Loading video...

Video Failed to Load

Go Home

Touring Impulse with the Stove Guy Sam D'Amico and allen featuring never-seen-before AI cooking features. Sam is reinventing the stove from first principles: battery-powered architecture, software-defined hardware, & OTA updates. In this episode, we discuss: • Why Impulse centered the stove around a battery • How they achieved extreme...

latentspacepod's profile picture

Latent.Space

29,134 subscribers

17,116 views • 5 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing
41:16

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 views • 3 months ago

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

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

Y Combinator

67,145 views • 4 months ago

there has been a lot of discussion of the SF housing crisis on the timeline recently so i brought on Sam D'Amico and Noah Smith 🐇🇺🇸🇺🇦🇹🇼 to break down how we got here while we cooked some steaks. finally MOTS gets political 00:00 - The SF Sam power rankings and using the Impulse stove 03:00 - Preparing steak 04:07 - Recent history of SF politics 07:48 - The rise of the NIMBY in SF 14:25 - SF Faction breakdown and inclusionary zoning 20:11 - The rise of the YIMBY in SF 24:33 - The progressive overreach in SF 28:23 - The weirdos in SF joined the AI labs 30:30 - It’s hard to know what you’re voting for 34:05 - Mayor Daniel Lurie 35:05 - SF unions 40:10 - The rise of non-profits in SF 45:35 - The board of supervisors and veto power of development
52:18

there has been a lot of discussion of the SF housing crisis on the timeline recently so i brought on Sam D'Amico and Noah Smith 🐇🇺🇸🇺🇦🇹🇼 to break down how we got here while we cooked some steaks. finally MOTS gets political 00:00 - The SF Sam power rankings and using the Impulse stove 03:00 - Preparing steak 04:07 - Recent history of SF politics 07:48 - The rise of the NIMBY in SF 14:25 - SF Faction breakdown and inclusionary zoning 20:11 - The rise of the YIMBY in SF 24:33 - The progressive overreach in SF 28:23 - The weirdos in SF joined the AI labs 30:30 - It’s hard to know what you’re voting for 34:05 - Mayor Daniel Lurie 35:05 - SF unions 40:10 - The rise of non-profits in SF 45:35 - The board of supervisors and veto power of development

atlas

89,714 views • 1 month ago

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 views • 5 months ago

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
39:32

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

107,806 views • 6 months ago

The US vs. China Manufacturing Debate My first debate between Sam D'Amico, founder of Impulse, and Aaron Slodov, founder of Atomic Industries. Timestamps: 0:39 Introductions 11:34 Is it possible to reverse America’s manufacturing decline? 16:37 Where would Sam invest $100M in a factory today? 21:28 How California made it illegal to do chemistry w metals 25:43 Elon on Tesla's Shanghai Gigafactory 32:01 Using the data center boom to pull advanced capabilities onshore 36:51 Building Chinese level capacity in America 38:33 The US Gov hasn't incentivized companies to build base level capacity 42:54 Why space companies have to vertically integrate their supply chains 45:06 The advantage of building a Chinese supply chain 49:10 Why Apple manufactures all their products in China 59:42 Apple exports engineering expertise 1:02:19 Consumer electronics 1:07:55 How the advent of humanoid robots will shift the balance of power 1:12:29 Why Apple spent hundreds of billions building their supply chains in China
1:16:56

The US vs. China Manufacturing Debate My first debate between Sam D'Amico, founder of Impulse, and Aaron Slodov, founder of Atomic Industries. Timestamps: 0:39 Introductions 11:34 Is it possible to reverse America’s manufacturing decline? 16:37 Where would Sam invest $100M in a factory today? 21:28 How California made it illegal to do chemistry w metals 25:43 Elon on Tesla's Shanghai Gigafactory 32:01 Using the data center boom to pull advanced capabilities onshore 36:51 Building Chinese level capacity in America 38:33 The US Gov hasn't incentivized companies to build base level capacity 42:54 Why space companies have to vertically integrate their supply chains 45:06 The advantage of building a Chinese supply chain 49:10 Why Apple manufactures all their products in China 59:42 Apple exports engineering expertise 1:02:19 Consumer electronics 1:07:55 How the advent of humanoid robots will shift the balance of power 1:12:29 Why Apple spent hundreds of billions building their supply chains in China

Ti Morse

232,737 views • 6 months ago

I sat down with Jordi Visser to discuss why bitcoin is down 50%, whether the bear market is over, why he's still buying through the dip, how AI agents will drive bitcoin adoption, and why the rotation from AI hardware to human software is the biggest investment opportunity right now. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:45 - Why bitcoin is down 50% & is the bear market over? 6:48 - What would convince Jordi to sell his bitcoin? 9:07 - Bitcoin as the S&P 500 of crypto 14:02 - Why bitcoin is safer than any company past 2030 17:45 - Bitcoin volatility vs. the stock market 22:22 - The five-layer AI stack & where revenues are missing 26:22 - Token budgets & the shift to specialized AI 35:10 - Are LLMs now commoditized? 40:30 - Peptides as the API key for the human body 49:24 - Healthcare, entitlements & the future outlook 55:12 - Jordi’s upcoming video & final thoughts
57:38

I sat down with Jordi Visser to discuss why bitcoin is down 50%, whether the bear market is over, why he's still buying through the dip, how AI agents will drive bitcoin adoption, and why the rotation from AI hardware to human software is the biggest investment opportunity right now. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:45 - Why bitcoin is down 50% & is the bear market over? 6:48 - What would convince Jordi to sell his bitcoin? 9:07 - Bitcoin as the S&P 500 of crypto 14:02 - Why bitcoin is safer than any company past 2030 17:45 - Bitcoin volatility vs. the stock market 22:22 - The five-layer AI stack & where revenues are missing 26:22 - Token budgets & the shift to specialized AI 35:10 - Are LLMs now commoditized? 40:30 - Peptides as the API key for the human body 49:24 - Healthcare, entitlements & the future outlook 55:12 - Jordi’s upcoming video & final thoughts

Anthony Pompliano 🌪

286,011 views • 3 months ago

EXCLUSIVE: Microsoft just launched Copilot Vision in Edge—the first AI that can navigate the internet with you in real time. I sat down with Mustafa Suleyman (CEO of Microsoft AI) to discuss how it works, infinite memory, AI companions, agents, and more. Timestamps: 0:00 Intro 0:58 Microsoft Copilot Vision rundown 02:35 Initial reactions and use cases 04:57 How Copilot Vision works 06:02 Teaching AI to remember 08:13 How Microsoft AI is differentiating from OpenAI 09:27 The push for the true AI companion 11:28 Living with a co-intelligence in 10+ years 14:04 How Microsoft handles your data 16:11 When and how can you try Copilot Vision? 18:15 Agentic AI that controls your computer 20:25 What's next: support, gaming, and more 22:34 Preparing the next generation with Copilot 25:11 Mustafa’s advice for students and businesses
26:21

EXCLUSIVE: Microsoft just launched Copilot Vision in Edge—the first AI that can navigate the internet with you in real time. I sat down with Mustafa Suleyman (CEO of Microsoft AI) to discuss how it works, infinite memory, AI companions, agents, and more. Timestamps: 0:00 Intro 0:58 Microsoft Copilot Vision rundown 02:35 Initial reactions and use cases 04:57 How Copilot Vision works 06:02 Teaching AI to remember 08:13 How Microsoft AI is differentiating from OpenAI 09:27 The push for the true AI companion 11:28 Living with a co-intelligence in 10+ years 14:04 How Microsoft handles your data 16:11 When and how can you try Copilot Vision? 18:15 Agentic AI that controls your computer 20:25 What's next: support, gaming, and more 22:34 Preparing the next generation with Copilot 25:11 Mustafa’s advice for students and businesses

Rowan Cheung

268,106 views • 1 year ago

The Sam Altman Interview You know him as the CEO of OpenAI — but he's also an avid writer. We spoke not once but twice about how Sam captures ideas, clarifies his thinking, edits his writing, decides what to work on, and uses ChatGPT. Timestamps: 1:47 Will LLMs change how we write? 8:39 How does Sam use ChatGPT? 11:26 How Sam became less anxious 17:24 Sam once dreamed of being a novelist 18:37 Lessons from Peter Thiel 21:35 Lessons from Paul Graham 26:02 The book Sam Altman wants to write 28:37 Advice for startup founders 30:20 How Y Combinator shapes OpenAI 35:55 How Sam chose to work on AGI 37:35 Writing strategy memos at OpenAI 41:34 Why isn’t ChatGPT a better storyteller? 44:20 Sam's obsessive note-taking method 47:12 Will AI put writers out of work?
48:38

The Sam Altman Interview You know him as the CEO of OpenAI — but he's also an avid writer. We spoke not once but twice about how Sam captures ideas, clarifies his thinking, edits his writing, decides what to work on, and uses ChatGPT. Timestamps: 1:47 Will LLMs change how we write? 8:39 How does Sam use ChatGPT? 11:26 How Sam became less anxious 17:24 Sam once dreamed of being a novelist 18:37 Lessons from Peter Thiel 21:35 Lessons from Paul Graham 26:02 The book Sam Altman wants to write 28:37 Advice for startup founders 30:20 How Y Combinator shapes OpenAI 35:55 How Sam chose to work on AGI 37:35 Writing strategy memos at OpenAI 41:34 Why isn’t ChatGPT a better storyteller? 44:20 Sam's obsessive note-taking method 47:12 Will AI put writers out of work?

David Perell

239,843 views • 2 years ago

My conversation with Eric Vishria of Benchmark. Eric has spent a decade investing across software and hardware, backing companies like Fireworks, Sierra, Sunday Robotics, and Cerebras. This one is about what history teaches us about the current moment, and a dispatch from inside the AI buildout through his companies. We discuss: - What AWS tells us about how big AI can get - How the goalposts have moved for every software company - Lessons from a decade with Cerebras - China and the energy bottleneck - Benchmark's return to growth investing - Robotics Enjoy! TIMESTAMPS 0:00 Intro 3:36 What Cloud Teaches Us About AI 12:52 Sierra, Sandcastles, and AI Products 17:35 The New SaaS Competitive Frontier 28:28 AI Demand and the Energy Bottleneck 31:28 The Cerebras Story and AI Chips 45:10 The Future of Robotics 52:53 Eric’s Venture Investing Philosophy 1:07:30 Going Public, AI Value, and Jobs
1:17:52

My conversation with Eric Vishria of Benchmark. Eric has spent a decade investing across software and hardware, backing companies like Fireworks, Sierra, Sunday Robotics, and Cerebras. This one is about what history teaches us about the current moment, and a dispatch from inside the AI buildout through his companies. We discuss: - What AWS tells us about how big AI can get - How the goalposts have moved for every software company - Lessons from a decade with Cerebras - China and the energy bottleneck - Benchmark's return to growth investing - Robotics Enjoy! TIMESTAMPS 0:00 Intro 3:36 What Cloud Teaches Us About AI 12:52 Sierra, Sandcastles, and AI Products 17:35 The New SaaS Competitive Frontier 28:28 AI Demand and the Energy Bottleneck 31:28 The Cerebras Story and AI Chips 45:10 The Future of Robotics 52:53 Eric’s Venture Investing Philosophy 1:07:30 Going Public, AI Value, and Jobs

Patrick OShaughnessy

675,860 views • 1 month ago

I spoke with Jordi Visser this week to break down: - AI mid-cycle slowdown - $90 trillion AI infrastructure buildout - memory and Micron's role in AI bottleneck - Fed under Warsh - what rate cuts could mean for markets - why bitcoin is positioned to be the best performing assets once the negatives of AI start to show up YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking? 9:00 - Sam Altman, OpenAI & the government getting involved 11:11 - American open source vs Chinese AI models 14:28 - AI compute demand & the infrastructure buildout 20:37 - AI slowdown: how bad, how long? 22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk 34:50 - Memory, Micron & the AI bottleneck 39:53 - The next 10-20 years: robots, AI & society 44:18 - The Fed, rate cuts & inflation's impact on AI 49:55 - Why bitcoin wins in the end
55:11

I spoke with Jordi Visser this week to break down: - AI mid-cycle slowdown - $90 trillion AI infrastructure buildout - memory and Micron's role in AI bottleneck - Fed under Warsh - what rate cuts could mean for markets - why bitcoin is positioned to be the best performing assets once the negatives of AI start to show up YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking? 9:00 - Sam Altman, OpenAI & the government getting involved 11:11 - American open source vs Chinese AI models 14:28 - AI compute demand & the infrastructure buildout 20:37 - AI slowdown: how bad, how long? 22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk 34:50 - Memory, Micron & the AI bottleneck 39:53 - The next 10-20 years: robots, AI & society 44:18 - The Fed, rate cuts & inflation's impact on AI 49:55 - Why bitcoin wins in the end

Anthony Pompliano 🌪

64,370 views • 2 months ago

I sat down with Jordi Visser to discuss why bitcoin is selling off, why software multiples are compressing, and how capital is shifting toward scarce, physical assets. We cover data centers, AI, energy, and semiconductors, plus insights on Elon Musk, Tesla, SpaceX, and what it all means for markets. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 – Intro 0:50 – Why is bitcoin crashing? 3:01 – The big crypto utility shift & why software multiples are collapsing 10:55 – The rotation: hardware/commodities outperforming software 17:14 – How AI agents disrupt legacy enterprise software 33:51 – Jordi’s “turbulence model” & what it’s signaling in markets 40:34 – Tesla/Elon: bull case, risks, & the path to $10T 50:25 – Should Elon merge Tesla + SpaceX + xAI + X? 53:22 – Wrap & what Jordi is publishing next
56:03

I sat down with Jordi Visser to discuss why bitcoin is selling off, why software multiples are compressing, and how capital is shifting toward scarce, physical assets. We cover data centers, AI, energy, and semiconductors, plus insights on Elon Musk, Tesla, SpaceX, and what it all means for markets. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 – Intro 0:50 – Why is bitcoin crashing? 3:01 – The big crypto utility shift & why software multiples are collapsing 10:55 – The rotation: hardware/commodities outperforming software 17:14 – How AI agents disrupt legacy enterprise software 33:51 – Jordi’s “turbulence model” & what it’s signaling in markets 40:34 – Tesla/Elon: bull case, risks, & the path to $10T 50:25 – Should Elon merge Tesla + SpaceX + xAI + X? 53:22 – Wrap & what Jordi is publishing next

Anthony Pompliano 🌪

44,334 views • 7 months ago

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing
1:26:12

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

Patrick OShaughnessy

49,418 views • 11 months ago

I sat down with Nicolas Sharp, founder of Attio, to talk in depth about how his company is disrupting the $80 billion CRM market. Attio has raised $116m, is 4x'ing ARR and is one of the fastest growing companies in Europe. We talk about: - Why Attio went against all conventional wisdom and spent years 3 years building the product before launching - Why Attio doesn’t hire ‘Software Engineers’ and who they hire instead. - How Attio chose investors who would back a long-term bet against multi-billion $ incumbents - How Attio is building CRM from first principles for the AI era - Who should you avoid hiring at all costs, and who should you hire for your startup And so much more. If you’re interested in learning about the story of how Nick built Attio into the incredible company it is today and is disrupting one of the most important software categories, you’re going to want to see this. Enjoy // Timestamps 00:00 Intro 00:39 Why Attio spent 3 years building their product before launch 5:05 The Power of Building Systems, Not a Box of Features 9:05 How to know when it’s time to launch 13:30 Nick’s Playbook For a Killer Product Launch 18:09 How To Go From an Investor to a Founder 23:05 How Failure Led to Attio's Big Break 28:07 Why startups need to hire "Hidden Gems," 34:05 Fundraising 49:36 Why Attio Invests In Inexperienced Talent 55:07 The Case For Not Hiring Software Engineers (& Who You Should Hire Instead) 1:02:46 The 8 Persona Hiring Framework 1:09:05 How Attio doesn’t use OKR's 1:27:11 Where does Nick's Ambition and Grit come from? 1:36:59 How Attio is Building CRM from First Principles for the AI era 1:45:29 How to Successfully Market In A Crowded Industry 1:51:10 Breaking Down Attio’s Viral Marketing Strategies 2:02:15 The Change That Had The Biggest Impact on Customer Conversion 2:05:04 Why Attio created A “Reverse Trial” 2:10:16 Why Nick is building from London, not Silicon Valley 2:22:48 The 10-Year Vision for Attio
2:26:34

I sat down with Nicolas Sharp, founder of Attio, to talk in depth about how his company is disrupting the $80 billion CRM market. Attio has raised $116m, is 4x'ing ARR and is one of the fastest growing companies in Europe. We talk about: - Why Attio went against all conventional wisdom and spent years 3 years building the product before launching - Why Attio doesn’t hire ‘Software Engineers’ and who they hire instead. - How Attio chose investors who would back a long-term bet against multi-billion $ incumbents - How Attio is building CRM from first principles for the AI era - Who should you avoid hiring at all costs, and who should you hire for your startup And so much more. If you’re interested in learning about the story of how Nick built Attio into the incredible company it is today and is disrupting one of the most important software categories, you’re going to want to see this. Enjoy // Timestamps 00:00 Intro 00:39 Why Attio spent 3 years building their product before launch 5:05 The Power of Building Systems, Not a Box of Features 9:05 How to know when it’s time to launch 13:30 Nick’s Playbook For a Killer Product Launch 18:09 How To Go From an Investor to a Founder 23:05 How Failure Led to Attio's Big Break 28:07 Why startups need to hire "Hidden Gems," 34:05 Fundraising 49:36 Why Attio Invests In Inexperienced Talent 55:07 The Case For Not Hiring Software Engineers (& Who You Should Hire Instead) 1:02:46 The 8 Persona Hiring Framework 1:09:05 How Attio doesn’t use OKR's 1:27:11 Where does Nick's Ambition and Grit come from? 1:36:59 How Attio is Building CRM from First Principles for the AI era 1:45:29 How to Successfully Market In A Crowded Industry 1:51:10 Breaking Down Attio’s Viral Marketing Strategies 2:02:15 The Change That Had The Biggest Impact on Customer Conversion 2:05:04 Why Attio created A “Reverse Trial” 2:10:16 Why Nick is building from London, not Silicon Valley 2:22:48 The 10-Year Vision for Attio

Wouter Teunissen

26,472 views • 1 year ago

The DoJ is using your tax dollars to sue any community that resists data centers. Clayton Morris on the AI takeover of America. 0:00 Clayton Morris’ Perspective on AI and Data Centers 4:26 Are Your Zoom Calls Being Recorded and Where Does the Data Go? 8:08 The AI Task Force Designed to Pummel Communities 14:29 How Much Water Does It Take to Cool a Data Center? 21:40 Why Build Data Centers in Areas Lacking Necessary Resources? 29:53 The Powerlessness of Communities Consumed by Data Centers 33:30 AI-China War, National Security, and Amazon Data Centers 41:27 Morris Debunks the AI Bubble Theory 47:34 The Upsides vs. Downsides of AI 54:26 Do We Serve the Machine or Does the Machine Serve Us? 58:20 How Anything With an Electrical Impulse Isn’t Under Your Control
1:01:04

The DoJ is using your tax dollars to sue any community that resists data centers. Clayton Morris on the AI takeover of America. 0:00 Clayton Morris’ Perspective on AI and Data Centers 4:26 Are Your Zoom Calls Being Recorded and Where Does the Data Go? 8:08 The AI Task Force Designed to Pummel Communities 14:29 How Much Water Does It Take to Cool a Data Center? 21:40 Why Build Data Centers in Areas Lacking Necessary Resources? 29:53 The Powerlessness of Communities Consumed by Data Centers 33:30 AI-China War, National Security, and Amazon Data Centers 41:27 Morris Debunks the AI Bubble Theory 47:34 The Upsides vs. Downsides of AI 54:26 Do We Serve the Machine or Does the Machine Serve Us? 58:20 How Anything With an Electrical Impulse Isn’t Under Your Control

Tucker Carlson

778,124 views • 1 month ago