AI and I

Dan Shipper
AI and I

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.

  1. 3 DAYS AGO

    How AI Will Change Science Forever - Ep. 43 with Alice Albrecht

    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

    1 hr

  2. DEC 12

    The Secret to Building Sticky AI Products - Ep. 42 with Chris Pedregal

    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!  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 for Spotify: Introduction (00:00:48) How Chris made early product decisions at Granola (00:09:14) Chris’s philosophy around product development (00:13:36) When to follow your intuition v. listen to your users (00:19:24) How to build a product with “soul” (00:20:40) Chris’s advice on becoming a better product thinker (00:25:12) The role travel plays in shaping Chris’s intuition (00:31:17) Why having fewer users is an advantage for AI startups (00:45:52) Why Chris is bullish on startups building specialized AI tools (00:52:09) Where Chris sees Granola in the next year (00:56:52) Links to resources mentioned in the episode:   Chris Pedregal: @cjpedregal Granola: http://Granola.ai, @meetgranola  The piece Chris wrote for Every about building useful AI products: https://every.to/thesis/how-to-build-a-truly-useful-ai-product

    1h 1m

  3. DEC 4

    Do 60-Minute Coding Tasks in 60 Seconds—With AI - Ep. 41 with Steve Krouse

    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!  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 (00:00:55) How programming changes the way you think (00:03:24) Building an app in less than 60 seconds (00:11:22) How Val Town’s AI assistant works (00:17:19) Steve’s contrarian take on the non-technical AI programmer (00:23:05) The nuances of building software that isn’t deterministic (00:33:38) How to design systems that can capitalize on the next leap in AI (00:39:05) What gives Val Town a competitive edge in a crowded market (00:40:47) The power of small, dense engineering teams (00:47:34) How Steve is positioning Val Town in a strategic niche (00:52:26) 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

    1h 1m

  4. NOV 27

    How We Incubate and Launch New Products With AI - Ep. 40 with Danny Aziz, Brandon Gell

    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!  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 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

    1h 1m

  5. NOV 20

    His GPT Wrapper Has Half a Million Users—And Keeps Growing - Ep. 39 with Vicente Silveira

    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!  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: (00:00:35) AI PDF’s story begins with an email to OpenAI’s Greg Brockman: (00:02:58) Why users choose AI PDF over ChatGPT: (00:05:41) How to compete—and thrive—as a GPT wrapper: (00:06:58) Why building with early adopters is key: (00:20:49) Being small and specialized is your biggest advantage: (00:27:53) When should AI startups raise capital: (00:31:47) The emerging role of humans who will manage AI agents: (00:34:53) Why AI is different from other tech revolutions: (00:45:25) A live demo of an agent integrated into AI PDF: (00:54:01)

    1h 3m

  6. NOV 13

    How to Win With Prompt Engineering - Ep. 38 with Jared Zoneraich

    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:  Jared Zoneraich: @imjaredz PromptLayer: @promptlayer, https://www.promptlayer.com/ A couple of Steven Wolfram’s articles on ChatGPT: What Is ChatGPT Doing … and Why Does It Work?, ChatGPT Gets Its “Wolfram Superpowers”!

    1h 2m

  7. NOV 8

    How Notion Cofounder Simon Last Builds AI for Millions of Users - Ep. 37 with Simon Last

    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: What he would build if he started Notion from scratch today with AI How to get high quality and reliable results from AI at scale The future of human creativity in a world with machines that think   This is a must-watch for anyone interested in building reliable AI products at scale. If you found this episode interesting, please like, subscribe, comment, and share!  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: 00:01:57 How AI changes the way we build the foundational elements of software: 00:02:28 Simon’s take on the impact of AI on data structures: 00:10:07 The way Simon would rebuild Notion with AI: 00:13:05 How to design good interfaces for LLMs: 00:23:39 An inside look at how Notion ships reliable AI systems at scale: 00:28:22 The tools Simon uses to code: 00:35:41 Simon’s thoughts on scaling inference compute as a new paradigm: 00:38:16 How the growing capabilities of AI will redefine human roles: 00:49:10 Simon’s AGI timeline: 00:50:28 Links to resources mentioned in the episode: Simon Last: @simonlast Notion AI: https://www.notion.so/product/ai The AI code editor Simon uses: Cursor OpenAI’s definition of AGI that Simon ascribes to: https://openai.com/charter/

    56 min

  8. OCT 30

    How Union Square Ventures Built an AI Brain for Venture Capital - Ep. 36 with Matt Cynamon

    Union Square Ventures is building an AI operating system to support their investment team.  But it’s not what you think: It’s a constellation of AI tools that captures and synthesizes the firm's collective wisdom. It’s evolving every day, and Matt Cynamon is the mad scientist in charge Matt calls himself a “regular” at USV. In practice that means he’s responsible for running experiments with AI for the firm. As an inherently curious person with the professional obligation to tinker, he’s built a suite of tools for the firm, including:  The Librarian, a chatbot trained on around 15,000 articles from USV’s blog Portfolio Tracker, a GPT that analyzes the investments made by the firm Meeting Notes, a tool that makes it possible for team members to interact with meetings   I sat down with Matt to talk about how AI is enabling him to bring his ideas to life as a generalist, get demos of the tools listed above, and exchange notes on all the other projects he has in the works at USV. We edit actionable insights extracted by an AI from meetings at USV and prepare them to be posted on the firm’s X handle live on the show. We even try out an art project at USV’s office called The Dream Machine, which generates art from conversations. Here’s a link to the episode transcript.    This is a must-watch for anyone interested in riding the AI wave by learning how to ship useful products quickly. If you found this episode interesting, please like, subscribe, comment, and share!  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: (00:00:52) How Matt became in charge of everything AI at USV: (00:01:56) How AI empowers generalists to be creators: (00:06:22) The Librarian, a chatbot trained on everything USV has published: (00:10:41) Portfolio Tracker, an AI tool to track USV’s investments: (00:21:09) The AI projects that Matt has in the pipeline at USV: (00:27:21) Meeting Notes, USV’s AI note-taking tool: (00:34:33) Prompting AI to generate a post for USV’s X handle: (00:44:57) Why it’s important to diversify ownership over data: (01:00:20) The Dream Machine, AI that generates images from conversations: (01:03:20) Links to resources mentioned in the episode: Matt Cynamon: @mattcynamon Union Square Ventures: @usv, https://www.usv.com/  More about the AI tools at USV: https://www.usv.com/people/the-librarian/, https://www.usv.com/writing/2024/02/ai-aesthetics/  The X post generated live on the show: https://x.com/usv/status/1847354782941663523

    1h 9m
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4.8
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About

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.

Information

  • Creator
    Dan Shipper
  • Years Active
    2023 - 2024
  • Episodes
    44
  • Rating
    Clean
  • Copyright
    © Dan Shipper
  • Show Website
    AI and I

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