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MCG x EasyA Ecosystem Day $CPX | CapIX Dev w/R.155 CapIX is building a liquid capacity exchange for raw GPU compute & AI inference grids Here's the breakdown: 00:48 - Meet the founder 01:17 - Built quantum compute as a service on AWS Bracket 04:00 - The problem 04:25...

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MCG

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50,156 views • 2 months ago •via X (Twitter)

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.CapIX Dev's vision is to bring quantum computing to retail The three layers underneath: - Layer 1: GPU mesh aggregating Surplus Intelligence, Pod the Squire, OpenRouter - Layer 2: Smart router (heavy tasks go to the GPU, light tasks go to the CPU, quantum computing in the future?) - Layer 3: Community cloud with end-to-end flows Ruqaiyah built it solo with an 86-agent dev The whole EasyA Kickstart stack is forming under the $CPX umbrella - ARTHUR LABS builds the 3D model - Bit Agents handles the commerce - Covenant Foundation governs the agents - Factory in China prints the physical product - All running on CapIX compute
5:31

.CapIX Dev's vision is to bring quantum computing to retail The three layers underneath: - Layer 1: GPU mesh aggregating Surplus Intelligence, Pod the Squire, OpenRouter - Layer 2: Smart router (heavy tasks go to the GPU, light tasks go to the CPU, quantum computing in the future?) - Layer 3: Community cloud with end-to-end flows Ruqaiyah built it solo with an 86-agent dev The whole EasyA Kickstart stack is forming under the $CPX umbrella - ARTHUR LABS builds the 3D model - Bit Agents handles the commerce - Covenant Foundation governs the agents - Factory in China prints the physical product - All running on CapIX compute

MCG

13,184 views • 2 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 • 2 months ago

Today on MCG $P0 | p0 Systems w/SerPepe P0 is building the operating system for creators... Highlights from our second convo with Cory: 01:21 - Cory is back 03:16 - The scale claim 04:40 - Bedrock Foundation incorporation 06:33 - Enables USDC staking from revenue, requires the legal structure 07:29 - They were Cloudflare's biggest gateway customer, got cut off as competition (the most postable beat) 09:00 - Why in-house GPUs? 10:24 - The China sourcing thesis 11:00 - The compute-rental vector 13:22 - Software-to-hardware shift 19:19 - "Training a model is like forking a GitHub" if you have the compute 20:41 - The Grok/xAI relationship 24:15 - Enterprise customers 31:23 - GPU depreciation/resale 35:17 - ~43K paying users (up from ~30K), ~320K active 38:41 - ~$480K MRR, ~$34K from the gateway alone 41:30 - Takeaways
50:27

Today on MCG $P0 | p0 Systems w/SerPepe P0 is building the operating system for creators... Highlights from our second convo with Cory: 01:21 - Cory is back 03:16 - The scale claim 04:40 - Bedrock Foundation incorporation 06:33 - Enables USDC staking from revenue, requires the legal structure 07:29 - They were Cloudflare's biggest gateway customer, got cut off as competition (the most postable beat) 09:00 - Why in-house GPUs? 10:24 - The China sourcing thesis 11:00 - The compute-rental vector 13:22 - Software-to-hardware shift 19:19 - "Training a model is like forking a GitHub" if you have the compute 20:41 - The Grok/xAI relationship 24:15 - Enterprise customers 31:23 - GPU depreciation/resale 35:17 - ~43K paying users (up from ~30K), ~320K active 38:41 - ~$480K MRR, ~$34K from the gateway alone 41:30 - Takeaways

MCG

31,314 views • 1 month ago

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead
1:09:53

My dear friend, Vlad Tenev, changed the landscape of investing forever! The rise of the retail investor is largely due to Robinhood's success... and in this new Journey Man, we discuss it all... Enjoy! 00:00 - Intro 00:53 - Introducing Vlad Tenev of Robinhood 01:27 - Why Take on Wall Street? 01:54 - Robinhood’s Zero-Fee Origin Story 02:53 - Inspiration from Instagram and Uber 04:24 - Reimagining Trading for Mobile 05:05 - The Challenge of Disrupting Finance 05:42 - Why Everything Is Hard 06:34 - Early Wrong Assumptions 07:42 - Raising Capital with a Small Vision 08:48 - Funding Robinhood on AngelList 09:50 - Early Investors Changed Their Lives 10:38 - The Crypto Explosion Begins 11:07 - Considering a Bitcoin Exchange First 12:17 - Bitcoin’s Early Skepticism and Growth 13:08 - Robinhood Launches Crypto in 2018 14:03 - 2020: Crypto Revenue Surges Overnight 15:04 - The Challenge of Crypto Cyclicality 16:11 - Staffing a Volatile Business 17:10 - Building Robinhood’s Lean Crypto Team 18:46 - Robinhood’s First Crypto Event Coming 19:38 - Where TradFi Meets DeFi 20:34 - Tokenizing Everything 21:09 - Robinhood’s Vision for Crypto + Finance 21:47 - Thoughts on Crypto Options Demand 23:04 - Why Crypto Options Haven’t Taken Off 24:09 - Millennials and the Speculative Economy 25:22 - Democratizing Trading for Everyone 26:08 - Why Buy-and-Hold Doesn’t Work for All 27:15 - Trading vs Investing: A Matter of Wealth 28:01 - Trading as a Skill Anyone Can Build 29:13 - Robinhood’s Role in Onboarding Millions 30:06 - The Fed's Role and Retail Insight 31:03 - The Rise of the Retail Macro Trader 32:17 - Helping Users Succeed with Robinhood Strategies 33:35 - Power of Community and the Hive Mind 34:55 - Will AI Disrupt Community Too? 36:14 - Technological Waves and Investor Opportunity 37:10 - Human Purpose in an AI World 37:52 - Tokenizing Human Connection 38:28 - Creators, Platforms, and Future-Proofing 39:26 - Vlad’s Long-Term View of the Future 40:05 - Financial Services at the Heart of Disruption 41:14 - If AI Replaces Jobs, What Happens to Investing? 42:25 - Entering the Economic Singularity 43:31 - What Happens When AIs Win the Markets? 44:16 - AI's Role in Capital and Markets 45:07 - Will AI Eliminate Human Emotion from Markets? 46:06 - HFT: The Original AI Traders 47:20 - AI and Long-Term Probabilistic Forecasting 48:48 - GPUs, Gaming, and the Origins of AI 50:01 - Nvidia, CUDA, and Wall Street Arms Races 51:04 - Flash Boys and Microwave Trading 51:54 - Will AI Costs Go to Zero? 52:52 - Lower Cost, Higher Usage 53:41 - Robinhood’s UX Won’t Be Just a Chatbox 55:16 - Cortex: AI-Powered Features at Robinhood 56:54 - Tokenization and the Future of Asset Management 57:44 - Crowdsourced, Tokenized Hedge Funds 58:48 - Portability of Tokenized Assets 59:39 - Blockchain as the New Rails of Finance 01:00:09 - The Trump Token and Capital Formation 01:01:00 - Capital Access Unlocks Innovation 01:01:49 - Why Crypto Needs Regulatory Clarity 01:03:17 - From Meme Coins to Real Assets 01:04:17 - Crypto's Path to $100 Trillion? 01:05:15 - The Financial System Will Run on Blockchains 01:06:00 - Platform Layer vs Application Layer Wealth 01:06:29 - AI Raises Money and Launches Tokens 01:07:39 - AIs Creating Software and Capital Formation 01:08:00 - Final Thoughts: A Wild Future Ahead 01:08:20 - When Will Vlad Buy a CryptoPunk? 01:08:51 - Wrapping Up: AI, Crypto, and the Road Ahead

Raoul Pal

172,640 views • 1 year ago

Here's my conversation with Demis Hassabis, CEO of Google DeepMind, all about the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini 3, scaling laws, compute, P vs NP, complexity, energy (solar & fusion), and much more. This was a fun & fascinating super-technical conversation. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:06 - Learnable patterns in nature 5:48 - Computation and P vs NP 14:26 - Veo 3 and understanding reality 18:50 - Video games 30:52 - AlphaEvolve 36:53 - AI research 41:17 - Simulating a biological organism 46:00 - Origin of life 52:15 - Path to AGI 1:03:01 - Scaling laws 1:06:17 - Compute 1:09:04 - Future of energy 1:13:00 - Human nature 1:17:54 - Google and the race to AGI 1:35:53 - Competition and AI talent 1:42:27 - Future of programming 1:48:53 - John von Neumann 1:58:07 - p(doom) 2:02:50 - Humanity 2:05:56 - Consciousness and quantum computation 2:12:06 - David Foster Wallace 2:19:20 - Education and research
2:28:14

Here's my conversation with Demis Hassabis, CEO of Google DeepMind, all about the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini 3, scaling laws, compute, P vs NP, complexity, energy (solar & fusion), and much more. This was a fun & fascinating super-technical conversation. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:06 - Learnable patterns in nature 5:48 - Computation and P vs NP 14:26 - Veo 3 and understanding reality 18:50 - Video games 30:52 - AlphaEvolve 36:53 - AI research 41:17 - Simulating a biological organism 46:00 - Origin of life 52:15 - Path to AGI 1:03:01 - Scaling laws 1:06:17 - Compute 1:09:04 - Future of energy 1:13:00 - Human nature 1:17:54 - Google and the race to AGI 1:35:53 - Competition and AI talent 1:42:27 - Future of programming 1:48:53 - John von Neumann 1:58:07 - p(doom) 2:02:50 - Humanity 2:05:56 - Consciousness and quantum computation 2:12:06 - David Foster Wallace 2:19:20 - Education and research

Lex Fridman

1,456,777 views • 1 year ago

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question
26:53

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question

The Humanoid Hub

96,465 views • 1 year ago

Here's my conversation with the founding team of Cursor, a popular code editor (based on VSCode) that specializes in AI-assisted programming. This is a super technical conversation that is bigger than just about one code editor. It's about the future of programming and, in general, the future of human-AI collaboration. It's here on X in full, and is up on everywhere else. Timestamps: 0:00 - Introduction 0:59 - Code editor basics 3:09 - GitHub Copilot 10:27 - Cursor 16:54 - Cursor Tab 23:08 - Code diff 31:20 - ML details 36:54 - GPT vs Claude 43:28 - Prompt engineering 50:54 - AI agents 1:04:51 - Running code in background 1:09:31 - Debugging 1:14:58 - Dangerous code 1:26:09 - Branching file systems 1:29:20 - Scaling challenges 1:43:32 - Context 1:48:39 - OpenAI o1 2:00:01 - Synthetic data 2:03:48 - RLHF vs RLAIF 2:05:34 - Fields Medal for AI 2:08:17 - Scaling laws 2:17:06 - The future of programming
2:28:55

Here's my conversation with the founding team of Cursor, a popular code editor (based on VSCode) that specializes in AI-assisted programming. This is a super technical conversation that is bigger than just about one code editor. It's about the future of programming and, in general, the future of human-AI collaboration. It's here on X in full, and is up on everywhere else. Timestamps: 0:00 - Introduction 0:59 - Code editor basics 3:09 - GitHub Copilot 10:27 - Cursor 16:54 - Cursor Tab 23:08 - Code diff 31:20 - ML details 36:54 - GPT vs Claude 43:28 - Prompt engineering 50:54 - AI agents 1:04:51 - Running code in background 1:09:31 - Debugging 1:14:58 - Dangerous code 1:26:09 - Branching file systems 1:29:20 - Scaling challenges 1:43:32 - Context 1:48:39 - OpenAI o1 2:00:01 - Synthetic data 2:03:48 - RLHF vs RLAIF 2:05:34 - Fields Medal for AI 2:08:17 - Scaling laws 2:17:06 - The future of programming

Lex Fridman

1,262,904 views • 1 year ago

My conversation with Tarun Chitra As a co-founder of Gauntlet and GP at Robot Ventures, Tarun has one of the sharpest frameworks for understanding market structure across both crypto and AI. In this episode we dig into why open source AI is unbundling faster than most people expect, and how the resulting stack looks surprisingly similar to DeFi. We spend time mapping the AI infrastructure layers directly onto crypto primitives and examining where value is actually going to accrue as models, harnesses, routers, and inference providers separate. At the center of the conversation is the belief that AI’s unbundling is creating a new competitive order-flow market (data centers competing like nodes, MEV-like dynamics for tokens/GPUs) while crypto itself has settled into a more mature “TradFi plus+” phase focused on trading, payments, and bringing real assets on-chain. We discuss: - The current state of crypto as TradFi+ and the decline of speculative narratives - Why AI is killing Bitcoin mining economics and weakening ETH value accrual - The architectural parallel between AI stacks and DeFi (Harnesses = Wallets, Routers = DEX aggregators, Models = Protocols, Inference Providers = LPs) - Why open source models are unbundling faster than traditional software - Agents as the next interface layer and the potential unbundling of ETFs -Cryptography, verifiable compute, and turning GPUs into digital assets - Onchain compute trading as the real crypto × AI opportunity - Sustainable business models and where value will ultimately capture Timestamps: 0:00 – Introduction & State of the Crypto Market 2:00 – Speculative Narratives Fade, Trading & Payments Remain 7:00 – AI’s Impact on Bitcoin Economics & Data Center Opportunity Cost 9:00 – ETH Value Accrual, Solana Positioning & DeFi Token Sustainability 15:00 – Trading Design Space & On-Chain Volume Upside 25:00 – AI Unbundling Thesis: Open-Source Models vs Data Centers 35:00 – The DeFi Mapping (Harnesses, Routers, Models, Inference) 48:00 – Agents, Preference Expression & Unbundling Traditional Products 1:00:00 – Real-Time Harness Generation & Active Learning 1:05:00 – Cryptography, Verifiable Compute & On-Chain GPU Markets 1:10:00 – Closing Thoughts: Where Value Accrues Next Enjoy!
1:13:04

My conversation with Tarun Chitra As a co-founder of Gauntlet and GP at Robot Ventures, Tarun has one of the sharpest frameworks for understanding market structure across both crypto and AI. In this episode we dig into why open source AI is unbundling faster than most people expect, and how the resulting stack looks surprisingly similar to DeFi. We spend time mapping the AI infrastructure layers directly onto crypto primitives and examining where value is actually going to accrue as models, harnesses, routers, and inference providers separate. At the center of the conversation is the belief that AI’s unbundling is creating a new competitive order-flow market (data centers competing like nodes, MEV-like dynamics for tokens/GPUs) while crypto itself has settled into a more mature “TradFi plus+” phase focused on trading, payments, and bringing real assets on-chain. We discuss: - The current state of crypto as TradFi+ and the decline of speculative narratives - Why AI is killing Bitcoin mining economics and weakening ETH value accrual - The architectural parallel between AI stacks and DeFi (Harnesses = Wallets, Routers = DEX aggregators, Models = Protocols, Inference Providers = LPs) - Why open source models are unbundling faster than traditional software - Agents as the next interface layer and the potential unbundling of ETFs -Cryptography, verifiable compute, and turning GPUs into digital assets - Onchain compute trading as the real crypto × AI opportunity - Sustainable business models and where value will ultimately capture Timestamps: 0:00 – Introduction & State of the Crypto Market 2:00 – Speculative Narratives Fade, Trading & Payments Remain 7:00 – AI’s Impact on Bitcoin Economics & Data Center Opportunity Cost 9:00 – ETH Value Accrual, Solana Positioning & DeFi Token Sustainability 15:00 – Trading Design Space & On-Chain Volume Upside 25:00 – AI Unbundling Thesis: Open-Source Models vs Data Centers 35:00 – The DeFi Mapping (Harnesses, Routers, Models, Inference) 48:00 – Agents, Preference Expression & Unbundling Traditional Products 1:00:00 – Real-Time Harness Generation & Active Learning 1:05:00 – Cryptography, Verifiable Compute & On-Chain GPU Markets 1:10:00 – Closing Thoughts: Where Value Accrues Next Enjoy!

Logan Jastremski

132,162 views • 24 days ago

Today on MCG: Pop Punk 🇺🇸 | Pumpcade | solana:Eg2ymQ2aQqjMcibnmTt8erC6Tvk9PVpJZCxvVPJz2agu Pop Punk came back on MCG to give updates on Pumpcade, the prediction market for instant-resolution markets as short as 60 seconds, focused only on markets that resolve through verifiable data Built on Solana Highlights include: 00:14 - Raised another $5M since the last MCG appearance 00:37 - First project to do the ACE round, which has since become a trend 03:06 - PopPunk's background 04:21 - Solo-built the product for 6 months without paying himself, then won the Pump Fun Hackathon 05:32 - The ACE round mechanic 06:33 - "We wanted CT degens to sit on the same cap table as top-tier S-tier investors" 09:30 - Final ACE numbers (6-7% of total supply converted for equity, locked for 1 year. Combined with 10% investor allocation = 16% of supply locked for 12 months) 19:23 - The product thesis 20:48 - Polymarket criticism 22:55 - GTM strategy 30:51 - "The only place to build our consumer application right now is on Solana" 38:25 - Parimutuel mechanic explained 39:34 - White-label resolution engine 41:28 - Biggest announcement yet is on the way 🚨
46:19

Today on MCG: Pop Punk 🇺🇸 | Pumpcade | solana:Eg2ymQ2aQqjMcibnmTt8erC6Tvk9PVpJZCxvVPJz2agu Pop Punk came back on MCG to give updates on Pumpcade, the prediction market for instant-resolution markets as short as 60 seconds, focused only on markets that resolve through verifiable data Built on Solana Highlights include: 00:14 - Raised another $5M since the last MCG appearance 00:37 - First project to do the ACE round, which has since become a trend 03:06 - PopPunk's background 04:21 - Solo-built the product for 6 months without paying himself, then won the Pump Fun Hackathon 05:32 - The ACE round mechanic 06:33 - "We wanted CT degens to sit on the same cap table as top-tier S-tier investors" 09:30 - Final ACE numbers (6-7% of total supply converted for equity, locked for 1 year. Combined with 10% investor allocation = 16% of supply locked for 12 months) 19:23 - The product thesis 20:48 - Polymarket criticism 22:55 - GTM strategy 30:51 - "The only place to build our consumer application right now is on Solana" 38:25 - Parimutuel mechanic explained 39:34 - White-label resolution engine 41:28 - Biggest announcement yet is on the way 🚨

MCG

39,285 views • 2 months ago

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

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

Patrick OShaughnessy

49,418 views • 11 months ago

Today on MCG $KARMA | Karma w/Amrix Karma started as a Solana neobank (USDC to IBAN, turning crypto into a real-world spendable balance), then evolved into a trading app with a social layer where users publish theses and get paid when others trade on them. He's now migrated to Robinhood Chain via Virtuals, relaunched the token on the ACF model, and committed 100% of swap revenue to the token for the first 90 days. Highlights: 01:04 - The journey 02:33 - The accidental launch 05:52 - Test everything, pivot on the data, "find where the money is made on chain" 06:17 - The neobank v1 09:43 - The last-click thesis 13:48 - Why Virtuals Protocol is everything Believe wasn't 18:42 - The ACF adoption 19:20 - Robinhood Crypto makes RWAs liquid, Karma is the accessibility layer that lets you spend them 22:17 - 100% of swap revenue for 90 days, 95% of in-app swaps to buy $KARMA back 25:00 - Building in public
29:01

Today on MCG $KARMA | Karma w/Amrix Karma started as a Solana neobank (USDC to IBAN, turning crypto into a real-world spendable balance), then evolved into a trading app with a social layer where users publish theses and get paid when others trade on them. He's now migrated to Robinhood Chain via Virtuals, relaunched the token on the ACF model, and committed 100% of swap revenue to the token for the first 90 days. Highlights: 01:04 - The journey 02:33 - The accidental launch 05:52 - Test everything, pivot on the data, "find where the money is made on chain" 06:17 - The neobank v1 09:43 - The last-click thesis 13:48 - Why Virtuals Protocol is everything Believe wasn't 18:42 - The ACF adoption 19:20 - Robinhood Crypto makes RWAs liquid, Karma is the accessibility layer that lets you spend them 22:17 - 100% of swap revenue for 90 days, 95% of in-app swaps to buy $KARMA back 25:00 - Building in public

MCG

18,050 views • 1 month ago

BITCOIN: HOW BAD IS THIS!!!! (hack) 🚨🚨🚨 00:00 Coldcard "Hack" News 01:20 Never roll your own Cryptography! 02:10 Key Generation was not Random 03:10 Maxis - No self custody! 04:25 Influencers rekt with Coldcard advice 05:40 35 YO loses everything on Coldcard 06:40 AI or Back door? 09:00 All wallets have risk! 12:00 Diversify 14:00 Signs of end of Bear market 15:00 Multisig with different wallets 16:30 Bitgo hack challenge 17:25 Strive buying BTC 18:10 Randomness is the key? Ledger 19:20 Quantum threat to BTC? 22:25 Galaxy Research on Coldcard 24:30 Willy Woo: Self custody vs Exchange 26:45 Ben Cowen Tweet 29:30 Stocks summary 32:25 Last week for Clarity Act? 34:00 Hunter Biden pro BTC??? 40:00 Oil 40:15 Random indicator of recession? 41:25 Join wait list for Bullmania 41:40 Bullmania AI to be launched 44:10 Fugazi, don't own US stocks? 47:30 Market update 48:35 Q and A 48:45 AI BS holding up stocks? 53:00 How is Moneyline performing now? 54:20 Buffet Megacrash? 58:00 Outro
58:08

BITCOIN: HOW BAD IS THIS!!!! (hack) 🚨🚨🚨 00:00 Coldcard "Hack" News 01:20 Never roll your own Cryptography! 02:10 Key Generation was not Random 03:10 Maxis - No self custody! 04:25 Influencers rekt with Coldcard advice 05:40 35 YO loses everything on Coldcard 06:40 AI or Back door? 09:00 All wallets have risk! 12:00 Diversify 14:00 Signs of end of Bear market 15:00 Multisig with different wallets 16:30 Bitgo hack challenge 17:25 Strive buying BTC 18:10 Randomness is the key? Ledger 19:20 Quantum threat to BTC? 22:25 Galaxy Research on Coldcard 24:30 Willy Woo: Self custody vs Exchange 26:45 Ben Cowen Tweet 29:30 Stocks summary 32:25 Last week for Clarity Act? 34:00 Hunter Biden pro BTC??? 40:00 Oil 40:15 Random indicator of recession? 41:25 Join wait list for Bullmania 41:40 Bullmania AI to be launched 44:10 Fugazi, don't own US stocks? 47:30 Market update 48:35 Q and A 48:45 AI BS holding up stocks? 53:00 How is Moneyline performing now? 54:20 Buffet Megacrash? 58:00 Outro

Ivan on Tech 🍳📈💰 Head Trader @ Bullmania

17,451 views • 1 month ago

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack
1:05:15

Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

Matt Turck

11,908 views • 3 months 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

내가 보려고 모은 #조이 역대 브릿지 파트 모음.zip (Ice Cream Cake~Sweet Dreams) 💚 * 레드벨벳 앨범만 포함했습니다 00:00 Somethin Kinda Crazy 00:05 Take It Slow 00:13 사탕 (Candy) 00:20 Dumb Dumb 00:22 Campfire 00:31 Lady’s Room 00:33 Day 1 00:43 Coold World 00:51 My Dear 00:55 Bad Dracula 00:58 Happily Ever After 01:03 빨간 맛 (Red Flavor) 01:09 여름빛 (Mojito) 01:13 I Just 01:17 Kingdom Come 01:23 두 번째 데이트 (My Second Date) 01:27 Attaboy 01:36 Perfect 10 01:42 달빛소리 01:53 Bad Boy 01:59 Time To Love 02:09 #Cookie Jar 02:13 Aitai-tai 02:20 Power Up 02:23 한 여름의 크리스마스 (With You) 02:30 Mr.E 02:35 Mosquito 02:41 RBB (Really Bad Boy) 02:47 So Good 02:51 멋있게 (Sassy Me) 02:54 Swimming Pool 02:57 Sayonara 03:04 짐살라빔 (Zimzalabim) 03:12 Sunny Side Up! 03:16 Milkshake 03:21 친구가 아냐 (Bing Bing) 03:29 안녕, 여름 (Parade) 03:32 LP 03:41 음파음파 (Umpah Umpah) 03:48 카풀 (Carpool) 03:57 Love Is The Way 04:05 Jumpin’ 04:11 눈 맞추고, 손 맞대고 (Eyes Locked, Hand Locked) 04:17 Psycho 04:24 In & Out 04:34 Remember Forever 04:44 Milky Way 04:52 Queendom 05:00 Pose 05:07 Knock On Wood 05:15 Better Be 05:25 Feel My Rhythm 05:29 Rainbow Halo 05:36 Beg For Me 05:43 In My Dreams 05:48 Marionette 05:50 WILDSIDE 05:54 Jackpot 06:00 Snap Snap 06:08 ‘Cause it’s you 06:14 Color of Love 06:16 Birthday 06:19 BYE BYE 06:22 롤러코스터 (On A Ride) 06:29 Zoom 06:40 Celebrate 06:48 Chill Kill 06:56 Knock Knock (Who’s There?) 06:59 Nightmare 07:03 One Kiss 07:10 Wings 07:21 풍경화 (Scenery) 07:32 Sunflower 07:38 Love Arcade 07:44 Bubble 07:51 Night Drive 07:57 Sweet Dreams
8:06

내가 보려고 모은 #조이 역대 브릿지 파트 모음.zip (Ice Cream Cake~Sweet Dreams) 💚 * 레드벨벳 앨범만 포함했습니다 00:00 Somethin Kinda Crazy 00:05 Take It Slow 00:13 사탕 (Candy) 00:20 Dumb Dumb 00:22 Campfire 00:31 Lady’s Room 00:33 Day 1 00:43 Coold World 00:51 My Dear 00:55 Bad Dracula 00:58 Happily Ever After 01:03 빨간 맛 (Red Flavor) 01:09 여름빛 (Mojito) 01:13 I Just 01:17 Kingdom Come 01:23 두 번째 데이트 (My Second Date) 01:27 Attaboy 01:36 Perfect 10 01:42 달빛소리 01:53 Bad Boy 01:59 Time To Love 02:09 #Cookie Jar 02:13 Aitai-tai 02:20 Power Up 02:23 한 여름의 크리스마스 (With You) 02:30 Mr.E 02:35 Mosquito 02:41 RBB (Really Bad Boy) 02:47 So Good 02:51 멋있게 (Sassy Me) 02:54 Swimming Pool 02:57 Sayonara 03:04 짐살라빔 (Zimzalabim) 03:12 Sunny Side Up! 03:16 Milkshake 03:21 친구가 아냐 (Bing Bing) 03:29 안녕, 여름 (Parade) 03:32 LP 03:41 음파음파 (Umpah Umpah) 03:48 카풀 (Carpool) 03:57 Love Is The Way 04:05 Jumpin’ 04:11 눈 맞추고, 손 맞대고 (Eyes Locked, Hand Locked) 04:17 Psycho 04:24 In & Out 04:34 Remember Forever 04:44 Milky Way 04:52 Queendom 05:00 Pose 05:07 Knock On Wood 05:15 Better Be 05:25 Feel My Rhythm 05:29 Rainbow Halo 05:36 Beg For Me 05:43 In My Dreams 05:48 Marionette 05:50 WILDSIDE 05:54 Jackpot 06:00 Snap Snap 06:08 ‘Cause it’s you 06:14 Color of Love 06:16 Birthday 06:19 BYE BYE 06:22 롤러코스터 (On A Ride) 06:29 Zoom 06:40 Celebrate 06:48 Chill Kill 06:56 Knock Knock (Who’s There?) 06:59 Nightmare 07:03 One Kiss 07:10 Wings 07:21 풍경화 (Scenery) 07:32 Sunflower 07:38 Love Arcade 07:44 Bubble 07:51 Night Drive 07:57 Sweet Dreams

맑음쨩 🍏

20,678 views • 1 year ago

NEW EP: Inside NEAR Protocol 10+ Years Building Toward the Agent Economy with Illia Polosukhin (Illia (root.near) (🇺🇦, ⋈)) From pioneering the transformer architecture now used in every AI stack to building the rails for the agent economy, NEAR Co-Founder Illia Polosukhin connects the full arc in this inaugural episode of “Inside NEAR,” a new quarterly series on The Rollup. Illia breaks down NEAR’s early architectural bets that are now paying off, why privacy had to be a base-layer choice, and how it all culminates in NEAR becoming AI money: the unit of account for inference, compute, and every agent-to-agent transaction settling onchain. With $20 billion+ in Intents volume and a path to deflation, the thesis is simple: NEAR’s TAM isn't crypto, it's all of commerce. Timestamps: 00:00 Intro 01:38 State Of NEAR Quarterly 04:32 The Transformer's Origin Story 06:51 NEAR Started As AI 11:40 Post-Quantum Crypto Coming Soon 13:48 Root Of Trust Solved 19:36 Target Market All Commerce 20:41 AI Frontend Blockchain Backend 22:44 Confidential Intents TVL Rising 28:51 Fee Switch Turned On 34:04 NEAR Is AI Money 36:30 One Line Changes Reality Full episode links below.
40:22

NEW EP: Inside NEAR Protocol 10+ Years Building Toward the Agent Economy with Illia Polosukhin (Illia (root.near) (🇺🇦, ⋈)) From pioneering the transformer architecture now used in every AI stack to building the rails for the agent economy, NEAR Co-Founder Illia Polosukhin connects the full arc in this inaugural episode of “Inside NEAR,” a new quarterly series on The Rollup. Illia breaks down NEAR’s early architectural bets that are now paying off, why privacy had to be a base-layer choice, and how it all culminates in NEAR becoming AI money: the unit of account for inference, compute, and every agent-to-agent transaction settling onchain. With $20 billion+ in Intents volume and a path to deflation, the thesis is simple: NEAR’s TAM isn't crypto, it's all of commerce. Timestamps: 00:00 Intro 01:38 State Of NEAR Quarterly 04:32 The Transformer's Origin Story 06:51 NEAR Started As AI 11:40 Post-Quantum Crypto Coming Soon 13:48 Root Of Trust Solved 19:36 Target Market All Commerce 20:41 AI Frontend Blockchain Backend 22:44 Confidential Intents TVL Rising 28:51 Fee Switch Turned On 34:04 NEAR Is AI Money 36:30 One Line Changes Reality Full episode links below.

The Rollup

25,703 views • 2 months 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

103,934 views • 3 months ago

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training
1:07:13

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

EigenCloud

62,099 views • 11 months ago