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Wabi Is Building Personalized Software for Everyone In a recent report from OpenAI, almost 80% of ChatGPT usage was for practical guidance, seeking information, and writing. AI usage shouldn’t be limited to search and writing. At wabi, Eugenia Kuyda is building a new kind of AI interface so that... anyone can create and share personalized AI-generated software. Eugenia joined a16z’s Anish Acharya, Justine Moore, and Erik Torenberg to explain why we’re still in the MS-DOS era of AI, how mini apps could change the way we interact with AI, how she started building AI products in 2012 and never stopped, and what she saw in her conversations with the early OpenAI team. 00:00 Introduction 00:54 AI companions & personal software 14:17 Mini apps as community catalysts 19:07 Wabi as a framework for memory, context, and expression 23:11 Prompt sharing as an emergent user behavior 28:11 100x’ing the world’s meaningful software 30:21 The creator economy on Wabi 39:25 Visiting the OpenAI office 43:31 Predicting consumer behavior Eugenia Kuyda Anish Acharya Justine Moore Erik Torenbergshow more

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83,856 просмотров • 10 месяцев назад

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon Aish and Kiriti Badam have built 50+ enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. Our goal with this conversation is to save you and your team a lot of pain and suffering while building your AI products. We discuss: 🔸 Two key ways AI products differ from traditional software 🔸 Common patterns and anti-patterns when building successful AI products 🔸 Their framework for iteratively building AI products 🔸 Why evals aren’t a cure-all 🔸 Why obsessing about customer trust and reliability is an underrated driver of successful AI products 🔸 The skills that matter most for builders in the AI era Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Merge — The fastest way to ship 220+ integrations: 🏆 Strella — The AI-powered customer research platform: 🏆 Brex — The banking solution for startups:

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon Aish and Kiriti Badam have built 50+ enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. Our goal with this conversation is to save you and your team a lot of pain and suffering while building your AI products. We discuss: 🔸 Two key ways AI products differ from traditional software 🔸 Common patterns and anti-patterns when building successful AI products 🔸 Their framework for iteratively building AI products 🔸 Why evals aren’t a cure-all 🔸 Why obsessing about customer trust and reliability is an underrated driver of successful AI products 🔸 The skills that matter most for builders in the AI era Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Merge — The fastest way to ship 220+ integrations: 🏆 Strella — The AI-powered customer research platform: 🏆 Brex — The banking solution for startups:

Lenny Rachitsky

176,800 просмотров • 7 месяцев назад

Where are all the consumer AI apps??? This is the question I posed to s-tier consumer investors scott belsky (Uber, Pinterest) and steve jang (Coinbase, Postmates). We're almost 3 years into the AI hype the we're still all mostly just talking about ChatGPT, Perplexity, and Claude. We explore the answer through the ways in which the entire consumer stack (input, memory, personalization, browsers, hardware) are being re-written by AI and how that may give birth to the consumer opportunity we've all been waiting for. Chapters: 00:00 Welcome and Introductions 00:43 The Current State of AI Products 02:48 Surprises and Challenges with LLMs 05:24 Future of Consumer AI and Personal AI 15:49 Memory and Personalization in AI 21:52 Potential Risks and Business Models 24:22 The Battle for User Data in AI 25:11 Optimism in AI Model Inference 27:00 The Role of Open Source Models 28:13 The Future of AI Hardware 28:54 Challenges and Opportunities in AI Hardware Startups 30:29 The Evolution of Consumer Hardware 38:36 The Reinvention of Browsers 46:23 The Potential of AI-Enhanced Browsers 47:54 Conclusion and Final Thoughts Many thanks to my good friends for joining me in this discussion 🙏

Where are all the consumer AI apps??? This is the question I posed to s-tier consumer investors scott belsky (Uber, Pinterest) and steve jang (Coinbase, Postmates). We're almost 3 years into the AI hype the we're still all mostly just talking about ChatGPT, Perplexity, and Claude. We explore the answer through the ways in which the entire consumer stack (input, memory, personalization, browsers, hardware) are being re-written by AI and how that may give birth to the consumer opportunity we've all been waiting for. Chapters: 00:00 Welcome and Introductions 00:43 The Current State of AI Products 02:48 Surprises and Challenges with LLMs 05:24 Future of Consumer AI and Personal AI 15:49 Memory and Personalization in AI 21:52 Potential Risks and Business Models 24:22 The Battle for User Data in AI 25:11 Optimism in AI Model Inference 27:00 The Role of Open Source Models 28:13 The Future of AI Hardware 28:54 Challenges and Opportunities in AI Hardware Startups 30:29 The Evolution of Consumer Hardware 38:36 The Reinvention of Browsers 46:23 The Potential of AI-Enhanced Browsers 47:54 Conclusion and Final Thoughts Many thanks to my good friends for joining me in this discussion 🙏

Michael Mignano

25,155 просмотров • 1 год назад

AI has ignited a consumer renaissance. My friend Kevin Rose is a legend in consumer tech. He founded a number of early internet startups, including Digg, where he invented an early predecessor to the now ubiquitous like button. He’s now a partner at True Ventures and recently relaunched Digg. We had a wide-ranging conversation on all things consumer tech, including why I think AI code will be an entire industry, why Kevin looks for “weird and working” products, Kevin’s dinners with Mark Zuckerberg, and how AI products will affect the human experience. 00:00 Intro 02:10 How we met 07:50 AI’s Renaissance for Consumer Products 11:48 Companionship apps and the future of human connection 14:01 The optimistic and pessimistic views on AI relationships 19:18 Poke and emotional primitives 21:05 Weird and working: how to spot great founders 30:05 AI companions and AI in relationships 40:08 The modern AI dev stack and building apps solo 47:25 AI music, creativity, and the next cultural wave 53:50 Curiosity, risk, and finding the next big thing 01:15:00 Always-on recording and social norms in tech

AI has ignited a consumer renaissance. My friend Kevin Rose is a legend in consumer tech. He founded a number of early internet startups, including Digg, where he invented an early predecessor to the now ubiquitous like button. He’s now a partner at True Ventures and recently relaunched Digg. We had a wide-ranging conversation on all things consumer tech, including why I think AI code will be an entire industry, why Kevin looks for “weird and working” products, Kevin’s dinners with Mark Zuckerberg, and how AI products will affect the human experience. 00:00 Intro 02:10 How we met 07:50 AI’s Renaissance for Consumer Products 11:48 Companionship apps and the future of human connection 14:01 The optimistic and pessimistic views on AI relationships 19:18 Poke and emotional primitives 21:05 Weird and working: how to spot great founders 30:05 AI companions and AI in relationships 40:08 The modern AI dev stack and building apps solo 47:25 AI music, creativity, and the next cultural wave 53:50 Curiosity, risk, and finding the next big thing 01:15:00 Always-on recording and social norms in tech

Anish Acharya

38,002 просмотров • 10 месяцев назад

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 просмотров • 1 год назад