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"This is not a product category." "They're not actually going to work on this." "They're going to acquihire themselves to somebody." When Christina Cacioppo (Christina Cacioppo) first pitched her startup in 2018, investors couldn't believe anyone would want to build compliance software. Eight years later, Vanta works with 16K+...

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Alex Konrad

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57,500 Aufrufe • vor 2 Monaten •via X (Twitter)

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Palantir CEO Alex Karp says people think we’re in an AI bubble because a lot of AI just doesn’t work: “If you just buy LLMs off the shelf and try to do any of this, it won’t work.” “It’s not precise enough. You can’t do underwriting. You can’t do these things that are regulated.” “People have tried things that just can never work. You buy a LLM, put it on your stack, and wonder why it’s not working.” “What you’re going to see, especially in America, is people trying to do something like Ontology by hand.” “Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.” “There’s a lot of discussion on if we’re in an AI bubble. What is the meaning of this bubble? If anything, we’re just in a lag. There’s a lot of AI, some of it works.” “Go back to the battlefield context: everybody in the world assumed this would not work. But now it does work. Now the question is, ‘How can I get it to work for my country?’” “Palantir barely has a sales force. In fact, it seems to be getting smaller and smaller every time I go see them.”

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Nicholas Thorne (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 early AI businesses I’ve ever seen. Nicholas is a good friend of mine and a general partner at Prehype, an incubator that launched BarkBox and Ro. 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 is using it to help entrepreneurs start businesses at scale—with AI. Nicholas tells me that he used @ChatGPTapp 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. Watch! ___ Timestamps: Introduction: 00:48 How AI can make you a more effective founder: 12:10 Live demo of Audos! 17:03 Why Nicholas built an AI tool to enable entrepreneurs: 24:07 How Audos puts you in “edit mode” instead of “create mode”: 25:35 Tools to gather customer feedback, generated by Audos: 28:12 How Audos actually works: 32:58 Nicholas uses ChatGPT to prototype a new feature: 35:07 How to establish checks and balances while using ChatGPT: 42:37 AI as a force for pushing entrepreneurship to new heights: 57:20

Dan Shipper 📧

36,351 Aufrufe • vor 2 Jahren

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
49:12

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,265 Aufrufe • vor 1 Jahr

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
1:00:52

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 Aufrufe • vor 1 Jahr

"We'll get to a point in intelligence where the human making that decision is going to be less efficient than the AI making that decision themselves." My full conversation with the head of AI at the Solana Foundation on the world coming fast, where AI starts earning, investing and outpacing the humans who built it. 00:00 Intro 02:46 The Wall Street dream he ditched 06:50 The cold email that changed everything 08:07 Meeting the founder of Stable Diffusion 11:36 Why Rishin left investing to build AI 20:31 When AI starts paying for things itself 23:36 The moment that predicted the future 31:35 Three people, five weeks, one product 38:01 When AI stops asking you first 41:09 When your customer is a robot 44:01 How much of the economy will AI run
48:32

"We'll get to a point in intelligence where the human making that decision is going to be less efficient than the AI making that decision themselves." My full conversation with the head of AI at the Solana Foundation on the world coming fast, where AI starts earning, investing and outpacing the humans who built it. 00:00 Intro 02:46 The Wall Street dream he ditched 06:50 The cold email that changed everything 08:07 Meeting the founder of Stable Diffusion 11:36 Why Rishin left investing to build AI 20:31 When AI starts paying for things itself 23:36 The moment that predicted the future 31:35 Three people, five weeks, one product 38:01 When AI stops asking you first 41:09 When your customer is a robot 44:01 How much of the economy will AI run

JackGK

19,233 Aufrufe • vor 1 Monat

Ep #90: Scaling Amplitude to $300M ARR This conversation with Spenser Skates unpacks everything he's learned in 13 years of building Amplitude. We talk importance of data in product design, how Amplitude is thinking about AI, and the future of user responsive software. We also get into the early days of building Amplitude, when to go multi-product, how to construct your board as a startup, hiring executives at various company stages, lessons from closing three acquisitions, lessons scaling to $300 million in ARR, inside Amplitude’s 2021 IPO, and what most people get wrong about Founder Mode. Thanks to Numeral, the end-to-end platform for sales tax and compliance, for supporting this episode. Stream here on X, or links below. Timestamps: 4:45 Using data to build great products 8:31 Why data is existential to every business 13:14 How to go multi-product 15:48 Every startup becomes a distribution company 19:29 Lessons from three acquisitions 29:09 AI hasn’t changed B2B SaaS yet 31:24 Challenges of incorporating AI in B2B SaaS 33:09 Amplitude’s AI experiments 36:29 Navigating technology hype cycles as a public company 39:40 Amplitude’s opportunity in LLMs 43:08 User responsive software 46:16 Surprising things that slow your speed of execution 51:27 What people get wrong about Founder Mode 59:48 Pivoting into Amplitude after YC 1:04:42 Nine months to raise Amplitude’s first round 1:08:31 Surprises from closing the first customers 1:12:46 Two sales lessons for technical founders 1:13:44 Scaling to $300M+ ARR 1:17:14 How to choose board members 1:19:55 Inside Amplitude’s IPO 1:21:56 “Stock price is an output of the business” 1:26:36 Evolving from startup founder to public company CEO 1:31:54 How hiring execs changes as you scale 1:34:32 Why DEI is important at Amplitude 1:39:46 Relevance of gaming and startups
1:44:04

Ep #90: Scaling Amplitude to $300M ARR This conversation with Spenser Skates unpacks everything he's learned in 13 years of building Amplitude. We talk importance of data in product design, how Amplitude is thinking about AI, and the future of user responsive software. We also get into the early days of building Amplitude, when to go multi-product, how to construct your board as a startup, hiring executives at various company stages, lessons from closing three acquisitions, lessons scaling to $300 million in ARR, inside Amplitude’s 2021 IPO, and what most people get wrong about Founder Mode. Thanks to Numeral, the end-to-end platform for sales tax and compliance, for supporting this episode. Stream here on X, or links below. Timestamps: 4:45 Using data to build great products 8:31 Why data is existential to every business 13:14 How to go multi-product 15:48 Every startup becomes a distribution company 19:29 Lessons from three acquisitions 29:09 AI hasn’t changed B2B SaaS yet 31:24 Challenges of incorporating AI in B2B SaaS 33:09 Amplitude’s AI experiments 36:29 Navigating technology hype cycles as a public company 39:40 Amplitude’s opportunity in LLMs 43:08 User responsive software 46:16 Surprising things that slow your speed of execution 51:27 What people get wrong about Founder Mode 59:48 Pivoting into Amplitude after YC 1:04:42 Nine months to raise Amplitude’s first round 1:08:31 Surprises from closing the first customers 1:12:46 Two sales lessons for technical founders 1:13:44 Scaling to $300M+ ARR 1:17:14 How to choose board members 1:19:55 Inside Amplitude’s IPO 1:21:56 “Stock price is an output of the business” 1:26:36 Evolving from startup founder to public company CEO 1:31:54 How hiring execs changes as you scale 1:34:32 Why DEI is important at Amplitude 1:39:46 Relevance of gaming and startups

The Peel

33,495 Aufrufe • vor 1 Jahr

.Natalia rode so hard for Claude Code we devoted an episode to how she was using it to automate her job running Every 📧’s consulting practice. Fast forward to five months later, and she rides just as hard for Codex. I had her back on AI & I to talk about what caused her to make the switch, including how she ran a prompt in Codex before bed and woke up to a finished, custom CRM tool. We get into: - Why she finds Codex easier to use than Claude Code - How she’s using loops in Codex to create customized tools that work exactly how she needs them to - Why the consulting team still pays for SaaS products like Attio and Asana even though they could vibe code their own versions - How she built an app to manage her father’s medical care in Codex - How knowledge work is evolving from sculpting to gardening, in which you develop the context and logic you need for an agent to execute for you This is a must-watch for anyone trying to figure out whether to build their own tools or buy real software—and what it takes to get an AI agent to run unsupervised for hours and nail the output. Watch below! Timestamps 1. Introduction: 00:01:05 2. How Natalia manages Claudie, the consulting team’s AI project manager: 00:02:35 3. Why the consulting team still pays for SaaS products: 00:04:55 4. Codex as a game changer : 00:11:47 5. Building personalized learning guides and illustrated explainers with AI: 00:14:55 6. Inside Natalia's AI-powered email triage system: 00:21:40 7. The shift from knowledge work as sculpting to knowledge work as gardening: 00:26:44 8. Using Codex to on-shot a custom CRM: 00:28:57 9. Using Codex to build an app that coordinates her father’s medical care: 00:33:16
41:15

.Natalia rode so hard for Claude Code we devoted an episode to how she was using it to automate her job running Every 📧’s consulting practice. Fast forward to five months later, and she rides just as hard for Codex. I had her back on AI & I to talk about what caused her to make the switch, including how she ran a prompt in Codex before bed and woke up to a finished, custom CRM tool. We get into: - Why she finds Codex easier to use than Claude Code - How she’s using loops in Codex to create customized tools that work exactly how she needs them to - Why the consulting team still pays for SaaS products like Attio and Asana even though they could vibe code their own versions - How she built an app to manage her father’s medical care in Codex - How knowledge work is evolving from sculpting to gardening, in which you develop the context and logic you need for an agent to execute for you This is a must-watch for anyone trying to figure out whether to build their own tools or buy real software—and what it takes to get an AI agent to run unsupervised for hours and nail the output. Watch below! Timestamps 1. Introduction: 00:01:05 2. How Natalia manages Claudie, the consulting team’s AI project manager: 00:02:35 3. Why the consulting team still pays for SaaS products: 00:04:55 4. Codex as a game changer : 00:11:47 5. Building personalized learning guides and illustrated explainers with AI: 00:14:55 6. Inside Natalia's AI-powered email triage system: 00:21:40 7. The shift from knowledge work as sculpting to knowledge work as gardening: 00:26:44 8. Using Codex to on-shot a custom CRM: 00:28:57 9. Using Codex to build an app that coordinates her father’s medical care: 00:33:16

Dan Shipper 📧

25,701 Aufrufe • vor 1 Monat

Mike Cannon-Brookes thinks the market is massively underestimating how AI will reshape software. In this conversation with the Atlassian co-founder and CEO, Mike joins a16z's Alex Rampell and Erik Torenberg to cover: - The history of software as “turning filing cabinets into databases” from 1960 to 2022 - The three buckets of SaaS, and why investors keep pricing them like they are the same thing - Why usage and outcome-based pricing is harder than it looks because customers hate bills they cannot control - The real bottleneck for AI at work: trust, UX, and getting humans to actually adopt new workflows 00:00 Introduction 03:16 Risk level has gone up 06:44 Three types of SaaS companies 11:07 Vibecoding is preposterous 14:23 Businesses are a set of processes 31:25 AI credits as casino chips 35:24 How Atlassian is adapting 43:26 Why customer trust is hard Mike Cannon-Brookes 👨🏼‍💻🧢🇦🇺 Alex Rampell Erik Torenberg
54:18

Mike Cannon-Brookes thinks the market is massively underestimating how AI will reshape software. In this conversation with the Atlassian co-founder and CEO, Mike joins a16z's Alex Rampell and Erik Torenberg to cover: - The history of software as “turning filing cabinets into databases” from 1960 to 2022 - The three buckets of SaaS, and why investors keep pricing them like they are the same thing - Why usage and outcome-based pricing is harder than it looks because customers hate bills they cannot control - The real bottleneck for AI at work: trust, UX, and getting humans to actually adopt new workflows 00:00 Introduction 03:16 Risk level has gone up 06:44 Three types of SaaS companies 11:07 Vibecoding is preposterous 14:23 Businesses are a set of processes 31:25 AI credits as casino chips 35:24 How Atlassian is adapting 43:26 Why customer trust is hard Mike Cannon-Brookes 👨🏼‍💻🧢🇦🇺 Alex Rampell Erik Torenberg

a16z

154,337 Aufrufe • vor 5 Monaten