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Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers...

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Daniel Koss

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203,706 views • 2 months ago •via X (Twitter)

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My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us
39:46

My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us

Elliot Garreffa

29,384 views • 5 months ago

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.
1:22

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

Six Five Media

294,803 views • 3 months ago

Interview with $NBIS Nebius CRO marc boroditsky & Alasdair Mann If you enjoy the podcast, please share it with someone who’d appreciate it. Thank you ❤️ Timestamps: 00:00 Introduction to Mark Boroditsky and Nebius 00:41 Landing Major Deals with Microsoft and Meta 02:56 Building and Scaling Data Centers 04:36 Defining Contracted vs. Connected Power 05:45 Managing Demand and Pricing Strategy 07:43 Addressing AI Bottlenecks and Nvidia Partnership 09:54 Power Sources and Energy Strategy 13:39 Growth Metrics and Customer Expansion 23:02 Bitcoin Miners and AI Infrastructure 26:58 Valuable Assets and Market Opportunities 27:43 Skepticism and Market Realities 28:13 Future of Rental Environmental Business 29:05 Enterprise Demand and Market Trends 31:01 Vertical Integration and Competitive Edge 33:08 Specialty Infrastructure and AI 34:16 Cybersecurity in Integrated Cloud 36:19 Customer Feedback and Platform Advantages 43:19 Revenue and Profit Split Insights 47:57 Personal Journey and Joining Neas 51:23 Future Prospects and Execution
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Interview with $NBIS Nebius CRO marc boroditsky & Alasdair Mann If you enjoy the podcast, please share it with someone who’d appreciate it. Thank you ❤️ Timestamps: 00:00 Introduction to Mark Boroditsky and Nebius 00:41 Landing Major Deals with Microsoft and Meta 02:56 Building and Scaling Data Centers 04:36 Defining Contracted vs. Connected Power 05:45 Managing Demand and Pricing Strategy 07:43 Addressing AI Bottlenecks and Nvidia Partnership 09:54 Power Sources and Energy Strategy 13:39 Growth Metrics and Customer Expansion 23:02 Bitcoin Miners and AI Infrastructure 26:58 Valuable Assets and Market Opportunities 27:43 Skepticism and Market Realities 28:13 Future of Rental Environmental Business 29:05 Enterprise Demand and Market Trends 31:01 Vertical Integration and Competitive Edge 33:08 Specialty Infrastructure and AI 34:16 Cybersecurity in Integrated Cloud 36:19 Customer Feedback and Platform Advantages 43:19 Revenue and Profit Split Insights 47:57 Personal Journey and Joining Neas 51:23 Future Prospects and Execution

Daniel Koss

449,230 views • 7 months ago

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History
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Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

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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

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49,418 views • 9 months ago

INTERVIEW - Venice Is Here to Win: How Private AI Takes On OpenAI and Anthropic David Hoffman sits down with Jon ShapeShift and Jesse Proudman to unpack how Venice is taking on OpenAI and Anthropic with private AI. They talk about: - why AI is becoming the biggest data honeypot ever - private Grok access through Venice - unrestricted AI as a consumer wedge - how non-crypto users still feed the $VVV economy - $DIEM, tokenized inference, and AI agents --- TIMESTAMPS 0:00 Intro 1:12 Why Private AI Matters 3:01 The AI Data Honeypot 6:07 Venice’s Consumer AI Ambition 8:01 Beating the Big AI Labs 10:19 Model Aggregation and Agentic Chat 12:29 Open Source, Closed Source, and Private Grok 17:14 Who Actually Needs Private AI? 21:58 Venice’s User Base 23:53 What Drove Venice’s Recent Growth 27:21 $VVV, $DIEM, and Tokenized Inference 29:00 Where Venice Gets Its Compute 32:17 Bonding Curves and DIEM Monetary Policy 39:14 Real Tokenomics, Not Just a Pie Chart 45:46 AI Agents Need Inference to Exist 52:54 ShapeShift DNA Inside Venice 55:53 Regulation, AI, and Individual Rights
58:52

INTERVIEW - Venice Is Here to Win: How Private AI Takes On OpenAI and Anthropic David Hoffman sits down with Jon ShapeShift and Jesse Proudman to unpack how Venice is taking on OpenAI and Anthropic with private AI. They talk about: - why AI is becoming the biggest data honeypot ever - private Grok access through Venice - unrestricted AI as a consumer wedge - how non-crypto users still feed the $VVV economy - $DIEM, tokenized inference, and AI agents --- TIMESTAMPS 0:00 Intro 1:12 Why Private AI Matters 3:01 The AI Data Honeypot 6:07 Venice’s Consumer AI Ambition 8:01 Beating the Big AI Labs 10:19 Model Aggregation and Agentic Chat 12:29 Open Source, Closed Source, and Private Grok 17:14 Who Actually Needs Private AI? 21:58 Venice’s User Base 23:53 What Drove Venice’s Recent Growth 27:21 $VVV, $DIEM, and Tokenized Inference 29:00 Where Venice Gets Its Compute 32:17 Bonding Curves and DIEM Monetary Policy 39:14 Real Tokenomics, Not Just a Pie Chart 45:46 AI Agents Need Inference to Exist 52:54 ShapeShift DNA Inside Venice 55:53 Regulation, AI, and Individual Rights

Bankless

59,022 views • 1 month ago

Google and Cisco Execs: The Infrastructure Renaissance AI is causing the biggest buildout of physical infrastructure in modern history. The five largest players alone are projected to spend $342 billion on capex in 2025, more than the entire Apollo moon landing program adjusted for inflation. In this episode from a16z’s Runtime conference, General Partner Raghu Raghuram sits down with Google AI infra lead Amin Vahdat and Cisco President and Chief Product Officer Jeetu Patel to discuss the unprecedented scale of what’s happening, how data centers will transform over time as AI inference, RL, and distributed computing become more prevalent, and why we’re still in the very earliest phase of the capex supercycle. 00:00 Intro 01:16 The scale of the AI buildout 05:56 Scarcity and global power constraints 12:18 The next wave in processor innovation 16:14 Networking evolution, scale challenges 21:00 Inference architecture 27:30 Rewiring culture around rapid AI adoption 31:55 Future of AI models, agents, media Raghu Raghuram Jeetu Patel
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Google and Cisco Execs: The Infrastructure Renaissance AI is causing the biggest buildout of physical infrastructure in modern history. The five largest players alone are projected to spend $342 billion on capex in 2025, more than the entire Apollo moon landing program adjusted for inflation. In this episode from a16z’s Runtime conference, General Partner Raghu Raghuram sits down with Google AI infra lead Amin Vahdat and Cisco President and Chief Product Officer Jeetu Patel to discuss the unprecedented scale of what’s happening, how data centers will transform over time as AI inference, RL, and distributed computing become more prevalent, and why we’re still in the very earliest phase of the capex supercycle. 00:00 Intro 01:16 The scale of the AI buildout 05:56 Scarcity and global power constraints 12:18 The next wave in processor innovation 16:14 Networking evolution, scale challenges 21:00 Inference architecture 27:30 Rewiring culture around rapid AI adoption 31:55 Future of AI models, agents, media Raghu Raghuram Jeetu Patel

a16z

102,487 views • 8 months ago

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
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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

102,776 views • 1 month ago

Full Interview with Charles-Antoine Beyney "CAB" (CAB) - Founder & CEO of DataOne and Alasdair Mann As always - please share this interview, so we reach as many $NBIS (and AI infra investors) as possible! --- DataOne is building giga-scale AI data center infrastructure for the next decade of compute demand. Most importantly for $NBIS investors: DataOne is the company behind Nebius’ 300MW Vineland, New Jersey campus, one of the key capacity expansion projects that could determine how fast Nebius scales from here! Timestamps: 00:00 Meet the CEO of DataOne 00:29 50% of Datacenters Delayed 02:01 Paid Protesters 04:48 Real Water Consumption of His Data Centers 08:27 Condensation and Cooling 11:09 Pollutant Problem 12:14 Behind the Meter Power 15:50 US vs France Power Strategy 18:39 Speed to Power Crunch 19:56 Supply Chain Bottlenecks 23:46 Generative Video AI Economics 25:54 Building Faster Than Anyone 30:55 Budgeting With Rapidly Increasing Prices 31:32 Tariffs and Contingency Buffers 33:31 Supply Crunch Pricing Power 37:36 DataOne Infrastructure Strategy 38:44 Gas Turbines at Scale 40:40 Microturbines and Small Sites 42:30 Batteries and Renewables Mix 43:57 Nuclear and Grid Stability 44:51 Real World Lead Times Strategy 47:26 Cooling Knowhow and Market Hype 48:48 Building Under Extreme Pressure 50:36 AI as the New Enlightenment 54:23 Fusion and the Energy Endgame As always - please share
56:16

Full Interview with Charles-Antoine Beyney "CAB" (CAB) - Founder & CEO of DataOne and Alasdair Mann As always - please share this interview, so we reach as many $NBIS (and AI infra investors) as possible! --- DataOne is building giga-scale AI data center infrastructure for the next decade of compute demand. Most importantly for $NBIS investors: DataOne is the company behind Nebius’ 300MW Vineland, New Jersey campus, one of the key capacity expansion projects that could determine how fast Nebius scales from here! Timestamps: 00:00 Meet the CEO of DataOne 00:29 50% of Datacenters Delayed 02:01 Paid Protesters 04:48 Real Water Consumption of His Data Centers 08:27 Condensation and Cooling 11:09 Pollutant Problem 12:14 Behind the Meter Power 15:50 US vs France Power Strategy 18:39 Speed to Power Crunch 19:56 Supply Chain Bottlenecks 23:46 Generative Video AI Economics 25:54 Building Faster Than Anyone 30:55 Budgeting With Rapidly Increasing Prices 31:32 Tariffs and Contingency Buffers 33:31 Supply Crunch Pricing Power 37:36 DataOne Infrastructure Strategy 38:44 Gas Turbines at Scale 40:40 Microturbines and Small Sites 42:30 Batteries and Renewables Mix 43:57 Nuclear and Grid Stability 44:51 Real World Lead Times Strategy 47:26 Cooling Knowhow and Market Hype 48:48 Building Under Extreme Pressure 50:36 AI as the New Enlightenment 54:23 Fusion and the Energy Endgame As always - please share

Daniel Koss

99,456 views • 2 months ago

1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️
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1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️

Proof of Coverage | Tech’s Creative Agency

98,908 views • 9 months ago

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
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AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,140 views • 1 year ago

Breaking the Memory Wall: Micron’s Strategy for the AI Era Ben Bajarin and Jay Goldberg 顾忠南 talk micron's strategy for the AI era with Jeremy Werner SVP and GM of Micron’s Core Data Center Business Unit. Chapters: (00:00) Introduction and Guest Welcome (01:12) How the AI Cycle Differs from Past Volatility (04:23) Anticipating the Explosive Growth of AI (06:40) The Long-Term Sustainability of the AI Market (10:07) The "Memory Wall" in AI Inference (12:05) Explaining KV Cache and Context Windows (15:07) The Memory Hierarchy: From HBM to SSDs (20:21) Scaling for Billions of Simultaneous Users (23:05) Solving the Power and Efficiency Bottleneck (26:27) Why AI is Driving a Storage Shortage (29:57) Personal AI Agents and Persistent Memory Needs (34:13) Innovations in Ultra-High-Capacity SSDs (39:00) Accelerating the Pace of Engineering Innovation (43:50) Market Misunderstandings and the Future Potential of AI (47:38) The Global Race to Build New Fabs (51:34) Closing Remarks
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Breaking the Memory Wall: Micron’s Strategy for the AI Era Ben Bajarin and Jay Goldberg 顾忠南 talk micron's strategy for the AI era with Jeremy Werner SVP and GM of Micron’s Core Data Center Business Unit. Chapters: (00:00) Introduction and Guest Welcome (01:12) How the AI Cycle Differs from Past Volatility (04:23) Anticipating the Explosive Growth of AI (06:40) The Long-Term Sustainability of the AI Market (10:07) The "Memory Wall" in AI Inference (12:05) Explaining KV Cache and Context Windows (15:07) The Memory Hierarchy: From HBM to SSDs (20:21) Scaling for Billions of Simultaneous Users (23:05) Solving the Power and Efficiency Bottleneck (26:27) Why AI is Driving a Storage Shortage (29:57) Personal AI Agents and Persistent Memory Needs (34:13) Innovations in Ultra-High-Capacity SSDs (39:00) Accelerating the Pace of Engineering Innovation (43:50) Market Misunderstandings and the Future Potential of AI (47:38) The Global Race to Build New Fabs (51:34) Closing Remarks

The Circuit

94,249 views • 2 months ago

State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up
1:12:54

State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up

Matt Turck

49,373 views • 1 month ago

INTERVIEW: AI, Surveillance, & the Fight for Digital Sovereignty | Near's Illia Polosukhin What happens when powerful AI requires an ID, governments can restrict access by country, and your most private data flows through centralized systems? NEAR Protocol co-founder Illia (root.near) (🇺🇦, ⋈) joins David Hoffman to explain why AI export controls could fracture the internet, how nationalized AI could become a surveillance layer, and why decentralized, user-owned AI may be the only real alternative. They also unpack Ironclaw, confidential inference, private agents, and NEAR’s push to make AI and assets fully sovereign. [TIMESTAMPS] 0:00 Intro 3:47 Pandora’s Box Opens 6:03 AI Meets Government 7:16 Decentralizing the Stack 8:51 Why Ban Fable? 11:18 Internet Balkanization 13:57 KYC for AI? 18:43 Nationalized Models 21:07 Building Private AI 26:30 Agent Marketplace 30:26 User-Owned Alignment 35:28 Winning Against Giants 40:19 Regulation Fuels Crypto 42:24 Global AI Adoption 45:05 Compute Is the Bottleneck 49:00 Enterprise Use Cases 51:39 Confidential TVL Explained 55:25 Full Sovereign Mode
57:26

INTERVIEW: AI, Surveillance, & the Fight for Digital Sovereignty | Near's Illia Polosukhin What happens when powerful AI requires an ID, governments can restrict access by country, and your most private data flows through centralized systems? NEAR Protocol co-founder Illia (root.near) (🇺🇦, ⋈) joins David Hoffman to explain why AI export controls could fracture the internet, how nationalized AI could become a surveillance layer, and why decentralized, user-owned AI may be the only real alternative. They also unpack Ironclaw, confidential inference, private agents, and NEAR’s push to make AI and assets fully sovereign. [TIMESTAMPS] 0:00 Intro 3:47 Pandora’s Box Opens 6:03 AI Meets Government 7:16 Decentralizing the Stack 8:51 Why Ban Fable? 11:18 Internet Balkanization 13:57 KYC for AI? 18:43 Nationalized Models 21:07 Building Private AI 26:30 Agent Marketplace 30:26 User-Owned Alignment 35:28 Winning Against Giants 40:19 Regulation Fuels Crypto 42:24 Global AI Adoption 45:05 Compute Is the Bottleneck 49:00 Enterprise Use Cases 51:39 Confidential TVL Explained 55:25 Full Sovereign Mode

Bankless

59,518 views • 23 days ago

Figma co-founder and CEO Dylan Field (Dylan Field) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice
40:36

Figma co-founder and CEO Dylan Field (Dylan Field) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice

Y Combinator

178,646 views • 11 months ago

Today we announced a $150M seed round in Inferact, a new startup led by the maintainers of the vLLM project. Inferact will support the vLLM open source project through dedicated financial and developer resources and build what they see as the next generation commercial inference engine. Cofounders Simon Mo and Woosuk Kwon joined a16z GP Matt Bornstein for a conversation on how vLLM came to be, what they’ve learned from building it, and what they’re now doing at Inferact. They cover how vLLM began as a side project, why open source is essential to the world’s AI infrastructure, why inference is getting harder, and more. 00:00 Introduction 11:41 Community and collaboration in vLLM 19:19 Understanding inference engines 24:27 Cluster scale and GPU deployment 31:19 Belief in open source AI 35:45 Founding Inferact 40:00 The future of Inference at scale Simon Mo Woosuk Kwon Matt Bornstein
44:14

Today we announced a $150M seed round in Inferact, a new startup led by the maintainers of the vLLM project. Inferact will support the vLLM open source project through dedicated financial and developer resources and build what they see as the next generation commercial inference engine. Cofounders Simon Mo and Woosuk Kwon joined a16z GP Matt Bornstein for a conversation on how vLLM came to be, what they’ve learned from building it, and what they’re now doing at Inferact. They cover how vLLM began as a side project, why open source is essential to the world’s AI infrastructure, why inference is getting harder, and more. 00:00 Introduction 11:41 Community and collaboration in vLLM 19:19 Understanding inference engines 24:27 Cluster scale and GPU deployment 31:19 Belief in open source AI 35:45 Founding Inferact 40:00 The future of Inference at scale Simon Mo Woosuk Kwon Matt Bornstein

a16z

87,993 views • 5 months ago

State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI
1:14:27

State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI

Matt Turck

70,916 views • 27 days ago

As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future
1:03:13

As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future

Raoul Pal

280,145 views • 1 year ago

How is an open ecosystem powering the next generation of AI for developers? Recording live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. Together they unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models. This relentless pace of hardware and software innovation is reshaping the AI landscape. Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications. 00:00 Live from AMD's Advancing AI 2025 Event 00:30 Introduction to Anush Elangovan 01:38 The MI350 GPU Series Unveiled 04:57 CDNA4 Architecture Explained 07:00 The Future of AI Infrastructure 08:32 AMD's Developer Cloud and ROCm 7 11:50 Cultural Shift at AMD 14:48 Open Source and Community Contributions 18:35 Software Longevity and Ecosystem Strategy 22:19 AI Agents and Performance Gains 27:36 AI's Role in Solving Power Challenges 28:11 Thanking Anush 28:42 Introduction to Sharon Zhou 29:45 Sharon's Focus at AMD 30:39 Engaging Developers with AMD's AI Tools 31:24 Listening to the AI Community 33:56 Open Source and AI Development 45:04 Future of AI and Self-Improving Models 48:04 Final Thoughts and Farewell
49:13

How is an open ecosystem powering the next generation of AI for developers? Recording live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. Together they unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models. This relentless pace of hardware and software innovation is reshaping the AI landscape. Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications. 00:00 Live from AMD's Advancing AI 2025 Event 00:30 Introduction to Anush Elangovan 01:38 The MI350 GPU Series Unveiled 04:57 CDNA4 Architecture Explained 07:00 The Future of AI Infrastructure 08:32 AMD's Developer Cloud and ROCm 7 11:50 Cultural Shift at AMD 14:48 Open Source and Community Contributions 18:35 Software Longevity and Ecosystem Strategy 22:19 AI Agents and Performance Gains 27:36 AI's Role in Solving Power Challenges 28:11 Thanking Anush 28:42 Introduction to Sharon Zhou 29:45 Sharon's Focus at AMD 30:39 Engaging Developers with AMD's AI Tools 31:24 Listening to the AI Community 33:56 Open Source and AI Development 45:04 Future of AI and Self-Improving Models 48:04 Final Thoughts and Farewell

Galileo

37,186 views • 1 year ago