正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Wabi Is Building Personalized Software for Everyone In a recent report from OpenAI, almost 80% of ChatGPT usage was for practical guidance, seeking information, and writing. AI usage shouldn’t be limited to search and writing. At wabi, Eugenia Kuyda is building a new kind of AI interface so that... anyone can create and share personalized AI-generated software. Eugenia joined a16z’s Anish Acharya, Justine Moore, and Erik Torenberg to explain why we’re still in the MS-DOS era of AI, how mini apps could change the way we interact with AI, how she started building AI products in 2012 and never stopped, and what she saw in her conversations with the early OpenAI team. 00:00 Introduction 00:54 AI companions & personal software 14:17 Mini apps as community catalysts 19:07 Wabi as a framework for memory, context, and expression 23:11 Prompt sharing as an emergent user behavior 28:11 100x’ing the world’s meaningful software 30:21 The creator economy on Wabi 39:25 Visiting the OpenAI office 43:31 Predicting consumer behavior Eugenia Kuyda Anish Acharya Justine Moore Erik Torenbergshow more

a16z

903,435 subscribers

79,405 次观看 • 9 个月前 •via X (Twitter)

艺术教育科学技术

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

What our consumer team expects in 2026 for AI: - Enterprise usage drives consumer usage - More app generation - More multimodality: Anything-in, anything-out - Better models enable startups to fill niches with opinionated products a16z's Anish Acharya, Bryan Kim, Olivia Moore, and Justine Moore sat down for a conversation on the state of consumer AI, including what we saw in 2025 and what's coming in 2026. They cover the “winner-take-most” trend for general-purpose LLMs, the role of brand in distribution, great products that flew under the radar in 2025, and more. 00:00 Introduction & market overview 00:35 Who won consumer AI in 2025? 02:24 Image and video model innovations 09:11 Underhyped products and productivity apps 10:21 Prosumer tools and power user workflows 12:56 Distribution and brand 14:04 Product nuances: Onboarding and engagement 16:09 Social features 26:54 Looking to 2026: Enterprise, apps, and multimodality 32:02 Startup opportunities and app generation 36:27 Favorite products and recommendations 42:19 Building consumer products in 2026 Anish Acharya Bryan Kim Olivia Moore Justine Moore

a16z

66,374 次观看 • 7 个月前

The AI era of apps is here. In this discussion with a16z Apps Fund GPs Alex Rampell, David Haber, and Anish Acharya, they break down: - How AI is driving explosive product growth - The new product categories that couldn't exist before AI - Why AI-native startups should look for “greenfield” opportunities with unserved customers instead of competing with large incumbents - Why moats still matter - How software is eating labor 00:00 Introduction 02:17 AI's role in enterprise and consumer applications 05:03 Emerging AI trends and investment strategies 08:43 Traditional software going AI-native 14:40 Software eating labor 17:04 Case study: Eve 21:33 Building defensible moats 24:45 Case study: Salient 31:53 The walled garden 40:23 Incumbents vs startups 53:32 Consumer AI applications 56:03 Model aggregation strategy 57:06 Investment process & team 1:06:46 Customer retention & enterprise sales Alex Rampell David Haber Anish Acharya

The AI era of apps is here. In this discussion with a16z Apps Fund GPs Alex Rampell, David Haber, and Anish Acharya, they break down: - How AI is driving explosive product growth - The new product categories that couldn't exist before AI - Why AI-native startups should look for “greenfield” opportunities with unserved customers instead of competing with large incumbents - Why moats still matter - How software is eating labor 00:00 Introduction 02:17 AI's role in enterprise and consumer applications 05:03 Emerging AI trends and investment strategies 08:43 Traditional software going AI-native 14:40 Software eating labor 17:04 Case study: Eve 21:33 Building defensible moats 24:45 Case study: Salient 31:53 The walled garden 40:23 Incumbents vs startups 53:32 Consumer AI applications 56:03 Model aggregation strategy 57:06 Investment process & team 1:06:46 Customer retention & enterprise sales Alex Rampell David Haber Anish Acharya

a16z

87,658 次观看 • 7 个月前

Cursor is at the cutting edge of merging design and software development. Earlier this year Ryo Lu, Head of Design at Cursor, joined a16z's Jennifer Li and Erik Torenberg for a conversation on what AI means for software, creativity, and design. They cover: - How software development has fragmented and why AI could unify it again - What people misunderstand about taste - How constraints drive design - Why design isn't about aesthetics - Everything apps vs purposeful apps 00:00 Intro 01:45 Design becomes approachable to everyone 02:36 From years to minutes: Product feedback loops collapse 17:18 The lost art of being a complete builder 21:42 Design is not about aesthetics 28:57 User-centric vs system-centric philosophy 34:00 AI as a universal interface 38:42 Simplicity as a constraint 46:33 ryOS: Building a personal operating system Ryo Lu Jennifer Li Erik Torenberg

Cursor is at the cutting edge of merging design and software development. Earlier this year Ryo Lu, Head of Design at Cursor, joined a16z's Jennifer Li and Erik Torenberg for a conversation on what AI means for software, creativity, and design. They cover: - How software development has fragmented and why AI could unify it again - What people misunderstand about taste - How constraints drive design - Why design isn't about aesthetics - Everything apps vs purposeful apps 00:00 Intro 01:45 Design becomes approachable to everyone 02:36 From years to minutes: Product feedback loops collapse 17:18 The lost art of being a complete builder 21:42 Design is not about aesthetics 28:57 User-centric vs system-centric philosophy 34:00 AI as a universal interface 38:42 Simplicity as a constraint 46:33 ryOS: Building a personal operating system Ryo Lu Jennifer Li Erik Torenberg

a16z

111,755 次观看 • 8 个月前

Benedict Evans on Why AI Feels Like the Internet in 1997 Benedict Evans joins Erik Torenberg for a conversation on the state of AI, including how coding agents hit product-market fit, why foundation models should be thought of as infrastructure, the value of vertical products, and more. 00:00 Intro 00:44 What's changed since last year 05:53 OpenAI vs Anthropic strategy 10:31 The pricing crunch & platform history 22:48 What comes after coding 38:18 AI & the future of enterprise software 48:43 The CapEx problem 55:07 Will models become commodities? Benedict Evans Erik Torenberg

Benedict Evans on Why AI Feels Like the Internet in 1997 Benedict Evans joins Erik Torenberg for a conversation on the state of AI, including how coding agents hit product-market fit, why foundation models should be thought of as infrastructure, the value of vertical products, and more. 00:00 Intro 00:44 What's changed since last year 05:53 OpenAI vs Anthropic strategy 10:31 The pricing crunch & platform history 22:48 What comes after coding 38:18 AI & the future of enterprise software 48:43 The CapEx problem 55:07 Will models become commodities? Benedict Evans Erik Torenberg

a16z

103,588 次观看 • 2 个月前

Box CEO Aaron Levie on the AI Adoption Gap Aaron Levie joins Steven Sinofsky, Martin Casado, and Erik Torenberg to discuss how AI agents will revolutionize work, the growing pains of building software for the agent economy, what Wall Street gets wrong about AI, and more. 00:00 Intro 00:51 Building software for agents vs. humans 02:10 Can non-technical workers actually use AI agents? 14:31 CFO/CIO pushback: the real fear of agents doing integration 18:39 Treating agents like employees and why it breaks down 27:35 Diffusion gap: startups vs. enterprises 42:53 What Wall Street gets wrong Aaron Levie Steven Sinofsky martin_casado Erik Torenberg

Box CEO Aaron Levie on the AI Adoption Gap Aaron Levie joins Steven Sinofsky, Martin Casado, and Erik Torenberg to discuss how AI agents will revolutionize work, the growing pains of building software for the agent economy, what Wall Street gets wrong about AI, and more. 00:00 Intro 00:51 Building software for agents vs. humans 02:10 Can non-technical workers actually use AI agents? 14:31 CFO/CIO pushback: the real fear of agents doing integration 18:39 Treating agents like employees and why it breaks down 27:35 Diffusion gap: startups vs. enterprises 42:53 What Wall Street gets wrong Aaron Levie Steven Sinofsky martin_casado Erik Torenberg

a16z

276,989 次观看 • 4 个月前

David Sacks on Polytheistic AI, Better Crypto Regulation, Beating China, and Fixing SF David Sacks is the White House AI and Crypto Czar. He joined a16z’s Marc Andreessen, Ben Horowitz, and Erik Torenberg for a conversation covering the importance of beating China in the AI race, the need for a federal standard for crypto regulation, how AI doomerism is replacing climate doomerism, how to solve the energy bottleneck in AI development, the path to fixing San Francisco, and more. 00:00 The state of AI and crypto policy 16:20 Orwellian AI and the real risks of AI 32:26 AI capabilities and pullback from AGI hype 39:18 Open-source AI, decentralization, and software freedom 46:28 Winning the AI race through innovation and exports 53:38 The energy bottleneck and America’s infrastructure challenge 59:48 AI doomerism and political narratives 01:13:30 San Francisco’s future David Sacks Marc Andreessen 🇺🇸 benahorowitz.eth Erik Torenberg

David Sacks on Polytheistic AI, Better Crypto Regulation, Beating China, and Fixing SF David Sacks is the White House AI and Crypto Czar. He joined a16z’s Marc Andreessen, Ben Horowitz, and Erik Torenberg for a conversation covering the importance of beating China in the AI race, the need for a federal standard for crypto regulation, how AI doomerism is replacing climate doomerism, how to solve the energy bottleneck in AI development, the path to fixing San Francisco, and more. 00:00 The state of AI and crypto policy 16:20 Orwellian AI and the real risks of AI 32:26 AI capabilities and pullback from AGI hype 39:18 Open-source AI, decentralization, and software freedom 46:28 Winning the AI race through innovation and exports 53:38 The energy bottleneck and America’s infrastructure challenge 59:48 AI doomerism and political narratives 01:13:30 San Francisco’s future David Sacks Marc Andreessen 🇺🇸 benahorowitz.eth Erik Torenberg

a16z

2,229,761 次观看 • 9 个月前

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

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

a16z

154,337 次观看 • 5 个月前

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

“Coding will eat all knowledge work” Peter Yang joins a16z’s Anish Acharya to discuss the post-AI future of work, why AI will create more solopreneurs, why human ambition means there will always be new jobs, and more. 00:00 Intro 01:56 Using OpenClaw for voice, memory & daily life 06:14 Will agents kill apps & SaaS? 11:57 Coding agents: Claude Code vs. Codex 17:00 Future of work: small teams, agents & company culture 24:00 How agents change consumer products & the economy Peter Yang Anish Acharya

“Coding will eat all knowledge work” Peter Yang joins a16z’s Anish Acharya to discuss the post-AI future of work, why AI will create more solopreneurs, why human ambition means there will always be new jobs, and more. 00:00 Intro 01:56 Using OpenClaw for voice, memory & daily life 06:14 Will agents kill apps & SaaS? 11:57 Coding agents: Claude Code vs. Codex 17:00 Future of work: small teams, agents & company culture 24:00 How agents change consumer products & the economy Peter Yang Anish Acharya

a16z

96,476 次观看 • 4 个月前

At fintech's peak, 25% of venture dollars went into the category. In the second half of 2022, that number dropped to almost zero. And now, fintech is building momentum again as AI makes new software solutions possible. Plaid CEO Zach Perret joined a16z's David Haber and Erik Torenberg to cover the wild story of fintech's ups and downs and where the industry is headed, including: - How fintech startups went mainstream - Why AI has caused a significant increase in financial fraud, and how companies like Plaid are building safeguards - How fintech has solved the "access problem," so that many more people can use excellent financial services products - How Plaid uses big data to deliver financial freedom for consumers 00:00 Introduction 01:10 A recent history of fintech 04:43 VC surge and fintech summer 08:25 Solving the access problem 10:41 Is crypto fintech? 13:51 Plaid's evolution and industry impact 17:26 Consumer behavior and adoption 21:48 Global fintech expansion 27:43 2026 predictions: AI and fraud 40:26 Emerging trends in fintech 43:34 Plaid's anti-fraud and credit innovations Zachary Perret David Haber Erik Torenberg Plaid

At fintech's peak, 25% of venture dollars went into the category. In the second half of 2022, that number dropped to almost zero. And now, fintech is building momentum again as AI makes new software solutions possible. Plaid CEO Zach Perret joined a16z's David Haber and Erik Torenberg to cover the wild story of fintech's ups and downs and where the industry is headed, including: - How fintech startups went mainstream - Why AI has caused a significant increase in financial fraud, and how companies like Plaid are building safeguards - How fintech has solved the "access problem," so that many more people can use excellent financial services products - How Plaid uses big data to deliver financial freedom for consumers 00:00 Introduction 01:10 A recent history of fintech 04:43 VC surge and fintech summer 08:25 Solving the access problem 10:41 Is crypto fintech? 13:51 Plaid's evolution and industry impact 17:26 Consumer behavior and adoption 21:48 Global fintech expansion 27:43 2026 predictions: AI and fraud 40:26 Emerging trends in fintech 43:34 Plaid's anti-fraud and credit innovations Zachary Perret David Haber Erik Torenberg Plaid

a16z

44,987 次观看 • 8 个月前

Today, we’re thrilled to announce $20M in funding led by a16z, with support from sarah guo, Amjad Masad, Akshay Kothari, Garry Tan, Justin Kan, Shruti Gandhi / Array VC preseed rounds, Naval, scott belsky, Gokul Rajaram, Soleio, Kevin Hartz and more. wabi is ushering in a new era of personal software, where anyone effortlessly create, discover, remix, and share personalized mini apps. For 50 years, software was made for people. The next 50, it will be made by people. Just as YouTube unlocked creative power through video, Wabi will unlock creative power through software. The YouTube moment for apps is here. We can’t wait to see what you create.

Today, we’re thrilled to announce $20M in funding led by a16z, with support from sarah guo, Amjad Masad, Akshay Kothari, Garry Tan, Justin Kan, Shruti Gandhi / Array VC preseed rounds, Naval, scott belsky, Gokul Rajaram, Soleio, Kevin Hartz and more. wabi is ushering in a new era of personal software, where anyone effortlessly create, discover, remix, and share personalized mini apps. For 50 years, software was made for people. The next 50, it will be made by people. Just as YouTube unlocked creative power through video, Wabi will unlock creative power through software. The YouTube moment for apps is here. We can’t wait to see what you create.

Eugenia Kuyda

846,392 次观看 • 9 个月前

"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? Balaji Erik Torenberg

"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? Balaji Erik Torenberg

a16z

163,295 次观看 • 4 个月前

Marc Andreessen and Ben Horowitz on the State of AI In this closing keynote from a16z’s Runtime conference, General Partner Erik Torenberg speaks with our firm’s cofounders, Marc Andreessen and Ben Horowitz, about whether AI can learn leadership success and theory of mind, how new entrants can beat huge incumbents, and how supply constraints evolve over time. 00:00 Intro 03:32 Remix, originality, and the nature of human creativity 06:20 Ben on hip-hop, innovation, and creative genius 16:40 Embodied intelligence & the mind-body question 23:02 Are we in an AI bubble? 31:00 Coaching founders in a unique AI era 34:14 Talent & chips 37:10 The U.S.-China AI race and the robotics future Marc Andreessen 🇺🇸 benahorowitz.eth Erik Torenberg

Marc Andreessen and Ben Horowitz on the State of AI In this closing keynote from a16z’s Runtime conference, General Partner Erik Torenberg speaks with our firm’s cofounders, Marc Andreessen and Ben Horowitz, about whether AI can learn leadership success and theory of mind, how new entrants can beat huge incumbents, and how supply constraints evolve over time. 00:00 Intro 03:32 Remix, originality, and the nature of human creativity 06:20 Ben on hip-hop, innovation, and creative genius 16:40 Embodied intelligence & the mind-body question 23:02 Are we in an AI bubble? 31:00 Coaching founders in a unique AI era 34:14 Talent & chips 37:10 The U.S.-China AI race and the robotics future Marc Andreessen 🇺🇸 benahorowitz.eth Erik Torenberg

a16z

195,623 次观看 • 9 个月前

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 次观看 • 4 个月前

Software is eating labor & moats still matter: AI can do cheaply what has never been worth a person's time. Whole verticals have opened up, and unlike in previous product cycles, AI can actually complete work end-to-end. a16z GPs David Haber, Alex Rampell, and Erik Torenberg sat down for a conversation on how AI changed software and what fundamentals still hold true. They cover why moats still matter, the "greenfield strategies" that AI makes possible, the difference between differentiation & defensibility, why context is king, and more. 0:00 Intro 1:12 Why moats still matter 8:48 Why you won't vibecode Microsoft 10:09 The Goldilocks zone of pricing 11:21 Greenfield strategies 16:22 Steel man: Brand and velocity as moats 17:44 "Context is king" 19:58 Feature vs. product vs. company 27:05 Platform risk: Will they compete or tax you? 35:26 Will AI consolidate to winner-take-most? 39:16 Why Dropbox survived 44:06 Why AI is different: It's consensus 48:18 Jobs won't disappear – $1 tasks will explode David Haber Alex Rampell Erik Torenberg

a16z

61,997 次观看 • 8 个月前

We sat down with Reid Hoffman to talk about the future trajectory of copilots and agents, whether true AGI requires consciousness, and what friendship and purpose will ultimately mean in a world where machines can do everything. 00:00 Introduction 01:15 Investing in AI & Silicon Valley blind spots 05:45 Will AI replace doctors? 09:40 Limits of LLMs and reasoning 15:00 Bits vs atoms: the robotics challenge 18:00 AI savants & context awareness 20:10 Software eating labor & the “lazy and rich” heuristic 24:25 Scaling laws and the future of AI 31:15 Consciousness and agency in AI 35:45 Philosophy, idealism & simulation theory 38:15 LinkedIn’s durability & network effects 47:00 Friendship & human connection in the AI era Reid Hoffman Alex Rampell Erik Torenberg

We sat down with Reid Hoffman to talk about the future trajectory of copilots and agents, whether true AGI requires consciousness, and what friendship and purpose will ultimately mean in a world where machines can do everything. 00:00 Introduction 01:15 Investing in AI & Silicon Valley blind spots 05:45 Will AI replace doctors? 09:40 Limits of LLMs and reasoning 15:00 Bits vs atoms: the robotics challenge 18:00 AI savants & context awareness 20:10 Software eating labor & the “lazy and rich” heuristic 24:25 Scaling laws and the future of AI 31:15 Consciousness and agency in AI 35:45 Philosophy, idealism & simulation theory 38:15 LinkedIn’s durability & network effects 47:00 Friendship & human connection in the AI era Reid Hoffman Alex Rampell Erik Torenberg

a16z

83,856 次观看 • 10 个月前

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon Aish and Kiriti Badam have built 50+ enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. Our goal with this conversation is to save you and your team a lot of pain and suffering while building your AI products. We discuss: 🔸 Two key ways AI products differ from traditional software 🔸 Common patterns and anti-patterns when building successful AI products 🔸 Their framework for iteratively building AI products 🔸 Why evals aren’t a cure-all 🔸 Why obsessing about customer trust and reliability is an underrated driver of successful AI products 🔸 The skills that matter most for builders in the AI era Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Merge — The fastest way to ship 220+ integrations: 🏆 Strella — The AI-powered customer research platform: 🏆 Brex — The banking solution for startups:

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon Aish and Kiriti Badam have built 50+ enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. Our goal with this conversation is to save you and your team a lot of pain and suffering while building your AI products. We discuss: 🔸 Two key ways AI products differ from traditional software 🔸 Common patterns and anti-patterns when building successful AI products 🔸 Their framework for iteratively building AI products 🔸 Why evals aren’t a cure-all 🔸 Why obsessing about customer trust and reliability is an underrated driver of successful AI products 🔸 The skills that matter most for builders in the AI era Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Merge — The fastest way to ship 220+ integrations: 🏆 Strella — The AI-powered customer research platform: 🏆 Brex — The banking solution for startups:

Lenny Rachitsky

176,800 次观看 • 7 个月前

Where are all the consumer AI apps??? This is the question I posed to s-tier consumer investors scott belsky (Uber, Pinterest) and steve jang (Coinbase, Postmates). We're almost 3 years into the AI hype the we're still all mostly just talking about ChatGPT, Perplexity, and Claude. We explore the answer through the ways in which the entire consumer stack (input, memory, personalization, browsers, hardware) are being re-written by AI and how that may give birth to the consumer opportunity we've all been waiting for. Chapters: 00:00 Welcome and Introductions 00:43 The Current State of AI Products 02:48 Surprises and Challenges with LLMs 05:24 Future of Consumer AI and Personal AI 15:49 Memory and Personalization in AI 21:52 Potential Risks and Business Models 24:22 The Battle for User Data in AI 25:11 Optimism in AI Model Inference 27:00 The Role of Open Source Models 28:13 The Future of AI Hardware 28:54 Challenges and Opportunities in AI Hardware Startups 30:29 The Evolution of Consumer Hardware 38:36 The Reinvention of Browsers 46:23 The Potential of AI-Enhanced Browsers 47:54 Conclusion and Final Thoughts Many thanks to my good friends for joining me in this discussion 🙏

Where are all the consumer AI apps??? This is the question I posed to s-tier consumer investors scott belsky (Uber, Pinterest) and steve jang (Coinbase, Postmates). We're almost 3 years into the AI hype the we're still all mostly just talking about ChatGPT, Perplexity, and Claude. We explore the answer through the ways in which the entire consumer stack (input, memory, personalization, browsers, hardware) are being re-written by AI and how that may give birth to the consumer opportunity we've all been waiting for. Chapters: 00:00 Welcome and Introductions 00:43 The Current State of AI Products 02:48 Surprises and Challenges with LLMs 05:24 Future of Consumer AI and Personal AI 15:49 Memory and Personalization in AI 21:52 Potential Risks and Business Models 24:22 The Battle for User Data in AI 25:11 Optimism in AI Model Inference 27:00 The Role of Open Source Models 28:13 The Future of AI Hardware 28:54 Challenges and Opportunities in AI Hardware Startups 30:29 The Evolution of Consumer Hardware 38:36 The Reinvention of Browsers 46:23 The Potential of AI-Enhanced Browsers 47:54 Conclusion and Final Thoughts Many thanks to my good friends for joining me in this discussion 🙏

Michael Mignano

25,155 次观看 • 1 年前

AI has ignited a consumer renaissance. My friend Kevin Rose is a legend in consumer tech. He founded a number of early internet startups, including Digg, where he invented an early predecessor to the now ubiquitous like button. He’s now a partner at True Ventures and recently relaunched Digg. We had a wide-ranging conversation on all things consumer tech, including why I think AI code will be an entire industry, why Kevin looks for “weird and working” products, Kevin’s dinners with Mark Zuckerberg, and how AI products will affect the human experience. 00:00 Intro 02:10 How we met 07:50 AI’s Renaissance for Consumer Products 11:48 Companionship apps and the future of human connection 14:01 The optimistic and pessimistic views on AI relationships 19:18 Poke and emotional primitives 21:05 Weird and working: how to spot great founders 30:05 AI companions and AI in relationships 40:08 The modern AI dev stack and building apps solo 47:25 AI music, creativity, and the next cultural wave 53:50 Curiosity, risk, and finding the next big thing 01:15:00 Always-on recording and social norms in tech

AI has ignited a consumer renaissance. My friend Kevin Rose is a legend in consumer tech. He founded a number of early internet startups, including Digg, where he invented an early predecessor to the now ubiquitous like button. He’s now a partner at True Ventures and recently relaunched Digg. We had a wide-ranging conversation on all things consumer tech, including why I think AI code will be an entire industry, why Kevin looks for “weird and working” products, Kevin’s dinners with Mark Zuckerberg, and how AI products will affect the human experience. 00:00 Intro 02:10 How we met 07:50 AI’s Renaissance for Consumer Products 11:48 Companionship apps and the future of human connection 14:01 The optimistic and pessimistic views on AI relationships 19:18 Poke and emotional primitives 21:05 Weird and working: how to spot great founders 30:05 AI companions and AI in relationships 40:08 The modern AI dev stack and building apps solo 47:25 AI music, creativity, and the next cultural wave 53:50 Curiosity, risk, and finding the next big thing 01:15:00 Always-on recording and social norms in tech

Anish Acharya

38,002 次观看 • 10 个月前

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 次观看 • 1 年前