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Meesho never built a chatbot. Not once. Vidit explains why on Signal Dialogues: chat isn’t native to how India’s next 500M users shop, so Meesho bet on voice instead a harder problem, but the right one. The results so far: 75% of orders now come from AI recommendations, not... show more

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10,532 次观看 • 26 天前 •via X (Twitter)

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Cursor’s founder on why AI makes the next engineer more valuable, not less. The New York Times spends ~$150M-$200M a year on software R&D, even though most people do not think of it as a software company. Professional software engineering is still far from solved because building a new codebase is not the same as safely migrating an existing system like Rippling’s 30M-line codebase, with years of logic, dependencies, and customer workflows behind it. Cursor’s 20-person support team handling millions of daily users clearly shows that AI leverage for small teams is real. But the scope of software work is still vast, so the ROI of adding the next strong engineer is higher, not lower.

Cursor’s founder on why AI makes the next engineer more valuable, not less. The New York Times spends ~$150M-$200M a year on software R&D, even though most people do not think of it as a software company. Professional software engineering is still far from solved because building a new codebase is not the same as safely migrating an existing system like Rippling’s 30M-line codebase, with years of logic, dependencies, and customer workflows behind it. Cursor’s 20-person support team handling millions of daily users clearly shows that AI leverage for small teams is real. But the scope of software work is still vast, so the ROI of adding the next strong engineer is higher, not lower.

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In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,673 次观看 • 5 个月前

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

Anatoli Kopadze

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Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Rohan Paul

334,691 次观看 • 11 个月前

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Dan Shipper 📧

19,436 次观看 • 3 个月前

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Rohan Paul

59,460 次观看 • 1 天前

Aravind Srinivas just explained why China’s open-source AI may become more powerful than ever. And why Anthropic has lobbied very hard for export control. "the only reason why there is even a 12-month gap between open source and frontier models is export controls. But there is a chance that, because of that, they now get really good at the physical layer. One advantage they (China) have is that they can actually build data centers a lot faster. Power is not a problem. Permits are not a problem. People are not a problem. Labor is not a problem. Expertise is not a problem. And so, by forcing them to go out there and build all this, you are converting them into a far more potent competitor." --- From "20VC with Harry Stebbings" YouTube channel (Harry Stebbings ), link in comment

Aravind Srinivas just explained why China’s open-source AI may become more powerful than ever. And why Anthropic has lobbied very hard for export control. "the only reason why there is even a 12-month gap between open source and frontier models is export controls. But there is a chance that, because of that, they now get really good at the physical layer. One advantage they (China) have is that they can actually build data centers a lot faster. Power is not a problem. Permits are not a problem. People are not a problem. Labor is not a problem. Expertise is not a problem. And so, by forcing them to go out there and build all this, you are converting them into a far more potent competitor." --- From "20VC with Harry Stebbings" YouTube channel (Harry Stebbings ), link in comment

Rohan Paul

139,025 次观看 • 1 个月前

WHAT??? A 1911 historical GHOST SHIP spotted in Antartica. This isn’t AI. These people are on a cruise in Antarctica and from their cabin room window they spot this. But there is no way it could withstand over 100 years of storms and terrain, yet still be out to sea. Impossible. I went through their page and they really are on a cruise. Why are so many things being disclosed all at once? 👇 “It's the historic Bark Europa (built 1911, three-masted tall ship & true 'Ocean Wanderer') — NOT AI. Sailing through the ice like something out of a legend.” Credit: Lucy-Lulu

WHAT??? A 1911 historical GHOST SHIP spotted in Antartica. This isn’t AI. These people are on a cruise in Antarctica and from their cabin room window they spot this. But there is no way it could withstand over 100 years of storms and terrain, yet still be out to sea. Impossible. I went through their page and they really are on a cruise. Why are so many things being disclosed all at once? 👇 “It's the historic Bark Europa (built 1911, three-masted tall ship & true 'Ocean Wanderer') — NOT AI. Sailing through the ice like something out of a legend.” Credit: Lucy-Lulu

Melanie King

1,495,779 次观看 • 5 个月前

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,638 次观看 • 2 个月前

Today Meesho became a public company. Congratulations to Vidit Vidit Aatrey, Sanjeev Sanjeev Barnwal and the entire Meesho team on building one of the most mission-driven companies India has produced in the last decade. We at Elevation Capital Elevation Capital feel privileged to have been partners since 2017. Back then, it took us 1.5 years and multiple meetings before we finally partnered, and watching what's unfolded since then has been nothing short of remarkable. Last week, Vidit and I went back to Cafe Noir @ UB City where, in June 2017, we had decided to partner. Same place, same table. We hadn’t returned together in eight years. And sitting there, it felt like time folded in on itself. That day in 2017, Vidit cancelled another investor meeting to meet me. I’ve always remembered that as a small but decisive moment. What I learned only last week was that he thought the other fund was more likely to convert. And still, he chose to meet me. I can’t thank him enough for that decision - because it led to one of the most defining partnerships of my career and one of the most fulfilling relationships of my life! Sitting there, we ended up reminiscing about the whole arc: the early days with very little runway; the brutal months in 2016-17 that forged the culture; the courage to pivot from reseller-first to consumer-first; and the conviction to go zero commission when many people thought it was impossible. From those early conversations to today: 200 million+ customers, 500k+ sellers, and 100k+ livelihoods created through Valmo. Meesho has truly democratized e-commerce in India. And beyond the numbers, what has stood out for me the most is the character of the journey: deep customer obsession, the courage to make non-consensus decisions, and stellar execution to back them - again and again. Watching Vidit and Sanjeev has been a masterclass. Vidit's rare combination of super long term thinking and operational depth, seeing clearly where the world is heading, and having the conviction to act; Sanjeev’s quiet excellence and the stellar engineering team and culture he’s built - one that so many teams in India now look up to. And through it all, both staying humble and grounded. As they remind us, this is just chapter one. The e-commerce market in India is still in its early days and the mission is just getting started! I couldn't be more excited about what comes next. Congratulations once again to the exceptional team that has delivered on super auditious goals time and time again - Dhiresh, Roopa, Debdoot, Milan, Prasanna, Megha, Siddharth, Ashish, Sourabh, Steffie, Harshit, Prateek, Kirti, Jatin, UK. And thank you for the partnership. It has been a privilege! Penned down my reflections on the journey here:

Today Meesho became a public company. Congratulations to Vidit Vidit Aatrey, Sanjeev Sanjeev Barnwal and the entire Meesho team on building one of the most mission-driven companies India has produced in the last decade. We at Elevation Capital Elevation Capital feel privileged to have been partners since 2017. Back then, it took us 1.5 years and multiple meetings before we finally partnered, and watching what's unfolded since then has been nothing short of remarkable. Last week, Vidit and I went back to Cafe Noir @ UB City where, in June 2017, we had decided to partner. Same place, same table. We hadn’t returned together in eight years. And sitting there, it felt like time folded in on itself. That day in 2017, Vidit cancelled another investor meeting to meet me. I’ve always remembered that as a small but decisive moment. What I learned only last week was that he thought the other fund was more likely to convert. And still, he chose to meet me. I can’t thank him enough for that decision - because it led to one of the most defining partnerships of my career and one of the most fulfilling relationships of my life! Sitting there, we ended up reminiscing about the whole arc: the early days with very little runway; the brutal months in 2016-17 that forged the culture; the courage to pivot from reseller-first to consumer-first; and the conviction to go zero commission when many people thought it was impossible. From those early conversations to today: 200 million+ customers, 500k+ sellers, and 100k+ livelihoods created through Valmo. Meesho has truly democratized e-commerce in India. And beyond the numbers, what has stood out for me the most is the character of the journey: deep customer obsession, the courage to make non-consensus decisions, and stellar execution to back them - again and again. Watching Vidit and Sanjeev has been a masterclass. Vidit's rare combination of super long term thinking and operational depth, seeing clearly where the world is heading, and having the conviction to act; Sanjeev’s quiet excellence and the stellar engineering team and culture he’s built - one that so many teams in India now look up to. And through it all, both staying humble and grounded. As they remind us, this is just chapter one. The e-commerce market in India is still in its early days and the mission is just getting started! I couldn't be more excited about what comes next. Congratulations once again to the exceptional team that has delivered on super auditious goals time and time again - Dhiresh, Roopa, Debdoot, Milan, Prasanna, Megha, Siddharth, Ashish, Sourabh, Steffie, Harshit, Prateek, Kirti, Jatin, UK. And thank you for the partnership. It has been a privilege! Penned down my reflections on the journey here:

Mukul Arora

39,087 次观看 • 8 个月前

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now

X Freeze

591,415 次观看 • 4 个月前

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,354 次观看 • 1 个月前

AI spend is now one of the fastest-growing line items at every company, but here's the problem: it's invisible to traditional spend management. We experienced this firsthand at Brex, so we built Magpie: an internal tool that sources and normalizes AI spend across every vendor so we can break it down by model, customer, employee, and workflow. What Magpie has already unlocked: → ~85% cost reduction on an audit agent through prompt caching → Real customer cost-to-serve numbers that inform pricing and margin → Model routing governance we couldn't enforce before → A surprise: operational AI is much cheaper than we assumed So why is AI spend so hard to track? Because each one exposes usage data differently (some not at all). At Brex, tool-use costs weren't tracked alongside tokens, customer attribution was inconsistent, and a meaningful chunk of our daily AI spend was unattributable. Now with Magpie, we have reliable visibility. AI spend is more complex than anything finance teams have had to manage before, and it's exactly the kind of problem Brex is built to solve, shaping how we think about the future of our platform. More below.

AI spend is now one of the fastest-growing line items at every company, but here's the problem: it's invisible to traditional spend management. We experienced this firsthand at Brex, so we built Magpie: an internal tool that sources and normalizes AI spend across every vendor so we can break it down by model, customer, employee, and workflow. What Magpie has already unlocked: → ~85% cost reduction on an audit agent through prompt caching → Real customer cost-to-serve numbers that inform pricing and margin → Model routing governance we couldn't enforce before → A surprise: operational AI is much cheaper than we assumed So why is AI spend so hard to track? Because each one exposes usage data differently (some not at all). At Brex, tool-use costs weren't tracked alongside tokens, customer attribution was inconsistent, and a meaningful chunk of our daily AI spend was unattributable. Now with Magpie, we have reliable visibility. AI spend is more complex than anything finance teams have had to manage before, and it's exactly the kind of problem Brex is built to solve, shaping how we think about the future of our platform. More below.

Pedro Franceschi

28,580 次观看 • 2 个月前

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48

Dan Shipper 📧

36,359 次观看 • 4 个月前

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper 📧

58,849 次观看 • 5 个月前

THE GREATEST LIVING MATHEMATICIAN EXPLAINED HOW HE ACTUALLY USES AI AND HIS ANSWER QUIETLY KILLS BOTH THE HYPE AND THE COPE, IT DOES NOT REPLACE HIM, AND IT IS NOT A TOY EITHER 83 minutes with Terence Tao -- Fields Medal winner, often called the Mozart of math. -> His framing: today's AI is a brilliant but unreliable grad student. Great flashes, confident nonsense, and you must check every step. So it does not hand you answers. It removes friction -- the boring computations you used to avoid and lets you try crazier ideas. His bigger idea is "Big math": AI-checked proofs let hundreds of mathematicians collaborate without trusting each other's work. The machine verifies, so humans finally scale. For builders the signal is clear. The win is not "AI does it for me". It is AI clearing the grunt work so you aim higher. You thought the question was whether AI beats the experts. Tao shows it is how far one mind reaches once the friction is gone. Save this. It is the most grounded take on AI you'll hear ↓

THE GREATEST LIVING MATHEMATICIAN EXPLAINED HOW HE ACTUALLY USES AI AND HIS ANSWER QUIETLY KILLS BOTH THE HYPE AND THE COPE, IT DOES NOT REPLACE HIM, AND IT IS NOT A TOY EITHER 83 minutes with Terence Tao -- Fields Medal winner, often called the Mozart of math. -> His framing: today's AI is a brilliant but unreliable grad student. Great flashes, confident nonsense, and you must check every step. So it does not hand you answers. It removes friction -- the boring computations you used to avoid and lets you try crazier ideas. His bigger idea is "Big math": AI-checked proofs let hundreds of mathematicians collaborate without trusting each other's work. The machine verifies, so humans finally scale. For builders the signal is clear. The win is not "AI does it for me". It is AI clearing the grunt work so you aim higher. You thought the question was whether AI beats the experts. Tao shows it is how far one mind reaches once the friction is gone. Save this. It is the most grounded take on AI you'll hear ↓

slash1s

54,342 次观看 • 1 个月前

Marc Benioff just exposed the biggest hypocrisy in the AI boom. The companies building the AI that’s supposed to kill software are some of Salesforce’s largest customers. Benioff: “The AI companies love our products and they can’t buy enough of them. They’re some of our largest customers now: Anthropic, OpenAI, Google, Amazon, you name it.” Let that land. The most advanced AI labs on earth. The companies with more engineering talent and compute than anyone. The ones building the technology that analysts say will make traditional software obsolete. Still buying traditional software. At scale. Benioff: “No one has a company that’s running entirely on a large language model because it’s not real.” Not because they haven’t tried. Because an LLM is not a foundation. It’s a feature. Benioff: “Yeah, Minority Report, I watched the movie. Great guys, fantastic. But I’m in the present-moment reality right now. We’re living in this world. This is 2026.” The analysts writing reports about fully autonomous AI companies have never had to run one. Benioff is running one of the largest enterprise software companies on earth. The gap between those two perspectives is where billions of dollars are being misallocated. Benioff: “How are we doing our financials, our HR, our customer information? How are we doing all of these aspects of our business?” A neural network that hallucinates cannot execute a financial transaction that has to be right every single time. Cannot secure customer data with zero tolerance for error. Cannot provide the determinism that every real business runs on. Benioff: “We need the determinism, and the programmability, and the security, and the sharing.” AI doesn’t replace those requirements. It sits on top of them. Benioff: “I think the software industry is going to be bigger and broader and do more this year than ever before.” The future isn’t AI replacing software. It’s AI making software exponentially more powerful. The smartest people building the future already know this. They’re the ones still buying the software.

Marc Benioff just exposed the biggest hypocrisy in the AI boom. The companies building the AI that’s supposed to kill software are some of Salesforce’s largest customers. Benioff: “The AI companies love our products and they can’t buy enough of them. They’re some of our largest customers now: Anthropic, OpenAI, Google, Amazon, you name it.” Let that land. The most advanced AI labs on earth. The companies with more engineering talent and compute than anyone. The ones building the technology that analysts say will make traditional software obsolete. Still buying traditional software. At scale. Benioff: “No one has a company that’s running entirely on a large language model because it’s not real.” Not because they haven’t tried. Because an LLM is not a foundation. It’s a feature. Benioff: “Yeah, Minority Report, I watched the movie. Great guys, fantastic. But I’m in the present-moment reality right now. We’re living in this world. This is 2026.” The analysts writing reports about fully autonomous AI companies have never had to run one. Benioff is running one of the largest enterprise software companies on earth. The gap between those two perspectives is where billions of dollars are being misallocated. Benioff: “How are we doing our financials, our HR, our customer information? How are we doing all of these aspects of our business?” A neural network that hallucinates cannot execute a financial transaction that has to be right every single time. Cannot secure customer data with zero tolerance for error. Cannot provide the determinism that every real business runs on. Benioff: “We need the determinism, and the programmability, and the security, and the sharing.” AI doesn’t replace those requirements. It sits on top of them. Benioff: “I think the software industry is going to be bigger and broader and do more this year than ever before.” The future isn’t AI replacing software. It’s AI making software exponentially more powerful. The smartest people building the future already know this. They’re the ones still buying the software.

Dustin

203,575 次观看 • 5 个月前

Some personal hot takes from AI: engineer Miami follows... 1. Software development is a dead-end profession because anyone can be a software developer now. 2. Anyone can use Cursor or any other tool and generate code. Being a coder and being a software engineer are different. 3. Computers used to be gated; now everyone has the power to make computers malleable. Everyone is a software developer now, but that does not mean they are software engineers 4. If you cannot demonstrate how a coding agent works, you are just a consumer and have imposed an artificial glass ceiling on your career as a software engineer. 5. If you are curious, you will have a job. If you have not been curious in the last two years, you are replaceable. 6. SaaS per-seat economics may become unstable as customers need fewer people to achieve results, prompting founders to think about new unit economics 7. Most companies will take two or three years (or more!) to figure out AI transformation. 8. Some companies are already building AI native teams of five to ten people who can build with the grain of AI 9. There will be an explosion in the number of software developers. Software development is now essentially free, and tokens are cheaper than humans 10. Not enough engineers know what it means to be a product engineer 11. JIRA ticket monkeys are cooked 12. If your company has banned AI, you should quit that company 13. AI is more like a musical instrument than just a tool play with it, make discoveries, build intuition learn where AI is good and where it fails

Some personal hot takes from AI: engineer Miami follows... 1. Software development is a dead-end profession because anyone can be a software developer now. 2. Anyone can use Cursor or any other tool and generate code. Being a coder and being a software engineer are different. 3. Computers used to be gated; now everyone has the power to make computers malleable. Everyone is a software developer now, but that does not mean they are software engineers 4. If you cannot demonstrate how a coding agent works, you are just a consumer and have imposed an artificial glass ceiling on your career as a software engineer. 5. If you are curious, you will have a job. If you have not been curious in the last two years, you are replaceable. 6. SaaS per-seat economics may become unstable as customers need fewer people to achieve results, prompting founders to think about new unit economics 7. Most companies will take two or three years (or more!) to figure out AI transformation. 8. Some companies are already building AI native teams of five to ten people who can build with the grain of AI 9. There will be an explosion in the number of software developers. Software development is now essentially free, and tokens are cheaper than humans 10. Not enough engineers know what it means to be a product engineer 11. JIRA ticket monkeys are cooked 12. If your company has banned AI, you should quit that company 13. AI is more like a musical instrument than just a tool play with it, make discoveries, build intuition learn where AI is good and where it fails

geoff

65,789 次观看 • 1 个月前