business empire in the age of AI. 

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Brandon Gell: https://twitter.com/bran_don_gell

The piece Dan recently published about Every’s Master Plan: https://every.to/chain-of-thought/every-s-master-plan 

Dan’s piece about the unbundling of Excel, and why it serves as an important story in the age of AI: https://every.to/chain-of-thought/the-great-ai-unbundling 

Tomasz Tunguz, the VC who has also written about Excel: https://tomtunguz.com/ 

Every cofounder Nathan’s word processor, Lex: https://lex.page/ 

Spiral, the app that automates 80 percent of repetitive creative work: https://spiral.computer/ 

Sparkle, the app that automatically organizes your files: https://makeitsparkle.co/ 

Alex Wieckowski is on a mission to make you fall in love with reading again—and he thinks AI can help.

Alex, who writes a newsletter that captures lessons from books he’s read and tips to become a better reader, Alex & Books, is a creator with over 1 million followers across social platforms. He’s also the author of a book of quotes that will inspire you to read more, Learn to Love Reading.

We spent an hour talking about how Alex uses AI to be a smarter reader, and we tested out a few strategies live on the show, including:

• prompting ChatGPT to recommend books that will help me lead a creator-run business better, 
• understanding the deeper themes in Hermann Hesse’s novel Siddhartha with large language models, and
• using AI to create an actionable strategy for Alex to build a course based on the frameworks in Alex Hormozi’s business strategy book $100M Offers. 

Alex clued me into what he’s learned about developing a good reading habit, and his best advice on how to reignite your passion for books. We also discuss Alex’s predictions on how companies like Neuralink, which make use of a brain-computer interface—a technology that allows users to control external devices through brain activity—will change the future of reading and books. Here’s a link to the transcript of this episode.

This is a must-watch for book lovers, people struggling to finish books, and anyone who wants to take their reading to the next level with AI.

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

Want even more?

Sign up for Every to unlock our 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:

• Subscribe to Every: https://every.to/subscribe 
• Follow him on X: https://twitter.com/danshipper 

Links to resources mentioned in the episode:

Alex Wieckowski: https://twitter.com/AlexAndBooks_

Alex’s newsletter: https://alexandbooks.beehiiv.com/

The self-improvement book that got Alex into reading: How to Win Friends & Influence People by Dale Carnegie

The books that Dan is reading: Children of Memory by Adrian Tchaikovsky, Pragmatism as Anti-Authoritarianism by Richard Rorty 

The books that Alex is reading: Never Enough: From Barista to Billionaire by Andrew Wilkinson, $100M Offers by Alex Hormozi, Siddhartha by Hermann Hesse, Outlive by Peter Attia

One of the most influential voices in tech explains how AI helps him write and invest.

This episode is sponsored by Create. If you want to maximize your gains, both with your body and with ChatGPT, try creatinine gummies from Create. Place your order through this link to get a 30 percent discount: https://trycreate.co/products/creatine-monohydrate-gummies-270-count?discount=every24

Packy McCormick’s job is to find, articulate, and invest behind the next big idea.

He writes Not Boring, a newsletter that analyzes technology and startups for 200,000 subscribers every week. He also invests in early stage companies through his fund Not Boring Capital and is an advisor at a16z crypto.

I spent an hour with him to understand how he’s baked AI into the way he thinks, writes, and invests. We get into:

- How he uses AI to understand dense concepts and refine his arguments
- His thesis around vertically integrated businesses being the future of tech
- How Packy uses Claude Projects to edit his newsletter
- How he makes interactive graphics that represent concepts from his essays
- The tools Packy uses to research, write, and edit Not Boring
- When he thinks the next crypto bull run will take place

We also use Projects to build an AI tool that grades Packy’s essays live on the show.

This is a must-watch for writers, investors, and anyone trying to understand the cutting edge of technology.

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

Want even more?
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:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper

Timestamps:
00:00:00 - Teaser
00:01:24 - Introduction
00:02:40 - Packy's thesis about the future of technology
00:07:42 - What Packy quick takes on your crypto portfolio
00:14:31 - Use LLMs to validate your understanding of complex concepts
00:18:26 - How Packy used Claude Projects to write an essay he published recently
00:24:00 - Packy's process to make interactive visual graphics for his essays
00:31:10 - How to use AI to be thorough in your research
00:35:04 - How Packy uses Claude to edit his writing
00:36:44 - The tools Packy uses to create his newsletter
00:44:12 - Using Claude Projects to make a tool that grades Packy's essays

Links to resources mentioned in the episode:
Packy McCormick: https://twitter.com/packyM
Packy’s newsletter, Not Boring: https://www.notboring.co/
Packy’s fund, Not Boring Capital: https://www.notboring.co/p/introducing-not-boring-capital
One of Packy’s first essays, about natively integrated companies: https://www.packym.com/natively-integrated-companies
Anduril, the company Packy thinks is an example of a Techno-industrial: https://www.anduril.com/
Packy’s portfolio company that’s integration crypto into its product: https://v2.oncyber.io/
The interactive tool Packy made for a recent newsletter: https://goventvectorsum.replit.app/ for https://www.notboring.co/p/the-american-millennium
Packy’s essay about America’s tolerance for risk: https://www.notboring.co/p/riskophilia
Packy’s essays about Blackbird: https://www.notboring.co/p/blackbird

Keeping up with AI is Nathaniel Whittemore’s full-time job—and I spent an hour with him to understand how he does it.

Nathaniel is the host of a top-ranked AI podcast on the technology charts, The AI Daily Brief, which breaks down the most important news in AI every day. He is also the founder and CEO of Superintelligent, a platform that teaches you how to use AI for work and fun through interactive video tutorials.

We talked about how he curates information with X bookmarks, Google News, news aggregator Feedly, and research tool Perplexity; the workflow that helps him record and produce two daily podcasts; and why he thinks optimizing your processes with AI remains one of its most underrated applications.

Here’s what you’ll learn if you listen to or watch this episode:
 - How to curates AI news using X bookmarks, Google News, Perplexity, and other specialized tools
 - Nathaniel’s insights from producing 300-plus episodes of a top-ranked podcast
 - The granular details of the workflow that helps Nathaniel produce two daily podcasts
 - Actionable advice on how to get the most out of AI right now

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

Want even more?
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:
- Subscribe to Every: https://every.to/subscribe
- Follow him on X: https://twitter.com/danshipper

Links to resources mentioned in the episode:
- Nathaniel Whittemore: https://twitter.com/nlw
- The podcasts Nathaniel hosts: The AI Daily Brief https://www.youtube.com/@AIDailyBrief , The Breakdown Podcast https://www.youtube.com/@NathanielWhittemoreCrypto/videos
- Nathaniel’s AI education platform: Superintelligent https://besuper.ai/
- The tools Nathaniel uses to curate AI news: Google News, Feedly https://feedly.com/ , Perplexity https://www.perplexity.ai/
- The AI-powered content generation tools Nathaniel likes: Hoppy Copy https://www.hoppycopy.co/ , SEO.ai http://SEO.ai

This episode is sponsored by Command Bar, an embedded AI copilot designed to improve user experience on your web or mobile site. Find them here: https://www.commandbar.com/copilot/ 

Dwarkesh Patel is on a quest to know everything. 

He’s using LLMs to enhance how he reads, learns, thinks, and conducts interviews. 

Dwarkesh is a podcaster who’s interviewed a wide range of people, like Mark Zuckerberg, Tony Blair, and Marc Andreesen. Before conducting each of these interviews, Dwarkesh learns as much as he can about his guest and their area of expertise—AI hardware, tense geopolitical crises, and the genetics of human origins, to name a few. 

The most important tool in his learning arsenal? AI—specifically Claude, Claude Projects, and a few custom tools he’s built to accelerate his workflow.

He does this by researching extensively, and as his knowledge grows, each piece of new information builds upon the last, making it easier and easier to grasp meaningful insights. 

In this interview, I turn the tables on him to understand how the prolific podcaster uses AI to become a smarter version of himself. We get into:

- How he uses LLMs to remember everything
- His podcast prep workflow with Claude to understand complex topics
- Why it’s important to be an early adopter of technology
- His taste in books and how he uses LLMs to learn from them
- How he thinks about building a worldview 
- His quick takes on the AI’s existential questions—AGI and P(doom)

We also use Claude live on the show to help Dwarkesh research for an upcoming podcast recording.

This is a must-watch for curious people who want to use AI to become smarter.

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

Want even more?
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:
- Subscribe to Every: https://every.to/subscribe 
- Follow him on X: https://twitter.com/danshipper 

Timestamps:
00:00:00 - Teaser
00:01:44 - Introduction 
00:05:37 - How Dwarkesh uses LLMs to remember everything 
00:11:50 - Dwarkesh's taste in books and how he uses AI to learn from them 
00:17:58 - Why it's important to be an early adopter of technology 
00:20:44 - How Dwarkesh uses Claude to understand complex concepts
00:26:36 - Dwarkesh on how you can compound your intelligence 
00:28:21 - Why Dwarkesh is on a quest to know everything 
00:39:19 - Dan and Dwarkesh prep for an upcoming interview 
01:04:14 - How Dwarkesh uses AI for post-production of his podcast 
01:08:51 - Rapid fire on AI's biggest questions—AGI and P(doom)

Links to resources mentioned in the episode:
- Dwarkesh Patel: https://twitter.com/dwarkesh_sp
- Dwarkesh’s podcast and newsletter: https://www.dwarkeshpatel.com/; https://substack.com/@dwarkesh 
- Dwarkesh’s interview with researcher Andy Matuschak on spaced repetition: https://www.dwarkeshpatel.com/p/andy-matuschak 
- The book about technology and society that both Dan and Dwarkesh are reading: Medieval Technology and Social Change
- Dan’s interview with Reid Hoffman: https://every.to/chain-of-thought/reid-hoffman-on-how-ai-might-answer-our-biggest-questions 
- The book by Will Durant that inspires Dwarkesh: Fallen Leaves https://www.amazon.com/Fallen-Leaves-Last-Words-Life/dp/1476771553 
- One of the most interesting books Dwarkesh has read: The Great Divide https://www.amazon.com/Great-Divide-Nature-Human-World/dp/0061672467
- Upcoming guests on Dwarkesh’s podcast: David Reich  https://reich.hms.harvard.edu/ and Daniel Yergin https://www.danielyergin.com/

I interviewed Microsoft CTO Kevin Scott about the future of agents and software engineering for another special edition of AI & I. 

With 41 years of programming behind him, Kevin has lived through nearly every big shift in modern software development. Here’s his clear-eyed take on what’s changing with AI, and how we can navigate what’s next:

• The real breakthrough for the agentic web is better plumbing. Kevin thinks agents won’t be useful until they can take action on your behalf by using tools and fetching data. To do this, agents need access across your systems—and Microsoft’s answer is adopting Model Context Protocol, or “MCP,” that allows an agent to access tools and fresh data beyond its knowledge base, as their standard protocol for agents to move through contexts and get things done.
• How the agentic web echoes the early internet. Just as protocols like HTTP and HTML gave the web a shared language, Kevin believes the  agentic web needs its own infrastructure—the first glimpses of this include MCP (the HTTP of agents) and NLWeb, Microsoft’s push to make websites legible to agents (similar to what HTML did for browsers).
• Open ecosystems can coexist with strong security systems. Kevin argues that the “tradeoff” between ecosystems that allow “permissionless” innovation and robust security is a false dichotomy. With AI agents that understand your personal risk preferences—and know your communication habits across email, text, and other channels—they could detect when something suspicious is happening and act on your behalf. 
• The craftsman’s dilemma in the age of agents. Kevin is a lifelong maker—of software, ceramics, even handmade bags—and he cares deeply about how things are made. Because this can feel at odds with coding with AI agents, Kevin’s approach is to notice where the process matters most to him, and where it's okay to optimize for outcomes. After four decades of seeing breakthrough technologies, his advice is simple: be curious, try stuff, and use it if it works for you.
• The future of software engineering agents is plural. Kevin believes the future of software engineering agents will be diverse because developers who enjoy the freedom of playing with different tools is one of the most consistent patterns he’s seen in his decades in tech. What will drive this diversity, he says, is builders who deeply understand specific problems and tailor agents to solve them exceptionally well.
• How agentic workflows will evolve. Kevin sees a shift from short back-and-forth interactions with agents to longer, async feedback loops. As the agentic web matures and model reasoning improves, people will start handing off bigger, more ambitious tasks and letting agents run with them.

Timestamps:

1. Introduction: 00:01:44
2. The race to close the “capability overhang”: 00:02:49
3. How agents will evolve into practical, useful tools: 00:04:31
4. The role Kevin sees Microsoft playing in the agent ecosystem: 00:06:48
5. How robust security measures can coexist with open ecosystems: 00:12:05
6. Kevin's philosophy on being a craftsman in the age of agents: 00:15:39
7. How the landscape of software development agents will evolve: 00:20:52
8. The future of agentic workflows: 00:25:33

Steph Smith is the ultimate internet explorer.

I spent an hour talking to her about the future of creating on the internet in the age of AI. She’s our first-ever repeat guest, and if you watch the episode you’ll see why: It’s a curious, fun, experimental romp through the best of the digital world.

We try out four underrated AI products, go through a list of Steph’s favorite niche internet creators, and follow her creative process in Midjourney in granular detail.

We had a wide-ranging discussion about:
- How AI changes what humans perceive as valuable in art and creativity
- The type of AI tools that are poised for success  
- How AI narrows the gap between ideas and execution 

If you don’t know her, Steph is the host of the @a16z podcast and the creator behind Internet Pipes, a toolkit to surface useful insights on the internet, and many other cool internet projects. 

This is a must-watch if you make things on the internet and are interested in how AI is changing what it means to be a creator—and how creator businesses work.

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

Want even more?
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:
- Subscribe to Every: https://every.to/subscribe 
- Follow him on X: https://twitter.com/danshipper 

Timestamps:
00:00:00 - Teaser
00:00:46 - Introduction
00:09:08 - How Steph uses Midjourney to find her aesthetic
00:20:45 - Steph predicts how creating on the internet will evolve with AI
00:32:51 - Rapid-fire rundown of Steph's favorite niche creators
00:42:58 - How Steph trains her brain on better data
00:48:19 - The AI research tool Steph uses for health information
00:56:25 - The future of AI tools—and one of Steph's top picks
01:01:20 - Dan and Steph use AI to create a simulation of the internet
01:05:09 - How LLM hallucinations can be useful
01:12:06 - Dan and Steph make a song about what they learned on the show

Links to resources mentioned in the episode:
Steph Smith: https://twitter.com/stephsmithio
Internet Pipes: https://internetpipes.com/ 
Doing Content Right: https://doingcontentright.com/#features 
A few of Steph’s favorite niche creators: India Rose Crawford https://www.instagram.com/indiarosecrawford/?hl=en , Blackforager https://www.instagram.com/blackforager/?hl=en , David Zinn https://www.instagram.com/davidzinn/?hl=en , David Bird https://www.instagram.com/davidmbird/ , WatchMaggiePaint https://www.instagram.com/watchmaggiepaint/?hl=en
The podcast episode Dan did with filmmaker Dave Clark: https://every.to/chain-of-thought/how-a-hollywood-director-uses-ai-to-make-movies 
The AI tools Dan and Steph use on the show: Consensus https://consensus.app/ , Globe Explorer https://explorer.globe.engineer/ , websim.ai http://websim.ai , Granola https://www.granola.so/ , Suno https://suno.com/

Dr. Bradley Love is building a tool that can predict the future.
Dr. Bradley Love is transforming neuroscience research with AI.
He's the creator of BrainGPT, a large language model that can predict the results of neuroscience studies—before they’re conducted. And it performs better than human experts.
We spent 90 minutes exploring how AI is reshaping scientific research and our understanding of the brain.
Bradley argues that as scientific knowledge grows exponentially, we need new tools to make sense of it all. BrainGPT isn't just summarizing existing research—it's predicting future discoveries.

We get into:

• How BrainGPT outperforms neuroscience professors

• Why clean scientific explanations may be a thing of the past

• The challenges of interpreting complex biological systems

• How AI could change the way we approach scientific research

• The limitations of our intuitive understanding of the brain

This is a must-watch for anyone interested in the future of science, AI, and how we understand the human mind.

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

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Sign up for Every to unlock our ultimate guide to prompting ChatGPT here. 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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• Follow him on X: https://twitter.com/danshipper

Timestamps:
00:00:00 - Teaser
00:01:00 - Introduction 
00:01:58 - The motivations behind building a LLM that can predict the future 
00:11:14 - How studying the brain can solve the AI revolution’s energy problem 
00:13:32 - Dr. Love and his team have developed a new way to prompt AI 
00:18:27 - Dan’s take on how AI is changing science 
00:22:54 - Why clean scientific explanations are a thing of the past 
00:29:49 - How our understanding of explanations will evolve 
00:37:31 - Why Dr. Love thinks the way we do scientific research is flawed 
00:40:42 - Why humans are drawn to simple explanations 
00:45:03 - How Dr. Love would rebuild the field of science

Links to resources mentioned in the episode:
Dr. Bradley Love: https://bradlove.org/; https://twitter.com/ProfData
BrainGPT: https://braingpt.org/
Thomas Nagel’s book on the philosophy of science that Dr. Love recommends: The View From Nowhere
The essay that Thomas Nagel is famous for: What is it like to be a bat?   

Claire Vo built ChatPRD—an on-demand chief product officer powered by AI. It’s now used by over 10,000 product managers and is pulling in six figures in revenue. 
The best part? 
Claire has a demanding day job as the CPO at LaunchDarkly. So she built all of ChatPRD herself—over the weekend—with AI. 
I sat down with Claire to talk about how ChatPRD works, how she built it as a side hustle using AI, and all of the ways she’s using AI tools to accelerate her work and life. 
We get into:

• How she used AI to build ChatPRD over Thanksgiving break
• The part of product management that Claire thinks AI will disrupt
• Why the PMs of tomorrow will be “proto-managers” who create prototypes rather than just specs
• How junior PMs can use AI to upskill faster
• The ways in which ChatPRD is baked into her own workflow
• How building ChatPRD is making Claire a better PM
• How Claire uses AI as a tech-forward parent

This is a must-watch for anyone interested in turning their side hustle into a thriving business or who works in product. If you found this episode interesting, please like, subscribe, comment, and share!

Want even more?
Sign up for Every to unlock our ⁠ultimate guide to prompting ChatGPT here⁠. It’s usually only for paying subscribers, but you can get it here for free.

To hear more from Dan Shipper:

• Subscribe to Every: ⁠https://every.to/subscribe ⁠
• Follow him on X: ⁠https://twitter.com/danshipper⁠

Links to resources mentioned in the episode:

• Claire Vo: ⁠https://x.com/clairevo⁠
• ChatPRD: https://www.chatprd.ai/; ⁠https://x.com/chatprd⁠; ⁠https://www.linkedin.com/company/chatprd/⁠; ⁠https://www.youtube.com/@ChatPRD ⁠
• Some of the AI tools that Claire used to build ChatPRD: ⁠http://Clerk.dev⁠; ⁠https://tiptap.dev/ ⁠
• Greeking Out, the Greek mythology podcast that Claire’s son enjoys: ⁠https://www.nationalgeographic.com/podcasts/greeking-out⁠

An interview with best-selling sci-fi novelist Robin Sloan

One of my favorite fiction writers, New York Times best-selling author Robin Sloan, just wrote the first novel I’ve seen that’s inspired by LLMs.

The book is called Moonbound, and Robin originally wanted to write it with language models. He tried doing this in 2016 with a rudimentary model he built himself, and more recently with commercially available LLMs. Both times Robin found himself unsatisfied with the creative output generated by the models. AI couldn’t quite generate the fiction he was looking for—the kind that pushes the boundaries of literature.

He did, however, find himself fascinated by the inner workings of LLMs

Robin was particularly interested in how LLMs map language into math—the notion that each letter is represented by a unique series of numbers, allowing the model to understand human language in a computational way. He thinks LLMs are language personified, given its first heady dose of autonomy. 

Robin’s body of work reflects his deep understanding of technology, language, and storytelling. He’s the author of the novels Mr. Penumbra’s 24-hour Bookstore and Sourdough, and has also written for publications like the New York Times, the Atlantic, and MIT Technology Review. Before going full-time on fiction writing, he worked at Twitter and in traditional media institutions. 

In Moonbound, Robin puts LLMs into perspective as part of a broader human story. I sat down with Robin to unpack his fascination with LLMs, their nearly sentient nature, and what they reveal about language and our own selves. It was a wide-ranging discussion about technology, philosophy, ethics, and biology—and I came away more excited than ever about the possibilities that the future holds.

This is a must-watch for science-fiction enthusiasts, and anyone interested in the deep philosophical questions raised by LLMs and the way they function. 

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

Want even more?

Sign up for Every to unlock our 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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Follow him on X: https://twitter.com/danshipper 

Links to resources mentioned in the episode:

Robin Sloan: https://www.robinsloan.com/ 

Robin’s books: Mr. Penumbra's 24-Hour Bookstore, Sourdough, Moonbound

Dan’s first interview with Robin four years ago: https://every.to/superorganizers/tasting-notes-with-robin-sloan-25629085 

Anthropic AI’s paper about how concepts are represented inside LLMs: https://www.anthropic.com/news/mapping-mind-language-model 

Dan’s interview with Notion engineer Linus Lee: https://www.youtube.com/watch?v=OeKEXnNP2yA 

Big Biology, the podcast that Robin enjoys listening to: https://www.bigbiology.org/ 

We use it to find bestselling author Steven Berlin Johnson’s next project.

I sat down with bestselling author Steven Johnson to see if we could come up with a concept for his next project—using AI. The results were amazing.

We loaded 200,000 words of NASA transcripts and all of Steven’s reading notes since 1999 into NotebookLM, Google’s personalized research assistant. We wanted to see if it could help us explore the Apollo 1 fire and find relevant and surprising ideas from history that could work to explain it.

• NotebookLM condensed disparate 200,000 words of NASA transcripts into readable formats like FAQs and chronological timelines.
• It sifted through the material to identify the catalyst for the fire.
• The model even went through Steven’s Readwise notes to find a relevant, and unexpected, story from history that we could use to explain the history and origins of the fire

If you’re a fan of Steven Johnson’s work or you’re interested in AI as a creative tool, you need to watch this episode. 

All of this happens as a live exploration of NotebookLM, and it’s a seriously wild ride. 

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

Want even more?
Sign up for Every to unlock our 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:
⁠Subscribe to Every⁠
Follow him on ⁠X⁠

Links to resources mentioned in the episode:
⁠Follow Steven Johnson
⁠⁠NotebookLM⁠
Steven’s newsletter, ⁠Adjacent Possible⁠
Steven’s latest book about the rise of the modern detective: ⁠The Infernal Machine⁠
A few of Steven’s other books: ⁠
How We Got to Now
⁠⁠Where Good Ideas Come From
⁠⁠The Ghost Map
⁠⁠Emergence
⁠⁠The Invention of Air

Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others about how they use AI tools like ChatGPT, Claude, and Midjourney in their work and in their lives. We screen-share through their historical chats and then experiment with AI live on the show. Join us to discover how AI is changing how we think about our world—and ourselves. 

For more essays, interviews, and experiments at the forefront of AI: https://every.to/chain-of-thought?sort=newest. 

New York Times journalist Kevin Roose has 18 new friends—none of whom are human. 

Kevin formed a collection of “friends”—AI personas with distinct personalities and backstories—using apps like Kindroid and Nomi. Among these were fitness guru Jared, San Francisco-based therapist Peter, and pragmatic trial lawyer Anna. He talked to them every day for a month, sharing personal stories, seeking advice, and even asking for “fit” checks. And this wasn’t Kevin’s first unusual interaction with AI characters. A year ago, he was the infamous target of Bing’s chatbot Sydney’s romantic overtures.

I don’t think anyone has studied AI companionship as deeply as Kevin, and in this episode, I sat down with him to learn more about his experience.

Kevin is a tech columnist at the New York Times and cohost of the Hard Fork podcast. He’s also the author of three books, most recently Futureproof: 9 Rules for Humans in the Age of Automation, which is about how humans can be happy in a world designed for machines. During our conversation, we also talk about how Kevin is using AI in his work and life every day.

This is a must-watch for anyone curious about how AI is changing the way we form relationships.

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Kevin Roose: @kevinroose

Hardfork, the podcast that Kevin cohosts: https://www.nytimes.com/column/hard-fork 

Kevin’s latest book about being human in a world designed for machines: https://www.kevinroose.com/futureproof 

Kevin’s piece in the New York Times about his experience making AI friends: https://www.nytimes.com/2024/05/09/technology/meet-my-ai-friends.html?unlocked_article_code=1.qk0.9dZN.6XiiP3RjRZxv&smid=nytcore-ios-share&referringSource=articleShare&sgrp=c-cb 

Two of the apps that Kevin used to create AI companions: https://landing.kindroid.ai/; https://nomi.ai/ 

Dan’s piece that explains why AI writing will feel real through psychologist D.W. Winnicott’s theory: https://every.to/chain-of-thought/will-you-read-writing-from-an-ai   

Every’s piece that explores AI companion app Replika: https://every.to/cybernaut/artificial-intimacy 

Nick Dobos, maker of the #1 programming GPT, on prompt-gramming with AI

You can go from having an idea to deploying a live website in minutes.

All you have to do is prompt Grimoire, the number-one custom GPT for programming, with an image or even a single word about your idea. As you watch the LLM process your request, Grimoire works with a web host on the backend, and just minutes later, your website will be live on the internet.

Grimoire, which has facilitated over 1 million chats, can help you with a lot more than just making websites: It includes a comprehensive guide to learning how to code, from basic concepts to advanced instruction, and serves as a tool for programmers to resolve their questions in real time.

The creator of Grimoire is Nick Dobos, who was an iOS developer at Twitter until Musk bought the company and laid off a majority of its staff. With plenty of free time suddenly on his hands, Nick started experimenting with ChatGPT, and ended up building Grimoire. He’s since emerged as one of the foremost experts in the world on building successful custom GPTs and coding with ChatGPT. 

I think Grimoire is a platform to examine the possibilities that “prompt-gramming”—an emerging way of coding by prompting AI—can enable. I sat down with Nick to explore what this means about the future of programming, the best way to use the coding assistant, and the role AI plays in his life beyond coding. As we talk, Nick uses Grimoire to build a website about coffee and generate a QR code from its URL live on the show.

This is a must-watch for coders, creative people, and anyone curious about how AI is changing the way we interact with computers.

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Nick Dobos: @NickADobos

Grimoire: https://chat.openai.com/g/g-n7Rs0IK86-grimoire 

Nick’s website for his experiments with AI: https://mindgoblinstudios.com/ 

AI-first code editor Cursor: https://cursor.sh/ 

Open Interpreter: https://www.openinterpreter.com/ 

Lisa Feldman Barrett’s book: How Emotions Are Made

Demo Hume, the empathetic AI voice: https://demo.hume.ai/ 

The future of AI technology isn’t just faster or more powerful—it’s empathetic. My guest for this episode, Alan Cowen, is leading the charge with the first-ever emotionally intelligent AI.

Alan is the co-founder and CEO of Hume, an AI research laboratory developing models trained to identify and measure expressions of emotion from voice inflections and facial expressions. The best part? Once it understands these emotions, the AI is designed to interact with users in a way that optimizes for human well-being and leaves them with a positive emotional experience.  

Previously, Alan—who has a Ph.D. in computational psychology—helped set up Google’s research into affective computing, a field focused on developing technologies that can understand and respond to human emotions. He operates at the intersection of AI and psychology, and I sat down with him to understand the inner workings of Hume’s models. Alan walks me through the shortcomings of traditional theories of emotional science and breaks down how Hume is addressing these challenges. While talking about the potential applications of the models, we also discuss the tricky ethical concerns that come with creating an AI that can interpret human emotions.

This is a must-watch for anyone interested in the science of emotion and the future of human-AI interactions.

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Alan Cowen: @AlanCowen

Hume: @hume_AI; hume.ai

If you want to demo Hume: demo.hume.ai

The nonprofit associated with Hume: Hume Initiative

Lisa Feldman Barrett’s book: How Emotions Are Made

The serial based on Paul Ekman’s theory of emotion: Lie to Me

Learn how to use philosophy to run your business more effectively

Reid Hoffman thinks a masters in philosophy will help you run your business better than an MBA.

Reid is the cofounder of LinkedIn, a partner at venture capital firm Greylock Partners, the host of the Masters of Scale podcast, and a prolific author. But before he did any of these things, Reid studied philosophy—and by helping him understand how to think, it made him a better entrepreneur.

A good student of philosophy rigorously engages with questions about truth, human nature, and the meaning of life, and, over time, learns how to think clearly about the big picture. This is a powerful tool for founders faced with existential questions about their product, consumers, and competitors, and enables them to respond with well-reasoned answers and enviable clarity of thought.

This show is usually about the actionable ways in which people have incorporated ChatGPT into their lives, but in this episode, I sat down with Reid to tackle a deeper question: How is AI changing what it means to be human? How might it change the way we see ourselves and the world around us?

This episode is a must-watch for anyone curious about some of the bigger questions prompted by the rapid development of AI.

Thanks again to our sponsor CommandBar, the first AI user assistance platform, for helping make this video possible. ⁠https://www.commandbar.com/copilot/⁠

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

Want even more?

Sign up for Every to unlock our 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:

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

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

Links to resources mentioned in the episode:

Reid Hoffman: @reidhoffman

The podcast Reid hosts: Masters of Scale

Reid’s book: Impromptu

The book Reid recommends if you want to be more philosophically inclined: Gödel, Escher, Bach

Reid’s article in the Atlantic: "Technology Makes Us More Human"

The book about why psychology literature is wrong: The WEIRDest People in the World by Joseph Henrich

The book about how culture is driving human evolution: The Secrets of Our Success by Joseph Henrich

OpenAI just launched Codex, a brand-new coding agent that can build features and fix bugs autonomously. We’ve been testing it at Every for a few days, and I’m impressed.

I invited Alexander Embiricos, a member of the OpenAI product staff responsible for Codex, to demo the agent live on a special edition of AI & I. We talk through:

- What Codex is and how it works. Codex’s UI allows developers to see the list of tasks the agent is working on, how many lines were changed for each, and the status of the PR. It’s built for the senior software engineer who wants to delegate and review tasks efficiently.
- How OpenAI is thinking about agents. Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. 
- Why an “abundance mindset” is best for interacting with agents. Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task, like a difficult bug—it’s worth it even if only one of them succeeds.
- OpenAI’s vision for the future of programming. In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding.

Timestamps:

1. Introduction: 00:00:52
2. The product decisions behind Codex’s interface: 00:01:40
3. How Codex works under the hood: 00:06:20
4. Why you need an abundance mindset to work well with agents: 00:14:06
5. Setting Codex to work on a real task in “Ask” mode: 00:16:28
6. How OpenAI is thinking about designing agents: 00:18:54
7. The future of programming is social: 00:31:16
8. Reviewing Codex’s work live: 00:37:21
9. How the landscape of agents will evolve: 00:39:41

Seth-Stephens Davidowitz wrote a book in 30 days—and he did it with ChatGPT.

Seth is a data scientist, economist, and author who challenged himself to write a book—Who Makes the NBA?—in less than 1 month after realizing how fast he could work by using ChatGPT plugin Advanced Data Analysis. 

But along the way he discovered something else: Writing with AI wasn’t just faster, it was also way more fun. 

Seth outsourced the boring parts of data analysis—like cleaning data, merging files, and looking up code snippets—to AI. This left him to focus on what he loves: thinking up questions to ask the dataset.

In a world where AI can answer any question humans know the answer to, asking the right questions is becoming increasingly important—a skill Seth isn’t just really good at, but also finds joy in. 

In this episode, Seth walks me through how he used AI to analyze data and write a book in 30 days. We get into:

- How to create and edit complex charts with AI in seconds

- Using ChatGPT to brainstorm creative ideas 

- How AI is redefining who can be an artist 

- Why ChatGPT is an excellent tool to get a quick ballpark estimate

- Developing a sixth sense about when ChatGPT is wrong

- The power of AI instantly answering hard questions that would normally take months of research 

We also use ChatGPT to analyze a dataset of Olympic athletes live on the show—in pursuit of finding out which sport I’m best suited for!

This episode is a must-watch for anyone curious about data science and how AI is transforming the future of creativity (or who is just a fan of the NBA).

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

Want even more?

Sign up for Every to unlock our 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:

• Subscribe to ⁠Every ⁠
• Follow him on ⁠X⁠

Links to resources mentioned in the episode:

Seth Stephens-Davidowitz: ⁠https://twitter.com/SethS_D⁠ ⁠http://sethsd.com⁠

Seth’s books: ⁠Who Makes the NBA?⁠ , ⁠Everybody Lies ⁠ and ⁠Don’t Trust Your Gut 

Nicholas Thorne is building Squarespace for the AI age. It’s called Audos, and it’s an AI chatbot to help any entrepreneur go from idea to:
- Pitch deck
- Working website
- Custom GPT
- User interviews with real customers
All in just a few minutes. And he did it using ChatGPTapp. It’s AI all the way down—and it’s one of the most impressive AI businesses I’ve ever seen.
Nicholas is a general partner at Prehype, an incubator that launched Barkbox and Ro Health. It’s also where I started Every, so it was great to come full circle.
Nicholas’s job at Prehype is to launch new companies. He’s taken everything he’s learned running an incubator and used it to help entrepreneurs start businesses at scale—with AI.
As we talk, Nicholas walks me through the interactions of Audos’s chatbot with a user live on the show.
Nicholas tells me that he used ChatGPT to prototype most of Audos’s features—despite being non-technical himself. He shares exactly how he did this by showing me how he’s using AI to create a new feature for the product.
We get into:
- Ways AI can make you a more effective founder
- How to use ChatGPT to build your prototype
- Strategies to refine problem statements with AI
- Using GPTs to gather and synthesize customer feedback
This episode is a must-watch for anyone who has ever toyed with the idea of starting a business—and wants to do it with AI.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ⁠ultimate guide to prompting ChatGPT⁠. It’s usually only for paying subscribers, but you can get it here for free: https://every.ck.page/ultimate-guide-to-prompting-chatgpt

To hear more from Dan Shipper:
Subscribe to Every: ⁠https://every.to/subscribe⁠
Follow him on X: ⁠https://twitter.com/danshipper⁠

Timestamps:
00:00:00 - Teaser
00:00:48 - Introduction
00:12:10 - How AI can make you a more effective founder
00:17:03 - Live demo of Audos!
00:24:07 - Why Nicholas built an AI tool to enable entrepreneurs
00:25:35 - How Audos puts you in “edit mode” instead of “create mode”
00:28:12 - Tools to gather customer feedback, generated by Audos
00:32:58 - How Audos actually works
00:35:07 - Nicholas uses ChatGPT to prototype a new feature
00:42:37 - How to establish checks and balances while using ChatGPT
00:57:20 - AI as a force for pushing entrepreneurship to new heights

Links to resources mentioned in the episode:
Nicholas Thorne: ⁠@thorneny⁠; ⁠nicholas@prehype.com⁠
Audos: ⁠https://www.audos.com/⁠
Nicholas’s book, Me, My Customer, and AI, is slated to publish next month. Follow him on X for updates: https://mmcai.super.site/

Antidepressants changed my life.

I have OCD and antidepressants did what nearly a decade of therapy, meditation, and supplements couldn’t: they allowed me to live my life without being in a 24/7 spiral. (Bonus: they actually made therapy and meditation far more helpful once they started to work.)

I think antidepressants are seriously misunderstood. Yes, they blunt negative emotions. But they also operate on personality and sense of self: they can make you bolder, less sensitive to failure, and less risk-averse.

In short: they are a technology that changes how we see ourselves and the world.

That’s why I invited Dr. Peter D. Kramer on my show. Dr. Kramer is a psychiatrist and the author of eight books, including Listening to Prozac, which is an international bestseller. He has practiced psychiatry and taught psychotherapy at Brown University for nearly four decades.

Listening To Prozac is one of my favorite books, and it documents Dr. Kramer’s experiences as a psychiatrist seeing how antidepressants like Prozac changed his patients’ sense of self and personality.

Now, you might be wondering why have him on a show about ChatGPT? Well, technology can change who we are even if it comes as a software product rather than a pill. It’s undoubtedly true that as generations of humans learn to live with AI, it will change what it means to be human—and how we see ourselves and the world. I think that can be a good thing, but it could also be scary.

I wanted to talk to Dr. Kramer about his book, and see if we could apply some of his insights in Prozac to ChatGPT. It was an incredible conversation, and I was honored to talk to him.

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

Want even more?

Sign up for Every to unlock our 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:

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

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

To learn more about the topics in this episode:

Listening to Prozac by Peter D. Kramer

ChatGPT and the Future of the Human Mind by Dan Shipper

SSRIs by Scott Alexander

Links to resources mentioned in the episode:

Dr. Peter D. Kramer: https://twitter.com/PeterDKramer 

ChatGPT and the Future of the Human  Mind by Dan Shipper: https://every.to/chain-of-thought/chatgpt-and-the-future-of-the-human-mind 

Listening to Prozac  by Dr. Kramer: https://www.amazon.com/Listening-Prozac-Landmark-Antidepressants-Remaking/dp/0140266712 

Should You Leave? by Dr. Kramer: https://www.amazon.com/Should-You-Leave-Psychiatrist-Autonomy/dp/0140272798 

Against Depression by Dr. Kramer: https://www.amazon.com/Against-Depression-Peter-D-Kramer/dp/0143036963 

Ordinarily Well by Dr. Kramer: https://www.amazon.com/Ordinarily-Well-Antidepressants-Peter-Kramer/dp/0374536961 

Pierre Menard, Author of the Quixote by Jorge Luis Borges: https://raley.english.ucsb.edu/wp-content/Engl10/Pierre-Menard.pdf 

The Soul of A New Machine by Tracy Kidder: https://www.amazon.com/Soul-New-Machine-Tracy-Kidder/dp/0316491977 

Making Hay by Verlyn Klinkenborg: https://www.amazon.com/Making-Hay-Verlyn-Klinkenborg/dp/0941130185 

Oranges by John McPhee: https://www.amazon.com/Oranges-John-McPhee/dp/0374512973 

You can build and run a one-person internet business that earns half a million in annual revenue—with AI.

Ben Tossell showed me exactly how in this episode. Ben is the founder of Ben’s Bites—one of the best daily AI newsletters out there, which I love reading every day—and an investor in a number of promising early-stage AI startups. Ben is also an experienced founder whose no-code platform Makerpad was acquired by Zapier.

I think Ben is really good at starting profitable internet businesses that are sneakily big, but don’t require too many resources. Over the last couple of years, he’s assembled a war chest of AI tools including ChatGPT, Claude, Gemini, Lex, and Supernormal to help him do this. In this episode, we get into the weeds of how Ben has integrated AI into his workflow to find new business opportunities, run them well, and evaluate their performance. 

We get into:
- How to use ChatGPT as a business strategist
- Building your MVP with ChatGPT
- Turning interview transcripts into compelling articles
- Analyzing business data using AI tools
- How to generate persuasive landing page copy with ChatGPT
- Offload time-consuming tasks to AI

This episode is a must-watch for anyone who is curious about using AI to bootstrap a profitable internet business.

Want even more?
Sign up for Every to unlock our 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:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper

Links to resources mentioned in the episode:
- Ben Tossell: https://twitter.com/bentossell