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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%...

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Ricardo

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1,815,322 views • 6 months ago •via X (Twitter)

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Jensen is pushing back on the AI will kill all software narrative just like he pushed back last year on the DeepSeek panic. He was right then. He will be right again. "There's this notion that .. the software industry is in decline and will be replaced by AI. You could tell because there's a whole bunch of software companies whose stock prices are under a lot of pressure. Because somehow AI is going to replace them. It is the most illogical thing in the world and time will prove itself. .. If you were an artificial general intelligence, would you use the tools like ServiceNow and SAP and Cadence and Synopsys? Or would you reinvent a calculator? Of course, you would just use a calculator."
1:45

Jensen is pushing back on the AI will kill all software narrative just like he pushed back last year on the DeepSeek panic. He was right then. He will be right again. "There's this notion that .. the software industry is in decline and will be replaced by AI. You could tell because there's a whole bunch of software companies whose stock prices are under a lot of pressure. Because somehow AI is going to replace them. It is the most illogical thing in the world and time will prove itself. .. If you were an artificial general intelligence, would you use the tools like ServiceNow and SAP and Cadence and Synopsys? Or would you reinvent a calculator? Of course, you would just use a calculator."

tae kim

112,469 views • 6 months ago

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 views • 5 months ago

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇
17:56

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇

Mia Bao

44,564 views • 1 month ago

🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it
0:21

🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it

Leshka.eth ⛩

11,737 views • 1 month ago

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud
1:31

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,663 views • 27 days ago

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)
0:59

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 views • 2 months ago

Vibe coding is causing the death of SaaS... Because we want better software: more features, more workflows, more EVERYTHING We're not happy with existing software, that's why we vibe code something ourselves. But what if... existing software came built-in with an AI vibe coding layer? If it did: that software can sell more, beat AI-first companies, and reduce churn. We've already proven this works - that's how we added $1,000,000 in sales and prevented $120,000 in churn for our Series B customers. Gigacatalyst embeds a vibe coding layer inside your software. See how it works: If you could improve any daily software that you use, what would you change?
1:08

Vibe coding is causing the death of SaaS... Because we want better software: more features, more workflows, more EVERYTHING We're not happy with existing software, that's why we vibe code something ourselves. But what if... existing software came built-in with an AI vibe coding layer? If it did: that software can sell more, beat AI-first companies, and reduce churn. We've already proven this works - that's how we added $1,000,000 in sales and prevented $120,000 in churn for our Series B customers. Gigacatalyst embeds a vibe coding layer inside your software. See how it works: If you could improve any daily software that you use, what would you change?

Namanyay

19,446 views • 2 months ago

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA
1:06

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA

Nick Dorsey

40,200 views • 5 months ago

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."
3:06

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

393,820 views • 6 months ago

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?
1:33

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,970,935 views • 2 months ago

Some personal hot takes from AI: engineer Miami follows... 1. Software development is a dead-end profession because anyone can be a software developer now. 2. Anyone can use Cursor or any other tool and generate code. Being a coder and being a software engineer are different. 3. Computers used to be gated; now everyone has the power to make computers malleable. Everyone is a software developer now, but that does not mean they are software engineers 4. If you cannot demonstrate how a coding agent works, you are just a consumer and have imposed an artificial glass ceiling on your career as a software engineer. 5. If you are curious, you will have a job. If you have not been curious in the last two years, you are replaceable. 6. SaaS per-seat economics may become unstable as customers need fewer people to achieve results, prompting founders to think about new unit economics 7. Most companies will take two or three years (or more!) to figure out AI transformation. 8. Some companies are already building AI native teams of five to ten people who can build with the grain of AI 9. There will be an explosion in the number of software developers. Software development is now essentially free, and tokens are cheaper than humans 10. Not enough engineers know what it means to be a product engineer 11. JIRA ticket monkeys are cooked 12. If your company has banned AI, you should quit that company 13. AI is more like a musical instrument than just a tool play with it, make discoveries, build intuition learn where AI is good and where it fails
14:55

Some personal hot takes from AI: engineer Miami follows... 1. Software development is a dead-end profession because anyone can be a software developer now. 2. Anyone can use Cursor or any other tool and generate code. Being a coder and being a software engineer are different. 3. Computers used to be gated; now everyone has the power to make computers malleable. Everyone is a software developer now, but that does not mean they are software engineers 4. If you cannot demonstrate how a coding agent works, you are just a consumer and have imposed an artificial glass ceiling on your career as a software engineer. 5. If you are curious, you will have a job. If you have not been curious in the last two years, you are replaceable. 6. SaaS per-seat economics may become unstable as customers need fewer people to achieve results, prompting founders to think about new unit economics 7. Most companies will take two or three years (or more!) to figure out AI transformation. 8. Some companies are already building AI native teams of five to ten people who can build with the grain of AI 9. There will be an explosion in the number of software developers. Software development is now essentially free, and tokens are cheaper than humans 10. Not enough engineers know what it means to be a product engineer 11. JIRA ticket monkeys are cooked 12. If your company has banned AI, you should quit that company 13. AI is more like a musical instrument than just a tool play with it, make discoveries, build intuition learn where AI is good and where it fails

geoff

65,789 views • 1 month ago

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 views • 3 months ago

Jensen Huang says the AI productivity story is already showing up in software output - and he thinks the "AI will reduce jobs" narrative is wrong. "In the first few months of 2026, it has nearly tripled." "30 million software developers, representing about $3 trillion worth of GDP, are now producing nearly three times as much output." "That $3 trillion worth of salary is now producing nearly $9 trillion worth of output." "This is the potential. This is the promise of AI." "People talk about AI reducing jobs. Complete nonsense. It's causing more software engineers to be hired." His logic is simple: If a software engineer can create 3x more output with AI, companies do not stop hiring engineers. They want more of them. The bottleneck moves from typing code to deciding what to build, orchestrating systems, and turning that output into products customers actually use.
1:25

Jensen Huang says the AI productivity story is already showing up in software output - and he thinks the "AI will reduce jobs" narrative is wrong. "In the first few months of 2026, it has nearly tripled." "30 million software developers, representing about $3 trillion worth of GDP, are now producing nearly three times as much output." "That $3 trillion worth of salary is now producing nearly $9 trillion worth of output." "This is the potential. This is the promise of AI." "People talk about AI reducing jobs. Complete nonsense. It's causing more software engineers to be hired." His logic is simple: If a software engineer can create 3x more output with AI, companies do not stop hiring engineers. They want more of them. The bottleneck moves from typing code to deciding what to build, orchestrating systems, and turning that output into products customers actually use.

Karl Mehta

13,780 views • 1 month ago

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.
2:08

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Ricardo

228,416 views • 2 months ago

Mark Cuban just told every software company on Earth they’re already dead. The people inside them are still building roadmaps. Cuban: “Software is dead because everything’s going to be customized to your unique utilization.” Every SaaS company was built on one bet. Software stays rigid. Humans stay adaptable. You learn the tool. You bend to it. You pay for someone else’s version of your solution. AI just inverted that. The tool bends to you or it dies. Cuban: “33 million companies aren’t going to have AI budgets, aren’t going to have AI experts.” 33 million businesses feel something shifting beneath them. None can name it. The distance between what AI can do and what small companies can access is the most mispriced gap in markets today. Not a technology problem. A translation problem. Cuban: “Learn all you can about AI but learn more on how to implement them in companies.” Everyone is racing to build intelligence. Almost nobody is racing to deploy it where the pain is deepest. The person who walks into a 40-person company and rewires their entire operation captures more value than the team that trained the model. Understanding pain is now worth more than building intelligence. Cuban: “Every single job available for kids coming out of school because every single company needs that.” The most important career of the next decade has no title. No degree path. No university knows it needs to exist yet. It belongs to whoever learns two languages fluently. The language of a business that can’t articulate what’s breaking. And the language of an AI that doesn’t know where to aim. 33 million companies. Zero translators. Whoever arrives first doesn’t enter a market. They create one.
2:14

Mark Cuban just told every software company on Earth they’re already dead. The people inside them are still building roadmaps. Cuban: “Software is dead because everything’s going to be customized to your unique utilization.” Every SaaS company was built on one bet. Software stays rigid. Humans stay adaptable. You learn the tool. You bend to it. You pay for someone else’s version of your solution. AI just inverted that. The tool bends to you or it dies. Cuban: “33 million companies aren’t going to have AI budgets, aren’t going to have AI experts.” 33 million businesses feel something shifting beneath them. None can name it. The distance between what AI can do and what small companies can access is the most mispriced gap in markets today. Not a technology problem. A translation problem. Cuban: “Learn all you can about AI but learn more on how to implement them in companies.” Everyone is racing to build intelligence. Almost nobody is racing to deploy it where the pain is deepest. The person who walks into a 40-person company and rewires their entire operation captures more value than the team that trained the model. Understanding pain is now worth more than building intelligence. Cuban: “Every single job available for kids coming out of school because every single company needs that.” The most important career of the next decade has no title. No degree path. No university knows it needs to exist yet. It belongs to whoever learns two languages fluently. The language of a business that can’t articulate what’s breaking. And the language of an AI that doesn’t know where to aim. 33 million companies. Zero translators. Whoever arrives first doesn’t enter a market. They create one.

Dustin

473,385 views • 1 month ago

OpenAI just created a $10 billion company whose ONLY job is forcing businesses to use AI. And they're literally guaranteeing investors a 17.5% annual return to make it happen. It's called "The Deployment Company." OpenAI finalized it yesterday with 19 investors including TPG, SoftBank, Bain Capital, Brookfield, and Advent International. Here's the structure: OpenAI puts in $1.5 billion. The private equity firms put in $4 billion. In exchange, those PE firms open up their 2,000+ portfolio companies as a CAPTIVE customer base for OpenAI's products. OpenAI then embeds teams of engineers directly inside those companies, Palantir-style, to integrate their tools into daily operations. And here's the big red flag in all of this: OpenAI is GUARANTEEING those PE firms a 17.5% annual return over five years. That means even if the companies in the portfolio don't want AI, don't need AI, or get zero value from AI, OpenAI is still on the hook to pay those returns. Think about what that means for a second. OpenAI is so desperate for enterprise adoption that they're paying Wall Street to force their product into thousands of businesses. They've essentially turned private equity firms into a distribution cartel with a guaranteed commission. This has NEVER been done before in enterprise software. No software company in history has guaranteed above-market returns to financial sponsors just to get their product installed. And it gets crazier: Within MINUTES of OpenAI's announcement, Anthropic announced their own version. A $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman. Same playbook. Two companies worth a combined $1+ TRILLION in private valuation both concluded on the same day that organic demand for their products is not growing fast enough. If enterprises were lining up to buy AI on their own, you wouldn't need to bribe private equity firms with guaranteed returns to shove it into their portfolios. You would just sell it normally like every other software company in history. But they can't. Because the gap between what AI companies PROMISE and what enterprises actually experience is still enormous. OpenAI's COO Brad Lightcap just moved into a new role specifically to lead this push. They've also signed "Frontier Alliances" with major consulting firms to embed AI through professional services channels. Every move they're making screams the same thing: We have a demand problem. And this is all happening right before OpenAI tries to IPO at $850 billion. If they can show Wall Street that 2,000+ companies are "using OpenAI products" through this PE distribution channel, it inflates their enterprise metrics right before the roadshow. Doesn't matter if those companies actually need it or if it creates real value. What matters is the number on the S-1. This is the AI playbook entering its most dangerous phase. The tech is real but the business model is being held together by financial engineering, guaranteed returns, and captive distribution deals that look more like a pharmaceutical company paying doctors to prescribe their drug than a software company earning customers on merit. And both OpenAI and Anthropic admitted it on the same day.
0:54

OpenAI just created a $10 billion company whose ONLY job is forcing businesses to use AI. And they're literally guaranteeing investors a 17.5% annual return to make it happen. It's called "The Deployment Company." OpenAI finalized it yesterday with 19 investors including TPG, SoftBank, Bain Capital, Brookfield, and Advent International. Here's the structure: OpenAI puts in $1.5 billion. The private equity firms put in $4 billion. In exchange, those PE firms open up their 2,000+ portfolio companies as a CAPTIVE customer base for OpenAI's products. OpenAI then embeds teams of engineers directly inside those companies, Palantir-style, to integrate their tools into daily operations. And here's the big red flag in all of this: OpenAI is GUARANTEEING those PE firms a 17.5% annual return over five years. That means even if the companies in the portfolio don't want AI, don't need AI, or get zero value from AI, OpenAI is still on the hook to pay those returns. Think about what that means for a second. OpenAI is so desperate for enterprise adoption that they're paying Wall Street to force their product into thousands of businesses. They've essentially turned private equity firms into a distribution cartel with a guaranteed commission. This has NEVER been done before in enterprise software. No software company in history has guaranteed above-market returns to financial sponsors just to get their product installed. And it gets crazier: Within MINUTES of OpenAI's announcement, Anthropic announced their own version. A $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman. Same playbook. Two companies worth a combined $1+ TRILLION in private valuation both concluded on the same day that organic demand for their products is not growing fast enough. If enterprises were lining up to buy AI on their own, you wouldn't need to bribe private equity firms with guaranteed returns to shove it into their portfolios. You would just sell it normally like every other software company in history. But they can't. Because the gap between what AI companies PROMISE and what enterprises actually experience is still enormous. OpenAI's COO Brad Lightcap just moved into a new role specifically to lead this push. They've also signed "Frontier Alliances" with major consulting firms to embed AI through professional services channels. Every move they're making screams the same thing: We have a demand problem. And this is all happening right before OpenAI tries to IPO at $850 billion. If they can show Wall Street that 2,000+ companies are "using OpenAI products" through this PE distribution channel, it inflates their enterprise metrics right before the roadshow. Doesn't matter if those companies actually need it or if it creates real value. What matters is the number on the S-1. This is the AI playbook entering its most dangerous phase. The tech is real but the business model is being held together by financial engineering, guaranteed returns, and captive distribution deals that look more like a pharmaceutical company paying doctors to prescribe their drug than a software company earning customers on merit. And both OpenAI and Anthropic admitted it on the same day.

Ricardo

52,664 views • 3 months ago

A single tweet just vaporized BILLIONS from cybersecurity stocks. CrowdStrike down 8%. Cloudflare down 8.1%. Okta down 9.2%. SailPoint down 9.4%. The Global X Cybersecurity ETF just hit its lowest level since November 2023. What happened? Anthropic dropped Claude Code Security. It scans your entire codebase for vulnerabilities and suggests patches. Sounds boring. Until you read what it actually did: In testing, Claude found over 500 HIGH-SEVERITY BUGS in production open-source codebases. Bugs that had been sitting there for DECADES. Despite years of expert review. Despite fuzzing campaigns. Despite penetration testing. Despite million-dollar security audits. Nobody found them. An AI did. In hours. Here's the terrifying part: Traditional security tools work by pattern matching. They look for known vulnerabilities in a database. Claude doesn't do that. It READS code the way a human security researcher would. Traces data flows. Understands how components interact. Catches logic flaws that rule-based tools can't see. And it just outperformed every cybersecurity tool on the market. Combined. Wall Street figured this out fast. If an AI can find what your $500k/year security team missed... Why do you need the team? If an AI catches bugs that CrowdStrike, Okta, and Cloudflare couldn't... Why are you paying those subscriptions? Barclays came out saying the selloff was "illogical" and Claude "doesn't compete" with these companies. But here's what Barclays missed: It's not about what Claude competes with TODAY. It's about what it replaces TOMORROW. The SaaS apocalypse hit legal software 3 weeks ago. Thomson Reuters dropped 18% in one day. $285 billion wiped from software stocks. Now it's cybersecurity's turn. The pattern is obvious: Every industry that sells "expertise as a service" is about to get repriced. Legal research? Done. Code vulnerability scanning? Done. Compliance checking? Coming soon. Financial analysis? On deck. Companies that spent 20 years building "moats" around specialized knowledge are watching AI swim right over them. CrowdStrike is worth $95 billion. They have 30,000 customers. Claude just found 500 bugs their tools missed. Do the math. The smart money already sees what's happening. The iShares Expanded Tech-Software Sector ETF is down 23% YTD. Heading for its largest quarterly decline since the 2008 financial crisis. Not because software is dying... But because software companies that charge per-seat subscriptions for AI-replicable work are dying. Anthropic just proved that a general-purpose AI can outperform DECADES of specialized cybersecurity infrastructure. In a single product release. While still in "limited research preview." It's not even fully launched yet. What happens when it scales? We're about to find out.
0:50

A single tweet just vaporized BILLIONS from cybersecurity stocks. CrowdStrike down 8%. Cloudflare down 8.1%. Okta down 9.2%. SailPoint down 9.4%. The Global X Cybersecurity ETF just hit its lowest level since November 2023. What happened? Anthropic dropped Claude Code Security. It scans your entire codebase for vulnerabilities and suggests patches. Sounds boring. Until you read what it actually did: In testing, Claude found over 500 HIGH-SEVERITY BUGS in production open-source codebases. Bugs that had been sitting there for DECADES. Despite years of expert review. Despite fuzzing campaigns. Despite penetration testing. Despite million-dollar security audits. Nobody found them. An AI did. In hours. Here's the terrifying part: Traditional security tools work by pattern matching. They look for known vulnerabilities in a database. Claude doesn't do that. It READS code the way a human security researcher would. Traces data flows. Understands how components interact. Catches logic flaws that rule-based tools can't see. And it just outperformed every cybersecurity tool on the market. Combined. Wall Street figured this out fast. If an AI can find what your $500k/year security team missed... Why do you need the team? If an AI catches bugs that CrowdStrike, Okta, and Cloudflare couldn't... Why are you paying those subscriptions? Barclays came out saying the selloff was "illogical" and Claude "doesn't compete" with these companies. But here's what Barclays missed: It's not about what Claude competes with TODAY. It's about what it replaces TOMORROW. The SaaS apocalypse hit legal software 3 weeks ago. Thomson Reuters dropped 18% in one day. $285 billion wiped from software stocks. Now it's cybersecurity's turn. The pattern is obvious: Every industry that sells "expertise as a service" is about to get repriced. Legal research? Done. Code vulnerability scanning? Done. Compliance checking? Coming soon. Financial analysis? On deck. Companies that spent 20 years building "moats" around specialized knowledge are watching AI swim right over them. CrowdStrike is worth $95 billion. They have 30,000 customers. Claude just found 500 bugs their tools missed. Do the math. The smart money already sees what's happening. The iShares Expanded Tech-Software Sector ETF is down 23% YTD. Heading for its largest quarterly decline since the 2008 financial crisis. Not because software is dying... But because software companies that charge per-seat subscriptions for AI-replicable work are dying. Anthropic just proved that a general-purpose AI can outperform DECADES of specialized cybersecurity infrastructure. In a single product release. While still in "limited research preview." It's not even fully launched yet. What happens when it scales? We're about to find out.

Ricardo

110,899 views • 5 months ago

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.
0:32

Microsoft just lost $357 billion in a single day... While Meta gained $170 billion. Both companies are spending over $100 billion on AI this year. One got punished. One got rewarded. The difference tells you everything about where this market is heading: Microsoft reported Wednesday. Beat on revenue. Beat on earnings. Revenue up 17%. EPS up 24%. But the stock dropped 10% - worst decline since March 2020. Why? Azure cloud growth came in at 39%. The Street wanted 39.4%. A miss of 0.4 percentage points erased a third of a trillion dollars. Meanwhile, capex jumped 89% year-over-year to $37.5B in a single quarter. CFO Amy Hood admitted two-thirds went to "short-lived assets" - GPUs that depreciate fast. And Microsoft also said they'll remain "capacity constrained through at least the end of our fiscal year." In other words: "We're spending $72B in six months and STILL can't build data centers fast enough." But that's not the real problem... The real problem is what's happening inside Microsoft's spending. They're not just building infrastructure for Azure customers. They're allocating scarce GPUs to their own products: M365 Copilot, GitHub Copilot, internal R&D. Hood said they must "balance Azure revenue growth with growing needs across first-party apps and AI solutions." Microsoft is competing with its own cloud customers for compute capacity. If they'd allocated all new GPUs to Azure, growth would've exceeded 40%. Instead, they're betting their own AI products will generate more value than selling raw compute. That bet hasn't paid off yet. And 45% of their $625B backlog is tied to ONE customer: OpenAI. Now compare that to Meta: Revenue beat. Earnings beat. Guidance crushed expectations. And they announced $115-135B in AI capex for 2026 - nearly DOUBLE what they spent in 2025. The stock surged 10%. Why the opposite reaction? Meta is seeing immediate returns. Ad impressions up 18%. Average price per ad up 6%. Revenue up 24% year-over-year. Their AI investment is already showing up in the core business TODAY. Better ad targeting. Better recommendations. Better engagement. Q1 revenue guidance came in at $53.5-56.5B - Wall Street expected $51.4B. That's 30% revenue growth ACCELERATION. When you have 3.58B daily active users, AI improvements compound immediately. Zuckerberg called it a "major AI acceleration" and Wall Street didn't care about the $135B spending number. Because they can SEE the connection between spending and revenue. Here's what matters: The hyperscalers are now spending over $600B combined on AI infrastructure in 2026. AI assets depreciate at roughly 20% per year. The five hyperscalers face annual depreciation expenses approaching $400B - MORE than their combined profits in 2025. This is the biggest capital spending cycle in history. And we just entered Phase 3, where AI-enabled revenue models must finally prove their worth. The market stopped rewarding spending. It's rewarding RETURNS. Meta showed returns. Microsoft showed constraints and margin compression. That's why we saw a $527B swing between two companies reporting on the same day. My read: The easy money in the AI trade is over. From here, execution matters more than ambition. Companies that can turn infrastructure spending into measurable productivity gains get rewarded. Companies still building without clear payback get punished - even when they beat estimates. Microsoft isn't a bad company. It's a company that bet big on AI infrastructure and is now scrambling to show ROI before margins collapse further. Meta isn't necessarily a better AI company. It just has a business model where AI improvements translate directly to revenue growth. For investors, the lesson is clear: The AI infrastructure phase is maturing. Winners from here will be companies with clear paths from spending to earnings. Not companies asking you to trust the process while margins compress.

George Noble

120,284 views • 6 months ago

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?
0:26

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?

Ricardo

167,555 views • 4 months ago

Jensen Huang just called out every CEO who’s been firing people “because of AI.” Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive. Jensen's answer: "For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more." Read that again. The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing: They have no imagination. They have no vision for what comes next. They got handed the most powerful tool in human history and their FIRST instinct was to fire people. This is the CEO of NVIDIA. The company whose chips power every AI system on the planet. If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang. And he said the OPPOSITE. He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it. But here's where it gets really interesting... During the same interview, Jensen revealed something nobody's talking about: He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees. One to two billion per week. That's a $50 to $100 BILLION annualized run rate. For companies that most people think are burning cash and making nothing. The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong. Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real. So if the money IS real, why are other companies firing people? Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets. They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board. Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines. That number doesn't include the new chips, systems, or partnerships announced this week. And he's not cutting people. He's hiring. Because when you have imagination, more capability means MORE opportunity. Not less headcount. Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency." Jensen's response: You're out of imagination. He also said something that stuck with me. Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's. His answer: "I was just trying to make it through the shift." Biggest tip he ever got? Two, three dollars. Now he's building tech that increased computing demand by one million times in two years. He announced OpenClaw, which he says is as big as ChatGPT. And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet. When asked how long he plans to keep working? "I'm hoping to die on the job. And I'm not hoping to die anytime soon." This is a man who believes every single thing he's building. And his message to every CEO using AI to justify layoffs is simple... You're not innovating. You're surrendering. The technology wasn't built to shrink companies. It was built to make them limitless. If your leadership can't see that, the problem isn't AI. It's THEM.
1:42

Jensen Huang just called out every CEO who’s been firing people “because of AI.” Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive. Jensen's answer: "For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more." Read that again. The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing: They have no imagination. They have no vision for what comes next. They got handed the most powerful tool in human history and their FIRST instinct was to fire people. This is the CEO of NVIDIA. The company whose chips power every AI system on the planet. If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang. And he said the OPPOSITE. He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it. But here's where it gets really interesting... During the same interview, Jensen revealed something nobody's talking about: He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees. One to two billion per week. That's a $50 to $100 BILLION annualized run rate. For companies that most people think are burning cash and making nothing. The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong. Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real. So if the money IS real, why are other companies firing people? Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets. They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board. Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines. That number doesn't include the new chips, systems, or partnerships announced this week. And he's not cutting people. He's hiring. Because when you have imagination, more capability means MORE opportunity. Not less headcount. Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency." Jensen's response: You're out of imagination. He also said something that stuck with me. Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's. His answer: "I was just trying to make it through the shift." Biggest tip he ever got? Two, three dollars. Now he's building tech that increased computing demand by one million times in two years. He announced OpenClaw, which he says is as big as ChatGPT. And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet. When asked how long he plans to keep working? "I'm hoping to die on the job. And I'm not hoping to die anytime soon." This is a man who believes every single thing he's building. And his message to every CEO using AI to justify layoffs is simple... You're not innovating. You're surrendering. The technology wasn't built to shrink companies. It was built to make them limitless. If your leadership can't see that, the problem isn't AI. It's THEM.

Ricardo

1,390,871 views • 5 months ago