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Technology Wars, AI's Atomic Risk & Value with the legend Vinod Khosla Time stamps: 00:00 - Intro 01:15 - Interview begins 01:46 - AI's Atomic Risk 03:27 - Frontier labs: Leaders, caution & winners 04:54 - Capital incentives & economic redistribution 06:25 - Investing in AI when most ventures...

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Madison Malone

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12,660 views • 1 year ago •via X (Twitter)

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Madison Malone1 year ago

On YouTube here:

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SpaceDerivatives1 year ago

@vkhosla Thanks for selling $RKLB at the bottom @vkhosla 👏

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sean1 year ago

@vkhosla Great interview again, Madison 👍. Interesting answers "Assessing the founder" is very important as we see with Musks Grok, manipulating the platforms for self benefit. AI incorporated into businesses will be more scrutinized or divided into need-based requirements in my view.

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Harry Stebbings

270,091 views • 6 months ago

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters
1:10:31

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

University of Austin (UATX)

27,770 views • 7 months ago

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

155,794 views • 1 month ago

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:
1:11:41

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 views • 4 months ago

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think
1:16:39

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

33,195 views • 3 months ago

The biggest inflection point in markets this year is NOT the SpaceX IPO Arthur Hayes believes Trump will pop the AI bubble to win the midterm elections "He can't fix inflation, he can't end the war... the only option he has left is to flip on the AI bros." He predicts H2 will be risk off across crypto and equities as Trump Watch the full interview now 00:00 Welcome Arthur Hayes + The HYPE Trade 02:00 Arthur’s Trading System and Narrative Shifts 04:15 Why HYPE Became Attractive Again 06:11 Why HYPE Stayed Strong vs Competitors 08:20 H2 Macro Outlook and the Iran/Oil Risk 11:21 Why Higher Energy Prices Threaten the AI Story 14:13 Why Arthur Dumped Risk Assets 15:48 Balancing Macro Signals with Momentum 17:20 Liquidity, Treasury Issuance, and the AI Bubble 18:21 Has Crypto Delivered on Its Original Promise? 20:21 Bitcoin to $1M and the Money Printing Thesis 21:34 Bitcoin vs Gold and Long-Term Store of Value 22:34 Can You Still “Make It” in Crypto? 24:52 Trading vs HODLing: What Actually Works Now 25:49 Lifestyle, Money, and What Actually Buys Happiness 27:48 Arthur’s Big Contrarian Prediction on AI 30:17 Trump, Politics, and the Power Game 32:21 Attention, Entertainment, and Financial Commentary 34:31 Why Being Entertaining Matters More Than Being Right 36:20 Arthur’s Bitcoin Bottom Prediction 36:58 House Music, Ibiza Picks, and Final Thoughts
37:47

The biggest inflection point in markets this year is NOT the SpaceX IPO Arthur Hayes believes Trump will pop the AI bubble to win the midterm elections "He can't fix inflation, he can't end the war... the only option he has left is to flip on the AI bros." He predicts H2 will be risk off across crypto and equities as Trump Watch the full interview now 00:00 Welcome Arthur Hayes + The HYPE Trade 02:00 Arthur’s Trading System and Narrative Shifts 04:15 Why HYPE Became Attractive Again 06:11 Why HYPE Stayed Strong vs Competitors 08:20 H2 Macro Outlook and the Iran/Oil Risk 11:21 Why Higher Energy Prices Threaten the AI Story 14:13 Why Arthur Dumped Risk Assets 15:48 Balancing Macro Signals with Momentum 17:20 Liquidity, Treasury Issuance, and the AI Bubble 18:21 Has Crypto Delivered on Its Original Promise? 20:21 Bitcoin to $1M and the Money Printing Thesis 21:34 Bitcoin vs Gold and Long-Term Store of Value 22:34 Can You Still “Make It” in Crypto? 24:52 Trading vs HODLing: What Actually Works Now 25:49 Lifestyle, Money, and What Actually Buys Happiness 27:48 Arthur’s Big Contrarian Prediction on AI 30:17 Trump, Politics, and the Power Game 32:21 Attention, Entertainment, and Financial Commentary 34:31 Why Being Entertaining Matters More Than Being Right 36:20 Arthur’s Bitcoin Bottom Prediction 36:58 House Music, Ibiza Picks, and Final Thoughts

EllioTrades

152,550 views • 2 months ago

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not
1:31:19

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not

Harry Stebbings

106,162 views • 11 months ago

New episode with former US Treasury Secretary and current OpenAI board member Larry Summers (Lawrence H. Summers). We discuss: - How he's been learning about AI. - The odds of the technology delivering a much faster economic growth regime (akin to the Industrial Revolution). - How AGI might change economic policymaking. Enjoy! Timestamps: (0:00:00) - Introduction. (0:00:46) - Larry's journey teaching himself about AI & deep learning since joining OpenAI’s board. (0:09:15) - How many hours per week has Larry been spending on OpenAI-related stuff? (0:10:16) - Which bottleneck to AI scaling does Larry think is the most underrated? (0:12:22) - Approximately what share of time do today's AI researchers spend on tasks that AI will be doing for them in five years? (0:15:01) - What explains the remarkable steadiness of US economic growth over the last 150 years? (0:19:42) - How likely is it that AI initiates a new growth regime with average growth that’s ~10x faster than today? (0:21:36) - What are the best economic arguments for believing AI won’t deliver a regime of ever-increasing growth rates? (0:25:33) - How much could AGI boost economic growth in developing countries merely by helping their policymakers make better decisions? (0:28:20) - How much better could monetary policy be if the Fed had AGI? (0:31:40) - How much would having AGI have helped US economic policymakers during the financial crisis & Great Recession? (0:36:07) - Is the CCP infiltrating and stealing the IP of major AI labs in the US and UK? (0:39:35) - At what point should AI be nationalised? (0:42:39) - How would Bill Clinton or Barack Obama be thinking about AI governance? (0:44:27) - If OpenAI restructures to a public benefit corporation, how does that change its incentives? (0:46:18) - What does Daron Acemoglu miss in his analysis of the economic impacts of AI?
47:57

New episode with former US Treasury Secretary and current OpenAI board member Larry Summers (Lawrence H. Summers). We discuss: - How he's been learning about AI. - The odds of the technology delivering a much faster economic growth regime (akin to the Industrial Revolution). - How AGI might change economic policymaking. Enjoy! Timestamps: (0:00:00) - Introduction. (0:00:46) - Larry's journey teaching himself about AI & deep learning since joining OpenAI’s board. (0:09:15) - How many hours per week has Larry been spending on OpenAI-related stuff? (0:10:16) - Which bottleneck to AI scaling does Larry think is the most underrated? (0:12:22) - Approximately what share of time do today's AI researchers spend on tasks that AI will be doing for them in five years? (0:15:01) - What explains the remarkable steadiness of US economic growth over the last 150 years? (0:19:42) - How likely is it that AI initiates a new growth regime with average growth that’s ~10x faster than today? (0:21:36) - What are the best economic arguments for believing AI won’t deliver a regime of ever-increasing growth rates? (0:25:33) - How much could AGI boost economic growth in developing countries merely by helping their policymakers make better decisions? (0:28:20) - How much better could monetary policy be if the Fed had AGI? (0:31:40) - How much would having AGI have helped US economic policymakers during the financial crisis & Great Recession? (0:36:07) - Is the CCP infiltrating and stealing the IP of major AI labs in the US and UK? (0:39:35) - At what point should AI be nationalised? (0:42:39) - How would Bill Clinton or Barack Obama be thinking about AI governance? (0:44:27) - If OpenAI restructures to a public benefit corporation, how does that change its incentives? (0:46:18) - What does Daron Acemoglu miss in his analysis of the economic impacts of AI?

Joseph Noel Walker

85,856 views • 1 year ago

“We are not allowed to believe in luck at a16z. We must see every deal in our domain. We must win 100% of them. I have not lost a deal in 6 years”. It has never been a more uncertain time to be an investor. - Is SaaS dead? - Do margins matter? - Is triple, triple, double, double dead? - Will foundation models eat the apps layer? Spotify 👉 Youtube 👉 Apple Podcasts 👉 My 8 takeaways with Anish Acharya 👇 Timestamps: 00:00 Intro 00:56 Why building an AI company requires being in San Francisco 04:01 The "SaaS Apocalypse" myth: Why "vibe coding" everything is a lie 05:18 How AI agents are finally breaking the lock-in of legacy software providers 07:41 Incumbents vs. Startups: Who actually wins the AI distribution war? 12:28 Why the developer tool market looks more like Cloud than Uber and Lyft 20:18 The death of the Chatbox? Why browse-based interfaces are still preferable 25:32 Why power users are 10x more valuable in the age of AI consumption 26:40 Do margins matter in a world of AI? 28:21 Why we are definitively not in an AI bubble right now 31:07 Why the Legal & Customer Support industries will have dozens of winners 37:42 Lessons from Marc Andreessen: Why the "quality of being right" supersedes process 42:59 Is "Triple, Triple, Double, Double" dead? 57:34 Open vs Closed Source 01:02:58 Is Kingmaking Real? 01:09:10 Quick-Fire Round 01:12:51 The a16z Playbook: How to win 100% of the deals you chase
1:20:02

“We are not allowed to believe in luck at a16z. We must see every deal in our domain. We must win 100% of them. I have not lost a deal in 6 years”. It has never been a more uncertain time to be an investor. - Is SaaS dead? - Do margins matter? - Is triple, triple, double, double dead? - Will foundation models eat the apps layer? Spotify 👉 Youtube 👉 Apple Podcasts 👉 My 8 takeaways with Anish Acharya 👇 Timestamps: 00:00 Intro 00:56 Why building an AI company requires being in San Francisco 04:01 The "SaaS Apocalypse" myth: Why "vibe coding" everything is a lie 05:18 How AI agents are finally breaking the lock-in of legacy software providers 07:41 Incumbents vs. Startups: Who actually wins the AI distribution war? 12:28 Why the developer tool market looks more like Cloud than Uber and Lyft 20:18 The death of the Chatbox? Why browse-based interfaces are still preferable 25:32 Why power users are 10x more valuable in the age of AI consumption 26:40 Do margins matter in a world of AI? 28:21 Why we are definitively not in an AI bubble right now 31:07 Why the Legal & Customer Support industries will have dozens of winners 37:42 Lessons from Marc Andreessen: Why the "quality of being right" supersedes process 42:59 Is "Triple, Triple, Double, Double" dead? 57:34 Open vs Closed Source 01:02:58 Is Kingmaking Real? 01:09:10 Quick-Fire Round 01:12:51 The a16z Playbook: How to win 100% of the deals you chase

Harry Stebbings

199,225 views • 6 months ago