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Long Live Outputmaxxing: AI compute grids, Anthropic’s coding takeoff, data center backlash, & frontier systems AMP PBC founder Anjney Midha explains why 95% GPU utilization was considered an outage at Google, why the AI race is no longer just about buying more GPUs, how AMP is trying to make... show more

Latent.Space

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30,621 просмотров • 28 дней назад •via X (Twitter)

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AI is getting much better at hacking and fast. Data from the UK AI Security Institute, the Center for AI Standards and Innovations and others, shows how AI models are becoming dramatically more capable at cyberattacks. Sky's Rowland Manthorpe explains why this is no longer just a warning about one powerful model, but a much bigger industry-wide shift. 📺

AI is getting much better at hacking and fast. Data from the UK AI Security Institute, the Center for AI Standards and Innovations and others, shows how AI models are becoming dramatically more capable at cyberattacks. Sky's Rowland Manthorpe explains why this is no longer just a warning about one powerful model, but a much bigger industry-wide shift. 📺

Sky News

18,021 просмотров • 1 месяц назад

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier

Nathan Lands

28,827 просмотров • 3 месяцев назад

"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.

"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.

Exabits

13,895 просмотров • 1 год назад

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google

Latent.Space

529,014 просмотров • 5 месяцев назад

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

64,952 просмотров • 1 месяц назад

SoftBank is creating the biggest data center. On Fox Business, Arm CEO and SoftBank Group International CEO Rene Haas was asked what he thinks about Meta selling compute, which could mean a compute supply glut. Haas disagreed, pointing to SpaceX's compute deal with Anthropic as proof of real demand, not oversupply. He said Meta's situation is similar, and that only a handful of companies, SpaceX, Meta, and SoftBank, have the financial muscle to compete at gigawatt scale, which is why SoftBank is moving in.

SoftBank is creating the biggest data center. On Fox Business, Arm CEO and SoftBank Group International CEO Rene Haas was asked what he thinks about Meta selling compute, which could mean a compute supply glut. Haas disagreed, pointing to SpaceX's compute deal with Anthropic as proof of real demand, not oversupply. He said Meta's situation is similar, and that only a handful of companies, SpaceX, Meta, and SoftBank, have the financial muscle to compete at gigawatt scale, which is why SoftBank is moving in.

Lark Davis

34,017 просмотров • 12 дней назад

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

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 просмотров • 7 месяцев назад

Joelle built the AI lab at Meta She worked and learnt from Zuck She is one of the leading minds in AI What everyone thinks they know about AI but get wrong 👇 - Why Scaling Laws Will Continue - Why You Can’t Buy Success in AI with Talent Acquisitions - Future of Synthetic Data & What It Means for Models - Why AI Coding is Akin to Image Generation in 2015 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:16 How Meta Shaped How I Think About AI Research 02:22 Challenges in Reinforcement Learning 08:33 Is It Possible To Be Capital Efficient in AI 13:47 AI in Enterprise: Efficiency and Adoption 21:51 Security Concerns with AI Agents 28:06 Can Zuck Win By Buying The Superstars of AI 32:11 The Rising Cost of Data 36:38 Synthetic Data and Model Degradation 38:42 Why AI Coding is Akin to Image Generation in 2015 51:16 If Joelle Was a VC Where Would She Invest? 51:50 Quick-Fire Round: Lessons from Zuck, Biggest Mindset Shift My 5 takeaways with Joelle Pineau 👇

Joelle built the AI lab at Meta She worked and learnt from Zuck She is one of the leading minds in AI What everyone thinks they know about AI but get wrong 👇 - Why Scaling Laws Will Continue - Why You Can’t Buy Success in AI with Talent Acquisitions - Future of Synthetic Data & What It Means for Models - Why AI Coding is Akin to Image Generation in 2015 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:16 How Meta Shaped How I Think About AI Research 02:22 Challenges in Reinforcement Learning 08:33 Is It Possible To Be Capital Efficient in AI 13:47 AI in Enterprise: Efficiency and Adoption 21:51 Security Concerns with AI Agents 28:06 Can Zuck Win By Buying The Superstars of AI 32:11 The Rising Cost of Data 36:38 Synthetic Data and Model Degradation 38:42 Why AI Coding is Akin to Image Generation in 2015 51:16 If Joelle Was a VC Where Would She Invest? 51:50 Quick-Fire Round: Lessons from Zuck, Biggest Mindset Shift My 5 takeaways with Joelle Pineau 👇

Harry Stebbings

19,398 просмотров • 8 месяцев назад

From early engineer at Glean to Partner at Menlo Ventures co-leading the Anthology Fund with Anthropic, Deedy Das has gone from shipping “boring” enterprise search to backing frontier labs, infra, and research plays like Anthropic, OpenRouter, Goodfire, Prime Intellect, and Whisper. We sat down with Deedy to unpack how Glean quietly built a real AI moat before LLMs were cool, why enterprise search is significantly different than consumer search, where value actually accrues in the model vs app stack, Anthropic’s rise and the importance of Claude Code, the Anthology Fund thesis, OpenRouter’s wedge, mechanistic interpretability, the coming compute arms race, coding agents and “LLM psychosis,” and what all of this means for AI engineers deciding what to build. Deedy swyx 🔜 NeurIPS + #DevWritersRetreat Alessio Fanelli

From early engineer at Glean to Partner at Menlo Ventures co-leading the Anthology Fund with Anthropic, Deedy Das has gone from shipping “boring” enterprise search to backing frontier labs, infra, and research plays like Anthropic, OpenRouter, Goodfire, Prime Intellect, and Whisper. We sat down with Deedy to unpack how Glean quietly built a real AI moat before LLMs were cool, why enterprise search is significantly different than consumer search, where value actually accrues in the model vs app stack, Anthropic’s rise and the importance of Claude Code, the Anthology Fund thesis, OpenRouter’s wedge, mechanistic interpretability, the coming compute arms race, coding agents and “LLM psychosis,” and what all of this means for AI engineers deciding what to build. Deedy swyx 🔜 NeurIPS + #DevWritersRetreat Alessio Fanelli

Latent.Space

269,074 просмотров • 8 месяцев назад

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

Matrix AI Network

60,855 просмотров • 1 месяц назад

$META is already using AI to improve its ad engine which makes it a clear winner in the AI economy. The market still wants proof that its massive infrastructure spend can create revenue beyond better ads which is why the compute business matters. If Meta monetizes just 20% of its data center capacity then it could generate $25B in profits and that optionality still doesn't look fully priced into the stock.

$META is already using AI to improve its ad engine which makes it a clear winner in the AI economy. The market still wants proof that its massive infrastructure spend can create revenue beyond better ads which is why the compute business matters. If Meta monetizes just 20% of its data center capacity then it could generate $25B in profits and that optionality still doesn't look fully priced into the stock.

Shay Boloor

85,425 просмотров • 9 дней назад

Adaption Labs CEO Sara Hooker says data centers in space are "pretty bonkers", and explains what the current consensus gets wrong: "Most co-located hardware is pretty much for training. I think that's why you care [about Space], otherwise you can distribute. Inference compute, which is where everything's moving towards, you can spread that compute more easily. You can have multiple data centers [on Earth]." "So if you care about space, you probably only care about training compute. I think people underestimate the amount of failures that happen, and you don't want to get your training job interrupted." "The real issue is that GPUs still have failure rates. The 2% of GPUs that are just considered done every year, that's really your cost. It's how quickly you can replace those [in space] and what that looks like."

Adaption Labs CEO Sara Hooker says data centers in space are "pretty bonkers", and explains what the current consensus gets wrong: "Most co-located hardware is pretty much for training. I think that's why you care [about Space], otherwise you can distribute. Inference compute, which is where everything's moving towards, you can spread that compute more easily. You can have multiple data centers [on Earth]." "So if you care about space, you probably only care about training compute. I think people underestimate the amount of failures that happen, and you don't want to get your training job interrupted." "The real issue is that GPUs still have failure rates. The 2% of GPUs that are just considered done every year, that's really your cost. It's how quickly you can replace those [in space] and what that looks like."

TBPN

37,445 просмотров • 5 месяцев назад

Krishna Rao is the CFO of Anthropic, and this is his first podcast appearance. He joined the company two years ago when run-rate revenue was about $250M. Today it is $30B. He has helped raise ~$75B and is responsible for the procurement and allocation of compute. I feel lucky we get to hear what it is like to sit inside a company this consequential at a moment this pivotal. We discuss: - The cone of uncertainty - How he allocates compute across Trainium, TPUs, and GPUs - What investors misunderstand about model companies - Why the returns to frontier intelligence keep rising - Platform vs application and where Anthropic builds its own products - How Anthropic uses Claude internally I have asked my closing question about the kindest thing more than 500 times. Krishna's answer is one I have never heard before. Enjoy! Timestamps: 0:00 Intro 2:38 The Compute Canvas 6:51 The "Cone of Uncertainty" 11:58 Why the Returns to Frontier Intelligence Are So High 16:45 Recursive Self-Improvement 20:20 Scaling Laws 23:30 Sourcing $100 Billion in Compute 28:05 Platform vs. Application Strategy 32:52 Pricing Dynamics 38:48 How Anthropic’s Finance Team Uses Claude 43:24 Raising Capital & Overcoming Investor Skepticism 52:32 Public Perception, Risks, and Government Regulation 57:25 Mythos Release 1:12:33 What Could Derail the AI Revolution? 1:13:47 Biotech and Healthcare 1:15:31 The Kindest Thing

Krishna Rao is the CFO of Anthropic, and this is his first podcast appearance. He joined the company two years ago when run-rate revenue was about $250M. Today it is $30B. He has helped raise ~$75B and is responsible for the procurement and allocation of compute. I feel lucky we get to hear what it is like to sit inside a company this consequential at a moment this pivotal. We discuss: - The cone of uncertainty - How he allocates compute across Trainium, TPUs, and GPUs - What investors misunderstand about model companies - Why the returns to frontier intelligence keep rising - Platform vs application and where Anthropic builds its own products - How Anthropic uses Claude internally I have asked my closing question about the kindest thing more than 500 times. Krishna's answer is one I have never heard before. Enjoy! Timestamps: 0:00 Intro 2:38 The Compute Canvas 6:51 The "Cone of Uncertainty" 11:58 Why the Returns to Frontier Intelligence Are So High 16:45 Recursive Self-Improvement 20:20 Scaling Laws 23:30 Sourcing $100 Billion in Compute 28:05 Platform vs. Application Strategy 32:52 Pricing Dynamics 38:48 How Anthropic’s Finance Team Uses Claude 43:24 Raising Capital & Overcoming Investor Skepticism 52:32 Public Perception, Risks, and Government Regulation 57:25 Mythos Release 1:12:33 What Could Derail the AI Revolution? 1:13:47 Biotech and Healthcare 1:15:31 The Kindest Thing

Patrick OShaughnessy

3,223,985 просмотров • 2 месяцев назад

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."

a16z

19,416 просмотров • 2 месяцев назад

AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

Six Five Media

131,501 просмотров • 4 месяцев назад

A perp marketplace asked Carmen Li to make a market in her own index. On Bits + Bips, she tells Steven Ehrlich why she refused, and what it says about the standards crypto has let slide. ⚠️ Timestamps: 💡 02:54 The elevator pitch: what Silicon Data and Compute Exchange are really building 🧮 04:37 No two GPUs are alike. So how do you build an index that tracks them all? 📈 07:37 The numbers that stop you cold: 1,000% month over month growth into a market worth $6T to $30T 🏛️ 10:15 Inside the CFTC: Carmen on Mike Selig and the race to put cash settled GPU futures on the CME 🔗 13:06 Why every perp marketplace and DEX suddenly wants her GPU index ⛏️ 15:28 The great pivot: Bitcoin miners walking away from mining for long term AI compute ⚠️ 25:06 The request that set off alarm bells: be the market maker of your own index 🛡️ 33:21 The manipulation defense: why 200 data sources crush transaction only feeds 💵 36:19 Billion dollar deals in stablecoins? Why Compute Exchange is saying yes 🌐 39:34 A bifurcating economy and the trillion dollar question: can crypto ride AI's wave

A perp marketplace asked Carmen Li to make a market in her own index. On Bits + Bips, she tells Steven Ehrlich why she refused, and what it says about the standards crypto has let slide. ⚠️ Timestamps: 💡 02:54 The elevator pitch: what Silicon Data and Compute Exchange are really building 🧮 04:37 No two GPUs are alike. So how do you build an index that tracks them all? 📈 07:37 The numbers that stop you cold: 1,000% month over month growth into a market worth $6T to $30T 🏛️ 10:15 Inside the CFTC: Carmen on Mike Selig and the race to put cash settled GPU futures on the CME 🔗 13:06 Why every perp marketplace and DEX suddenly wants her GPU index ⛏️ 15:28 The great pivot: Bitcoin miners walking away from mining for long term AI compute ⚠️ 25:06 The request that set off alarm bells: be the market maker of your own index 🛡️ 33:21 The manipulation defense: why 200 data sources crush transaction only feeds 💵 36:19 Billion dollar deals in stablecoins? Why Compute Exchange is saying yes 🌐 39:34 A bifurcating economy and the trillion dollar question: can crypto ride AI's wave

Laura Shin

12,773 просмотров • 19 дней назад

Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

Manish Gupta, Senior Director at Google DeepMind India, sits down with Aakrit Vaish and Pratyush Choudhury at Mumbai Tech Week for a rare on-record conversation about the frontier AI research happening out of Bangalore. Gupta makes a pointed case against the narrative that India lacks AI research talent: a team of roughly 75 researchers, a third of them fresh out of college, producing work on par with the best in the world and feeding directly into Gemini. The conversation goes deep on what DeepMind India actually builds, why Gemini is considered the most efficient model on the planet, and what India needs to become a research leader rather than a fast follower. In this conversation, they go deep on: 0:00 Intro: Manish Gupta of DeepMind India at Mumbai Tech Week 1:23 What DeepMind India does, and why it's a "mystery" 1:59 The three roles: languages, efficiency, continual learning 2:19 The Haryanvi demo at Google I/O and the cultural playbook 2:38 Making models efficient: from mobile to servers 3:25 Matryoshka transformers and why nested models win 4:20 Why Gemini is the most efficient model on the planet 5:11 Continual learning: using Gemini to improve Gemini 5:50 Google's India plans: consumers, agents, enterprise, government 7:44 Government officials live-coding with AI Studio and NotebookLM 8:30 The India AI talent debate and how the team is structured 10:25 Why India lacks courage and R&D investment, not talent 11:16 DeepMind's global labs and India's outsized impact 13:15 On-device AI: Gemma 3n and 4n 14:08 The headline: 75 world-class researchers in Bangalore 14:58 25 of the 75 are fresh out of college If you're a founder, builder or researcher thinking about AI, frontier models, or India's place in global AI research, this one's for you. Aakrit Vaish Pratyush Choudhury (PC) Google DeepMind Google India Manish Gupta

Activate

14,136 просмотров • 1 месяц назад

$60 Billion SpaceX Cursor Deal? This week on The Brainstorm ⬇️ 0:00 — SpaceX buying Cursor for $60 B? We think this is the reason why 6:05 — SpaceX has reportedly committed ~$310B in 12 months acquiring its way toward the AI frontier 9:55 — "If you're not getting rate-limited by Claude, you might just be bad at using AI" 12:24 — The compute crunch that could hit in 2 years, and why we think few are pricing it in 15:10 — Tim Cook is out. Could Apple finally be about to start cooking? 18:05 — The AI gap at Apple that, if Microsoft is any guide, could prove costly 20:14 — Why AirPods, not the iPhone, may be Apple's most underrated AI weapon 33:49 — OpenAI is building hardware with Qualcomm. Why we don't think it'll really be a "phone" 40:12 — Why Google could go from paying Apple ~$20B/yr to Apple paying Google

$60 Billion SpaceX Cursor Deal? This week on The Brainstorm ⬇️ 0:00 — SpaceX buying Cursor for $60 B? We think this is the reason why 6:05 — SpaceX has reportedly committed ~$310B in 12 months acquiring its way toward the AI frontier 9:55 — "If you're not getting rate-limited by Claude, you might just be bad at using AI" 12:24 — The compute crunch that could hit in 2 years, and why we think few are pricing it in 15:10 — Tim Cook is out. Could Apple finally be about to start cooking? 18:05 — The AI gap at Apple that, if Microsoft is any guide, could prove costly 20:14 — Why AirPods, not the iPhone, may be Apple's most underrated AI weapon 33:49 — OpenAI is building hardware with Qualcomm. Why we don't think it'll really be a "phone" 40:12 — Why Google could go from paying Apple ~$20B/yr to Apple paying Google

ARK Invest

64,826 просмотров • 2 месяцев назад

Anthropic CEO Dario Amodei: AI Progress isn’t magic, it’s just compute, data, and training. "All the cleverness, all the techniques, all of the “we need a new method,” doesn’t matter very much. There are only a few things that matter, and I listed seven of them. One is how much raw compute you have. Two is the quantity of data you have. Three is the quality and distribution of the data, meaning it needs to be broad. Four is how long you train for. Five is an objective function that can scale to the moon. Pre-training is one. Another is an RL-style objective: you have a goal and you go reach it. That includes more objective rewards (like math and coding), and more subjective rewards (like RL from human feedback, or higher-order versions of that). Six and seven are about normalization/conditioning: keeping numerical stability so the huge blob of compute flows cleanly instead of blowing up or getting weird." --- From 'Dwarkesh Patel' YT Channel (link in comment)

Anthropic CEO Dario Amodei: AI Progress isn’t magic, it’s just compute, data, and training. "All the cleverness, all the techniques, all of the “we need a new method,” doesn’t matter very much. There are only a few things that matter, and I listed seven of them. One is how much raw compute you have. Two is the quantity of data you have. Three is the quality and distribution of the data, meaning it needs to be broad. Four is how long you train for. Five is an objective function that can scale to the moon. Pre-training is one. Another is an RL-style objective: you have a goal and you go reach it. That includes more objective rewards (like math and coding), and more subjective rewards (like RL from human feedback, or higher-order versions of that). Six and seven are about normalization/conditioning: keeping numerical stability so the huge blob of compute flows cleanly instead of blowing up or getting weird." --- From 'Dwarkesh Patel' YT Channel (link in comment)

Rohan Paul

74,166 просмотров • 4 месяцев назад

"Just because you're using a ton of AI doesn't mean you're producing good things." - Alex Finn Anthropic filed to go public. The layoff wave keeps rolling. Data centers are getting protested in middle America. And a single GPU now burns more power than ever, with energy about to become AI's hard ceiling. This week, we covered these topics and more with: - Naveen Rao (Naveen Rao), building brain-inspired analog chips to crash the cost of compute at Unconventional AI. - Alex Finn (Alex Finn), building an AI agent for everyone at Henry Intelligent Machines (Henry Intelligent Machines). 7 moments worth your time: 0:00 Welcome back to This Week in AI 1:23 The AI cost panic: real, or just token maxing? 8:19 Surgeons vs. shotguns: how to actually use AI 17:16 How energy overtook chips as the cost of compute 20:13 The energy wall nobody is pricing in 24:31 AI's PR problem & the data center backlash 29:17 Data center taxes & the politics of AI upside 36:33 The Anthropic IPO & if the guests would buy They join alex 🏴‍☠️🇺🇸🇺🇦 on This Week in AI Episode 16, out now. @jason

"Just because you're using a ton of AI doesn't mean you're producing good things." - Alex Finn Anthropic filed to go public. The layoff wave keeps rolling. Data centers are getting protested in middle America. And a single GPU now burns more power than ever, with energy about to become AI's hard ceiling. This week, we covered these topics and more with: - Naveen Rao (Naveen Rao), building brain-inspired analog chips to crash the cost of compute at Unconventional AI. - Alex Finn (Alex Finn), building an AI agent for everyone at Henry Intelligent Machines (Henry Intelligent Machines). 7 moments worth your time: 0:00 Welcome back to This Week in AI 1:23 The AI cost panic: real, or just token maxing? 8:19 Surgeons vs. shotguns: how to actually use AI 17:16 How energy overtook chips as the cost of compute 20:13 The energy wall nobody is pricing in 24:31 AI's PR problem & the data center backlash 29:17 Data center taxes & the politics of AI upside 36:33 The Anthropic IPO & if the guests would buy They join alex 🏴‍☠️🇺🇸🇺🇦 on This Week in AI Episode 16, out now. @jason

This Week in AI

28,106 просмотров • 1 месяц назад