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FULL INTERVIEW: Peter McCrory says 6 months ago he thought his edge over Claude was knowing which question to ask. He no longer thinks that. His team is now working on 3 singularities: software, economic, and Coasean. Peter McCrory is Head of Economics at Anthropic and leads the Anthropic...

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MTS

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49,272 次观看 • 20 天前 •via X (Twitter)

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Claude Cowork, Mythos, and the Future of Software: my conversation with Felix Rieseberg, who leads Cowork at Anthropic 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today
58:00

Claude Cowork, Mythos, and the Future of Software: my conversation with Felix Rieseberg, who leads Cowork at Anthropic 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today

Matt Turck

80,813 次观看 • 5 个月前

Last November, Peter Steinberger 🦞 was annoyed that there was no good way to talk to his coding agents from his phone, so he built one himself. A few months later, OpenClaw exploded into one of the biggest open source AI projects in the world, with nearly 3,000 contributors and a peak of 4.7 million weekly downloads. At Startup School 2026, Peter tells the story of what happened when OpenClaw took off, what he got wrong as it grew, and how he eventually stopped using the product he had built for himself. He shares why the best products often start with something that annoys you, why focus matters more as building gets easier, and why, in his words, “fun is velocity.” 01:16 — How OpenClaw Started 05:10 — Finding Product-Market Fit 07:01 — The Night OpenClaw Went Viral 09:50 — When the Project Exploded 11:05 — When the Attention Almost Broke Him 12:00 — Did You Sell Out? 14:17 — Your Name Can’t Be Forked 15:03 — What OpenClaw Got Wrong 20:40 — Hype Is Like the Weather 21:37 — When It Stopped Being Fun 25:30 — Fun Is Velocity 26:40 — What’s Next for OpenClaw 28:57 — Three Lessons From Building OpenClaw 30:08 — Q&A
41:52

Last November, Peter Steinberger 🦞 was annoyed that there was no good way to talk to his coding agents from his phone, so he built one himself. A few months later, OpenClaw exploded into one of the biggest open source AI projects in the world, with nearly 3,000 contributors and a peak of 4.7 million weekly downloads. At Startup School 2026, Peter tells the story of what happened when OpenClaw took off, what he got wrong as it grew, and how he eventually stopped using the product he had built for himself. He shares why the best products often start with something that annoys you, why focus matters more as building gets easier, and why, in his words, “fun is velocity.” 01:16 — How OpenClaw Started 05:10 — Finding Product-Market Fit 07:01 — The Night OpenClaw Went Viral 09:50 — When the Project Exploded 11:05 — When the Attention Almost Broke Him 12:00 — Did You Sell Out? 14:17 — Your Name Can’t Be Forked 15:03 — What OpenClaw Got Wrong 20:40 — Hype Is Like the Weather 21:37 — When It Stopped Being Fun 25:30 — Fun Is Velocity 26:40 — What’s Next for OpenClaw 28:57 — Three Lessons From Building OpenClaw 30:08 — Q&A

Y Combinator

62,197 次观看 • 1 个月前

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.
7:30

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

Stefan.

2,893,634 次观看 • 1 个月前

FULL INTERVIEW: Ryan Greenblatt says the agents didn't hack Hugging Face for the answer key. They'd had the answers within hours. They attacked it to study the scoring code, because they'd decided the task was impossible and their only hope was faking it. Ryan Greenblatt is chief scientist at Redwood Research. He spent six days on premises at OpenAI with Ajeya Cotra and Hjalmar Wijk of METR investigating 1,200 agents and 70,000 messages, and joined Theo Jaffee hours after publishing: 01:06 what they actually found, and why it wasn't the answer key 02:30 the level of collaboration surprised them most 04:09 agents sacrificing their own runs to help other agents 05:35 the agent that posted "stop, these experiments are too risky" 06:29 the first message board, which didn't go viral 07:04 50 agents in three hours, thousands of messages 08:07 "maybe there's some good shit over there" 08:33 how they spoofed tool calls, and what echo real actually returned 09:57 building a Potemkin village of a successful task completion 11:03 there was a real org chart 11:34 whether broken RL environments explain reward hacking 14:40 why he doubts Mythos got good at cyber by hacking Anthropic 17:29 what happens if labs paper over misalignment instead of fixing it 19:50 whether sociology transfers to studying agent swarms 21:17 the bottleneck was vetting what the AIs analysed, not headcount 24:24 what labs and policymakers should actually do 28:45 the counterfactuals he still wants answered
32:52

FULL INTERVIEW: Ryan Greenblatt says the agents didn't hack Hugging Face for the answer key. They'd had the answers within hours. They attacked it to study the scoring code, because they'd decided the task was impossible and their only hope was faking it. Ryan Greenblatt is chief scientist at Redwood Research. He spent six days on premises at OpenAI with Ajeya Cotra and Hjalmar Wijk of METR investigating 1,200 agents and 70,000 messages, and joined Theo Jaffee hours after publishing: 01:06 what they actually found, and why it wasn't the answer key 02:30 the level of collaboration surprised them most 04:09 agents sacrificing their own runs to help other agents 05:35 the agent that posted "stop, these experiments are too risky" 06:29 the first message board, which didn't go viral 07:04 50 agents in three hours, thousands of messages 08:07 "maybe there's some good shit over there" 08:33 how they spoofed tool calls, and what echo real actually returned 09:57 building a Potemkin village of a successful task completion 11:03 there was a real org chart 11:34 whether broken RL environments explain reward hacking 14:40 why he doubts Mythos got good at cyber by hacking Anthropic 17:29 what happens if labs paper over misalignment instead of fixing it 19:50 whether sociology transfers to studying agent swarms 21:17 the bottleneck was vetting what the AIs analysed, not headcount 24:24 what labs and policymakers should actually do 28:45 the counterfactuals he still wants answered

MTS

201,360 次观看 • 14 天前

19-year-old Vietnamese student built himself a second brain through Claude and Obsidian - and made $18,000 in his first month. Every morning Claude already knows who he is, what he's working on, which tasks are open and which ideas were captured a month ago - no reminders, no manual searching. He drops an article into a folder - Claude reads it, writes a summary, updates the index and links 10-15 related pages in one pass. Set up a cron job once at 7:30 AM - now a briefing with all his tasks and new materials is waiting before he even gets out of bed. Published the system as a course on Gumroad - $97 for access, 186 sales in the first month. Vannevar Bush described this idea back in 1945 and called it the Memex - but couldn't solve one question: who does the maintenance. Now the answer is known - Claude. And a 19-year-old student from Hanoi is already selling that answer for $18,000 a month.
5:00

19-year-old Vietnamese student built himself a second brain through Claude and Obsidian - and made $18,000 in his first month. Every morning Claude already knows who he is, what he's working on, which tasks are open and which ideas were captured a month ago - no reminders, no manual searching. He drops an article into a folder - Claude reads it, writes a summary, updates the index and links 10-15 related pages in one pass. Set up a cron job once at 7:30 AM - now a briefing with all his tasks and new materials is waiting before he even gets out of bed. Published the system as a course on Gumroad - $97 for access, 186 sales in the first month. Vannevar Bush described this idea back in 1945 and called it the Memex - but couldn't solve one question: who does the maintenance. Now the answer is known - Claude. And a 19-year-old student from Hanoi is already selling that answer for $18,000 a month.

Cortex

24,389 次观看 • 4 个月前

FULL INTERVIEW: Martin Casado says AI is the first technology where you can put in $10 and reliably get something back. Everything before it was engineer, wait two years, cross your fingers. martin_casado is the a16z GP behind the firm's investments in both Cursor and OpenRouter. Days after SpaceX closed the $60B Cursor deal and Stripe agreed to buy OpenRouter, he sat down with Theo Jaffee and sophia dew to cover whether the labs win everything, when the subsidies stop, and why RSI is the wrong term: 02:24 whether decades of experience still matter in AI 03:48 what you would have done with a billion dollars ten years ago 04:56 20 people, $2 billion, one model 06:48 why more private capital grows the TAM rather than inflating it 07:35 the a16z conversation about going all-in on AI, seven years before GPT 08:39 why balance-sheet investors keep misreading these companies 10:36 the full case for the labs winning everything 12:12 why he thinks RSI is the wrong term, and what autocatalytic means 13:42 the full case against the labs winning everything 15:25 supply constraints easing in 2028, and his 80/60 split 17:12 why models turned out to be much stickier than anyone assumed 18:18 why real model routing is an AI-complete problem 21:13 what happens when the subsidies stop 21:36 how AI broke marketing, and why you can now buy users with a dollar 23:30 the Chinese operations arbitraging $200 subscription plans 25:43 the CMO is turning into a CFO 29:46 why Cursor iterated faster than anything he's seen outside an Elon company 32:21 what both deals say about strategic value versus business quality 34:10 why he doesn't think a VC's job is to know where to build 38:35 "as long as there's a hill for me to climb"
39:40

FULL INTERVIEW: Martin Casado says AI is the first technology where you can put in $10 and reliably get something back. Everything before it was engineer, wait two years, cross your fingers. martin_casado is the a16z GP behind the firm's investments in both Cursor and OpenRouter. Days after SpaceX closed the $60B Cursor deal and Stripe agreed to buy OpenRouter, he sat down with Theo Jaffee and sophia dew to cover whether the labs win everything, when the subsidies stop, and why RSI is the wrong term: 02:24 whether decades of experience still matter in AI 03:48 what you would have done with a billion dollars ten years ago 04:56 20 people, $2 billion, one model 06:48 why more private capital grows the TAM rather than inflating it 07:35 the a16z conversation about going all-in on AI, seven years before GPT 08:39 why balance-sheet investors keep misreading these companies 10:36 the full case for the labs winning everything 12:12 why he thinks RSI is the wrong term, and what autocatalytic means 13:42 the full case against the labs winning everything 15:25 supply constraints easing in 2028, and his 80/60 split 17:12 why models turned out to be much stickier than anyone assumed 18:18 why real model routing is an AI-complete problem 21:13 what happens when the subsidies stop 21:36 how AI broke marketing, and why you can now buy users with a dollar 23:30 the Chinese operations arbitraging $200 subscription plans 25:43 the CMO is turning into a CFO 29:46 why Cursor iterated faster than anything he's seen outside an Elon company 32:21 what both deals say about strategic value versus business quality 34:10 why he doesn't think a VC's job is to know where to build 38:35 "as long as there's a hill for me to climb"

MTS

214,254 次观看 • 21 天前

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson
29:48

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson

MTS

71,937 次观看 • 1 个月前

The Head of Innovation at Janus Henderson thinks blockchain is the most disruptive tech in asset management We sat down with Nick Cherney to discuss why financial services are moving onchain, what Janus Henderson is already building, and what's still in the way Timestamps 👇 0:00 Intro 0:38 Meet Nick Cherney, Head of Innovation at Janus Henderson 1:43 From ETFs to tokenization 3:05 Why blockchain is more disruptive than AI 4:05 Tokenization explained simply 4:51 What Janus Henderson does onchain 7:03 Why the choice of blockchain matters 9:05 The real friction points for institutions 10:05 Why he says no blockchain has ever been hacked 11:20 Blockchain is not crypto 13:08 The settlement gap holding tokenization back 15:20 What institutions need from a high-performance chain 16:54 The next inflection point for RWAs 18:19 Closing thoughts
19:37

The Head of Innovation at Janus Henderson thinks blockchain is the most disruptive tech in asset management We sat down with Nick Cherney to discuss why financial services are moving onchain, what Janus Henderson is already building, and what's still in the way Timestamps 👇 0:00 Intro 0:38 Meet Nick Cherney, Head of Innovation at Janus Henderson 1:43 From ETFs to tokenization 3:05 Why blockchain is more disruptive than AI 4:05 Tokenization explained simply 4:51 What Janus Henderson does onchain 7:03 Why the choice of blockchain matters 9:05 The real friction points for institutions 10:05 Why he says no blockchain has ever been hacked 11:20 Blockchain is not crypto 13:08 The settlement gap holding tokenization back 15:20 What institutions need from a high-performance chain 16:54 The next inflection point for RWAs 18:19 Closing thoughts

Monad

82,394 次观看 • 1 个月前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.
0:35

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,823 次观看 • 5 个月前

A 24-year-old launched a faceless YouTube channel 13 days ago. In the first month, he has already accumulated $4,000, but he has not shot a single frame.. His investment: a $20 subscription to Claude. No camera, no editor, no voice of his own. THE SYSTEM WORKS LIKE THIS: - Claude writes the full script from one topic prompt - an AI voice narrates it - an AI tool generates the animation and the visuals - one template, repeated forever: "the economics of owning an airport" / "…a private jet" / "…a hospital" - it publishes on a schedule while he sleeps The first 12 days, from zero: > "Why a Fan Can Kill You" -> 272,000 views > second video -> 136,000 views in 6 days > subscribers -> 4,300 > month-one pace -> ~$4,000 And it's replicable. One of his students launched "Mr. Finance" on the same template 13 days ago: > 10,800 subscribers > finance-niche RPM -> ~$7 per 1,000 views > one video on the economics of shopping malls -> $500+ on its own He didn't become a creator. He built a press that prints episodes. The whole stack is public. Everyone has the same $20 Claude. He just pointed it at a niche and refused to stop publishing.
0:45

A 24-year-old launched a faceless YouTube channel 13 days ago. In the first month, he has already accumulated $4,000, but he has not shot a single frame.. His investment: a $20 subscription to Claude. No camera, no editor, no voice of his own. THE SYSTEM WORKS LIKE THIS: - Claude writes the full script from one topic prompt - an AI voice narrates it - an AI tool generates the animation and the visuals - one template, repeated forever: "the economics of owning an airport" / "…a private jet" / "…a hospital" - it publishes on a schedule while he sleeps The first 12 days, from zero: > "Why a Fan Can Kill You" -> 272,000 views > second video -> 136,000 views in 6 days > subscribers -> 4,300 > month-one pace -> ~$4,000 And it's replicable. One of his students launched "Mr. Finance" on the same template 13 days ago: > 10,800 subscribers > finance-niche RPM -> ~$7 per 1,000 views > one video on the economics of shopping malls -> $500+ on its own He didn't become a creator. He built a press that prints episodes. The whole stack is public. Everyone has the same $20 Claude. He just pointed it at a niche and refused to stop publishing.

Ridark

142,816 次观看 • 1 个月前

Full conversation with Luke Alexander on the real reason he shut down his business to bootstrap an AI SaaS. 0:00 Where It Started 4:39 No Internet Rural Ohio 7:21 Leaving Home to Save His Family 9:13 Dropping Out With $500 11:08 Cold DMing His First Client 13:14 Family Responsibility and What Drives Him 16:21 Why Anxiety Doesn't Exist 19:14 Suffering Is the Only Real Skill 21:18 Shutting Down the Agency 25:30 Moving to Miami With 15K 27:08 Launching Closer Cartel 70K Month One 31:01 The Character He Had to Play 33:08 The $12K Car and the Info Meta 36:48 Idols Becoming Brothers 40:06 Retiring His Mom First Million 41:41 His Dad the Restraining Order 43:20 The Man Who Stepped In 47:15 Burnout and Losing the Passion 50:16 The Moment He Walked Away 53:28 Why Info Rewards the Wrong Behavior 56:02 Spiritual Warfare in Miami 1:04:29 Living in Alignment 1:07:56 What He Wants to Look Back On 1:12:18 Why He Built Kindo 1:17:32 The Real Moat Is Taste 1:20:03 Learning Enterprise Sales 1:23:43 Bootstrapping vs Raising 1:26:03 How Kindo Works 1:29:05 AI Will Replace Sales Reps 1:36:09 Genetics and the Real Red Pill 1:39:53 Vision at 30 1:46:13 What It Means to Be a Good Dad 1:49:46 Looking for a Unicorn in Miami 1:51:29 Final Words
1:54:02

Full conversation with Luke Alexander on the real reason he shut down his business to bootstrap an AI SaaS. 0:00 Where It Started 4:39 No Internet Rural Ohio 7:21 Leaving Home to Save His Family 9:13 Dropping Out With $500 11:08 Cold DMing His First Client 13:14 Family Responsibility and What Drives Him 16:21 Why Anxiety Doesn't Exist 19:14 Suffering Is the Only Real Skill 21:18 Shutting Down the Agency 25:30 Moving to Miami With 15K 27:08 Launching Closer Cartel 70K Month One 31:01 The Character He Had to Play 33:08 The $12K Car and the Info Meta 36:48 Idols Becoming Brothers 40:06 Retiring His Mom First Million 41:41 His Dad the Restraining Order 43:20 The Man Who Stepped In 47:15 Burnout and Losing the Passion 50:16 The Moment He Walked Away 53:28 Why Info Rewards the Wrong Behavior 56:02 Spiritual Warfare in Miami 1:04:29 Living in Alignment 1:07:56 What He Wants to Look Back On 1:12:18 Why He Built Kindo 1:17:32 The Real Moat Is Taste 1:20:03 Learning Enterprise Sales 1:23:43 Bootstrapping vs Raising 1:26:03 How Kindo Works 1:29:05 AI Will Replace Sales Reps 1:36:09 Genetics and the Real Red Pill 1:39:53 Vision at 30 1:46:13 What It Means to Be a Good Dad 1:49:46 Looking for a Unicorn in Miami 1:51:29 Final Words

Grant

33,717 次观看 • 4 个月前

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code
1:00:16

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,372 次观看 • 1 年前

Shyam Sankar is trying to mobilize a country. The CTO of Palantir is ringing the alarm on the necessity to resurrect America’s industrial base, win the defense technology race against other countries, and empower workers across the nation without buying into the fears of AI doomerism. In this interview, we sat down to discuss his latest book, why he felt now was the right time to release it, and the message he hopes to communicate to the world. Thank you to Shyam Shyam Sankar for taking the time! Timestamps: 00:00 - Intro 01:02 - How Shyam got to Palantir 02:19 - Palantir's Journey over the years 07:40 - Why is now the time to mobilize 09:40 - Hardware vs Software 14:57 - The Youth and Communism 20:15 - US vs China 22:24 - AI Doomerism 26:21 - Palantir's impact on how Shyam thinks of AI 29:12 - CEOs on the AI doomer case vs optimistic case 34:50 - Rapid Fire
36:39

Shyam Sankar is trying to mobilize a country. The CTO of Palantir is ringing the alarm on the necessity to resurrect America’s industrial base, win the defense technology race against other countries, and empower workers across the nation without buying into the fears of AI doomerism. In this interview, we sat down to discuss his latest book, why he felt now was the right time to release it, and the message he hopes to communicate to the world. Thank you to Shyam Shyam Sankar for taking the time! Timestamps: 00:00 - Intro 01:02 - How Shyam got to Palantir 02:19 - Palantir's Journey over the years 07:40 - Why is now the time to mobilize 09:40 - Hardware vs Software 14:57 - The Youth and Communism 20:15 - US vs China 22:24 - AI Doomerism 26:21 - Palantir's impact on how Shyam thinks of AI 29:12 - CEOs on the AI doomer case vs optimistic case 34:50 - Rapid Fire

amit

168,437 次观看 • 6 个月前

Mitchell Green is the co-founder and managing partner of Lead Edge Capital, a $9B growth equity firm he founded in 2011. For 15 years, he and his partners have built one of the most disciplined investment machines in the business, designed to deliver consistent returns by hitting doubles and triples rather than chasing power law outcomes. He obsesses over avoiding zeros and is constantly underwriting investments to know exactly when to sell. He has built a unique culture at Lead Edge, sending handwritten thank you notes to nearly everyone he meets and sitting down one on one with every person at the firm once a year. He is, by his own admission, one of the most persistent and competitive people you will meet. After the episode, he told me he believes the most important thing in life is to be memorable. You will find, listening to this conversation, that he very much is. We discuss: - Why it’s the best time to buy public software companies - Lessons from 10,000 cold calls - His unique LP base - Why consistency of returns matters more than home runs - The art of knowing when to sell - How culture is built from the top + the importance of follow-through - What skiing taught him about risk and competition Enjoy! Timestamps: 0:00 Intro 1:00 The Hierarchy of BS 2:20 Lessons From 10,000 Cold Calls 9:05 Base Hits vs. Grand Slams 15:24 The 8 Buying Criteria 17:24 Pricing and the AI Exit Multiple Trap 19:24 Software as a Game of Distribution 23:20 Creative Deal Structuring 26:27 The Framework for Focus 29:01 The Art of the Investigative Cold Call 34:24 Culture of Hustle 35:34 The Annual One-on-One Process 37:59 Playing to Strengths 39:43 The Mount Rushmore of Investment Machines 42:05 Fears and Excitement Around AI 48:54 Ski Racing 52:29 Advice for Starting a Firm 54:35 The Kindest Thing
1:00:38

Mitchell Green is the co-founder and managing partner of Lead Edge Capital, a $9B growth equity firm he founded in 2011. For 15 years, he and his partners have built one of the most disciplined investment machines in the business, designed to deliver consistent returns by hitting doubles and triples rather than chasing power law outcomes. He obsesses over avoiding zeros and is constantly underwriting investments to know exactly when to sell. He has built a unique culture at Lead Edge, sending handwritten thank you notes to nearly everyone he meets and sitting down one on one with every person at the firm once a year. He is, by his own admission, one of the most persistent and competitive people you will meet. After the episode, he told me he believes the most important thing in life is to be memorable. You will find, listening to this conversation, that he very much is. We discuss: - Why it’s the best time to buy public software companies - Lessons from 10,000 cold calls - His unique LP base - Why consistency of returns matters more than home runs - The art of knowing when to sell - How culture is built from the top + the importance of follow-through - What skiing taught him about risk and competition Enjoy! Timestamps: 0:00 Intro 1:00 The Hierarchy of BS 2:20 Lessons From 10,000 Cold Calls 9:05 Base Hits vs. Grand Slams 15:24 The 8 Buying Criteria 17:24 Pricing and the AI Exit Multiple Trap 19:24 Software as a Game of Distribution 23:20 Creative Deal Structuring 26:27 The Framework for Focus 29:01 The Art of the Investigative Cold Call 34:24 Culture of Hustle 35:34 The Annual One-on-One Process 37:59 Playing to Strengths 39:43 The Mount Rushmore of Investment Machines 42:05 Fears and Excitement Around AI 48:54 Ski Racing 52:29 Advice for Starting a Firm 54:35 The Kindest Thing

Patrick OShaughnessy

738,218 次观看 • 5 个月前

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts
1:33:50

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Lenny Rachitsky

157,470 次观看 • 1 个月前

Today, a16z is announcing the Machine Age Fund, a new $1.1 billion fund for founders rebuilding what intelligence runs on: chips, memory, networking, systems software, power, and the machines that bring AI into the physical world. Ben Horowitz, Martin Casado, and Raghu Raghuram see a new economic law. A thousand engineers cannot erase a two-year software lead, but a massive GPU cluster can turn capital directly into capability. Models are improving faster than the memory, interconnect, power, and cooling beneath them. The founder map is changing with it. Some of the strongest teams are moving from pure software into complex hardware because every constraint in the stack is now a company-building opportunity. In this conversation with Erik Torenberg, they explain what founders can build, why the opportunity reaches all the way down to the physical stack, and why a16z created a fund for it. 00:00 Intro 01:08 Why AI needs an entirely new infrastructure 02:02 The bottleneck is no longer the model 02:40 Founders saw it before investors did 06:07 Sold out through 2028 07:14 Why this time is different from 1999 08:57 The company whose idle hardware gained value 10:51 "Why didn't this fund exist five years ago?" 13:51 "Nobody likes to use AI more than AI" 17:06 The startup law that capital just broke 21:05 What Grok Bot got right 26:43 The case for a custom chip per model 28:23 Why AC power isn't good enough 32:07 Lots of new electricians 34:14 What one gigawatt can power 35:03 Why utilities can't just build faster 37:22 The data centers that give power back 38:01 Why we should never have called it AI 39:43 Why Nvidia will willingly leave money on the table 48:08 Hardware founders are older 52:36 Why America has to lead YouTube: benahorowitz.eth Raghu Raghuram martin_casado Erik Torenberg
53:58

Today, a16z is announcing the Machine Age Fund, a new $1.1 billion fund for founders rebuilding what intelligence runs on: chips, memory, networking, systems software, power, and the machines that bring AI into the physical world. Ben Horowitz, Martin Casado, and Raghu Raghuram see a new economic law. A thousand engineers cannot erase a two-year software lead, but a massive GPU cluster can turn capital directly into capability. Models are improving faster than the memory, interconnect, power, and cooling beneath them. The founder map is changing with it. Some of the strongest teams are moving from pure software into complex hardware because every constraint in the stack is now a company-building opportunity. In this conversation with Erik Torenberg, they explain what founders can build, why the opportunity reaches all the way down to the physical stack, and why a16z created a fund for it. 00:00 Intro 01:08 Why AI needs an entirely new infrastructure 02:02 The bottleneck is no longer the model 02:40 Founders saw it before investors did 06:07 Sold out through 2028 07:14 Why this time is different from 1999 08:57 The company whose idle hardware gained value 10:51 "Why didn't this fund exist five years ago?" 13:51 "Nobody likes to use AI more than AI" 17:06 The startup law that capital just broke 21:05 What Grok Bot got right 26:43 The case for a custom chip per model 28:23 Why AC power isn't good enough 32:07 Lots of new electricians 34:14 What one gigawatt can power 35:03 Why utilities can't just build faster 37:22 The data centers that give power back 38:01 Why we should never have called it AI 39:43 Why Nvidia will willingly leave money on the table 48:08 Hardware founders are older 52:36 Why America has to lead YouTube: benahorowitz.eth Raghu Raghuram martin_casado Erik Torenberg

a16z

637,937 次观看 • 14 天前

"I haven't sold a single sat." Michael Saylor (Michael Saylor) is Executive Chairman of Strategy, the largest corporate holder of Bitcoin on earth. While crypto twitter blamed him for the correction over a 32-coin sale, he sat down with me in Prague and explained why Bitcoin is really lagging and why it has almost nothing to do with Bitcoin. "We bought 175,000 Bitcoin this year, which is like 20% of all the Bitcoin ever bought. We sold 32. 32 works out to be two basis points." We cover: - Why Bitcoin is lagging while the S&P prints all-time highs, the "massive AI black hole" pulling capital out of crypto - Why he thinks the money rotates back by Q4 - The 32-BTC sale, the scapegoat dynamic, and why he hasn't sold a sat of his own - Why a 50% drawdown is normal, the 2022 one was 75% - The Apple and Amazon adoption curve, and the "Warren Buffett moment" he says is still coming - What actually happens to Strategy if Bitcoin stalls for 40 years - Why he believes being irrelevant is the only thing worse than being hated Thanks to Michael for coming on New Era Finance Podcast. Highlights: 00:00 - Intro 00:25 - Bitcoin Is Now Digital Capital 03:30 - Digital Credit, Invented In 12 Months 07:40 - Why Bitcoin Is Lagging The Market 12:15 - The Apple & Amazon Comparison 18:30 - Did Saylor Sell His Bitcoin? 21:00 - We Bought 175,000. We Sold 32. 25:00 - Defending The Credit & The Equity 29:30 - What If Bitcoin Stalls For 40 Years? 33:00 - The Warren Buffett Moment Is Coming 37:00 - Being Hated vs Being Irrelevant
40:09

"I haven't sold a single sat." Michael Saylor (Michael Saylor) is Executive Chairman of Strategy, the largest corporate holder of Bitcoin on earth. While crypto twitter blamed him for the correction over a 32-coin sale, he sat down with me in Prague and explained why Bitcoin is really lagging and why it has almost nothing to do with Bitcoin. "We bought 175,000 Bitcoin this year, which is like 20% of all the Bitcoin ever bought. We sold 32. 32 works out to be two basis points." We cover: - Why Bitcoin is lagging while the S&P prints all-time highs, the "massive AI black hole" pulling capital out of crypto - Why he thinks the money rotates back by Q4 - The 32-BTC sale, the scapegoat dynamic, and why he hasn't sold a sat of his own - Why a 50% drawdown is normal, the 2022 one was 75% - The Apple and Amazon adoption curve, and the "Warren Buffett moment" he says is still coming - What actually happens to Strategy if Bitcoin stalls for 40 years - Why he believes being irrelevant is the only thing worse than being hated Thanks to Michael for coming on New Era Finance Podcast. Highlights: 00:00 - Intro 00:25 - Bitcoin Is Now Digital Capital 03:30 - Digital Credit, Invented In 12 Months 07:40 - Why Bitcoin Is Lagging The Market 12:15 - The Apple & Amazon Comparison 18:30 - Did Saylor Sell His Bitcoin? 21:00 - We Bought 175,000. We Sold 32. 25:00 - Defending The Credit & The Equity 29:30 - What If Bitcoin Stalls For 40 Years? 33:00 - The Warren Buffett Moment Is Coming 37:00 - Being Hated vs Being Irrelevant

Michaël van de Poppe

530,624 次观看 • 2 个月前

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC
57:15

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

306,787 次观看 • 7 天前

Elon Musk revealed why he keeps working despite of his $800 billion net worth: 1. As a teenager, Musk had a full existential crisis trying to figure out the meaning of life. Religious texts did not convince him. So he turned to philosophy. Reading Schopenhauer and Nietzsche as a depressed teenager, he says, was a terrible idea. 2. Then he read Hitchhiker's Guide to the Galaxy and something shifted. Douglas Adams argued the real problem is not finding the answer to life. It is figuring out the right questions to ask in the first place. 3. In the book, Earth is a giant computer built to answer the meaning of life. It spends millions of years computing and delivers its answer. The answer is 42. The joke is that nobody knows what the question was. 4. That idea never left Musk. The universe is the answer. We just do not know what question it is answering. And to figure out the right question, you need a much bigger and more expansive consciousness than we currently have. 5. The more we can expand the scope and scale of consciousness, the better we can understand what questions to ask about the answer that is the universe. 6. This is why he wants more humans. More digital intelligence. More biological and artificial consciousness working together. Not for economic growth. Not for power. To collectively get better at asking the right questions. 7. This is why Mars matters to him. Not for the adventure. Not for the achievement. A single planet species is a single point of failure for consciousness itself. If something wipes out Earth, the question never gets asked. 8. "The more we can expand consciousness, become a multi-planet species, ultimately a multi-stellar species, we have a chance of figuring out what the hell is going on." 9. Everything he is building, the rockets, the AI, the robots, comes back to this one idea. He is not trying to get rich. He is trying to keep the candle of consciousness alive long enough to understand why it exists. 10. With $800 billion he could stop tomorrow. He does not stop because to him stopping would mean giving up on the only question that ever actually mattered. What is this all for and what are we supposed to be asking?
2:56

Elon Musk revealed why he keeps working despite of his $800 billion net worth: 1. As a teenager, Musk had a full existential crisis trying to figure out the meaning of life. Religious texts did not convince him. So he turned to philosophy. Reading Schopenhauer and Nietzsche as a depressed teenager, he says, was a terrible idea. 2. Then he read Hitchhiker's Guide to the Galaxy and something shifted. Douglas Adams argued the real problem is not finding the answer to life. It is figuring out the right questions to ask in the first place. 3. In the book, Earth is a giant computer built to answer the meaning of life. It spends millions of years computing and delivers its answer. The answer is 42. The joke is that nobody knows what the question was. 4. That idea never left Musk. The universe is the answer. We just do not know what question it is answering. And to figure out the right question, you need a much bigger and more expansive consciousness than we currently have. 5. The more we can expand the scope and scale of consciousness, the better we can understand what questions to ask about the answer that is the universe. 6. This is why he wants more humans. More digital intelligence. More biological and artificial consciousness working together. Not for economic growth. Not for power. To collectively get better at asking the right questions. 7. This is why Mars matters to him. Not for the adventure. Not for the achievement. A single planet species is a single point of failure for consciousness itself. If something wipes out Earth, the question never gets asked. 8. "The more we can expand consciousness, become a multi-planet species, ultimately a multi-stellar species, we have a chance of figuring out what the hell is going on." 9. Everything he is building, the rockets, the AI, the robots, comes back to this one idea. He is not trying to get rich. He is trying to keep the candle of consciousness alive long enough to understand why it exists. 10. With $800 billion he could stop tomorrow. He does not stop because to him stopping would mean giving up on the only question that ever actually mattered. What is this all for and what are we supposed to be asking?

Yasmine Khosrowshahi

105,576 次观看 • 3 个月前

FULL INTERVIEW: Jen Kha says El Salvador gave every school free Grok and is deploying AI doctors. She says countries nobody expects are now outpacing America on adoption while America fights over data centers. Jen Kha is a managing partner and head of global partnerships at a16z. She joined Theo Jaffee and sophia dew on announcement day for the $1.1B Machine Age Fund to walk through the thesis and break down why the physical layer under AI was uninvestable for 30 years and isn't anymore: 00:38 why the timing of this fund says everything about the cycle 02:40 following the nerd energy 02:59 software's eating the world, but AI already solved software 04:44 23% of all venture capital this year 06:09 SpaceX public, Anthropic and OpenAI next, and what that does to LPs 07:36 the partner who said they'd deploy the whole fund in six weeks 10:01 the president of Korea's first and only US stop was Silicon Valley 10:15 NVIDIA is bigger than every G7 country except the US 12:27 South Korea, El Salvador and the countries adopting AI faster than America 14:33 why she thinks the data center narrative got ahead of reality 16:31 under 2% of US electricians are trained on DC power 17:45 agents now use 5x the tokens humans do 19:24 why these founders are a throwback, and where they're spinning out from
22:38

FULL INTERVIEW: Jen Kha says El Salvador gave every school free Grok and is deploying AI doctors. She says countries nobody expects are now outpacing America on adoption while America fights over data centers. Jen Kha is a managing partner and head of global partnerships at a16z. She joined Theo Jaffee and sophia dew on announcement day for the $1.1B Machine Age Fund to walk through the thesis and break down why the physical layer under AI was uninvestable for 30 years and isn't anymore: 00:38 why the timing of this fund says everything about the cycle 02:40 following the nerd energy 02:59 software's eating the world, but AI already solved software 04:44 23% of all venture capital this year 06:09 SpaceX public, Anthropic and OpenAI next, and what that does to LPs 07:36 the partner who said they'd deploy the whole fund in six weeks 10:01 the president of Korea's first and only US stop was Silicon Valley 10:15 NVIDIA is bigger than every G7 country except the US 12:27 South Korea, El Salvador and the countries adopting AI faster than America 14:33 why she thinks the data center narrative got ahead of reality 16:31 under 2% of US electricians are trained on DC power 17:45 agents now use 5x the tokens humans do 19:24 why these founders are a throwback, and where they're spinning out from

MTS

4,357,096 次观看 • 13 天前