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.
The podcast AI and I is created by Dan Shipper. The podcast and the artwork on this page are embedded on this page using the public podcast feed (RSS).
After two Jhana meditation retreats Nadia Asparouhova could silence her mind, change her emotional state at will, and even intentionally slip out of consciousness. It challenged the idea that our minds are not under our control—and made her wonder if we’re more like AI than we realize.
Nadia is a writer and researcher of technology and culture. She published Working in Public, a book about the evolution of open-source development, with Stripe Press in 2020. Her latest book, Antimemetics, is about why some ideas don’t go viral even though they’re powerful.
I had her on the show to talk about her experience with Jhana meditation and how it reshaped the way she thinks about being human in the age of AI. We get into:
Jhana as a means to nurture profound joy and calm. Unlike many meditation practices that emphasize passive observation, Jhana is goal-oriented—practitioners proactively cultivate states of concentrated bliss. Apart from helping her regulate her emotions, it prompted Nadia to reexamine deep questions of our human existence.
Self-talk is not essential as it seems. Nadia describes how advanced meditation quieted her inner voice—challenging the idea that self-talk is core to being human.
How years of cultural evolution have shaped our sense of self. According to Nadia, our modern conception of “self” isn’t as timeless as we assume. She draws on psychologist Julian Jaynes’s theory that our inner dialogue—what we often equate with consciousness—only emerged in humans a few thousand years ago; a provocation to reconsider the benchmarks we use to assess the intelligence or sentience of LLMs.
What it is like to experience a “cessation.” On her last meditation retreat, Nadia experiences a cessation where your consciousness abruptly winks out—like suddenly flipping a switch. Nadia described it as slipping into nothingness, then returning with the jarring realization that even your sense of self can vanish and reappear.
Why she likes the unknowability of AI. The mechanics of exactly how LLMs predict their next token remain a mystery. Driven by thousands of subtle, context-dependent correlations, they’re too complex to distill into a simple explanation. Nadia finds joy in the unknowability of it all, seeing the ambiguity as an invitation to explore.
How she uses AI as a writing partner. Nadia believes the trope of the solitary, brooding writer is beginning to shift with the rise of LLMs. For her, ChatGPT has made writing feel less isolating. She turns to it at both ends of the process: to help make sense of early ideas, and later, to sharpen phrasing and land on just the right words.
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Timestamps:
Introduction: 00:01:15
The beginning of Nadia’s journey with Jhana: 00:02:34
How Jhana is different from other meditation practices: 00:05:51
Jhana reframed the way Nadia thinks about being human: 00:09:52
How Nadia integrates her experience with Jhana into her life: 00:14:16
Nadia describes her experience of the final stage of Jhana: 00:16:44
Why our modern sense of self isn’t as timeless as you might assume: 00:19:11
How new technologies can be a mirror to ourselves: 00:23:53
Nadia embraces the feeling of not knowing how AI precisely works: 00:33:55
How Nadia uses ChatGPT to make writing less isolating: 00:38:03
Links mentioned:
Nadia Asparouhova: https://nadia.xyz/
Her deep dive on Jhana meditation: https://nadia.xyz/jhanas
Nadia’s book: Working in Public, Antimemetics
Books about how new technology can change our sense of self: The WEIRDest People in the World, Listening to Prozac
Sarah Tavel thinks it's criminal that ChatGPT isn’t inherently social.
There’s no easy way to discover great prompts or share the ones that worked. As a venture partner at Benchmark, Sarah believes that the next wave of consumer AI will be built on this missing social layer—by product-driven founders who understand people, not just models.
Sarah has seen this shift before. As one of Pinterest’s first product managers, she saw the company grow from a niche consumer tool to a beloved global community. On this episode of Every's podcast AI & I, we talk about how she’s applying the lessons she learned to AI—and what it takes to build a breakout consumer AI app today.
We get into:
Why product geniuses win as new tech matures. In the early days of a new technology, companies win by wrangling raw innovation into something usable. But as the infrastructure matures, Sarah says the edge shifts to product thinkers—founders who turn new capabilities into delightful user experiences.
The future of prompting is social. When Sarah had to dig through Reddit to find a prompt to help her interpret her blood test results, she saw a gap: The best prompt creators are invisible. Sarah bets that a social AI product that makes them discoverable and followable would gain traction.
Sarah’s method to spot exceptional founders. Sarah backs founders for whom building a company feels like a calling—or even an affliction. These are people who have fallen in love with the process and are obsessed with learning how to grow alongside their companies.
How to tell if your startup really has network effects. Founders raising money love to say that their business has “network effects.” Sarah has learned to look for early signs they’re real—like traction in a small, white-hot segment of the market. If there’s no evidence the flywheel is already starting to spin, it’s probably not a network effect.
How LLMs change the way the best VCs invest. Sarah thinks the future of venture will be shaped by how well VCs can turn the decisions they make into training data. After every pitch, she logs what she liked, what she didn’t, the deal terms, and her reasoning. Over time, she’s building a dataset of her own judgment—one an LLM could help her use to pressure-test decisions and avoid past mistakes.
This is a must-listen for if you’re building a consumer AI product and want to see ahead of the curve.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
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To hear more from Dan Shipper:
Sponsor:
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Timestamps:
Introduction: 00:01:10
Why the future of consumer AI belongs to founders with product intuition: 00:02:26
What Sarah sees as ChatGPT’s biggest weakness: 00:11:09
How Sarah would design a consumer AI app with social DNA: 00:18:45
The kind of founders Sarah invests in: 00:25:04
How to know if your startup’s network-effects are real: 00:29:26
What’s catching Sarah’s eye beyond AI: 00:36:33
How AI will change the way top venture capitalists invest: 00:41:35
Links to resources mentioned in the episode:
Sarah Tavel: @sarahtavel
Sarah’s substack: https://www.sarahtavel.com/
Eugene Wei’s essay about Status-as-a-Service: https://www.eugenewei.com/blog/2019/2/19/status-as-a-service
The book Sarah talks about in the context of founders who become CEOs in pursuit of status: The Five Temptations of a CEO
Kevin Kelly has spent more time thinking about the future than almost anyone else.
From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what’s next.
Today, as Wired's senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering.
He’s a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on the show. We get into:
How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn’t staying at the edge forever; it's returning with a story to tell.
Why history is so important to help you understand the future To stay grounded while exploring what’s new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands.
From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked ChatGPT to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself.
What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we’re building intelligent machines without really understanding what intelligence is.
Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn’t a single force, but a compound of many cognitive elements. He draws from Marvin Minsky’s “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today.
Your competitive advantage is being yourself. Don’t aim to be the best—aim to be the only. Kevin realized the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work.
This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired.
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Timestamps:
Introduction: 00:00:50
Why Kevin and I love Annie Dillard: 00:01:10
Learn how to predict the future like Kevin: 00:12:50
What the history of electricity can teach us about AI: 00:16:08
How Kevin thinks about the nature of intelligence: 00:20:11
Kevin’s advice on discovering your competitive advantage: 00:27:21
The story of how Kevin assembled a bench of star writers for Wired: 00:31:07
How Kevin used ChatGPT to co-create a book: 00:36:17
Using AI as a mirror for your mind: 00:40:45
What Kevin learned from betting on VR in the 1980s: 00:45:16
Links to resources mentioned:
Kevin Kelly: @kevin2kelly
Kelly’s books: https://kk.org/books
Annie Dillard books that Kelly and Dan discuss: Pilgrim at Tinker Creek, Teaching a Stone to Talk, Holy the Firm, The Writing Life
Dillard’s account of the total eclipse: "Total Eclipse"
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?
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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
Timestamps:
Introduction: 00:01:30
Talking to the Portola CEO’s Tolan, Clarence: 00:04:07
How Portola went from building software for kids to AI companions: 00:09:11
Why response time is everything for voice-based AI interfaces: 00:23:40
Tolans don’t use scripted prompts—they’re taught to improvise: 00:29:54
How to know which AI personalities your users will click with: 00:37:23
Developing the character traits of an AI companion: 00:42:27
What does it mean to build technology that makes us flourish: 00:49:48
How Portola evaluates whether Tolans are resonating with users: 01:01:10
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.
Lucas Crespo is the mastermind behind Every's visual vibe—and he does it one prompt at a time.
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.
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:
Introduction: 00:01:41
How AI changed the course of Lucas’s career: 00:04:02
Why Every’s aesthetic feels both familiar and fresh: 00:08:00
Why Lucas thinks minimalism is overrated: 00:14:53
Art direction matters more than ever in the age of AI: 00:20:38
How to reimagine what a website can be with AI: 00:23:42
Lucas’s process in Midjourney to generate cover images: 00:33:08
Midjourney v. image generation in ChatGPT: 00:42:30
Behind the scenes of Cora’s design language: 00:49:07
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”
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.
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?
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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
Timestamps:
Introduction: 00:01:18
Science can deepen your sense of the spiritual: 00:02:36
The nature of consciousness: 00:11:31
AI might appear to be conscious, but it isn’t: 00:13:11
Why AI can be considered to be “natural”: 00:19:50
AI shifts the focus of science from explanations to predictions: 00:30:40
How modern neural networks simulate thinking: 00:33:48
Lightman’s vision for how humans and machines will merge: 00:39:38
Does AI know more about love than you?: 00:43:11
How technology is accelerating the pace of our lives: 00:49:18
Links to resources mentioned:
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:
Introduction: 00:01:31
Dan and Jonny’s approach to running non-traditional businesses: 00:02:18
How Jonny uses ChatGPT to deepen his meditation practice: 00:12:04
Jonny uses AI to research a theory of how trauma is stored in our bodies: 00:25:44
Dan’s theory around how AI is changing science: 00:31:28
Jonny’s method to build personalized AI coaches: 00:32:35
How Jonny used OpenAI’s deep research to plan a move to Costa Rica: 00:47:07
Dan is developing an app that can predict his OCD symptoms: 00:52:50
AI makes the idea of a “quantified self” useful: 00:55:42
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.
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?
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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
Timestamps for Spotify:
Introduction: 00:02:00
Why there should be a nation-wide framework to regulate AI: 00:04:31
How 61,000 state employees in New Jersey are adopting AI: 00:10:34
Why new tech is key to transforming government services: 00:12:20
The Governor is bringing startups back to New Jersey: 00:17:30
How to stimulate innovation at scale: 00:25:28
The Governor is making New Jersey a top choice for the best talent: 00:33:07
Balancing technological progress while ensuring the workforce isn’t left behind: 00:36:56
We’re moving toward an “allocation economy”: 00:41:39
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
Introduction: 00:01:07
The power of treating your startup as an evolving entity: 00:03:00
Building a business as a means of self-expression: 00:05:27
Prompting ChatGPT to do Jungian dream work: 00:17:45
Why you should listen to this episode, especially if it feels too “woo’” for you: 00:21:44
Visualizing Steve’s dream with ChatGPT: 00:36:31
Creating living records of meaning experiences with AI: 00:47:38
If you tend to think faster than you can type, lean into voice interfaces: 00:49:37
How Steve writes with AI: 00:52:13
How AI will disrupt traditional coaching and therapy: 00:54:03
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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.
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Timestamps for Spotify:
Introduction: 00:02:20
Innovate the business model, not just the product: 00:06:02
How startups can compete against the likes of OpenAI: 00:15:49
Mike’s take on DeepSeek: 00:19:34
Why the future has always belonged to the tinkerers: 00:21:44
How small teams today can make big money: 00:24:03
Find niches that incumbents can’t or don’t want to enter: 00:28:55
The qualities of the truly AI-native: 00:47:08
How AI changes the funding model for software companies: 00:53:46
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.
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Timestamps:
Introduction: 00:01:32
AI can simulate human personalities with remarkable precision: 00:04:30
How Michael simulated a Hacker News audience: 00:08:15
Push AI to be a good judge of your work: 00:15:04
Best practices to run evals: 00:19:00
How AI compresses years of learning into shorter feedback loops: 00:23:01
Why prompt engineering is becoming increasingly important: 00:27:01
Adopting a new technology is about risk appetite: 00:44:59
Michael demos Rally, his market research tool: 00:47:20
The AI tools Michael uses to ship new features: 00:55:03
Links to resources mentioned in the episode:
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 toolRoam Research, flipped real estate in Austin for a 6x return, and published abook with Random House. He’s now writing a book of science fiction and running a viralcourse 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.
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Timestamps:
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.
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To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
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Timestamps:
Introduction: 00:01:33
How to spot trends early: 00:03:18
Why you should be your own customer: 00:07:34
How to create an ecosystem of talent and ambition: 00:14:55
Why Guillermo doesn't identify as a coder: 00:17:29
AI is gearing us toward an allocation economy: 00:20:50
How Vercel’s copilot compares with other coding agents: 00:28:34
Guillermo’s advice on having better taste: 00:40:35
The future of AI agents is specialized: 00:42:46
How AI startups can compete with big tech: 00:47:50
Links to resources mentioned in the episode:
Guillermo Rauch: @rauchg
Vercel: https://vercel.com/
Our 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."
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.
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
We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways:
Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial
In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility
In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error
How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data
Why now may be finally be the era where quantified self tools become valuable
…and more, including everything in his new book Superagency, out this week.
It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI.
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Timestamps:
Introduction: 00:01:29
Patterns in how we’ve historically adopted technology: 00:02:50
Why humans have typically been fearful of new technologies: 00:07:02
How Reid developed his own sense of agency: 00:13:25
The way Reid thinks about making investment decisions: 00:20:08
AI as a “techno-humanist” compass: 00:29:40
How to prepare yourself for the way AI will change knowledge work: 00:35:30
Why equitable access to AI is important: 00:41:39
Reid’s take on why private commons will be beneficial for society: 00:45:15
How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23
The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14
Reid’s new book, Superagency: 01:03:29
Links to resources mentioned in the episode:
Reid Hoffman: @reidhoffman
Superagency, Reid’s newest book:
Nabeel Hyatt is looking for the “Japanese toilets” of AI—products that delight users in unexpected ways.
As a partner at Spark Capital, that investment philosophy has paid off. Despite making only 1-2 investments a year, he’s picked some of the biggest winners in AI so far: Descript, Cruise, and Granola.
We spent an hour unpacking:
How much “leash” top products give to AI agents—and why that matters
How he spots remarkable AI products
Why “sensitivity” is one of the most important traits of top founders
The huge opportunities for AI products to help users explore new “possibility spaces”
How Nabeel is actually using AI tools like ChatGPT, Claude, and AI code editor Windsurf in his life
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: 00:01:32
Why Nabeel doesn’t invest in more than two companies per year: 00:01:50
Why the words you use to describe your business matter: 00:06:49
What a product with soul looks like: 00:13:45
Patterns in the remarkable founders Nabeel has invested in: 00:16:48
How Nabeel evaluates popular coding agents: 00:24:12
AI has broadened the horizons of what Nabeel can do: 00:32:29
How funding models are changing as AI makes it cheaper to build software: 00:36:28
Nabeel’s framework for when to trust an LLM: 00:45:43
Guide AI to provide context (and not just quick answers): 00:55:39
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
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.
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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
Timestamps:
Introduction: 00:01:56
How the maker of Cora describes the product: 00:02:33
Our first mistake while building Cora: 00:06:31
The story of how Kieran shipped the first MVP overnight: 00:09:37
Why Dan believes software is becoming content: 00:13:44
Products with a point of view will win: 00:16:40
How Kieran approaches building a new product: 00:19:16
Best practices while using Cursor: 00:31:55
Hacking together a copy editor in Cursor live on the show: 00:41:05
The future of Cora, and the hardest challenge we face today: 00:53:58
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. Her startup re:collect built an app to augment human intelligence with AI and was acqui-hired by SmartNews earlier this year. She now heads up AI product there. We get into the contours of this new paradigm in science: - Whether research papers are still the best format to “release” science in - The increasing importance of data in scientific discovery - Why AI is making N-of-1 studies imperative—when they’re normally seen as unscientific - The case for big tech to open-source their data for scientific research - The power of unbundling data and interpretations, in science and media 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! 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:
Introduction: (00:00:59)
Everything Alice learned about growing an AI startup: (00:04:50)
Alice’s thesis about how AI can augment human intelligence: (00:09:08)
Whether chat is the best way for humans to interface with AI: (00:12:47)
Ideas to build an AI model that predicts OCD symptoms: (00:23:55)
Why Alice thinks LLMs aren’t the right models to do predictive work: (00:37:12)
How AI is broadening the horizons of science: (00:38:39)
The new format in which science will be released: (00:40:14)
Why AI makes N-of-1 studies more relevant: (00:45:39)
The power of separating data from interpretations: (00:50:42)
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 notepad for meetings. We use it all the time at Every—Granola listens in on a meeting and, when it ends, generates notes and a shareable transcript for anyone who missed it.
Granola is one of my favorite consumer AI products because it’s equal parts delightful and useful. So my question for Chris was:
How do you do it? How do you make an excellent product in AI?
We spent an hour talking about:
How Chris uses intuition while making product decisions
The importance of building products with “soul”
How to develop your product thinking muscles
When Chris trusts his gut over listening to user feedback
How fewer users gives startups a leg up over big tech
Why Chris is bullish on founders building specialized AI tools for professionals
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!
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Timestamps for Spotify:
Links to resources mentioned in the episode:
Chris Pedregal: @cjpedregal
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, Geoffrey Litt and I spent an hour coaxing ChatGPT and Replit to build an app live on my podcast.12 months later, Steve Krouse and I built the same app with one prompt in less than a 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. We used Townie, Val Town’s AI assistant, to build an app to keep track of time on the podcast, take notes, and generate questions for the guest.
Townie generated the app even before Steve could finish describing it on the show. As we demo Townie, we get into:
Why Steve believes programming can rewire the way you think
The rise of the non-technical AI developer and what that means for the future of coding
How Townie works under the hood, including the details of the system prompt
How Steve is evolving ValTown’s strategy as AI progress continues to unfold
The power of small, dense engineering teams
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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Timestamps:
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 AI products
Acquired tens of thousands of users
Released a new incubation in private alpha
The weird thing is: We’re a media company with < 10 full-time employees, and we’re mostly bootstrapped.
That’s not how things are supposed to work in startups.
When we started our product incubation arm six months ago, many people told us 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 our strategy: We launched a brand-new version of our AI product Spiral (https://spiral.computer) with Danny Aziz as GM—who left a $200K salary to join us.
The question is: Why? Why did he join us, and why is the model working when it “shouldn’t” be?
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, what makes our flywheel turn, where each of us sees ourselves one year from now, and what happens when you mix media, software, and AI under one roof.
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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To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: 00:01:08
All about Spiral, the tool we recently launched: 00:02:15
Why Danny left a $200,000 salary to work at a bootstrapped media company: 00:04:06
How we do a lot of things well at Every: 00:10:33
What makes Every’s flywheel turn: 00:14:44
The kind of people who fit right in at Every: 00:17:11
How Every is differentiated from a standard VC-backed startup: 00:23:25
How Danny found his way into the world of startups: 00:36:11
The tech industry’s affinity for potential over experience: 00:46:43
Where each of us sees ourselves in the next one year: 00:52:38
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 that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast.
Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building.
A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round.
I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into:
Why staying small and specialized is a bigger advantage than you think
The power of building with your early adopters
Why lean startups are better positioned than frontier AI companies to create radical solutions
When a growing startup should think about raising venture capital
The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents
We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements.
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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Timestamps:
Prompt engineering matters more than ever. But it’s evolving into something totally new:
A way for non-technical domain experts to solve complex problems with AI.
I spent an hour talking to prompt wizard Jared Zoneraich, cofounder and CEO of PromptLayer, about why the death of prompt engineering is greatly exaggerated. And why the future of prompting is equipping non-technical experts with the tools to manage, deploy, and evaluate prompts quickly.
We get into:
His theory around why the “irreducible” nature of problems will keep prompt engineering relevant
Prompt engineering best practices around prompts, evals, and datasets
Why it’s important to align your prompts with the language the model speaks
How to run evals when you don’t have ground truth
Why he believes that the companies who have domain experts to scope out the right problems will win in the age of gen AI
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!
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:
Introduction: 00:01:08
Jared’s hot AGI take: 00:09:54
An inside look at how PromptLayer works: 00:11:49
How AI startups can build defensibility by working with domain experts: 00:15:44
Everything Jared has learned about prompt engineering: 00:25:39
Best practices for evals: 00:29:46
Jared’s take on o-1: 00:32:42
How AI is enabling custom software just for you: 00:39:07
The gnarliest prompt Jared has ever run into: 00:42:02
Who the next generation of non-technical prompt engineers are: 00:46:39
Links to resources mentioned in the episode:
This episode is sponsored by Notion. I’ve been using Notion to manage my professional and personal life for almost 10 years. As a company, they pay attention to the craft and ideas underlying the software they build, and that comes through in the experience of using Notion every day. If you’re a startup, get up to 6 months of Notion Plus with unlimited AI—worth up to $6,000—for free by going to https://ntn.so/every, selecting Every in the drop-down partner list, and using the code EveryXNotion.
Notion cofounder Simon Last told me everything he’s learned from integrating AI 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 his first-ever podcast interview, we get into:
This is a must-watch for anyone interested in building reliable AI products at scale.
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To hear more from Dan Shipper:
Timestamps:
Links to resources mentioned in the episode:
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:
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.
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To hear more from Dan Shipper:
Timestamps:
Links to resources mentioned in the episode:
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 (each one named after a different animal), a coding agent called Ditto, a framework for building autonomous agents, and, most recently, BabyAGI 2o, a self-building autonomous agent (that follows OpenAI’s unfortunate naming convention).
Even more incredible, Yohei isn’t a professional developer. His day job is as the general partner of Untapped Capital (@UntappedVC).
I sat down with Yohei to talk about:
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.
This is a must-watch for anyone curious about autonomous agents, building cool AI tools on the internet, and the future of AI tooling.
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:
Timestamps:
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
This episode is sponsored by Reflect. It’s the ultra-fast note-taking app that’s about to change the way you take notes. To boost your productivity with advanced features like custom prompts and voice transcripts, give Reflect a try by clicking on this link: https://reflect.app/?utm_source=every&utm_medium=sponsorship&utm_campaign=september2024
Simon Eskildsen is a learning machine.
I first interviewed him in 2020 about how he leveled up from an intern at Shopify to 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. LLMs have made it possible to contextualize information like never before—and in this episode, I sat down with Simon to 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 easy and affordable to run at scale.
We get into:
How Simon’s learning rituals have evolved over time, as the cofounder of a growing startup and a new parent
The ways Simon has integrated ChatGPT, Claude, and Notion AI to do everything from writing legal documents to maintaining his rural cabin in Quebec
The custom AI commands in productivity tool Raycast that Simon uses to learn new words and cook creative dishes
Simon’s take on how language models will reshape the future of learning, especially skills like language acquisition, for the next generation
As we talk, we screenshare through his Anki setup, including the flashcard template he finds most useful, and try out his custom AI commands in Raycast to understand the meaning of two of my favorite obscure words, “lambent” and “eigengrau.”
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 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:
Introduction: 00:01:06
How entrepreneurship and parenthood changed Simon’s learning rituals: 00:02:51
How Simon accelerates his learning by using LLMs to find associations: 00:12:59
Simon’s Anki setup and the flashcard template he swears by: 00:18:24
The custom AI commands that Simon uses most often: 00:26:02
How Simon uses LLMs for DIY home projects: 00:37:45
Leveraging LLMs as intuitive translators: 00:40:48
Simon’s take on how AI is reshaping the future of learning: 00:51:38
How to use Notion AI to write: 00:59:10
The AI tools that Simon uses to write, read, and code: 01:08:53
Links to resources mentioned in the episode:
How do two professional writers use AI to do the best work of their lives?
In today’s show, Every’s lead writer Evan Armstrong and I conduct an expert workshop on how we use ChatGPT, Claude, AI-powered word processor Lex, and the prompt builder that Every launched, Spiral, to feed our obsession with words—and help us write for more than 78,000 readers every day.
We talk about how AI helps us:
Understand our taste—understanding what good is
Pick a topic—knowing what to write about
Craft our words—everything from sketching out an outline to writing and editing
Build an audience—learn how to reach people
We get into:
How I used Claude and ChatGPT to help me identify the kind of writing I like—and why that’s critically important for mastery
How Evan uses ChatGPT to explore his taste across books, movies, and paintings
The way I use Claude Projects to help me turn a vast amount of research into a clear thesis statement for major projects
The routine Evan swears by to publish two pieces every week
How Evan and I use Lex to push through writer’s block and catch common writing mistakes like passive voice
My workflow inside Claude to craft emphatic metaphors
How we use Spiral to write viral tweets
Evan is the lead writer at Every who writes the column Napkin Math twice a week. He’s smart, funny, curious—and has the rare combination of business acumen, way with words, and crazy required to be a professional writer.
This is a must-watch for aspiring writers, or anyone whose job involves writing more than six sentences in a row.
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:
Introduction: 00:01:04
How to develop good taste: 00:04:28
Dan uses Claude to articulate his taste in books: 00:13:34
How to use LLMs to explore art cross different mediums: 00:21:06
The way Evan chooses his next essay topic: 00:33:45
Go from research notes to clear thesis in Claude Projects: 00:38:20
How Evan uses AI to master new topics quickly: 00:46:51
Evan leverages AI to power through writer’s block: 00:59:21
How to use Claude to find good metaphors: 01:04:28
The role of AI in building an audience: 01:11:44
Links to resources mentioned in the episode:
The Browser Company isn’t just building a browser, they’re building a formidable brand—and they’re doing it with AI.
I sat down with Nashilu Mouen-Makoua, the head of storytelling at The Browser Company, to talk about how they tell stories that capture the cultural zeitgeist and connect authentically with their users—and how she integrates AI into her process for both.
We get into:
We also screen share through Nash’s conversation with ChatGPT as she conducted research for an exercise in how to position Arc, and use the LLM to simulate a typical Arc user and interview them 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 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:
Introduction: 00:00:47
Nash’s philosophy around storytelling: 00:04:03
The Browser Company’s strategy to come up with creative ideas: 00:09:07
Why Nash thinks building brands people can relate to is important: 00:15:00
How to avoid the tired narrative around AI products: 00:18:47
The ways Nash has integrated ChatGPT into her workflow: 00:22:21
Why understanding social context is important to position your product: 00:33:35
How Nash uses ChatGPT to get a gut check on her writing: 00:41:10
What Nash thinks is the gestalt of the current age: 00:49:50
Nash and Dan use ChatGPT to simulate and interview a typical Arc user: 00:52:01
Links to resources mentioned in the episode:
The journey to a calm, profitable business in the AI age
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:
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 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:
Introduction: 00:00:56
Dan’s childhood dream—to build a Microsoft competitor: 00:03:36
The first app Dan built in middle school: 00:07:07
The story of Dan’s first company that he sold in college: 00:18:52
How Every came to be: 00:33:56
The start of Brandon’s journey as a builder: 00:49:15
Brandon’s first software app—and why you should launch first, and iterate later: 00:57:05
Everything Brandon learned from running a B2B business for seven years: 01:08:49
What brought Brandon to Every—and the email he sent Dan before joining: 01:18:00
Every’s master plan to be a successful creator-run business: 01:29:15
Live demo of Spiral, the app that automates 80 percent of repetitive creative work: 01:38:11
Brandon and Dan’s take on how AI startups can find a valuable niche: 01:44:00
Live demo of Sparkle, the app that organizes your files for you: 01:50:52
Links to resources mentioned in the episode:
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:
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 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:
Timestamps:
Introduction: 00:00:34
Choose physical books over e-readers to boost your memory: 00:01:36
Alex’s take on how long books will stay relevant: 00:02:54
Prompt ChatGPT to find your next read: 00:07:40
Articulating Dan’s taste in books with AI: 00:13:50
Use AI to find books tailored to solve your problems: 00:15:46
How to use AI as a personal study buddy: 00:33:32
Prompt LLMs to turn insights from books into actionable strategies: 00:41:19
What Alex’s rule around buying a new book is: 01:02:10
Alex’s advice for anyone who feels like they don’t have time to read: 01:16:36
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:
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:
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:
Timestamps:
Links to resources mentioned in the episode:
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. 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 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 - Dwarkesh’s podcast and newsletter - Dwarkesh’s interview with researcher Andy Matuschak on spaced repetition - The book about technology and society that both Dan and Dwarkesh are reading: Medieval Technology and Social Change - Dan’s interview with Reid Hoffman - The book by Will Durant that inspires Dwarkesh: Fallen Leaves - One of the most interesting books Dwarkesh has read: The Great Divide - Upcoming guests on Dwarkesh’s podcast: David Reich and Daniel Yergin
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:
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:
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, Blackforager, David Zinn, David Bird, WatchMaggiePaint 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, Globe Explorer, websim.ai, Granola, Suno
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! 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
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:
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:
Links to resources mentioned in the episode:
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:
Links to resources mentioned in the episode:
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.
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 JohnsonNotebookLM 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 NowWhere Good Ideas Come FromThe Ghost MapEmergenceThe 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
The NYT’s Kevin Roose has 18 new friends—none of whom are human.
His new friends are AI personas that he made with Noma, Kindroid, and other AI companion apps. There’s fitness guru Jared, therapist Peter, trial lawyer Anna, and over a dozen more.
Kevin talked to them every day for a month, sharing his feelings, asking for parenting advice, and even using them for “fit” checks.
This isn’t the first time Kevin has had an…unusual interaction with an AI persona. A year ago, he was the target of Bing’s chatbot Sydney’s unhinged romantic affections.
Kevin has gone deeper into the world of AI companions than anyone I know. He is a tech columnist at the New York Times, cohost of the Hard Fork podcast, and the author of three books. In this episode, I sat down with Kevin to learn more about his interactions with AI. We dive into:
This is a must-watch for anyone curious about how AI is changing the way we form relationships.
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Links to resources mentioned in the episode:
Nick Dobos, maker of the #1 programming GPT, on prompt-gramming with AI
Nick Dobos showed me how to ship a website with two words and a single click.
He’s the creator of Grimoire, the #1 custom GPT for programming that has been used for over 1 million chats.
All he gave Grimoire was two words: “coffee website.” Just a minute later, Grimoire built the website and pushed it live to the internet. It was wild.
Grimoire can do a lot more than create websites—it’s a coding assistant with 75+ built-in hotkey commands and sample projects, a guide to learning how to code from scratch, and a tool for programmers to find answers to their questions in real-time.
Before he created Grimoire, Nick was an iOS developer at Twitter. When ChatGPT came out, Nick started experimenting with it—and ended up building Grimoire. Today, he’s at the leading edge of experimenting and building with AI.
I sat down with Nick to explore how people are using Grimoire and what it tells us about the age of programming by prompting. We dive into:
How AI is massively lowering the barriers to code
Why it’s important to solve the “blank canvas problem” that people experience while creating with AI
How AI tools can streamline your creative process
Why Grimoire has an edge over ordinary ChatGPT
The best ways to use Grimoire to code smarter and faster
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!
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Subscribe to Every: https://every.to/subscribe
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Timestamps:
Introduction: 00:00:31
How Nick built Grimoire, the top-ranked GPT for programming: 00:05:20
Ship a website with two words and a single click: 00:10:25
How Grimoire is solving the “blank canvas problem” in AI creation: 00:14:57
The coding curriculum that can take you from zero to full programmer: 00:16:30
Why Grimoire has an edge over ordinary ChatGPT: 00:23:29
Nick’s thoughts on building the system prompt for a GPT: 00:34:10
The utility of AI as a new layer on top of existing apps: 00:40:04
How Nick uses a custom GPT to unpack his emotions: 00:43:11
How to use AI to break down tasks—from programming to daily to-do lists: 00:50:35
Links to resources mentioned in the episode:
Nick Dobos: @NickADobos
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/
This AI can read emotions better than you can.
It was created by Alan Cowen, the cofounder and CEO of Hume, an AI research lab developing models that can read your face and your voice with uncanny accuracy. Before starting Hume, Alan helped set up Google’s research into affective computing and has a Ph.D. in computational psychology from Berkely.
Hume’s ultimate goal is to build AI models that can optimize for human well-being, and in this episode I sat down with Alan to understand how that might be possible.
We get into:
What an emotion actually is
Why traditional psychological theories of emotion are inadequate
How Hume is able to model human emotions
How Hume's API enables developers to build empathetic voice interfaces
Applications of the model in customer service, gaming, and therapy
Why Hume is designed to optimize for human well-being instead of engagement
The ethical concerns around creating an AI that can interpret human emotions
The future of psychology as a science
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!
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To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
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Timestamps:
Dan tells Hume’s empathetic AI model a secret: 00:00:00
Introduction: 00:01:13
What traditional psychology tells us about emotions: 00:10:17
Alan’s radical approach to studying human emotion: 00:13:46
Methods that Hume’s AI model uses to understand emotion: 00:16:46
How the model accounts for individual differences: 00:21:08
Dan’s pet theory on why it’s been hard to make progress in psychology: 00:27:19
The ways in which Alan thinks Hume can be used: 00:38:12
How Alan is thinking about the API v. consumer product question: 00:41:22
Ethical concerns around developing AI that can interpret human emotion: 00:44:42
Links to resources mentioned in the episode:
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 a founder, investor, podcaster, and author. But before he did any of these things, he studied philosophy—and it changed the way he thinks.
Studying philosophy trains you to think deeply about truth, human nature, and the meaning of life. It helps you see the big picture and reason through complex problems—invaluable skills for founders grappling with existential questions about their business.
I usually bring guests onto my podcast to discuss the actionable ways in which people have incorporated ChatGPT into their lives. But this episode is different.
I sat down with Reid to tackle a deeper question: How is AI changing what it means to be human?
It was honestly one of the most meaningful shows I’ve recorded yet. We dive into:
Reid also shares actionable uses of ChatGPT for people who want to think more clearly, like:
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!
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To hear more from Dan Shipper:
Links to resources mentioned in the episode:
Reid Hoffman: @reidhoffman
The podcasts that Reid hosts: Possible (possible.fm) and Masters of Scale (https://mastersofscale.com/)
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
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:
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!
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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; [email protected] 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.
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.
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To learn more about the topics in this episode:
Timestamps:
Links to resources mentioned in the episode:
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:
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
I made the greatest trade of my life with Jesse Beyroutey in 2019. We bought Nvidia shares when they were trading at $33. They’re worth nearly $800 today.
I sat down with Jesse to top that trade in 90 minutes using Gemini Pro 1.5’s incredible 1 million token context window—and make a $1,000 trade live on the show.
Jesse is a managing partner at IA Ventures, a $600 million venture fund with seed investments in companies like Wise and Digital Ocean. He’s also a very close friend and one of the smartest people I know.
We unpack our investment thesis for our Nvidia trade and leverage the power of Gemini Pro 1.5 and ChatGPT to orchestrate what we hope will be the best trade of our lives. We put our money where our mouth is and make a $1,000 trade while the cameras are still rolling.
There’s a plot twist at the end of this episode—so stick around to see the epilogue Jesse and I recorded just days after we made our investment.
We get into:
This is not investment advice, but it’s a must-watch for anyone who wants to leverage the power of AI to make smarter financial decisions.
If you found this episode interesting, please like, subscribe, comment, and share!
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You can break into Hollywood with a movie you made alone in your room.
Dave Clark can show you exactly how in < 60 minutes. He’s a film director with a body of work that includes both feature films and commercials for brands like Google. His latest achievement is a stunning sci-fi short that got Hollywood’s attention, one that Dave made exclusively using AI.
Dave and I make a movie live on this episode, iterating from rough ideas to a real motion picture in < 1 hour. It’s a noir short featuring Nicolas Cage using a haunted roulette ball to resurrect his dead movie career that you don’t want to miss.
We dive deep into the world of AI tools for image and video generation, specifically exploring their implications on lowering the barriers to enter the traditional movie industry. This episode is also packed with Dave’s wisdom on how to use these tools to create mind-bending movies.
We get into:
This episode is a must-watch for creative people interested in bringing their stories to life, movie buffs, and anyone curious about the future of creativity.
If you found this episode interesting, please like, subscribe, comment, and share!
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Timestamps:
Links to resources mentioned in the episode:
Borrowing Time, Dave’s viral sci-fi short
Forbes article that mentions Borrowing Time
Are you a curious person with a lot of ideas and little time?
Anne-Laure Le Cunff can show you how to do it all. Anne-Laure is the founder of one of my favorite internet communities for curious minds, Ness Labs, a prolific writer, and a neuroscience PhD candidate. She’s also writing a book, Liminal Minds, that’ll be out later this year.
And she said that the reason she can run a business, write a book, and do a PhD all at the same time is ChatGPT.
Anne-Laure is one of the busiest people I know, and in this episode we dive into how she uses ChatGPT to get everything done.
We get into:
This is a must-watch for curious, creative people who want to get more done.
If you found this episode interesting, please like, subscribe, comment, and share!
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Timestamps:
Links to resources mentioned in the episode:
Anne-Laure following ChatGPT’s recipe to make an obscure Algerian cheese
Steph Smith is the host of the a16z podcast and a prolific online creator.
Steph sees the internet through a high-definition lens that gives her a deep understanding of what people want.
She can isolate a clear signal from the noise, which she uses to build wonderfully creative, useful things.
In this episode, I dive deep with Steph on how she uses the internet and AI to unearth emerging trends and validate business ideas.
I pitch Steph two potential companies on the show, and we use an arsenal of tools and strategies to vet them live.
We get into:
This is a must-watch for anyone who spends time online and wants to discover the next big idea hiding on the internet.
If you found this episode interesting, please like, subscribe, comment, and share!
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You can build a video game without writing a single line of code.
Logan Kilpatrick and I use ChatGPT and GPT Builder to make our own video game in less than 60 minutes—live on this show.
Logan is OpenAI’s first dev relations and advocacy hire and is committed to empowering more people to build using AI.
It’s only fitting that we explore the depths of our own creativity by making a video game with GPT Builder—we start with a rough idea and iterate all the way up to a functional video game in < 1 hour.
This episode is full of Logan’s actionable insights on leveraging GPT Builder and ChatGPT to build any custom GPT that you’d like.
We get into:
This is a must-watch for anyone who wants to bring their creative ideas to life.
If you found this episode interesting, please like, subscribe, comment, and share!
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Timestamps
Links to resources mentioned in the episode:
Our video game, Allocator: https://chat.openai.com/g/g-oooxUbOkj-allocator
Dr. Gena Gorlin is a clinical psychologist at UT Austin whose goal is to raise the ceiling on human potential.
I sat down with her to discuss how @ChatGPTapp has become a key tool in her quest for radical self-betterment.
In this episode, she feeds ChatGPT a list of her old journal entries, and it conducts the most thorough and insightful annual review and goal-setting session you’ve ever seen:
Ultimately, it acts as both a mirror and mentor for her—one that’s always on, responds instantly, and can take on any personality or psychological modality that she needs.
It’s a mind-bending example of how ChatGPT can unlock your potential.
If you found this episode interesting, please like, subscribe, comment, and share!
Want more?
Dan is running a course with Dr. Gorlin called Maximize Your Mind With ChatGPT. It’s a four-week cohort-based course marrying the cutting edge of AI with the best of what psychology knows about how to reach your potential.
Learn more: https://maxyourmind.xyz
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Sign up for Dan’s newsletter 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:
Timestamps:
Links to resources mentioned in the episode:
In Defense of Radical Self-Betterment
Tyler Cowen is an economist who has been thinking about the impact of technology on life, work, and the economy for the past decade.
He is a prolific writer behind the leading economic blog Marginal Revolution, a professor of economics at George Mason University, and the author of 17 books.
In this episode, I dive deep with him on how ChatGPT will change the economy, and how he uses it in his own life. We get into:
This is a must-watch for anyone who wants insights on adapting to the future of work.
If you found this episode interesting, please like, subscribe, comment, and share. And sign up for Every to get our ultimate guide to prompting ChatGPT.
To hear more from Dan Shipper:
Subscribe to Every
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Register for his course, Maximize Your Mind With ChatGPT
Timestamps:
David Perell is one of the best known internet writers of his generation.
He’s amassed almost a half million followers on X, hosts the popular podcast How I Write, and founded Write of Passage, which has taught thousands of students how to be digital writers.
We go deep on using ChatGPT to:
If you found this episode interesting, please like, subscribe, comment, and share.
Timestamps:
This show might be a first in the history of podcasts:
Researcher Geoffrey Litt and I built an app together using ChatGPTapp and Replit in under 60 minutes—while we talked.
We wanted to show how AI and ChatGPT change who gets to build software and how they usher in a world where everyone can modify and remix the apps they use every day.
So we did it live, and ChatGPT delivered a working prototype at the end of the episode.
It was a tiny glimpse of the future—and it pushes the boundaries of what a show can be. It honestly left me speechless and it'll change the way you think about software. If it does, make sure to subscribe, share, and leave us a review!
I spent an hour and a half with Nathan Labenz, who went deep with me on using ChatGPT to:
Save days of time on programming projects
Boost his creativity
Provide access to limitless expertise
We covered both the practical and philosophical, including:
Using AI in copilot versus delegation mode
How ChatGPT relieves the hidden drudgery of creative work
Prompting techniques to maximize your thought partnership with ChatGPT
How he believes AI will do for cognitive labor what the Industrial Revolution did for manual labor
Nathan is an expert and one of the most interesting thinkers in AI today. I’m excited to share his thinking with you—like, comment, subscribe, and share if you liked this episode.
TL;DR: Today we’re releasing a new episode of our podcast How Do You Use ChatGPT? I go in-depth with Notion research engineer Linus Lee on how he uses ChatGPT and Notion AI to maximize creative control. Watch on X, YouTube, or Spotify.
You might think that being an AI researcher would mostly involve solving complicated programming problems and thinking through mathematical equations. Instead, a big part of the job is rewriting parts of your prompts in ALL CAPS in order to make sure the AI model you’re working with follows your directions. “All caps works!” Linus Lee told me in this interview. “If you look at OpenAI's system prompts for a lot of their tools, all caps works.”
Linus is a research engineer at Notion who works on its AI team, prototyping new experiences, like a Q&A chatbot. He is a deep thinker who is obsessed with building AI that enables human creativity and agency. He came on the show to talk about how AI might augment our thinking, how he thinks about prompting to get the best results, and how he uses ChatGPT and Notion AI in his work and life.
I first interviewed him a year ago, when he showed off dozens of AI prototypes he’d been building to try to understand the future of this technology. Our latest interview is a mixture of theory and practice. Linus talks about how the tools we use shape the work we can create and what the future of AI-driven interfaces might be. We watch him demo personal tools he’s built, like an AI chatbot that he communicates with over iMessage. And we peek over his shoulder to see his chats with ChatGPT to understand how he talks to it to get the best results.
Here’s a taste of what we talk about. Read on for more analysis from me at the bottom.
What do you use ChatGPT for? Have you found any interesting or surprising use cases? We want to hear from you—and we might even interview you. Reply here to talk to me!
Miss an episode? Catch up on my recent conversations with writer Nat Eliason and Gumroad CEO Sahil Lavingia and learn how they use ChatGPT.
How Nat Eliason uses ChatGPT to write books: Nat Eliason is a shape-shifter. He’s a writer with a book deal from Random House, a crypto trader, a Roam Research aficionado, a marketer, a book podcaster, a parent, and a seed oil iconoclast. He's amassed thousands of newsletter subscribers, 70,000 followers on X, and 110,000 on TikTok. His secret weapon for all of his exploring? ChatGPT. Nat took me through why he uses it every day for his work and his life. In this interview we talk about using ChatGPT for: Identifying his taste in writing. He uses ChatGPT to help him identify the kind of writing he likes, so that he can produce more of it. Finding new books to read for inspiration. ChatGPT helps him find writers and books that he never would've encountered through Googling or in his daily life. Generating story outlines and character descriptions. He uses ChatGPT to help him outline the sci-fi novel he's writing and learn how to create vivid descriptions. Settling bar bets. Air in the atmosphere contains carbon—which can technically be converted into diamond. So, how much air would be required to make a diamond? It's the kind of thing you might argue about over drinks with a friend—and exactly the kind of question ChatGPT is built to answer. Reading the news. Nat doesn't read the news. But every once in a while he wants to know what's going on about a particular topic. ChatGPT is the perfect news summarizer. Generating recipes. Nat is a frequent chef. ChatGPT is his recipe companion: surfacing ideas, and easily modifying them based on what he has at hand and his family's dietary preferences.
About the show
I believe that ChatGPT is the most important creative tool of the decade. I think it can help us write better, create art, efficiently ship products, build great businesses, make smart decisions, and even learn something about ourselves,.
But it’s still so early. Most of us don’t even really know how to use ChatGPT. We have a feeling that it’s powerful, interesting, and important—but we haven’t figured out how to incorporate it into our lives.
There are a few people, though, who are living in the future. They have the time and curiousity to use ChatGPT in their everyday lives, taking the opportunity to make the technology work for them. In this way, they light the way for everyone else.
That’s what this interview series, How I Use ChatGPT, is all about. We go in-depth with the most interesting people in the world to learn concrete ways they are already using ChatGPT. It won’t be theoretical—or limited to audio: we’ll screen-share and see their actual prompts and responses, so you can see how ChatGPT helps them perform better at work and improve their lives—one conversation at a time.
About this episode
My first guest is Sahil Lavingia, the co-founder and CEO of Gumroad, one of the largest platforms for creators to sell their work online. He shared how he uses ChatGPT for:
Buying a building. He wants to buy a New York City hangout for Gumroad employees and customers, so he asked ChatGPT to research the history of real estate in NYC, suggest which neighborhoods might be best to target, generate questions for brokers, and even detail what the design of a particular property might look like.
Writing tweets. Sahil is a prolific Twitter/X user. He often uses ChatGPT to help him flesh out an idea. He says, “I [start] with a tweet, which is like a thesis, and then I just say, ‘Add three to four paragraphs to make the point compelling—also suggest more examples.’” We explore his precise process for using ChatGPT to help him brainstorm short tweets and longer essays in this episode.
Pressure-testing ideas. For Sahil, ChatGPT is like upgrading his peripheral vision. It lets him see around the corners, ask better questions of himself and other people, and avoid poor decisions. He told me, “I think a lot of people sort of delude themselves into thinking they have [good ideas]… I think that one of the most useful things about [ChatGPT] is it focuses your research on what actually matters.” It’s the ultimate tool to help him think better.
Also in this episode: how ChatGPT could have helped Sahil save $70 million, how he thinks it will improve the most-talented creatives, and why he thinks—in the age of AI—people have no excuse for not knowing the answer to something anymore.
Timestamps:
En liten tjänst av I'm With Friends. Finns även på engelska.