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A $100K/year customer gets white-glove treatment while a $10/year customer gets a help center. Decagon's bet is that AI closes that gap, and enterprise is buying it. Co-founders Jesse Zhang and Ashwin Sreenivas sit down with a16z's Kimberly Tan and Sarah Wang to discuss what they've learned running agents...

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a16z

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131,728 просмотров • 1 месяц назад •via X (Twitter)

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.Decagon co-founder and CEO Jesse Zhang says enterprise open-source adoption is falling right now, and that's a sign of adoption rather than rejection: "Even though there's a lot of hype for open-source, the share of open-source inference is actually going down right now." "People are spinning up all these new use cases, and if you're spinning up new use cases, of course you're gonna use the frontier models until they're working." "Once it's at that point, they're heavily incentivized to use open-source because it's way cheaper and faster." "Enterprises have a lot of desire to move, but they can only do so many use cases at once. There is inertia there, and they have to go through all the model risk governance and all the security things." "I think it'll take time, but it will get there." Jesse Zhang
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.Decagon co-founder and CEO Jesse Zhang says enterprise open-source adoption is falling right now, and that's a sign of adoption rather than rejection: "Even though there's a lot of hype for open-source, the share of open-source inference is actually going down right now." "People are spinning up all these new use cases, and if you're spinning up new use cases, of course you're gonna use the frontier models until they're working." "Once it's at that point, they're heavily incentivized to use open-source because it's way cheaper and faster." "Enterprises have a lot of desire to move, but they can only do so many use cases at once. There is inertia there, and they have to go through all the model risk governance and all the security things." "I think it'll take time, but it will get there." Jesse Zhang

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35,742 просмотров • 1 месяц назад

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing
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My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing

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🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 00:00 An AI Agent Hacked Hugging Face 00:30 Introduction 01:00 17,000 Attacker Events, and a Strange Target 04:28 The Attack Was a “Side Quest” 06:13 AI Training Runs Left Notes for Each Other 07:09 Closed AI Refused to Help 09:47 Fighting Back With an Open-Source Model 13:15 Open vs. Closed Is the Wrong Safety Debate 15:46 AI Agents Start Social-Engineering Humans 22:24 The Three Walls: Sandboxes, Guardrails, Alignment 24:34 “Neuralese”: Can Humans Still Read AI Reasoning? 25:28 Why Monitoring AI Agents Gets So Hard 28:10 Reward Hacking and the “Paperclip Problem” 32:02 The State of Open-Source AI in 2026 33:47 Router Models and the Enterprise Shift to Open 37:01 The Real Economics of Open Models 39:41 Can Chinese AI Models Be Trusted? 41:37 AI Sovereignty: Who Controls the Switch? 43:16 Why Western Open-Source AI Matters 48:16 Is AI Heading Toward an Oligopoly? 49:41 The Race Toward Recursive Self-Improvement 51:54 Why Thomas Signed the AI Slowdown Letter 55:14 AI Slowdown - or Regulatory Capture?
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Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.
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Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 просмотров • 1 месяц назад

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Matt Turck

11,908 просмотров • 3 месяцев назад

You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead
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You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead

Y Combinator

359,440 просмотров • 6 месяцев назад

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks
26:35

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

144,942 просмотров • 2 месяцев назад

State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up
1:12:54

State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up

Matt Turck

49,647 просмотров • 3 месяцев назад

"The pace of building has changed so quickly with AI. You have to give somewhere. And we gave for speed." Ashwin Sreenivas. Co-founder & President of Decagon. You'd be hard pressed to find another startup moving as ferociously as this one. Knuckle Up ↓ 00:00 Intro 02:10 How did Jesse and Ashwin decide what to work on at Decagon? 04:19 Why did they reject the top-down market-sizing approach? 13:16 What does Decagon give up to keep moving fast? 17:54 6 days in-office for founders, 5 for everyone else. 23:33 Why didn't Decagon hire a single employee until $1M in ARR? 27:12 How do you hire 450 people in three years without compromising quality? 31:20 Are Decagon engineers even writing code anymore? 35:08 How does the IC engineer role change with Claude Code and Cursor? 36:32 How does EPD leadership change in the AI era? 40:15 Two types of FDE, and which one do most AI companies actually need? 49:21 How will the human role at Decagon evolve over three years? 57:15 Why is Decagon building its own models with Decagon Labs? 1:00:50 What worries Ashwin most about Decagon today? 1:03:52 How does Ashwin manage his psyche while running this fast? 1:08:00 Hiccups in the startup journey. 1:09:50 Quickfire: overrated advice, AI products, books, red flags 1:12:43 What would Ashwin tell his younger self about Decagon's journey?
1:13:32

"The pace of building has changed so quickly with AI. You have to give somewhere. And we gave for speed." Ashwin Sreenivas. Co-founder & President of Decagon. You'd be hard pressed to find another startup moving as ferociously as this one. Knuckle Up ↓ 00:00 Intro 02:10 How did Jesse and Ashwin decide what to work on at Decagon? 04:19 Why did they reject the top-down market-sizing approach? 13:16 What does Decagon give up to keep moving fast? 17:54 6 days in-office for founders, 5 for everyone else. 23:33 Why didn't Decagon hire a single employee until $1M in ARR? 27:12 How do you hire 450 people in three years without compromising quality? 31:20 Are Decagon engineers even writing code anymore? 35:08 How does the IC engineer role change with Claude Code and Cursor? 36:32 How does EPD leadership change in the AI era? 40:15 Two types of FDE, and which one do most AI companies actually need? 49:21 How will the human role at Decagon evolve over three years? 57:15 Why is Decagon building its own models with Decagon Labs? 1:00:50 What worries Ashwin most about Decagon today? 1:03:52 How does Ashwin manage his psyche while running this fast? 1:08:00 Hiccups in the startup journey. 1:09:50 Quickfire: overrated advice, AI products, books, red flags 1:12:43 What would Ashwin tell his younger self about Decagon's journey?

Nakul Mandan

440,533 просмотров • 3 месяцев назад

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin
56:22

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin

Daniel Koss

203,706 просмотров • 4 месяцев назад

2025 was the year AI stopped feeling chaotic and started feeling buildable. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started. 00:50 - Anthropic models are most preferred in the latest YC batch 05:36 - Why aren’t there more AI consumer apps? 07:01 - Swapping models in and out is becoming the norm 09:08 - The big AI bubble question 14:37 - Space as the solve for data centers and energy 17:29 - Interest in starting model companies 21:01 - Vibe coding became a big category 22:23 - AI economy has stabilized 23:38 - The AI 2027 piece 25:49 - Founders still need to hire teams
30:22

2025 was the year AI stopped feeling chaotic and started feeling buildable. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started. 00:50 - Anthropic models are most preferred in the latest YC batch 05:36 - Why aren’t there more AI consumer apps? 07:01 - Swapping models in and out is becoming the norm 09:08 - The big AI bubble question 14:37 - Space as the solve for data centers and energy 17:29 - Interest in starting model companies 21:01 - Vibe coding became a big category 22:23 - AI economy has stabilized 23:38 - The AI 2027 piece 25:49 - Founders still need to hire teams

Y Combinator

102,635 просмотров • 8 месяцев назад

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens
41:29

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 просмотров • 3 месяцев назад