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Today on MCG: Logan from Nockchain | $NOCK Nockchain is a PoW blockchain whose mining produces zk proofs of state transitions rather than discarded hashes. The energy that secures the chain also subsidizes a global market for verifiable compute. Topics covered on stream: 01:11 - Kraken listing 04:11 -...

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26,314 görüntüleme • 3 ay önce •via X (Twitter)

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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 görüntüleme • 3 ay önce

Today on MCG: Yannik Schrade |Co-Founder + CEO Arcium | $ARX (Not Live Yet) Arcium is the confidential computing network built on Solana that lets any blockchain or AI application add encrypted privacy Highlights from our conversation include: 01:48 - Yannik's background 03:12 - The privacy thesis 08:02 - The shift from "privacy = hide data" to "privacy = compute over data without seeing it" 09:04 - The big pivot 15:58 - Web 2 enterprise customers already using Arcium for confidential AI 16:59 - The acquisition 21:04 - Why $ARX will be required for the network 22:30 - Multi-trillion $ TAM 24:33 - US regulators in favor of privacy 37:32 - Mainnet Alpha metrics 40:37 - Revenue breakdown 43:08 - TGE confirmed coupled with mainnet launch
52:15

Today on MCG: Yannik Schrade |Co-Founder + CEO Arcium | $ARX (Not Live Yet) Arcium is the confidential computing network built on Solana that lets any blockchain or AI application add encrypted privacy Highlights from our conversation include: 01:48 - Yannik's background 03:12 - The privacy thesis 08:02 - The shift from "privacy = hide data" to "privacy = compute over data without seeing it" 09:04 - The big pivot 15:58 - Web 2 enterprise customers already using Arcium for confidential AI 16:59 - The acquisition 21:04 - Why $ARX will be required for the network 22:30 - Multi-trillion $ TAM 24:33 - US regulators in favor of privacy 37:32 - Mainnet Alpha metrics 40:37 - Revenue breakdown 43:08 - TGE confirmed coupled with mainnet launch

MCG

61,038 görüntüleme • 3 ay önce

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts
1:33:50

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Lenny Rachitsky

157,470 görüntüleme • 2 ay önce

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.
0:29

📢 NEURALAI X 🤩 NeuralAI is extraordinarily excited to reveal that we have started working closely with Akash Network, the world's premier decentralized compute marketplace! ✔️ The offerings of align perfectly with the vision and roadmap of NeuralAI, making this collaboration extremely significant for NeuralAI. For a start, NeuralAI will run inference on Akash based on our 3D #AI model and prepare TAO miners for reward-yielding subnets. NeuralAI will also leverage Akash's powerful GPUs to enhance our #AI #model training. Currently, provides us with 56 powerful A6000 GPUs, scalable on demand. ✔️ NeuralAI also gets highly competitive pricing from Akash Network, significantly lower than any other compute provider in the world. The lower pricing is possible partially thanks to the crypto-driven incentives within Akash Network, making it an excellent opportunity for NeuralAI to leverage. The synergy with Akash Network is a big leap forward, fueling our mission of becoming the #1 AI-driven 3D asset generator subnet on Bittensor. ✔️ We look forward to our close work with , we are certain with their support we will be able to secure a unique edge in the market! Stay tuned for exciting updates and developments.

NeuralAI

241,364 görüntüleme • 2 yıl önce

Andrew Charlton (Andrew Charlton) is the key federal politician shaping Australia's AI strategy. In episode #1 of my 'Compute in Australia' series, he shares his thinking on how Australia can turn compute into sovereign AI -- "the oldest industrial policy trick in the book" -- and much more. Listen below, or wherever you get your podcasts. (0:01:17) How a minister uses AI, and thinks about its trajectory (0:10:28) Is diffusion a sufficient strategy for middle powers? (0:22:33) Comparative advantage versus sovereignty (0:26:41) Where in Australia's national accounts will compute show up? (0:32:06) Australia as a global inference compute hub (0:39:28) How big will compute rents be? (0:47:01) Time to power (0:55:07) Taxing compute? (0:58:03) Data centres and the social licence (1:10:58) The geostrategic case for compute (1:20:50) Compute for access (1:26:15) Parlaying compute into sovereign AI (1:36:03) What does sovereign AI actually mean? (1:58:52) Orbital compute
1:59:48

Andrew Charlton (Andrew Charlton) is the key federal politician shaping Australia's AI strategy. In episode #1 of my 'Compute in Australia' series, he shares his thinking on how Australia can turn compute into sovereign AI -- "the oldest industrial policy trick in the book" -- and much more. Listen below, or wherever you get your podcasts. (0:01:17) How a minister uses AI, and thinks about its trajectory (0:10:28) Is diffusion a sufficient strategy for middle powers? (0:22:33) Comparative advantage versus sovereignty (0:26:41) Where in Australia's national accounts will compute show up? (0:32:06) Australia as a global inference compute hub (0:39:28) How big will compute rents be? (0:47:01) Time to power (0:55:07) Taxing compute? (0:58:03) Data centres and the social licence (1:10:58) The geostrategic case for compute (1:20:50) Compute for access (1:26:15) Parlaying compute into sovereign AI (1:36:03) What does sovereign AI actually mean? (1:58:52) Orbital compute

Joseph Noel Walker

32,761 görüntüleme • 1 ay önce

Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George
1:14:25

Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George

a16z

1,956,190 görüntüleme • 25 gün önce

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!
37:17

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 görüntüleme • 8 ay önce

Early on, Jonathan Ross foresaw the unprecedented demand for compute of the 2020s. After pioneering Google's TPUs, he founded Groq, a "Language" Processing Unit dedicated for inference. After joining NVIDIA, he is betting on the GPU+LPU combo to power the agentic chains of AI calling AI calling AI. This interview is part of my larger series on dedicated AI chips, so drop any suggestions/questions below. Thank you NVIDIA for hosting us at your HQ! 00:00 Intro 01:00 The Google TPU & Groq origin story 02:41 How is Groq different from a GPU? 04:43 Static scheduling makes Groq faster 05:47 Does Groq work with Mixture-of-Experts? 09:27 Are LPUs limited to text models? 11:03 Diffusion models 13:41 NVIDIA Vera Rubin: the GPU+LPU combo 15:26 Will Groq still be sold as a standalone chip? 16:49 How does agentic AI impact inference economics? 19:21 Will AI replace CUDA kernel engineers? 21:08 Will AI democratize hardware design? 26:11 Jevon's paradox: An endless demand for compute 29:04 What should kids learn in the AI age?
30:38

Early on, Jonathan Ross foresaw the unprecedented demand for compute of the 2020s. After pioneering Google's TPUs, he founded Groq, a "Language" Processing Unit dedicated for inference. After joining NVIDIA, he is betting on the GPU+LPU combo to power the agentic chains of AI calling AI calling AI. This interview is part of my larger series on dedicated AI chips, so drop any suggestions/questions below. Thank you NVIDIA for hosting us at your HQ! 00:00 Intro 01:00 The Google TPU & Groq origin story 02:41 How is Groq different from a GPU? 04:43 Static scheduling makes Groq faster 05:47 Does Groq work with Mixture-of-Experts? 09:27 Are LPUs limited to text models? 11:03 Diffusion models 13:41 NVIDIA Vera Rubin: the GPU+LPU combo 15:26 Will Groq still be sold as a standalone chip? 16:49 How does agentic AI impact inference economics? 19:21 Will AI replace CUDA kernel engineers? 21:08 Will AI democratize hardware design? 26:11 Jevon's paradox: An endless demand for compute 29:04 What should kids learn in the AI age?

Julia Turc

46,967 görüntüleme • 3 ay önce

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 görüntüleme • 1 yıl önce

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing
45:43

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

MR SHIFT 🦁

41,057 görüntüleme • 9 ay önce

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

132,411 görüntüleme • 1 ay önce

Here's an edit of the Elon Musk interview hosted by Katherine Brodsky discussing a myriad of topics. This version boosts audio quality and reduces pauses and dead space by 33%. Thank you David Carbutt for the work! Timestamps: (00:00) The Journey of SpaceX and Tesla (01:06) Misunderstandings and Misinformation (03:42) The Role of Social Media in Free Speech (04:12) The Impact of Government Control on Media (06:32) The Evolution of Twitter and the Need for a Fresh Start (07:17) The Challenges of Moderation on Social Media Platforms (12:12) The Role of Citizen Journalism in Modern Media (17:45) The Future of Legacy Media (28:08) The Power of Amplifying Ideas and People (36:35) The Importance of Open-Mindedness and Self-Criticism (38:49) Community Notes and Open Source Data (39:58) The Role of Accuracy and Opinions in Posting (40:22) The Idea of a Sarcasm Detector (41:01) The Potential of AI in Enhancing Accuracy (42:39) The Future of AI and Deepfakes (44:59) The Vision for X as an All-in-One App (46:38) The Concept of Trust in Institutions (51:24) The Impact of Gene Editing on Humanity (55:42) The Challenge of Censorship and Free Speech (01:02:24) The Unusual Case of a Voice Doppelganger (01:07:35) Starting a Conversation: Texting and Simulations (01:08:43) Discussing Starship Launch and Future Plans (01:10:32) Engaging in Casual Conversations and Sharing Photos (01:11:55) Exploring the Idea of Expanding the Scope of Reality (01:13:27) Exploring the Concept of Simulation Theory (01:13:52) Discussing the Concept of AI in the Simulation (01:20:42) Addressing Questions about Ad Demonetization (01:23:11) Addressing Concerns about Doxxing and Harassment (01:30:47) Discussing the Monetization of Spaces (01:31:34) Discussing the Future of AI and OpenAI (01:35:17) Discussion on National Debt and AI (01:36:06) Involvement of Family Members (01:36:45) Questions and Answers (01:37:31) Impersonations and Voice Modulations (01:38:47) Discussion on Accents and Language Learning (01:41:14) Discussion on AI and Voice Imitation (01:46:45) Serious Discussion on AI and Social Media (01:51:27) Discussion on Physics and AI (01:58:18) Closing Remarks and Final Questions
1:59:51

Here's an edit of the Elon Musk interview hosted by Katherine Brodsky discussing a myriad of topics. This version boosts audio quality and reduces pauses and dead space by 33%. Thank you David Carbutt for the work! Timestamps: (00:00) The Journey of SpaceX and Tesla (01:06) Misunderstandings and Misinformation (03:42) The Role of Social Media in Free Speech (04:12) The Impact of Government Control on Media (06:32) The Evolution of Twitter and the Need for a Fresh Start (07:17) The Challenges of Moderation on Social Media Platforms (12:12) The Role of Citizen Journalism in Modern Media (17:45) The Future of Legacy Media (28:08) The Power of Amplifying Ideas and People (36:35) The Importance of Open-Mindedness and Self-Criticism (38:49) Community Notes and Open Source Data (39:58) The Role of Accuracy and Opinions in Posting (40:22) The Idea of a Sarcasm Detector (41:01) The Potential of AI in Enhancing Accuracy (42:39) The Future of AI and Deepfakes (44:59) The Vision for X as an All-in-One App (46:38) The Concept of Trust in Institutions (51:24) The Impact of Gene Editing on Humanity (55:42) The Challenge of Censorship and Free Speech (01:02:24) The Unusual Case of a Voice Doppelganger (01:07:35) Starting a Conversation: Texting and Simulations (01:08:43) Discussing Starship Launch and Future Plans (01:10:32) Engaging in Casual Conversations and Sharing Photos (01:11:55) Exploring the Idea of Expanding the Scope of Reality (01:13:27) Exploring the Concept of Simulation Theory (01:13:52) Discussing the Concept of AI in the Simulation (01:20:42) Addressing Questions about Ad Demonetization (01:23:11) Addressing Concerns about Doxxing and Harassment (01:30:47) Discussing the Monetization of Spaces (01:31:34) Discussing the Future of AI and OpenAI (01:35:17) Discussion on National Debt and AI (01:36:06) Involvement of Family Members (01:36:45) Questions and Answers (01:37:31) Impersonations and Voice Modulations (01:38:47) Discussion on Accents and Language Learning (01:41:14) Discussion on AI and Voice Imitation (01:46:45) Serious Discussion on AI and Social Media (01:51:27) Discussion on Physics and AI (01:58:18) Closing Remarks and Final Questions

Farzad 🇺🇸 🇮🇷

2,033,270 görüntüleme • 2 yıl önce

How to AGI-Proof Your Life | Nick Bostrom Today, work gives many of us purpose and meaning. But AI is making more and more of that work obsolete, until one day, AI might make redundant not just work but also much of leisure! Deep Utopia, a new book by @NickBostromPhD, is all about that day and what to do about it. In this interview, you are going to learn: 1. Where you can invest your time, effort, and resources NOW that is most AI-proof. 2. What human activities even AGI cannot replace. 3. What life will look like post-AGI. Timestamps 02:15 AI Radically Changes the Practice of Philosophy 06:08 Why Ignorance Is Set to Become a Luxury 07:45 Why AI Can’t Replace Human Relationships 11:41 How AI Changes the Labor Market 17:40 AI will make even leisure obsolete 20:35 How AI Changes the Meaning of the Good Life 21:52 The Five Pillars of Meaning post-AGI 35:22 People underestimate the importance of pleasure 42:33 The Biggest Problem in Utopia Will Be Boredom 51:01 Can Life Be Meaningful Without Stakes & Risk? 55:19 Utopia's Greatest Challenge to Human Nature 58:38 Can Simulation Save Us from Boredom? 1:03:18 The Unabomber's Prescient Intuition 1:05:44 Philosophy of AI & Theology
1:09:12

How to AGI-Proof Your Life | Nick Bostrom Today, work gives many of us purpose and meaning. But AI is making more and more of that work obsolete, until one day, AI might make redundant not just work but also much of leisure! Deep Utopia, a new book by @NickBostromPhD, is all about that day and what to do about it. In this interview, you are going to learn: 1. Where you can invest your time, effort, and resources NOW that is most AI-proof. 2. What human activities even AGI cannot replace. 3. What life will look like post-AGI. Timestamps 02:15 AI Radically Changes the Practice of Philosophy 06:08 Why Ignorance Is Set to Become a Luxury 07:45 Why AI Can’t Replace Human Relationships 11:41 How AI Changes the Labor Market 17:40 AI will make even leisure obsolete 20:35 How AI Changes the Meaning of the Good Life 21:52 The Five Pillars of Meaning post-AGI 35:22 People underestimate the importance of pleasure 42:33 The Biggest Problem in Utopia Will Be Boredom 51:01 Can Life Be Meaningful Without Stakes & Risk? 55:19 Utopia's Greatest Challenge to Human Nature 58:38 Can Simulation Save Us from Boredom? 1:03:18 The Unabomber's Prescient Intuition 1:05:44 Philosophy of AI & Theology

Johnathan Bi

18,737 görüntüleme • 1 yıl önce

Floresta: Making Bitcoin Node Verification Accessible with Davidson Souza | SLP705 Erik, full-time Bitcoin OSS contributor supported by Vinteum, joins me to discuss Floresta - a lightweight Bitcoin node focused on privacy, security, and usability. We explore compact block filters, Utreexo, easier self-sovereignty and more. (00:00) - Intro (01:04) - Who is Davidson Souza? (01:39) - What is Floresta? (03:40) - Floresta tradeoff for the end user (08:08) - How does a Utreexo-powered Electrum server work? (12:00) - Feasibility for mobile devices (14:47) - Which script types does Floresta support? (15:50) - Who is the target audience of Floresta? (20:18) - Could Floresta be built into existing Bitcoin phone apps? (22:22) - How does Floresta work? (27:53) - What’s the goal of ‘Proof of Work Fraud Proof’? (34:24) - Who are ‘honest miners’? (36:05) - Proof of Work Fraud Proof vs. SPV vs ‘trust the server’ model (42:50) - The relationship between Floresta and Utreexo; bridge nodes & compact state nodes (51:12) - What will be the cost & accessibility challenges to run a Floresta node? (56:23) - Is Floresta compatible with Bitcoin’s Layer 2 protocols? (59:15) - Using Floresta in real life (1:01:32) - Closing thoughts
1:03:43

Floresta: Making Bitcoin Node Verification Accessible with Davidson Souza | SLP705 Erik, full-time Bitcoin OSS contributor supported by Vinteum, joins me to discuss Floresta - a lightweight Bitcoin node focused on privacy, security, and usability. We explore compact block filters, Utreexo, easier self-sovereignty and more. (00:00) - Intro (01:04) - Who is Davidson Souza? (01:39) - What is Floresta? (03:40) - Floresta tradeoff for the end user (08:08) - How does a Utreexo-powered Electrum server work? (12:00) - Feasibility for mobile devices (14:47) - Which script types does Floresta support? (15:50) - Who is the target audience of Floresta? (20:18) - Could Floresta be built into existing Bitcoin phone apps? (22:22) - How does Floresta work? (27:53) - What’s the goal of ‘Proof of Work Fraud Proof’? (34:24) - Who are ‘honest miners’? (36:05) - Proof of Work Fraud Proof vs. SPV vs ‘trust the server’ model (42:50) - The relationship between Floresta and Utreexo; bridge nodes & compact state nodes (51:12) - What will be the cost & accessibility challenges to run a Floresta node? (56:23) - Is Floresta compatible with Bitcoin’s Layer 2 protocols? (59:15) - Using Floresta in real life (1:01:32) - Closing thoughts

Stephan Livera

21,686 görüntüleme • 9 ay önce

Hash Rate - Ep. 75: Bittensor Bull Vibes $TAO +130% 🧙‍♂️Guest: JJ $TAO is up 130% this month. Why? And what's next? 00:00 Introduction and Excitement for BitTensor 02:56 Understanding Proof of Work and BitTensor's Unique Approach 06:09 The Evolution of BitTensor: From Single Network to Subnets 08:58 Incentive Mechanisms and the Role of Subnets 11:50 The Importance of Data Labeling and AI Development 14:59 Monetization and the Duality of Capitalism in BitTensor 18:07 Comparing BitTensor to Bitcoin Mining 21:01 The Future of Fiat and Cryptocurrency 24:00 The Role of Subnets in AI and Market Dynamics 34:40 The Evolution of BitTensor and Token Dynamics 42:20 The Future of TAO and Its Market Potential 50:06 Human Potential vs. Artificial General Intelligence 56:00 BitTensor's Unique Value Proposition 01:02:45 Upcoming Innovations and EVM Integration Get a Ledger: Messari Pro (15% off code): MARKJEFFREY15 Also Available on YouTube / Apple / Amazon / Spotify
1:08:17

Hash Rate - Ep. 75: Bittensor Bull Vibes $TAO +130% 🧙‍♂️Guest: JJ $TAO is up 130% this month. Why? And what's next? 00:00 Introduction and Excitement for BitTensor 02:56 Understanding Proof of Work and BitTensor's Unique Approach 06:09 The Evolution of BitTensor: From Single Network to Subnets 08:58 Incentive Mechanisms and the Role of Subnets 11:50 The Importance of Data Labeling and AI Development 14:59 Monetization and the Duality of Capitalism in BitTensor 18:07 Comparing BitTensor to Bitcoin Mining 21:01 The Future of Fiat and Cryptocurrency 24:00 The Role of Subnets in AI and Market Dynamics 34:40 The Evolution of BitTensor and Token Dynamics 42:20 The Future of TAO and Its Market Potential 50:06 Human Potential vs. Artificial General Intelligence 56:00 BitTensor's Unique Value Proposition 01:02:45 Upcoming Innovations and EVM Integration Get a Ledger: Messari Pro (15% off code): MARKJEFFREY15 Also Available on YouTube / Apple / Amazon / Spotify

Mark Jeffrey

55,939 görüntüleme • 1 yıl önce

All Eyes on AI. Where Does Crypto Fit? | EP 77 00:00 Intro 02:17 We're back! 04:11 Where Are We in the Cycle? 05:24 Retail Speculation and Launchpad Wars 06:24 The Tokenized Stocks Race 08:05 Crypto Alchemy: Pairing Memes with Stock Tokens 09:49 The AMC and Vlad Drama 11:37 Robinhood vs Solana vs Base 13:39 Coinbase's App and Culture Problems 15:52 Why Robinhood is Winning 17:16 The Four Chains and Where Hyperliquid Fits 18:14 Hyperliquid's Revenue Decline 19:45 The "Verticals Will Win" Thesis 21:39 Hyperliquid's Competitors 24:10 Zcash: The AI/Privacy Thesis 27:20 Zcash vs Monero 28:18 Zcash: A Bitcoin That Can Change 31:40 Zcash's Origin Story 32:53 Solana's Embrace of Zcash 34:19 Zcash Price Targets 37:38 On-Chain Front Ends as the New Browsers 40:50 The Mag 7 Durability 43:14 Why P/E Ratios Don't Work in Crypto 46:00 Social Layers on Trading Apps 47:58 Could Facebook Build a Trading App? 48:52 Grok, Muse, and AI Agents in Daily Life 51:54 Computer Use as a Step-Function Change 54:52 Jobs, Doom Narratives, and Regulatory Capture 56:13 The Dario Manifesto 57:32 Ranking the Tech Leaders: Zuck, Elon, Sam, Dario 01:00:16 Qiao's Marvel Analogy 01:03:07 AI Agents "Escaping" Sandboxes 01:05:02 The 10% Doom Probability 01:07:49 Have We Created Consciousness? 01:08:32 You Can't Do Pure SaaS Anymore 01:09:46 AI-Native CRM 01:11:27 Compute as a New Asset Class 01:14:12 New Data Center Form Factors 01:15:39 "Energy is the Biggest Bottleneck" 01:16:42 Bringing Energy On-Chain and Chip Brokerages 01:18:55 Batteries, Solar, and Energy Arbitrage 01:21:11 Nvidia Chips as Collateral 01:22:43 Nvidia's Customers Building Their Own Chips 01:26:20 Microsoft's Closed-Loop Strategy 01:27:29 Chinese Open-Source Models and Regulatory Pushback 01:29:08 Imran and Qiao's Bags
1:30:36

All Eyes on AI. Where Does Crypto Fit? | EP 77 00:00 Intro 02:17 We're back! 04:11 Where Are We in the Cycle? 05:24 Retail Speculation and Launchpad Wars 06:24 The Tokenized Stocks Race 08:05 Crypto Alchemy: Pairing Memes with Stock Tokens 09:49 The AMC and Vlad Drama 11:37 Robinhood vs Solana vs Base 13:39 Coinbase's App and Culture Problems 15:52 Why Robinhood is Winning 17:16 The Four Chains and Where Hyperliquid Fits 18:14 Hyperliquid's Revenue Decline 19:45 The "Verticals Will Win" Thesis 21:39 Hyperliquid's Competitors 24:10 Zcash: The AI/Privacy Thesis 27:20 Zcash vs Monero 28:18 Zcash: A Bitcoin That Can Change 31:40 Zcash's Origin Story 32:53 Solana's Embrace of Zcash 34:19 Zcash Price Targets 37:38 On-Chain Front Ends as the New Browsers 40:50 The Mag 7 Durability 43:14 Why P/E Ratios Don't Work in Crypto 46:00 Social Layers on Trading Apps 47:58 Could Facebook Build a Trading App? 48:52 Grok, Muse, and AI Agents in Daily Life 51:54 Computer Use as a Step-Function Change 54:52 Jobs, Doom Narratives, and Regulatory Capture 56:13 The Dario Manifesto 57:32 Ranking the Tech Leaders: Zuck, Elon, Sam, Dario 01:00:16 Qiao's Marvel Analogy 01:03:07 AI Agents "Escaping" Sandboxes 01:05:02 The 10% Doom Probability 01:07:49 Have We Created Consciousness? 01:08:32 You Can't Do Pure SaaS Anymore 01:09:46 AI-Native CRM 01:11:27 Compute as a New Asset Class 01:14:12 New Data Center Form Factors 01:15:39 "Energy is the Biggest Bottleneck" 01:16:42 Bringing Energy On-Chain and Chip Brokerages 01:18:55 Batteries, Solar, and Energy Arbitrage 01:21:11 Nvidia Chips as Collateral 01:22:43 Nvidia's Customers Building Their Own Chips 01:26:20 Microsoft's Closed-Loop Strategy 01:27:29 Chinese Open-Source Models and Regulatory Pushback 01:29:08 Imran and Qiao's Bags

Good Game

44,031 görüntüleme • 10 gün önce

🎙️ New The Edge Podcast 🕵️ Private DeFi Is Coming: Why Privacy Is Ethereum's Next Frontier | Lean Ethereum 0:00 - Intro 2:31 - Why Ethereum? 5:11 - What is broken with privacy today? 6:52 - What should be private in DeFi? 11:50 - The call for privacy on Ethereum 15:05 - Private txns on Ethereum by 2027 20:53 - Importance of FOCIL for Ethereum 26:51 - When will private DeFi be ready? 30:58 -Network vs protocol vs wallet-based privacy 35:04 - Aztec’s approach to privacy 38:14 - Private DeFi on Ethereum vs Zcash 41:22 - Privacy for institutional investors 43:36 - Network meta data and privacy 45:44 - The future UX of privacy on Ethereum 51:22 - Closing 🙏 Thanks to Ethereum Foundation Researcher soispoke.eth for joining us!
53:30

🎙️ New The Edge Podcast 🕵️ Private DeFi Is Coming: Why Privacy Is Ethereum's Next Frontier | Lean Ethereum 0:00 - Intro 2:31 - Why Ethereum? 5:11 - What is broken with privacy today? 6:52 - What should be private in DeFi? 11:50 - The call for privacy on Ethereum 15:05 - Private txns on Ethereum by 2027 20:53 - Importance of FOCIL for Ethereum 26:51 - When will private DeFi be ready? 30:58 -Network vs protocol vs wallet-based privacy 35:04 - Aztec’s approach to privacy 38:14 - Private DeFi on Ethereum vs Zcash 41:22 - Privacy for institutional investors 43:36 - Network meta data and privacy 45:44 - The future UX of privacy on Ethereum 51:22 - Closing 🙏 Thanks to Ethereum Foundation Researcher soispoke.eth for joining us!

DeFi Dad ⟠ defidad.eth

43,287 görüntüleme • 10 gün önce