Загрузка видео...

Не удалось загрузить видео

На главную

SOFTWARE IS DEAD: "The software companies frankly got fat & happy. I think what will happen over time is ChatGPT & Claude will end up sitting on top of basically the entire enterprise software stack & almost everything else will end up being a dumb data pipe into those...

WilfredFrost's profile picture

Wilfred Frost

46,420 subscribers

64,633 просмотров • 2 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

BREAKING: How a $15+ Trillion AUM Firm is Thinking About the Systemic $10T Rebuild Around AI Inside BlackRock's Fundamental Equities w/ Managing Director & Head of Global Tech, Tony Kim (Tony Kim) The irony of "the trillion dollars of CapEx this year & the $10T over the next 5 years that are coming.. is to move data centimeters & millimeters. That's AI." "Today we're all talking about compute, compute, compute. I think the primacy of memory will become even more important." Tony invests across public & private tech markets, covering semiconductors, memory, data centers, power, software, quantum, & robotics We Cover: › The shift from a software centric world to a compute centric world › The 10-20-30 trillion market cap breakdown, software, Mag 7, and hardware › RAMpocalypse, why memory becomes as important as compute › How BlackRock allocates, 90% in the 3-year AI vortex, 10% on frontier bets converging on 2030 › 140 Chinese robotics companies and the 30-40 IPOs coming this year › Following the "token flow" Special thank you to Brex, MongoDB, & AssemblyAI for helping make this RAISE AI Summit mini-series in Paris, France happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Tony Kim, Head of BlackRock Fundamental Equities Global Technology (01:10) RAISE AI Summit in Paris (03:52) Why Compute now rules everything (05:17) Why old data centers can't survive AI (07:43) Why data centers are ditching Copper for Light (10:44) The shortage nobody saw coming: RAM (15:20) Only three companies control memory (16:03) Tony's Playbook for Investing in the AI Era (18:16) The 20-year lie: "Compute is just a Commodity" (22:38) How Hardware quietly became bigger than Software (27:49) The Investing Rule: Will you still be cool in 5 years? (32:41) 2030: the year every frontier technology bet converges (38:16) Chips were never a Commodity (41:30) Rack design, materials science, & the physical-world renaissance (43:30) Inside the architecture of a Robot's mind (45:03) Why China Is winning the Robotics race (47:37) Why the biggest Robotics market might be companionship (51:34) Speech models are growing faster than anyone expected (53:07) "Token Flow": Tony's Framework for the Future Enterprise (57:56) Are PE roll-ups the next big AI disruption play? (1:00:48) What Could Go Right (& Wrong) in AI This Year (1:03:51) The mentors who shaped Tony Kim's worldview
1:08:44

BREAKING: How a $15+ Trillion AUM Firm is Thinking About the Systemic $10T Rebuild Around AI Inside BlackRock's Fundamental Equities w/ Managing Director & Head of Global Tech, Tony Kim (Tony Kim) The irony of "the trillion dollars of CapEx this year & the $10T over the next 5 years that are coming.. is to move data centimeters & millimeters. That's AI." "Today we're all talking about compute, compute, compute. I think the primacy of memory will become even more important." Tony invests across public & private tech markets, covering semiconductors, memory, data centers, power, software, quantum, & robotics We Cover: › The shift from a software centric world to a compute centric world › The 10-20-30 trillion market cap breakdown, software, Mag 7, and hardware › RAMpocalypse, why memory becomes as important as compute › How BlackRock allocates, 90% in the 3-year AI vortex, 10% on frontier bets converging on 2030 › 140 Chinese robotics companies and the 30-40 IPOs coming this year › Following the "token flow" Special thank you to Brex, MongoDB, & AssemblyAI for helping make this RAISE AI Summit mini-series in Paris, France happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Tony Kim, Head of BlackRock Fundamental Equities Global Technology (01:10) RAISE AI Summit in Paris (03:52) Why Compute now rules everything (05:17) Why old data centers can't survive AI (07:43) Why data centers are ditching Copper for Light (10:44) The shortage nobody saw coming: RAM (15:20) Only three companies control memory (16:03) Tony's Playbook for Investing in the AI Era (18:16) The 20-year lie: "Compute is just a Commodity" (22:38) How Hardware quietly became bigger than Software (27:49) The Investing Rule: Will you still be cool in 5 years? (32:41) 2030: the year every frontier technology bet converges (38:16) Chips were never a Commodity (41:30) Rack design, materials science, & the physical-world renaissance (43:30) Inside the architecture of a Robot's mind (45:03) Why China Is winning the Robotics race (47:37) Why the biggest Robotics market might be companionship (51:34) Speech models are growing faster than anyone expected (53:07) "Token Flow": Tony's Framework for the Future Enterprise (57:56) Are PE roll-ups the next big AI disruption play? (1:00:48) What Could Go Right (& Wrong) in AI This Year (1:03:51) The mentors who shaped Tony Kim's worldview

Molly O’Shea

341,794 просмотров • 1 месяц назад

I think most public company CEOs are not telling the truth on the impact AI will have on business and jobs. Seb is 0 BS, 100% truthtelling. This man is not afraid to ruffle some feathers. Full show below with Sebastian Siemiatkowski 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:16 The real Threat to SaaS 05:58 What revenue multiple will software companies trade at in the future? 10:31 Why you need to build your own customer service AI to win 22:12 Klarna has two times the customer base of Revolut. They will beat Revolut 24:17 How I lost a billion dollars not investing in Nubank 25:54 Why Nubank are more likely to win the US than Revolut? 33:59 We used to be 6,000 people. Now we are just 3,000 40:14 When is a high valuation too high and can be dangerous? 41:27 How we got Sequoia to invest & Michael Moritz to join the board 53:19 Investors who don’t build will lose 01:07:56 What CEOs really think about AI 01:13:34 I have changed my mind on the adoption cycle
1:29:45

I think most public company CEOs are not telling the truth on the impact AI will have on business and jobs. Seb is 0 BS, 100% truthtelling. This man is not afraid to ruffle some feathers. Full show below with Sebastian Siemiatkowski 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:16 The real Threat to SaaS 05:58 What revenue multiple will software companies trade at in the future? 10:31 Why you need to build your own customer service AI to win 22:12 Klarna has two times the customer base of Revolut. They will beat Revolut 24:17 How I lost a billion dollars not investing in Nubank 25:54 Why Nubank are more likely to win the US than Revolut? 33:59 We used to be 6,000 people. Now we are just 3,000 40:14 When is a high valuation too high and can be dangerous? 41:27 How we got Sequoia to invest & Michael Moritz to join the board 53:19 Investors who don’t build will lose 01:07:56 What CEOs really think about AI 01:13:34 I have changed my mind on the adoption cycle

Harry Stebbings

270,091 просмотров • 6 месяцев назад

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

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

Matt Turck

70,916 просмотров • 2 месяцев назад

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 просмотров • 2 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
1:22:03

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

3,231,415 просмотров • 3 месяцев назад

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
1:11:41

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 просмотров • 4 месяцев назад

Jensen Huang just replaced the most important metric in global economics. Not trade volume. Not oil output. Not manufacturing. Compute. Huang: “Compute equals GDP. I know that for certain.” He did not say probably. He said certain. If your nation does not produce compute, it does not produce intelligence. If it does not produce intelligence, it does not produce revenue. Two links in the chain. Miss one and the whole thing breaks. Huang: “Not one country in the future will say, ‘Guess what, we’re gonna opt out on intelligence.’” Because opting out of compute is not a strategic decision. It is an extinction schedule. Every country that does not build its own inference capacity becomes a tenant in someone else’s infrastructure. Not an ally. Not a partner. A dependent. And dependents do not negotiate terms. They accept them. But this is not just a story about nations. Huang: “The entire software industry will be token-driven.” Every product. Every platform. Every service you touch. The entire business model of software is about to be measured in tokens consumed. Not seats sold. Not licenses renewed. Tokens burned. Software used to be a thing you bought. Now it is a thing that thinks. And thinking costs compute. Every query. Every action. Every decision the machine makes on your behalf. The meter is always running. Huang: “The entire internet industry could take 100% of their CapEx and make it AI because it’s better.” Not ten percent. Not a pilot program. One hundred percent. The moment any internet service rebuilds itself on generative intelligence, it outperforms every version that came before it. Search. Ads. Recommendation. Infrastructure. All of it. Better on contact. CapEx follows. All of it. Trillions moving in one direction with no offramp. The companies still budgeting AI as a line item are telling you exactly how much they understand. AI is not the line item. AI is the budget. The global economy is being re-denominated in a currency most people have not even heard of yet. Tokens. Whoever controls the supply of that currency is not playing in the new economy. They are the house. And the house does not lose.
2:07

Jensen Huang just replaced the most important metric in global economics. Not trade volume. Not oil output. Not manufacturing. Compute. Huang: “Compute equals GDP. I know that for certain.” He did not say probably. He said certain. If your nation does not produce compute, it does not produce intelligence. If it does not produce intelligence, it does not produce revenue. Two links in the chain. Miss one and the whole thing breaks. Huang: “Not one country in the future will say, ‘Guess what, we’re gonna opt out on intelligence.’” Because opting out of compute is not a strategic decision. It is an extinction schedule. Every country that does not build its own inference capacity becomes a tenant in someone else’s infrastructure. Not an ally. Not a partner. A dependent. And dependents do not negotiate terms. They accept them. But this is not just a story about nations. Huang: “The entire software industry will be token-driven.” Every product. Every platform. Every service you touch. The entire business model of software is about to be measured in tokens consumed. Not seats sold. Not licenses renewed. Tokens burned. Software used to be a thing you bought. Now it is a thing that thinks. And thinking costs compute. Every query. Every action. Every decision the machine makes on your behalf. The meter is always running. Huang: “The entire internet industry could take 100% of their CapEx and make it AI because it’s better.” Not ten percent. Not a pilot program. One hundred percent. The moment any internet service rebuilds itself on generative intelligence, it outperforms every version that came before it. Search. Ads. Recommendation. Infrastructure. All of it. Better on contact. CapEx follows. All of it. Trillions moving in one direction with no offramp. The companies still budgeting AI as a line item are telling you exactly how much they understand. AI is not the line item. AI is the budget. The global economy is being re-denominated in a currency most people have not even heard of yet. Tokens. Whoever controls the supply of that currency is not playing in the new economy. They are the house. And the house does not lose.

Dustin

43,505 просмотров • 5 месяцев назад

Chamath just asked the question nobody in AI wants to answer (Save this). "Okay guys, you've spent $3 trillion in the last four years. What is the ROI of these tokens?" It is the most important question in technology right now and the data suggests most of the people being asked cannot answer it. A PwC CEO survey published in January 2026 found that 56% of CEOs report no increase in revenue and no decrease in costs attributable to AI over the past year meaning the majority of companies deploying AI tools have not yet produced a single dollar of auditable return. And only 12% reported experiencing both benefits. Hyperscalers alone are on track to spend $675 billion on AI infrastructure in 2026, up 63% year over year, with total global AI investment approaching $2.5 trillion this year alone against a backdrop where most enterprise buyers cannot yet quantify what any of it produced. Chamath's answer to the question is the real insight. He said what happens next is that enterprises go to guys like Mark Benioff and say: "please sell my tokens." In other words, the AI labs built the capability but the enterprise software giants are the ones who have the customer relationships, the distribution, the workflows and the trust to actually convert token consumption into measurable business outcomes and therefore into revenue that justifies the spend. Mark Benioff was sitting in the same conversation and confirmed exactly that, he said Salesforce is about to spend $300 million on Anthropic. But listen to what Benioff did with Salesforce's own balance sheet at the same time. He announced the largest stock buyback in enterprise software history $50 billion, or 28% of Salesforce's entire market cap while simultaneously admitting the stock has fallen 36% over the past year. In March, Salesforce launched the largest accelerated share repurchase in history to execute $25 billion of it immediately, financed in part with debt it will be carrying until 2066. Chamath is pointing at the underlying structural problem that has triggered the SaaS rout of 2026, software forward P/E multiples have now fallen below the S&P 500 for the first time in history, the iShares software ETF is down over 21% year to date and 30% from its September 2025 peak, and companies like Adobe, and Workday have seen their valuation multiples drop 47-54% in a single year. The core fear is not that AI does not work but rather that AI is breaking the seat based model that built the entire B2B software industry. If one AI agent can do the work of five employees, enterprises stop buying 500 seats and start buying 100, or renegotiate entirely and the recurring revenue that made SaaS stocks trade at 40 times forward earnings simply evaporates. Chamath's prediction is that AI multiples come way back down while infrastructure plays go back up and find a balance is essentially already happening in real time.
1:06

Chamath just asked the question nobody in AI wants to answer (Save this). "Okay guys, you've spent $3 trillion in the last four years. What is the ROI of these tokens?" It is the most important question in technology right now and the data suggests most of the people being asked cannot answer it. A PwC CEO survey published in January 2026 found that 56% of CEOs report no increase in revenue and no decrease in costs attributable to AI over the past year meaning the majority of companies deploying AI tools have not yet produced a single dollar of auditable return. And only 12% reported experiencing both benefits. Hyperscalers alone are on track to spend $675 billion on AI infrastructure in 2026, up 63% year over year, with total global AI investment approaching $2.5 trillion this year alone against a backdrop where most enterprise buyers cannot yet quantify what any of it produced. Chamath's answer to the question is the real insight. He said what happens next is that enterprises go to guys like Mark Benioff and say: "please sell my tokens." In other words, the AI labs built the capability but the enterprise software giants are the ones who have the customer relationships, the distribution, the workflows and the trust to actually convert token consumption into measurable business outcomes and therefore into revenue that justifies the spend. Mark Benioff was sitting in the same conversation and confirmed exactly that, he said Salesforce is about to spend $300 million on Anthropic. But listen to what Benioff did with Salesforce's own balance sheet at the same time. He announced the largest stock buyback in enterprise software history $50 billion, or 28% of Salesforce's entire market cap while simultaneously admitting the stock has fallen 36% over the past year. In March, Salesforce launched the largest accelerated share repurchase in history to execute $25 billion of it immediately, financed in part with debt it will be carrying until 2066. Chamath is pointing at the underlying structural problem that has triggered the SaaS rout of 2026, software forward P/E multiples have now fallen below the S&P 500 for the first time in history, the iShares software ETF is down over 21% year to date and 30% from its September 2025 peak, and companies like Adobe, and Workday have seen their valuation multiples drop 47-54% in a single year. The core fear is not that AI does not work but rather that AI is breaking the seat based model that built the entire B2B software industry. If one AI agent can do the work of five employees, enterprises stop buying 500 seats and start buying 100, or renegotiate entirely and the recurring revenue that made SaaS stocks trade at 40 times forward earnings simply evaporates. Chamath's prediction is that AI multiples come way back down while infrastructure plays go back up and find a balance is essentially already happening in real time.

Milk Road AI

115,726 просмотров • 3 месяцев назад

I sat down with Matt Hougan, CIO of @BitwiseInvest, the firm managing $15 billion in crypto assets. He told me the 4-year cycle is self-fulfilling. That Bitcoin holders sold in anticipation of the crash and created it themselves. "You haven't seen anything yet. This is the pregame. The game hasn't started yet." Very pleased to have Matt on New Era Finance Podcast. TIMESTAMPS: 0:00 Introduction 0:38 Why Bitcoin really collapsed 5:16 Paper Bitcoin vs physical demand 7:44 Gold stole Bitcoin's spotlight 9:44 Why gold's rally is actually bullish for Bitcoin 11:40 The 4-year cycle is dead — here's the data 15:07 How institutions invest differently 15:55 Institutions are licking their chops 17:19 Why central banks bought gold (and will buy Bitcoin) 20:52 Stablecoins will onboard billions 24:47 What Trump actually delivered for crypto 28:55 The Genius Act: "You haven't seen anything yet" 30:13 Will the Clarity Act pass? 32:09 Quantum FUD is slowing institutions 36:59 Every AI scenario leads back to Bitcoin 41:03 What will drag us out of this crypto winter 44:30 Is the market undervaluing Ethereum? 47:43 The Mount Rushmore of crypto: BTC, ETH, SOL, LINK 51:14 Best advice for a 25-year-old investor 52:55 How to stay sane in a bear market
58:45

I sat down with Matt Hougan, CIO of @BitwiseInvest, the firm managing $15 billion in crypto assets. He told me the 4-year cycle is self-fulfilling. That Bitcoin holders sold in anticipation of the crash and created it themselves. "You haven't seen anything yet. This is the pregame. The game hasn't started yet." Very pleased to have Matt on New Era Finance Podcast. TIMESTAMPS: 0:00 Introduction 0:38 Why Bitcoin really collapsed 5:16 Paper Bitcoin vs physical demand 7:44 Gold stole Bitcoin's spotlight 9:44 Why gold's rally is actually bullish for Bitcoin 11:40 The 4-year cycle is dead — here's the data 15:07 How institutions invest differently 15:55 Institutions are licking their chops 17:19 Why central banks bought gold (and will buy Bitcoin) 20:52 Stablecoins will onboard billions 24:47 What Trump actually delivered for crypto 28:55 The Genius Act: "You haven't seen anything yet" 30:13 Will the Clarity Act pass? 32:09 Quantum FUD is slowing institutions 36:59 Every AI scenario leads back to Bitcoin 41:03 What will drag us out of this crypto winter 44:30 Is the market undervaluing Ethereum? 47:43 The Mount Rushmore of crypto: BTC, ETH, SOL, LINK 51:14 Best advice for a 25-year-old investor 52:55 How to stay sane in a bear market

Michaël van de Poppe

190,693 просмотров • 5 месяцев назад

Anthropic's CEO just leaked the most INSANE revenue numbers in AI history. And what he said about the next 12 months will change how you think about every business decision you're making right now. Dario Amodei told Dwarkesh Patel on the interview that Anthropic went from: - 2023: $0 to $100M - 2024: $100M to $1B - 2025: $1B to $9-10B That's 10x revenue growth. Every. Single. Year. "In January alone, we added another few billion to revenue." One month. A few billion dollars. Think about what that means. Most companies would kill for $1B in annual revenue. Anthropic added multiple billions in 30 days. But Dario said something even more interesting: "We are near the end of the exponential." Not the end of AI progress. The end of people understanding how close we actually are. His exact words: "It is absolutely wild that you have people talking about the same tired political issues, when we are near the end of the exponential." What does "end of the exponential" mean? In 1-3 years, we get what he calls a "country of geniuses in a data center." AI systems that can: - Do end-to-end software engineering - Navigate any computer interface - Learn new skills like humans do - Replace entire categories of knowledge work And here's the contradiction: If Anthropic really believed this was 1-3 years away, why aren't they buying $1 trillion in compute? Dario's answer exposes the real game: "If you're off by only a year in your prediction, you go bankrupt." So even the CEO who's most bullish on AI timelines is hedging. He's buying hundreds of billions in compute. Not trillions. Because the gap between "AI can do the job" and "companies actually pay for it" is massive. He calls it "economic diffusion." I call it the gap that's going to make some people very rich and destroy everyone who ignores it. The models are already better than people think. Claude Code writes 90% of code at Anthropic right now. But Dario says there's a huge difference between: - 90% of code written by AI - 100% of code written by AI - 90% of end-to-end SWE tasks done by AI - 100% of end-to-end SWE tasks done by AI We're moving through that spectrum "very quickly." His prediction: FULL end-to-end software engineering in 1-2 years. But here's what's scary: The technology is advancing faster than anyone outside the AI labs understands. And the revenue is following faster than any technology in history. But it's still not instant. Dario expects 10-20% annual GDP growth. Not 300%. Which means we're in this weird middle zone: Fast enough to destroy unprepared businesses. Slow enough that most people are ignoring it. Dario's big takeaway: If you're running a business right now, you have maybe 12-18 months to figure out how AI changes your model. Not to "add AI features." To fundamentally rethink what you're selling and who can do the work. Because the companies that get this right will 10x. And the ones that don't will be explaining to investors why revenue is flat while everyone else is printing money. The exponential is ending. But most people literally still don't even know it started.
2:05

Anthropic's CEO just leaked the most INSANE revenue numbers in AI history. And what he said about the next 12 months will change how you think about every business decision you're making right now. Dario Amodei told Dwarkesh Patel on the interview that Anthropic went from: - 2023: $0 to $100M - 2024: $100M to $1B - 2025: $1B to $9-10B That's 10x revenue growth. Every. Single. Year. "In January alone, we added another few billion to revenue." One month. A few billion dollars. Think about what that means. Most companies would kill for $1B in annual revenue. Anthropic added multiple billions in 30 days. But Dario said something even more interesting: "We are near the end of the exponential." Not the end of AI progress. The end of people understanding how close we actually are. His exact words: "It is absolutely wild that you have people talking about the same tired political issues, when we are near the end of the exponential." What does "end of the exponential" mean? In 1-3 years, we get what he calls a "country of geniuses in a data center." AI systems that can: - Do end-to-end software engineering - Navigate any computer interface - Learn new skills like humans do - Replace entire categories of knowledge work And here's the contradiction: If Anthropic really believed this was 1-3 years away, why aren't they buying $1 trillion in compute? Dario's answer exposes the real game: "If you're off by only a year in your prediction, you go bankrupt." So even the CEO who's most bullish on AI timelines is hedging. He's buying hundreds of billions in compute. Not trillions. Because the gap between "AI can do the job" and "companies actually pay for it" is massive. He calls it "economic diffusion." I call it the gap that's going to make some people very rich and destroy everyone who ignores it. The models are already better than people think. Claude Code writes 90% of code at Anthropic right now. But Dario says there's a huge difference between: - 90% of code written by AI - 100% of code written by AI - 90% of end-to-end SWE tasks done by AI - 100% of end-to-end SWE tasks done by AI We're moving through that spectrum "very quickly." His prediction: FULL end-to-end software engineering in 1-2 years. But here's what's scary: The technology is advancing faster than anyone outside the AI labs understands. And the revenue is following faster than any technology in history. But it's still not instant. Dario expects 10-20% annual GDP growth. Not 300%. Which means we're in this weird middle zone: Fast enough to destroy unprepared businesses. Slow enough that most people are ignoring it. Dario's big takeaway: If you're running a business right now, you have maybe 12-18 months to figure out how AI changes your model. Not to "add AI features." To fundamentally rethink what you're selling and who can do the work. Because the companies that get this right will 10x. And the ones that don't will be explaining to investors why revenue is flat while everyone else is printing money. The exponential is ending. But most people literally still don't even know it started.

Ricardo

62,043 просмотров • 6 месяцев назад

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06
53:53

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Dan Shipper 📧

19,436 просмотров • 3 месяцев назад

Marc Benioff just exposed the biggest hypocrisy in the AI boom. The companies building the AI that’s supposed to kill software are some of Salesforce’s largest customers. Benioff: “The AI companies love our products and they can’t buy enough of them. They’re some of our largest customers now: Anthropic, OpenAI, Google, Amazon, you name it.” Let that land. The most advanced AI labs on earth. The companies with more engineering talent and compute than anyone. The ones building the technology that analysts say will make traditional software obsolete. Still buying traditional software. At scale. Benioff: “No one has a company that’s running entirely on a large language model because it’s not real.” Not because they haven’t tried. Because an LLM is not a foundation. It’s a feature. Benioff: “Yeah, Minority Report, I watched the movie. Great guys, fantastic. But I’m in the present-moment reality right now. We’re living in this world. This is 2026.” The analysts writing reports about fully autonomous AI companies have never had to run one. Benioff is running one of the largest enterprise software companies on earth. The gap between those two perspectives is where billions of dollars are being misallocated. Benioff: “How are we doing our financials, our HR, our customer information? How are we doing all of these aspects of our business?” A neural network that hallucinates cannot execute a financial transaction that has to be right every single time. Cannot secure customer data with zero tolerance for error. Cannot provide the determinism that every real business runs on. Benioff: “We need the determinism, and the programmability, and the security, and the sharing.” AI doesn’t replace those requirements. It sits on top of them. Benioff: “I think the software industry is going to be bigger and broader and do more this year than ever before.” The future isn’t AI replacing software. It’s AI making software exponentially more powerful. The smartest people building the future already know this. They’re the ones still buying the software.
2:27

Marc Benioff just exposed the biggest hypocrisy in the AI boom. The companies building the AI that’s supposed to kill software are some of Salesforce’s largest customers. Benioff: “The AI companies love our products and they can’t buy enough of them. They’re some of our largest customers now: Anthropic, OpenAI, Google, Amazon, you name it.” Let that land. The most advanced AI labs on earth. The companies with more engineering talent and compute than anyone. The ones building the technology that analysts say will make traditional software obsolete. Still buying traditional software. At scale. Benioff: “No one has a company that’s running entirely on a large language model because it’s not real.” Not because they haven’t tried. Because an LLM is not a foundation. It’s a feature. Benioff: “Yeah, Minority Report, I watched the movie. Great guys, fantastic. But I’m in the present-moment reality right now. We’re living in this world. This is 2026.” The analysts writing reports about fully autonomous AI companies have never had to run one. Benioff is running one of the largest enterprise software companies on earth. The gap between those two perspectives is where billions of dollars are being misallocated. Benioff: “How are we doing our financials, our HR, our customer information? How are we doing all of these aspects of our business?” A neural network that hallucinates cannot execute a financial transaction that has to be right every single time. Cannot secure customer data with zero tolerance for error. Cannot provide the determinism that every real business runs on. Benioff: “We need the determinism, and the programmability, and the security, and the sharing.” AI doesn’t replace those requirements. It sits on top of them. Benioff: “I think the software industry is going to be bigger and broader and do more this year than ever before.” The future isn’t AI replacing software. It’s AI making software exponentially more powerful. The smartest people building the future already know this. They’re the ones still buying the software.

Dustin

203,575 просмотров • 5 месяцев назад

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05
53:21

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

Dan Shipper 📧

35,342 просмотров • 11 месяцев назад

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters
1:10:31

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

University of Austin (UATX)

27,770 просмотров • 7 месяцев назад

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.
1:54

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.

Ricardo

1,815,638 просмотров • 6 месяцев назад

Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50
56:15

Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50

Dan Shipper 📧

186,985 просмотров • 1 год назад

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 просмотров • 1 месяц назад

Why is Palantir so expensive? You don’t need to look at spreadsheets. Just consider this: The market knows NVIDIA sells the shovels for the AI goldrush. The market is realizing that AI isn’t being monetized at the commercial level because although it’s cool, it’s not unlocking any real insights yet. The market now anticipates that Palantir is selling the maps to find the gold…. Gold being AI-driven insights that actually solve difficult problems. Software that works. Since 2021, NVIDIA’s revenue has exploded from $16B to $96B. Palantir’s TTM revenue is $2.5B. The trajectory of Palantir has changed since AIP released in 2023, which is enabling the company to scale. If NVIDIA sells the shovels, and Palantir provides the maps, then the market believes Palantir will see the same explosion of growth within the commercial market, which the market believes has an almost unlimited TAM for Palantir. A lot of people missed out on NVIDIA. While Palantir’s market cap is expensive at $95B, it is nothing compared to NVIDIA’s $3.26T market cap in terms of size. The market doesn’t want to miss out on the next big thing. At this point, investors have thrown all standard methods of valuation out of the window… Those days were years ago. To me, at this point, buying the stock is betting on NVIDIA-like growth (No I’m not saying the company will shoot to a $3T market cap in 2 years — you get the point). If the company does not show this sort of revenue growth, the stock will be punished. This is the risk investors are willing to take. While I am very bullish on the company in the long run, I, like everyone else, have no clue what will actually happen in the short term. This is not a stock to play on the short term. This is why I continue to hold, regardless of how “expensive” the stock gets. I personally believe Palantir does in fact carry the potential to see explosive revenue growth to more than enough justify its current ratios. I’m not saying it will happen this quarter. But the potential is there. It’s a matter of when, in my opinion. I would never risk selling what I view as my golden ticket to wealth with the justification of “it’s too expensive, the price will come back down and I can buy even more then”. If the stock crashes, I can start buying more shares regardless — I don’t want to get greedy and try to time the market. I would never forgive myself if I sold and the stock ended up soaring so high that even after a crash, it would be far too expensive for me to get back in with my original position size (plus capital gains tax). I don’t care who agrees with me or who thinks I’m crazy for saying this — it’s a real risk to me and I’m not willing to take it. This is not me telling you to buy $PLTR. My average is $8.50. Only you can decide what is right, and your decision should be made on your own level of conviction from studying the company — nothing else. This is me telling you why it’s so expensive. Again, I believe that if the stock does not continue to crush earnings each quarter, even the slightest miss, the stock will be punished in the short term. For longs, it’s another opportunity to accumulate more. This is my opinion, of course. 5-10 years from now, we’ll see who was right. Chips & Ontology.
1:59

Why is Palantir so expensive? You don’t need to look at spreadsheets. Just consider this: The market knows NVIDIA sells the shovels for the AI goldrush. The market is realizing that AI isn’t being monetized at the commercial level because although it’s cool, it’s not unlocking any real insights yet. The market now anticipates that Palantir is selling the maps to find the gold…. Gold being AI-driven insights that actually solve difficult problems. Software that works. Since 2021, NVIDIA’s revenue has exploded from $16B to $96B. Palantir’s TTM revenue is $2.5B. The trajectory of Palantir has changed since AIP released in 2023, which is enabling the company to scale. If NVIDIA sells the shovels, and Palantir provides the maps, then the market believes Palantir will see the same explosion of growth within the commercial market, which the market believes has an almost unlimited TAM for Palantir. A lot of people missed out on NVIDIA. While Palantir’s market cap is expensive at $95B, it is nothing compared to NVIDIA’s $3.26T market cap in terms of size. The market doesn’t want to miss out on the next big thing. At this point, investors have thrown all standard methods of valuation out of the window… Those days were years ago. To me, at this point, buying the stock is betting on NVIDIA-like growth (No I’m not saying the company will shoot to a $3T market cap in 2 years — you get the point). If the company does not show this sort of revenue growth, the stock will be punished. This is the risk investors are willing to take. While I am very bullish on the company in the long run, I, like everyone else, have no clue what will actually happen in the short term. This is not a stock to play on the short term. This is why I continue to hold, regardless of how “expensive” the stock gets. I personally believe Palantir does in fact carry the potential to see explosive revenue growth to more than enough justify its current ratios. I’m not saying it will happen this quarter. But the potential is there. It’s a matter of when, in my opinion. I would never risk selling what I view as my golden ticket to wealth with the justification of “it’s too expensive, the price will come back down and I can buy even more then”. If the stock crashes, I can start buying more shares regardless — I don’t want to get greedy and try to time the market. I would never forgive myself if I sold and the stock ended up soaring so high that even after a crash, it would be far too expensive for me to get back in with my original position size (plus capital gains tax). I don’t care who agrees with me or who thinks I’m crazy for saying this — it’s a real risk to me and I’m not willing to take it. This is not me telling you to buy $PLTR. My average is $8.50. Only you can decide what is right, and your decision should be made on your own level of conviction from studying the company — nothing else. This is me telling you why it’s so expensive. Again, I believe that if the stock does not continue to crush earnings each quarter, even the slightest miss, the stock will be punished in the short term. For longs, it’s another opportunity to accumulate more. This is my opinion, of course. 5-10 years from now, we’ll see who was right. Chips & Ontology.

Jack Prescott

258,450 просмотров • 1 год назад

Matthew Smith has spent the last 18 months modeling every well, pipeline, storage facility, and power plant in the American natural gas system. His conclusion is that the US is heading toward a natural gas shortage with no precedent, beginning in 2028. By 2030, he believes we could exhaust our working natural gas storage entirely. The fuel everyone in AI is counting on, and that everyone assumes is abundant, is not there. And because gas sets the price of electricity in most of the country, he argues Americans will pay for the shortage in their power bills. Matthew has worked in energy markets for over 20 years and is the CIO of Chronometer Partners. This is his second time on the show, and he's one of my favorite people to talk to about energy. We discuss: - Why the bottleneck is moving from power to fuel - Why we can't just shut off exports - 2028 as the inflection point - Large-scale nuclear v. SMRs - Who wins, who loses, and what can still be done Enjoy! TIMESTAMPS 0:00 Intro 1:30 What Drives the Deficit 11:00 Why Supply Can’t Catch Up 20:35 The 2030 Gas Crisis 25:05 Winners and Losers 29:00 Nuclear and Solar 33:30 Consumers Pay the Bill 37:20 AI’s Next Shortage 45:25 Solutions and Global Stakes 51:15 The Coming Gas Knife Fight
59:44

Matthew Smith has spent the last 18 months modeling every well, pipeline, storage facility, and power plant in the American natural gas system. His conclusion is that the US is heading toward a natural gas shortage with no precedent, beginning in 2028. By 2030, he believes we could exhaust our working natural gas storage entirely. The fuel everyone in AI is counting on, and that everyone assumes is abundant, is not there. And because gas sets the price of electricity in most of the country, he argues Americans will pay for the shortage in their power bills. Matthew has worked in energy markets for over 20 years and is the CIO of Chronometer Partners. This is his second time on the show, and he's one of my favorite people to talk to about energy. We discuss: - Why the bottleneck is moving from power to fuel - Why we can't just shut off exports - 2028 as the inflection point - Large-scale nuclear v. SMRs - Who wins, who loses, and what can still be done Enjoy! TIMESTAMPS 0:00 Intro 1:30 What Drives the Deficit 11:00 Why Supply Can’t Catch Up 20:35 The 2030 Gas Crisis 25:05 Winners and Losers 29:00 Nuclear and Solar 33:30 Consumers Pay the Bill 37:20 AI’s Next Shortage 45:25 Solutions and Global Stakes 51:15 The Coming Gas Knife Fight

Patrick OShaughnessy

1,634,222 просмотров • 1 месяц назад

Mark my words, Nebius will be the first Trillion dollar Neo-cloud company and here is why (Save this). Roman Chernin, CEO of Nebius just said on 20VC that Nebius raised prices and demand didn't move. When a company can raise prices and still have more demand than supply, that's the opportunity. Chernin also explained why he is deliberately not charging the maximum. As AI shifts from training, a one time cost to inference, which is the ongoing cost of serving every user and every query, compute pricing becomes the cost structure of the entire AI economy. If Nebius prices customers out, those customers cannot grow, and Nebius cannot grow with them. That is the compounding flywheel built directly into the revenue model. The numbers are already confirming it. Q1 2026 revenue came in at $399 million, up 684% year over year. The AI cloud segment grew 840% and represented 98% of total revenue. Adjusted EBITDA flipped positive to $129.5 million. And Nebius signed a long-term agreement with Meta worth up to $27 billion over five years, a hyperscaler outsourcing its own AI compute stack to a neocloud, which tells you that even companies with $50 billion capex budgets cannot build fast enough. Goldman Sachs says the consensus is underestimating 2027 hyperscaler capex by $500 billion. Every dollar hyperscalers cannot provision themselves flows to neoclouds like Nebius. As that gap widens, Nebius captures the overflow with 3 gigawatts of contracted power already secured and a CEO who just told you raising prices did not dent demand. Our subscribers are already up massively on Nebius and come join Milk Road Pro for our full breakdown, how to size Nebius against the broader neocloud opportunity, and our full AI thesis. Link below!
0:47

Mark my words, Nebius will be the first Trillion dollar Neo-cloud company and here is why (Save this). Roman Chernin, CEO of Nebius just said on 20VC that Nebius raised prices and demand didn't move. When a company can raise prices and still have more demand than supply, that's the opportunity. Chernin also explained why he is deliberately not charging the maximum. As AI shifts from training, a one time cost to inference, which is the ongoing cost of serving every user and every query, compute pricing becomes the cost structure of the entire AI economy. If Nebius prices customers out, those customers cannot grow, and Nebius cannot grow with them. That is the compounding flywheel built directly into the revenue model. The numbers are already confirming it. Q1 2026 revenue came in at $399 million, up 684% year over year. The AI cloud segment grew 840% and represented 98% of total revenue. Adjusted EBITDA flipped positive to $129.5 million. And Nebius signed a long-term agreement with Meta worth up to $27 billion over five years, a hyperscaler outsourcing its own AI compute stack to a neocloud, which tells you that even companies with $50 billion capex budgets cannot build fast enough. Goldman Sachs says the consensus is underestimating 2027 hyperscaler capex by $500 billion. Every dollar hyperscalers cannot provision themselves flows to neoclouds like Nebius. As that gap widens, Nebius captures the overflow with 3 gigawatts of contracted power already secured and a CEO who just told you raising prices did not dent demand. Our subscribers are already up massively on Nebius and come join Milk Road Pro for our full breakdown, how to size Nebius against the broader neocloud opportunity, and our full AI thesis. Link below!

Milk Road AI

15,677 просмотров • 2 месяцев назад