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Three years ago, two Harvard dropouts set out to build a better AI chip than the largest companies in the world. Almost everyone I called at the time said it was impossible. Today, Etched (Etched) comes out of stealth with $800M total raised, $1B in signed customer contracts, and...

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Patrick OShaughnessy

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1,461,301 views • 2 months ago •via X (Twitter)

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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
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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

1,919,731 views • 2 days ago

Robert Wachen (Robert Wachen) dropped out of Harvard at 21 with $10,000 in a bank account. Three years later Etched has raised $1B+, is valued over $10B, and has signed $1B+ in customer contracts. He's 24. His explanation for why it worked: "We were absolutely not the best people in the world to solve this. We were the only people willing to." More below in this talk he gave to hundreds of interns.
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Robert Wachen (Robert Wachen) dropped out of Harvard at 21 with $10,000 in a bank account. Three years later Etched has raised $1B+, is valued over $10B, and has signed $1B+ in customer contracts. He's 24. His explanation for why it worked: "We were absolutely not the best people in the world to solve this. We were the only people willing to." More below in this talk he gave to hundreds of interns.

Cory Levy

22,785 views • 22 days ago

This is my sixth conversation with Gavin Baker. As always with Gavin, the conversation covers a lot of ground, but we spend the most time on watts and wafers. We discuss: - Why the wafer shortage may prevent an AI bubble - Data centers in space (reframed) - Elon's Terafab and the new chip companies challenging Nvidia - Usage-based pricing - The disaggregation of GPUs - DRAM, frontier tokens, and open source Enjoy! Timestamps: 0:00 Intro 7:55 Anthropic and OpenAI Valuations 12:58 Watts, Wafers, and Infrastructure 14:39 Orbital Compute and Data Centers in Space 22:49 Avoiding the AI Bubble 28:26 Terafab and the Future of US Manufacturing 32:16 Returns to the Frontier 37:23 Continual Learning 42:03 New Chip Companies 48:52 Extending GPU Lifespans and Private Credit 51:22 The Application Layer 57:32 The Token Path and Open-Source Dynamics 1:01:37 Cybersecurity 1:05:46 Diversity Breakdown 1:11:59 Assessing the Big Tech Players in AI 1:19:02 Geopolitics, Personal Safety, and the AI Horizon
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Patrick OShaughnessy

1,676,900 views • 3 months ago

Ben Horowitz and Alex Rampell on AI Infrastructure, Crypto, and the Future of Venture a16z's Ben Horowitz and Alex Rampell sit down to discuss why the fundamental rules of building software companies have changed, how AI is creating massive demand for crypto infrastructure, why the next 15 years will make everyone's lives dramatically better, and more. 00:00 Intro 01:39 The new laws of physics for tech companies 06:37 Why not every legacy SaaS company is dead 10:30 America's AI infrastructure bottleneck 14:27 AI + crypto: Why they're more connected than you think 19:54 The future of VC 24:06 AI and the history of technological change benahorowitz.eth Alex Rampell
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a16z

24,904 views • 4 months ago

Etched came out of stealth at $800M and by lunch X had NVIDIA in the ground We do this every few months. A chip launches, the deck says killer, the timeline holds a funeral, and NVIDIA closes green anyway Etched hardwires the transformer into silicon. That is where the speed comes from, nearly the whole die on one job instead of the ~30% a GPU uses. It is also the trap. The day that chip tapes out is the best it will ever be. You cannot patch it. You burned progress into a wafer and now pray the field stops moving NVIDIA made the opposite bet. Same board, faster every quarter in software. Dynamo is pulling more tokens per watt out of the same rack, on version 1.0 The depreciation risk the bears aimed at NVIDIA for two years does not live at NVIDIA. It lives here, on the chip built to bury it Etched is not a fraud. It is a niche tool priced like a general one, and $800M is not enough to run a frontier supply chain. The rest get bought on the next down cycle Bury the lead, not the leader. Full case with Jack Farley and Max Wiethe on MTS And special thanks for Baseten for the cool T Shirt! Chapters 00:00 Switching from bonds to semis 00:33 What Etched actually is 01:31 Faster and cheaper, but how much HBM 02:56 Maturing market, not an NVIDIA killer 05:01 $1B in contracts and a Taiwan factory 05:20 Why these startups all get absorbed 06:48 Tiered inference and the obsolescence trap 09:39 Etched vs TPUs and Trainium 12:17 Is the CUDA moat weakening 13:21 Co-design, squeezing every token per watt 14:37 NVIDIA is a software company that sells a chip 14:58 Who is NVIDIA's most dangerous competitor 16:23 The NVIDIA killers, ranked 18:19 A rich man's game 18:43 AMD's MI500 vs Rubin Ultra 20:19 The neocloud business decision 22:46 Lightning round, Rambus the toll on HBM 25:11 The CXL run-up on Astera, Marvell, Credo 26:22 Use AI less, go to the booth 28:10 EDA is not dead
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Etched came out of stealth at $800M and by lunch X had NVIDIA in the ground We do this every few months. A chip launches, the deck says killer, the timeline holds a funeral, and NVIDIA closes green anyway Etched hardwires the transformer into silicon. That is where the speed comes from, nearly the whole die on one job instead of the ~30% a GPU uses. It is also the trap. The day that chip tapes out is the best it will ever be. You cannot patch it. You burned progress into a wafer and now pray the field stops moving NVIDIA made the opposite bet. Same board, faster every quarter in software. Dynamo is pulling more tokens per watt out of the same rack, on version 1.0 The depreciation risk the bears aimed at NVIDIA for two years does not live at NVIDIA. It lives here, on the chip built to bury it Etched is not a fraud. It is a niche tool priced like a general one, and $800M is not enough to run a frontier supply chain. The rest get bought on the next down cycle Bury the lead, not the leader. Full case with Jack Farley and Max Wiethe on MTS And special thanks for Baseten for the cool T Shirt! Chapters 00:00 Switching from bonds to semis 00:33 What Etched actually is 01:31 Faster and cheaper, but how much HBM 02:56 Maturing market, not an NVIDIA killer 05:01 $1B in contracts and a Taiwan factory 05:20 Why these startups all get absorbed 06:48 Tiered inference and the obsolescence trap 09:39 Etched vs TPUs and Trainium 12:17 Is the CUDA moat weakening 13:21 Co-design, squeezing every token per watt 14:37 NVIDIA is a software company that sells a chip 14:58 Who is NVIDIA's most dangerous competitor 16:23 The NVIDIA killers, ranked 18:19 A rich man's game 18:43 AMD's MI500 vs Rubin Ultra 20:19 The neocloud business decision 22:46 Lightning round, Rambus the toll on HBM 25:11 The CXL run-up on Astera, Marvell, Credo 26:22 Use AI less, go to the booth 28:10 EDA is not dead

Ben Pouladian

29,969 views • 2 months ago

Had a lot of fun chatting again with my twin brother Dylan Patel We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone). And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities. One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. 0:00:00 – Two labs will soon control most of the world’s compute 0:07:01 – $6 billion in fab capex enables $1t+ of end revenue 0:13:08 – Compute prices will rise if the labs outbid everyone 0:18:22 – Which layer will capture most of the surplus? 0:25:40 – Will datacenter regulation slow down AI? 0:29:43 – Labs are shifting compute from inference to R&D 0:33:27 – China gets less than 10% of new compute, but its labs need less 0:48:48 – Will AI cause a sovereign debt crisis? 1:07:52 – Will the world's future workforce belong to a few companies?
1:16:52

Had a lot of fun chatting again with my twin brother Dylan Patel We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone). And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities. One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. 0:00:00 – Two labs will soon control most of the world’s compute 0:07:01 – $6 billion in fab capex enables $1t+ of end revenue 0:13:08 – Compute prices will rise if the labs outbid everyone 0:18:22 – Which layer will capture most of the surplus? 0:25:40 – Will datacenter regulation slow down AI? 0:29:43 – Labs are shifting compute from inference to R&D 0:33:27 – China gets less than 10% of new compute, but its labs need less 0:48:48 – Will AI cause a sovereign debt crisis? 1:07:52 – Will the world's future workforce belong to a few companies?

Dwarkesh Patel

995,190 views • 8 days ago

Neil Movva (Neil Movva) started his career at Nvidia, working on GPUs and kernels, and has an unusually deep understanding of inference, from software to chips to power. We spend a lot of time on each of those layers, how they connect, and where the important tradeoffs are. What makes this conversation special is how detailed it is (like a 401-level class), yet Neil makes it remarkably clear and easy to follow. Today he runs Sail Research, a company building infrastructure for agents to make tokens as cheap as possible. We discuss: - Latency versus throughput - Why there are no bad chips, only bad pricing - The end of kernel engineering - Buying chips and power no one else wants - New chip architectures - Nvidia lore + his contrarian view of the company - Open source and the frontier labs I learned a ton. Enjoy! TIMESTAMPS 0:00 Intro 0:38 Building a “Token Factory” 4:21 The Future of Background Agents 13:09 Nvidia and the GPU Stack 23:27 Chips, Memory, and Transformers 36:14 The Future of AI Training Data 44:32 Chip Scarcity and Compute Arbitrage 52:44 Reinventing the AI Data Center 59:01 Power and the “Scavenger Strategy” 1:10:10 Open vs. Closed AI
1:23:35

Neil Movva (Neil Movva) started his career at Nvidia, working on GPUs and kernels, and has an unusually deep understanding of inference, from software to chips to power. We spend a lot of time on each of those layers, how they connect, and where the important tradeoffs are. What makes this conversation special is how detailed it is (like a 401-level class), yet Neil makes it remarkably clear and easy to follow. Today he runs Sail Research, a company building infrastructure for agents to make tokens as cheap as possible. We discuss: - Latency versus throughput - Why there are no bad chips, only bad pricing - The end of kernel engineering - Buying chips and power no one else wants - New chip architectures - Nvidia lore + his contrarian view of the company - Open source and the frontier labs I learned a ton. Enjoy! TIMESTAMPS 0:00 Intro 0:38 Building a “Token Factory” 4:21 The Future of Background Agents 13:09 Nvidia and the GPU Stack 23:27 Chips, Memory, and Transformers 36:14 The Future of AI Training Data 44:32 Chip Scarcity and Compute Arbitrage 52:44 Reinventing the AI Data Center 59:01 Power and the “Scavenger Strategy” 1:10:10 Open vs. Closed AI

Patrick OShaughnessy

4,522,178 views • 9 days ago

“Is there an AI bubble?” Gavin Baker and David George In this conversation from a16z’s Runtime conference, Gavin Baker, Managing Partner and CIO of Atreides Management, joins David George, General Partner at a16z, to unpack the macro view of AI: the trillion-dollar data center buildout, the new economics of GPUs, and what this boom means for investors, founders, and the global economy. They discuss how this cycle compares to the dot-com era, why “there are no dark GPUs,” and what makes today’s infrastructure spending different from past bubbles. Gavin explains why he believes we’re still early in the AI supercycle, the changing margins in SaaS and software, and how business models and consumer interfaces are being rewritten in real time. 00:00 Intro 01:02 Are we in an AI Bubble? 02:41 Dark fiber vs. “no dark GPUs” 05:07 ROI on AI: Why this time is different 08:36 Round-tripping deals 12:22 The application layer: It’s still early 15:24 Margins, scaling laws 23:26 Reasoning models and the flywheel 27:18 The future of business models 29:41 Robotics and Elon’s Optimus vision Gavin Baker David George
31:52

“Is there an AI bubble?” Gavin Baker and David George In this conversation from a16z’s Runtime conference, Gavin Baker, Managing Partner and CIO of Atreides Management, joins David George, General Partner at a16z, to unpack the macro view of AI: the trillion-dollar data center buildout, the new economics of GPUs, and what this boom means for investors, founders, and the global economy. They discuss how this cycle compares to the dot-com era, why “there are no dark GPUs,” and what makes today’s infrastructure spending different from past bubbles. Gavin explains why he believes we’re still early in the AI supercycle, the changing margins in SaaS and software, and how business models and consumer interfaces are being rewritten in real time. 00:00 Intro 01:02 Are we in an AI Bubble? 02:41 Dark fiber vs. “no dark GPUs” 05:07 ROI on AI: Why this time is different 08:36 Round-tripping deals 12:22 The application layer: It’s still early 15:24 Margins, scaling laws 23:26 Reasoning models and the flywheel 27:18 The future of business models 29:41 Robotics and Elon’s Optimus vision Gavin Baker David George

a16z

92,161 views • 10 months ago

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens
41:29

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 views • 3 months ago

My guests on Uncapped this week are Kevin Hartz and Bennett Siegel, co-founders of the early stage VC firm A*. They've backed companies like Notion, Mercor, Ramp, Decagon, Similie, and many more. They also announced a new $450m fund today. We discussed the state of venture capital firms in the current AI cycle, what it means for seed specialists, trends with great founders, and what they're seeing in AI. Timestamps: (0:00) Intro (0:25) The A* Capital story (1:16) Why big funds went into seed (7:50) The mother of all bubbles (10:46) Why founders are getting younger (13:00) Mapping talent, not markets (16:31) The rise of AI researcher founders (19:16) Why seed investing is so hard (22:54) Concentration and venture returns (27:34) The AI rollup craze (31:15) AI vs traditional software (33:15) Robotics and the future of AI (35:39) What’s next for A*
36:59

My guests on Uncapped this week are Kevin Hartz and Bennett Siegel, co-founders of the early stage VC firm A*. They've backed companies like Notion, Mercor, Ramp, Decagon, Similie, and many more. They also announced a new $450m fund today. We discussed the state of venture capital firms in the current AI cycle, what it means for seed specialists, trends with great founders, and what they're seeing in AI. Timestamps: (0:00) Intro (0:25) The A* Capital story (1:16) Why big funds went into seed (7:50) The mother of all bubbles (10:46) Why founders are getting younger (13:00) Mapping talent, not markets (16:31) The rise of AI researcher founders (19:16) Why seed investing is so hard (22:54) Concentration and venture returns (27:34) The AI rollup craze (31:15) AI vs traditional software (33:15) Robotics and the future of AI (35:39) What’s next for A*

Jack Altman

57,630 views • 3 months ago

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

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

Matt Turck

49,647 views • 3 months ago

Wolfgang Hammer helps founders and CEOs, at both startups and massive fortune 100 companies, use storytelling to better understand what they do, and why it matters. In many ways, this conversation is a manual for how to find that story and communicate it in a way that resonates. Wolfgang (Wolfgang Hammer) is a film producer and executive who helped create House of Cards and ran several major studios. He’s now building a new kind of film studio with support from Mitch Lasky and Marc Andreessen. We talk about how stories work, what great ones have in common, and why understanding your own story can be transformative. Wolfgang explains the three layers every story must have — the external, the emotional, and the philosophical — and how they apply to building companies and leading people. So many CEOs have Wolfgang to thank for changing how they think and talk about their business, and I hope this episode gives you the tools to do the same. Enjoy! Timestamps: 00:00 Intro 02:50 The Three Layers of Story 04:44 Prompts for the Three Layers 05:54 Why Companies Struggle to Tell Their Story 08:11 The 80/20 Rule: Familiar vs. New 10:17 Suspense from Minimal New Information 11:16 Understanding the Customer's Story First 14:07 What CEOs Can Learn From Filmmakers 15:30 The CEO's Job: Master Communicator 17:12 The Definition of an Iconic Character 18:23 All Stories Are About Death 19:20 Two Kinds of Stories: Desire Fulfilled or Anxiety Purged 21:09 Story as an Ultimate Concern 23:31 Deconstructing Great Stories 26:25 The Relentless Role of Status 30:57 The Story So Big It Terrifies You 32:29 Inner Conflict: The Heart vs. Itself 35:06 A Unified Theory of Storytelling 36:09 The Two Core Truths 38:15 Kindest Thing
43:35

Wolfgang Hammer helps founders and CEOs, at both startups and massive fortune 100 companies, use storytelling to better understand what they do, and why it matters. In many ways, this conversation is a manual for how to find that story and communicate it in a way that resonates. Wolfgang (Wolfgang Hammer) is a film producer and executive who helped create House of Cards and ran several major studios. He’s now building a new kind of film studio with support from Mitch Lasky and Marc Andreessen. We talk about how stories work, what great ones have in common, and why understanding your own story can be transformative. Wolfgang explains the three layers every story must have — the external, the emotional, and the philosophical — and how they apply to building companies and leading people. So many CEOs have Wolfgang to thank for changing how they think and talk about their business, and I hope this episode gives you the tools to do the same. Enjoy! Timestamps: 00:00 Intro 02:50 The Three Layers of Story 04:44 Prompts for the Three Layers 05:54 Why Companies Struggle to Tell Their Story 08:11 The 80/20 Rule: Familiar vs. New 10:17 Suspense from Minimal New Information 11:16 Understanding the Customer's Story First 14:07 What CEOs Can Learn From Filmmakers 15:30 The CEO's Job: Master Communicator 17:12 The Definition of an Iconic Character 18:23 All Stories Are About Death 19:20 Two Kinds of Stories: Desire Fulfilled or Anxiety Purged 21:09 Story as an Ultimate Concern 23:31 Deconstructing Great Stories 26:25 The Relentless Role of Status 30:57 The Story So Big It Terrifies You 32:29 Inner Conflict: The Heart vs. Itself 35:06 A Unified Theory of Storytelling 36:09 The Two Core Truths 38:15 Kindest Thing

Patrick OShaughnessy

594,089 views • 9 months ago

Etched is deploying two new technologies in chip design: low-voltage inference and cluster-scale memory. CEO Gavin Uberti says they'll make their chips much more power-efficient and way, way faster than today's leading GPUs. He breaks it down: "We looked at a lot of early research directions, and we realized the key things that models need are way more compute and way faster memory." "If you think about inference, there are two key parts: prefill and decode. For prefill, it's a compute-bound problem. You need to have more FLOPS, more operations per second on each of your chips." "On our GPU, the bottleneck's actually thermals. You can't really run a GPU at more than around 50% of what it could theoretically do, or it'll melt." "So we're using a new technology today called low-voltage inference to try to solve this problem. You bring the voltage of the chip down dramatically, which allows us to have way, way better efficiency in terms of how much power is drawn per unit of math, and thus fit way way more flops onto the chip..." "For decode, it's all about bandwidth. Not just bandwidth on a chip, but bandwidth across your cluster. That's why we have this technology we call cluster-scale memory. It reduces the amount of time it takes to communicate from one chip to another dramatically." "As a result we can go use all of our HBM, HBM bandwidth, SRAM, SRAM bandwidth, and our scale-up domain as a single coherent pool. And that means if you're a user, you can go get much faster tokens per second, while still keeping your costs low."
3:19

Etched is deploying two new technologies in chip design: low-voltage inference and cluster-scale memory. CEO Gavin Uberti says they'll make their chips much more power-efficient and way, way faster than today's leading GPUs. He breaks it down: "We looked at a lot of early research directions, and we realized the key things that models need are way more compute and way faster memory." "If you think about inference, there are two key parts: prefill and decode. For prefill, it's a compute-bound problem. You need to have more FLOPS, more operations per second on each of your chips." "On our GPU, the bottleneck's actually thermals. You can't really run a GPU at more than around 50% of what it could theoretically do, or it'll melt." "So we're using a new technology today called low-voltage inference to try to solve this problem. You bring the voltage of the chip down dramatically, which allows us to have way, way better efficiency in terms of how much power is drawn per unit of math, and thus fit way way more flops onto the chip..." "For decode, it's all about bandwidth. Not just bandwidth on a chip, but bandwidth across your cluster. That's why we have this technology we call cluster-scale memory. It reduces the amount of time it takes to communicate from one chip to another dramatically." "As a result we can go use all of our HBM, HBM bandwidth, SRAM, SRAM bandwidth, and our scale-up domain as a single coherent pool. And that means if you're a user, you can go get much faster tokens per second, while still keeping your costs low."

TBPN

20,404 views • 2 months ago

Full interview with the founder and CEO of Propr Lou sits down with me to discuss Hyperliquid and the future of prop trading Timestamps: 00:00:00 Intro 00:00:16 Meet Louis: from TradFi to crypto 00:01:52 How the crypto market has changed 00:03:49 Why build on Hyperliquid 00:06:52 How propr got started 00:08:40 Scaling a lean team (and the grind) 00:12:24 What a prop firm actually is 00:15:33 Agents, bots, and the future of trading 00:18:32 What Louis is most proud of 00:21:02 Thinking about the token and TGE 00:24:14 Where propr goes in 5 years 00:25:57 Best and worst trades 00:27:50 Life outside crypto + shoutouts
29:41

Full interview with the founder and CEO of Propr Lou sits down with me to discuss Hyperliquid and the future of prop trading Timestamps: 00:00:00 Intro 00:00:16 Meet Louis: from TradFi to crypto 00:01:52 How the crypto market has changed 00:03:49 Why build on Hyperliquid 00:06:52 How propr got started 00:08:40 Scaling a lean team (and the grind) 00:12:24 What a prop firm actually is 00:15:33 Agents, bots, and the future of trading 00:18:32 What Louis is most proud of 00:21:02 Thinking about the token and TGE 00:24:14 Where propr goes in 5 years 00:25:57 Best and worst trades 00:27:50 Life outside crypto + shoutouts

ryandcrypto

43,155 views • 3 months ago

This is my fifth conversation with Gavin Baker. Gavin understands semiconductors and AI as well as anyone I know and has a gift for making sense of the industry's complexity and nuance. We discuss: - Nvidia vs Google (GPUs + TPUs) - Scaling laws and reasoning models - The economics of AI compute - Why Blackwell's delay mattered - The bear case on the AI capex buildout - Data centers in space - The mistake SaaS companies are making Few people love investing more than Gavin. His closing answer about why he loves it turned into a full reflection on his investing origin story, which I had never heard before. Enjoy! Timestamps: 0:00 Intro 5:03 The Blackwell Transition 23:15 The Prisoner's Dilemma 27:12 The Bear Case: Edge AI 37:19 Meta, Open Source, and Model Depreciation 43:08 Geopolitics and Rare Earths 50:42 Data Centers in Space 56:06 Power Constraints as a Governor 1:11:31 The SaaS Mistake 1:16:17 Nuclear and Quantum 1:22:25 Gavin’s Investing Origins
1:28:25

This is my fifth conversation with Gavin Baker. Gavin understands semiconductors and AI as well as anyone I know and has a gift for making sense of the industry's complexity and nuance. We discuss: - Nvidia vs Google (GPUs + TPUs) - Scaling laws and reasoning models - The economics of AI compute - Why Blackwell's delay mattered - The bear case on the AI capex buildout - Data centers in space - The mistake SaaS companies are making Few people love investing more than Gavin. His closing answer about why he loves it turned into a full reflection on his investing origin story, which I had never heard before. Enjoy! Timestamps: 0:00 Intro 5:03 The Blackwell Transition 23:15 The Prisoner's Dilemma 27:12 The Bear Case: Edge AI 37:19 Meta, Open Source, and Model Depreciation 43:08 Geopolitics and Rare Earths 50:42 Data Centers in Space 56:06 Power Constraints as a Governor 1:11:31 The SaaS Mistake 1:16:17 Nuclear and Quantum 1:22:25 Gavin’s Investing Origins

Patrick OShaughnessy

2,859,057 views • 8 months ago

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.
5:13

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 views • 1 year ago

I spoke with Jordi Visser to break down why the last two weeks may be the most important stretch in bitcoin's history, along with the US-Japan yen intervention and a weakening labor market. We also discuss the AI stock rebound, physical AI and Tesla's Terafab, Google's talent exodus, and how to value SpaceX. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:54 - Why the last two weeks may be the most important in Bitcoin's history 8:17 - What should the Fed do next? 13:39 - AI stocks bounce hard: was that the bottom? 21:40 - Physical AI: Hadrian, Tesla's Terafab & the compute race 25:57 - How to position your portfolio for the compute boom 33:26 - What frontier models get wrong (and why big companies move slow) 38:40 - Google's talent exodus: is the stock at risk? 41:23 - Does calendar seasonality still matter? 42:43 - Valuing SpaceX: the compute business vs. the space hype 44:42 - What's coming in Jordi's video this week
47:30

I spoke with Jordi Visser to break down why the last two weeks may be the most important stretch in bitcoin's history, along with the US-Japan yen intervention and a weakening labor market. We also discuss the AI stock rebound, physical AI and Tesla's Terafab, Google's talent exodus, and how to value SpaceX. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:54 - Why the last two weeks may be the most important in Bitcoin's history 8:17 - What should the Fed do next? 13:39 - AI stocks bounce hard: was that the bottom? 21:40 - Physical AI: Hadrian, Tesla's Terafab & the compute race 25:57 - How to position your portfolio for the compute boom 33:26 - What frontier models get wrong (and why big companies move slow) 38:40 - Google's talent exodus: is the stock at risk? 41:23 - Does calendar seasonality still matter? 42:43 - Valuing SpaceX: the compute business vs. the space hype 44:42 - What's coming in Jordi's video this week

Anthony Pompliano 🌪

79,205 views • 25 days ago

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency
39:32

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,673 views • 5 months ago

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.
3:31

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 views • 2 months ago

Alex Behring and Daniel Schwartz have almost never spoken publicly about how 3G Capital invests and operates. The firm is different in many fascinating ways. Every fund they raise is designed to make exactly one investment. They invest more of their own money than any of their limited partners in every deal and send their own people to run the businesses as CEOs and CFOs. Over 20 years they have never lost money on a deal. The conversation includes the story of how they spent 15 years building a relationship with the founding family of Hunter Douglas before getting the opportunity to buy it. They describe receiving a two line rejection email from Tim Hortons after weeks of silence and how they managed to get back to the table and close the deal. They talk about how they bought Burger King for a billion dollars when no other firm showed up to compete. And they explain why Skechers being the third largest sneaker company in the world surprised even them. A major theme is how they develop talent. Daniel was an analyst who became CFO at 26 and CEO of Burger King at 32. Alex became CEO of the largest railroad in Latin America when he was 30. They explain what they look for in young people, why they give them significant responsibility faster than anywhere else, and what it takes to set them up to succeed while holding an unusually high bar. Daniel and Alex are two of the most talented and intense people I know. They are incredibly serious about business quality and disciplined about waiting for the right opportunity. They would rather do nothing than compromise. Enjoy! Timestamps: 00:00:00 Episode Intro: Daniel Schwartz & Alex Behring 00:01:20 The "One Investment Per Fund" Model 00:05:39 Characteristics of Great Businesses 00:08:40 The Unique Structure of 3G Capital 00:14:21 Why Hunter Douglas Was Appealing 00:20:15 Alex's Railroad Story 00:28:12 The "Burger King is Run by Children" Story 00:30:38 Negotiating with Tim Hortons 00:38:18 Be Wired for Urgency 00:46:43 3G's Operating System 00:55:35 Why Burger King Was Undervalued 00:59:43 From Zero to $2 Billion in France 01:01:42 Kraft Heinz: Concentration Risk 01:04:25 Skechers: Great Product Meets Great Distribution 01:13:10 Zero-Based Budgeting & When It Works 01:16:28 The Current State of Capital Markets 01:23:04 3G's Founder-Led Focus 01:28:57 The Kindest Thing
1:30:32

Alex Behring and Daniel Schwartz have almost never spoken publicly about how 3G Capital invests and operates. The firm is different in many fascinating ways. Every fund they raise is designed to make exactly one investment. They invest more of their own money than any of their limited partners in every deal and send their own people to run the businesses as CEOs and CFOs. Over 20 years they have never lost money on a deal. The conversation includes the story of how they spent 15 years building a relationship with the founding family of Hunter Douglas before getting the opportunity to buy it. They describe receiving a two line rejection email from Tim Hortons after weeks of silence and how they managed to get back to the table and close the deal. They talk about how they bought Burger King for a billion dollars when no other firm showed up to compete. And they explain why Skechers being the third largest sneaker company in the world surprised even them. A major theme is how they develop talent. Daniel was an analyst who became CFO at 26 and CEO of Burger King at 32. Alex became CEO of the largest railroad in Latin America when he was 30. They explain what they look for in young people, why they give them significant responsibility faster than anywhere else, and what it takes to set them up to succeed while holding an unusually high bar. Daniel and Alex are two of the most talented and intense people I know. They are incredibly serious about business quality and disciplined about waiting for the right opportunity. They would rather do nothing than compromise. Enjoy! Timestamps: 00:00:00 Episode Intro: Daniel Schwartz & Alex Behring 00:01:20 The "One Investment Per Fund" Model 00:05:39 Characteristics of Great Businesses 00:08:40 The Unique Structure of 3G Capital 00:14:21 Why Hunter Douglas Was Appealing 00:20:15 Alex's Railroad Story 00:28:12 The "Burger King is Run by Children" Story 00:30:38 Negotiating with Tim Hortons 00:38:18 Be Wired for Urgency 00:46:43 3G's Operating System 00:55:35 Why Burger King Was Undervalued 00:59:43 From Zero to $2 Billion in France 01:01:42 Kraft Heinz: Concentration Risk 01:04:25 Skechers: Great Product Meets Great Distribution 01:13:10 Zero-Based Budgeting & When It Works 01:16:28 The Current State of Capital Markets 01:23:04 3G's Founder-Led Focus 01:28:57 The Kindest Thing

Patrick OShaughnessy

43,117 views • 6 months ago