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🚨DID MICROSOFT JUST BAN ITS ENGINEERS FROM USING AI? The company that poured $5 billion into Anthropic discovered that Claude Code was costing more than the humans it was supposed to replace — so they’re canceling nearly all licenses by the end of June. Uber’s CTO admitted their full-year... show more

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19,103 次观看 • 3 个月前 •via X (Twitter)

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BREAKING: MICROSOFT JUST ANNOUNCED TO BAN ITS OWN ENGINEERS FROM USING AI DUE TO THE COST OF USING IT. VP OF NVIDIA SAID, “THE COST OF AI FOR MY TEAM WAS MORE THAN HUMANS” “AI CAN COST MORE THAN HUMAN WORKERS NOW”

BREAKING: MICROSOFT JUST ANNOUNCED TO BAN ITS OWN ENGINEERS FROM USING AI DUE TO THE COST OF USING IT. VP OF NVIDIA SAID, “THE COST OF AI FOR MY TEAM WAS MORE THAN HUMANS” “AI CAN COST MORE THAN HUMAN WORKERS NOW”

Vivek Sen

17,500,452 次观看 • 3 个月前

Microsoft is deceiving you by inflating its AI empire with money it handed its OWN customer first. They sold Wall Street a $37 billion AI business, then went silent the moment its own filing showed where that money came from. The line sits in the annual report for fiscal 2026: Microsoft recorded $24.1 billion of revenue from commercial arrangements with OpenAI, including revenue sharing payments. If you run that figure against Microsoft's own AI disclosures you'll find that OpenAI made up more than half, and likely around 70%, of everything the company counts as AI sales. ONE customer. A Microsoft spokesperson confirmed the figure covers all sales and revenue share from OpenAI. The 70% comes by assuming Microsoft's AI run rate kept growing at the 123% pace the company itself reported in March, which is the company's own optimistic math turned around on it. Now follow where that money starts: Microsoft has put around $12 billion into OpenAI since 2019. OpenAI spends its cash on computing power, and Microsoft is the cloud provider selling it. So the money leaves as an investment and comes back as an Azure bill. Microsoft then books that bill as AI revenue and shows it to investors as proof the AI business is "working." Microsoft invests in OpenAI -> OpenAI buys Microsoft compute -> Microsoft records the payment as AI revenue -> the AI growth story goes to Wall Street And a chunk of it never actually arrived. The same filing shows $6 billion of accounts receivable from OpenAI as of June 30. That is $6 billion of AI revenue Microsoft booked and had not been paid when the year closed. Now here's where it gets really concerning for anyone holding the stock... Microsoft has told the public how big its total AI business is exactly twice. Once for the quarter ending December 2024, when it said the unit was on pace for more than $13 billion a year. And once for the quarter ending March 2026, when Satya Nadella put it on pace for $37 billion. That $37 billion number went everywhere. It was the headline proof that Microsoft had won the AI race. Then fourth quarter earnings arrived, and Microsoft did NOT update it. The company that had been announcing the figure as its own scoreboard stopped announcing the figure. In the same stretch, the filing landed showing where most of it came from. So what is actually left underneath? The full year AI business ran near $34 billion. Take OpenAI out and roughly $10 billion remains. Microsoft has spent about $261 billion on capital expenditure since the start of 2022. That is the scale of the bet against what the rest of the AI business currently brings in. And the one customer holding it up is walking further away every quarter. In October, Microsoft's stake in OpenAI dropped to 27% from 32.5%. In April the partnership was rewritten so OpenAI can sell its products across any cloud it likes, which is how Amazon got a seat at the table. The exclusivity that made this arrangement valuable is gone. The compute bill and the unpaid $6 billion are still on Microsoft's books. Nadella spent two years telling the market Microsoft built the largest AI business in software. The filing shows one client bought most of it, on credit, using money Microsoft partly supplied. So watch the next earnings call: If Microsoft puts a fresh total AI number back on the board, the business found customers beyond OpenAI. If you hear a lot about AI momentum and never hear what it adds up to, you already know why the number went missing. But nonetheless, how is something like this even legal?

Microsoft is deceiving you by inflating its AI empire with money it handed its OWN customer first. They sold Wall Street a $37 billion AI business, then went silent the moment its own filing showed where that money came from. The line sits in the annual report for fiscal 2026: Microsoft recorded $24.1 billion of revenue from commercial arrangements with OpenAI, including revenue sharing payments. If you run that figure against Microsoft's own AI disclosures you'll find that OpenAI made up more than half, and likely around 70%, of everything the company counts as AI sales. ONE customer. A Microsoft spokesperson confirmed the figure covers all sales and revenue share from OpenAI. The 70% comes by assuming Microsoft's AI run rate kept growing at the 123% pace the company itself reported in March, which is the company's own optimistic math turned around on it. Now follow where that money starts: Microsoft has put around $12 billion into OpenAI since 2019. OpenAI spends its cash on computing power, and Microsoft is the cloud provider selling it. So the money leaves as an investment and comes back as an Azure bill. Microsoft then books that bill as AI revenue and shows it to investors as proof the AI business is "working." Microsoft invests in OpenAI -> OpenAI buys Microsoft compute -> Microsoft records the payment as AI revenue -> the AI growth story goes to Wall Street And a chunk of it never actually arrived. The same filing shows $6 billion of accounts receivable from OpenAI as of June 30. That is $6 billion of AI revenue Microsoft booked and had not been paid when the year closed. Now here's where it gets really concerning for anyone holding the stock... Microsoft has told the public how big its total AI business is exactly twice. Once for the quarter ending December 2024, when it said the unit was on pace for more than $13 billion a year. And once for the quarter ending March 2026, when Satya Nadella put it on pace for $37 billion. That $37 billion number went everywhere. It was the headline proof that Microsoft had won the AI race. Then fourth quarter earnings arrived, and Microsoft did NOT update it. The company that had been announcing the figure as its own scoreboard stopped announcing the figure. In the same stretch, the filing landed showing where most of it came from. So what is actually left underneath? The full year AI business ran near $34 billion. Take OpenAI out and roughly $10 billion remains. Microsoft has spent about $261 billion on capital expenditure since the start of 2022. That is the scale of the bet against what the rest of the AI business currently brings in. And the one customer holding it up is walking further away every quarter. In October, Microsoft's stake in OpenAI dropped to 27% from 32.5%. In April the partnership was rewritten so OpenAI can sell its products across any cloud it likes, which is how Amazon got a seat at the table. The exclusivity that made this arrangement valuable is gone. The compute bill and the unpaid $6 billion are still on Microsoft's books. Nadella spent two years telling the market Microsoft built the largest AI business in software. The filing shows one client bought most of it, on credit, using money Microsoft partly supplied. So watch the next earnings call: If Microsoft puts a fresh total AI number back on the board, the business found customers beyond OpenAI. If you hear a lot about AI momentum and never hear what it adds up to, you already know why the number went missing. But nonetheless, how is something like this even legal?

Ricardo

24,389 次观看 • 1 个月前

Chamath said AI is not like the internet. Every new user costs real money. And the infrastructure making it possible was built by everyone. His argument was the clearest case for government ownership of AI labs I have ever heard. And it had nothing to do with Bernie Sanders. Start with the internet comparison. Google and Facebook became the most profitable companies in human history because of one number. The marginal cost of adding a new user was effectively zero. One more search query cost Google nothing. One more Facebook profile cost Meta nothing. They could serve a billion people and the incremental cost of that billion person was rounding error. That is the money printer. Infinite scale at zero marginal cost. AI breaks that model completely. Every single user taxes a GPU. Every query costs electricity. Every response requires memory and compute. The marginal cost of AI is real, significant, and does not disappear at scale. You cannot print money the same way. Then Chamath made the point that landed hardest. The infrastructure these companies depend on, the power grid, the land, the data centers, the permitting, the national security apparatus that protects their chips from being stolen, none of that was built by Anthropic or OpenAI. It was built by the public. By taxpayers. By decades of government investment in the physical and legal foundation these companies are now running on. He compared it to the interstate highway system. If the federal government built the roads and two companies transported all the goods on them, a logical question at that point would be how much of that should I own? You are riding on my rails. His conclusion was direct. If he were running a sovereign wealth fund and had the negotiating leverage of the US government, he would own 75% of these companies when he was done. The internet had zero marginal cost. That is why the founders captured almost all of the value. AI has real marginal cost and runs on public infrastructure. That changes who has a claim on what gets built. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

79,378 次观看 • 3 个月前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,823 次观看 • 5 个月前

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

Dami-Defi

16,105 次观看 • 4 个月前

$Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?$

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

93,586 次观看 • 2 个月前

🇺🇸 Your AI bill could triple by year's end, and almost nobody has done the math on why. Jeff Currie connects the oil glut of 2020 to the AI spending bubble now forming, warning that energy is becoming scarcer than human capital. "The supply curve of a commodity is upward sloping. The old model on tech was infinitely scalable at zero marginal cost. When you start burning more and more commodities... the cost of the production of AI compute goes higher." The same drunken-sailor spending that cremated shale oil investors in 2014 is happening again, just with GPUs. Jeffrey Currie 🆔++

🇺🇸 Your AI bill could triple by year's end, and almost nobody has done the math on why. Jeff Currie connects the oil glut of 2020 to the AI spending bubble now forming, warning that energy is becoming scarcer than human capital. "The supply curve of a commodity is upward sloping. The old model on tech was infinitely scalable at zero marginal cost. When you start burning more and more commodities... the cost of the production of AI compute goes higher." The same drunken-sailor spending that cremated shale oil investors in 2014 is happening again, just with GPUs. Jeffrey Currie 🆔++

Mario Nawfal

44,809 次观看 • 20 天前

This Guy has Figured it out ! "AI won't take your job, But someone using it will " This Statement has been echoed everywhere , just to stop panic in the tech community bcoz of increasing layoffs and less hirings...Such a narrative will force many into spending money for learning AI tools and writing codes for it.. Again the Truth is Simple. AI is doing what it was always meant to do for The Big players ...Replace the work that can be automated by any means and Reduce Cost.. But The real Reason for This Downfall in the Tech Sector is De-Dollari$ation . Though it will be called the " AI Cr@sh " by Everybody because no one wants to call it that .. Those Jobs and High Payments which the majority of people in the tech sector enjoyed because of the Free Printing of Dollars from the Fed Reserve is not possible now ..

This Guy has Figured it out ! "AI won't take your job, But someone using it will " This Statement has been echoed everywhere , just to stop panic in the tech community bcoz of increasing layoffs and less hirings...Such a narrative will force many into spending money for learning AI tools and writing codes for it.. Again the Truth is Simple. AI is doing what it was always meant to do for The Big players ...Replace the work that can be automated by any means and Reduce Cost.. But The real Reason for This Downfall in the Tech Sector is De-Dollari$ation . Though it will be called the " AI Cr@sh " by Everybody because no one wants to call it that .. Those Jobs and High Payments which the majority of people in the tech sector enjoyed because of the Free Printing of Dollars from the Fed Reserve is not possible now ..

Susmit🔥AS Quotes🌌🌏🕉️

15,805 次观看 • 7 个月前

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Even if AI will write 90% of code. But it won’t replace engineers. Windsurf’s founder explains that even if AI generates most of the code, engineers still spend time reviewing it, debugging issues, designing systems, testing, deploying, and making architectural decisions. Those parts don’t disappear. He brings up Amdahl’s Law to make the point: speeding up one part of the workflow doesn’t dramatically change the total time unless everything else speeds up too. For example, if writing code takes 30 out of 100 total time units, and AI brings it down to 3, the total time still only drops to 73. That’s just a 27% improvement. More productivity doesn’t mean fewer engineers. It just increases the ROI of building tech. And when ROI goes up, companies are more likely to build—and hire more people to do it. Take JPMorgan Chase. They have 50,000+ engineers and a $17B software budget. If each engineer can ship more software, that doesn’t reduce headcount. It raises the cost of not building. Companies like Windsurf and Anthropic are hiring more engineers—not less—despite being at the edge of AI code generation.

Aish

27,268 次观看 • 1 年前

The corporations fired workers to cut costs. Now they’re raiding your pension to fund the AI that replaced them. BlackRock’s Larry Fink said it directly: the trillions needed to build the AI infrastructure will come from savings accounts and pension funds. Not from the corporations. From YOU. So let’s recap. They fired your colleagues to save money. The AI cost more than the employees anyway. The bill for building it gets forwarded to your retirement account. And while all of this happens, your data and IP gets stripped in the process. You didn’t just lose the job but handed your retirement to financing the machine that took it. You’re not angry enough. Sit with that.

The corporations fired workers to cut costs. Now they’re raiding your pension to fund the AI that replaced them. BlackRock’s Larry Fink said it directly: the trillions needed to build the AI infrastructure will come from savings accounts and pension funds. Not from the corporations. From YOU. So let’s recap. They fired your colleagues to save money. The AI cost more than the employees anyway. The bill for building it gets forwarded to your retirement account. And while all of this happens, your data and IP gets stripped in the process. You didn’t just lose the job but handed your retirement to financing the machine that took it. You’re not angry enough. Sit with that.

Sir Escanor (𝘏𝘰𝘱𝘪𝘶𝘮 𝘚𝘭𝘢𝘺𝘦𝘳)

18,833 次观看 • 3 个月前

🚨 Google and Anthropic are reportedly paying SpaceX more than $2.1 billion per month for AI compute capacity That's a revenue run rate of roughly $26 billion per year! Jeff Lutz 🔋 says the bigger story isn't the money. It's that some of the world's leading AI companies are choosing SpaceX to power their AI ambitions. A few years ago, SpaceX was known for rockets. Today, it may be becoming one of the most important AI infrastructure companies in the world. 🚀 $SPCX

🚨 Google and Anthropic are reportedly paying SpaceX more than $2.1 billion per month for AI compute capacity That's a revenue run rate of roughly $26 billion per year! Jeff Lutz 🔋 says the bigger story isn't the money. It's that some of the world's leading AI companies are choosing SpaceX to power their AI ambitions. A few years ago, SpaceX was known for rockets. Today, it may be becoming one of the most important AI infrastructure companies in the world. 🚀 $SPCX

Herbert Ong

33,815 次观看 • 3 个月前

🇺🇸 SpaceX is making a massive AI bet Elon says SpaceX is scaling AI compute at a pace he believes no one else can match, with more than 2 GW expected online by the end of this year. The company could push its cumulative AI compute capacity to nearly 10 GW by the end of next year and has decided to build exclusively on NVIDIA, calling its Vera Rubin platform the best available. If SpaceX delivers, it could become one of the world's AI superpowers. Writer: Val

🇺🇸 SpaceX is making a massive AI bet Elon says SpaceX is scaling AI compute at a pace he believes no one else can match, with more than 2 GW expected online by the end of this year. The company could push its cumulative AI compute capacity to nearly 10 GW by the end of next year and has decided to build exclusively on NVIDIA, calling its Vera Rubin platform the best available. If SpaceX delivers, it could become one of the world's AI superpowers. Writer: Val

Mario Nawfal

43,332 次观看 • 1 个月前

Anthropic CEO, Dario Amodei: "Half of entry-level white-collar jobs could be gone in one to five years." 3 things he said during the interview. The third, OpenAI completely ignores. One: inside Anthropic, engineers say "I don't write code anymore. The model does." Two: the real trigger isn't jobs. It's AI that builds AI, and the loop may close this year. Three: how soon superhuman AI actually lands. The massive buffers they are raising because the compute explosion is moving faster than anticipated. Save it. Reread in 2028 and see who was right.

Anthropic CEO, Dario Amodei: "Half of entry-level white-collar jobs could be gone in one to five years." 3 things he said during the interview. The third, OpenAI completely ignores. One: inside Anthropic, engineers say "I don't write code anymore. The model does." Two: the real trigger isn't jobs. It's AI that builds AI, and the loop may close this year. Three: how soon superhuman AI actually lands. The massive buffers they are raising because the compute explosion is moving faster than anticipated. Save it. Reread in 2028 and see who was right.

Ridark

14,555 次观看 • 2 个月前

Jensen Huang, CEO of NVIDIA, laid out the compute math almost nobody priced in: reasoning AI needs 1,000x more compute than generative AI. Then you multiply by 100x more users. "In order for AI to go through understanding, reasoning, planning, using tools to take action, the amount of computation necessary compared to generative AI is like a thousand times more." "In two years time, the number of cars you need in the world grew by a thousand times. In two years time, the number of airplanes you need in the world grew by 2,000 times. Whatever metric you want to use, the thousand times difference is incredible." "But then you multiply that by the number of people who now want to use it by a hundred times. Which is the reason why GPU consumption is going through the roof." "Even GPUs we sold 4 or 5 years ago are now rising in price faster than good wine. It has defied what everybody said was going to happen." The reasoning era did not make intelligence cheaper to run. It made a single answer cost 1,000x more compute, then handed that answer to 100x more people. Anyone still budgeting AI around "inference gets cheap" is planning for a curve that already snapped.

Jensen Huang, CEO of NVIDIA, laid out the compute math almost nobody priced in: reasoning AI needs 1,000x more compute than generative AI. Then you multiply by 100x more users. "In order for AI to go through understanding, reasoning, planning, using tools to take action, the amount of computation necessary compared to generative AI is like a thousand times more." "In two years time, the number of cars you need in the world grew by a thousand times. In two years time, the number of airplanes you need in the world grew by 2,000 times. Whatever metric you want to use, the thousand times difference is incredible." "But then you multiply that by the number of people who now want to use it by a hundred times. Which is the reason why GPU consumption is going through the roof." "Even GPUs we sold 4 or 5 years ago are now rising in price faster than good wine. It has defied what everybody said was going to happen." The reasoning era did not make intelligence cheaper to run. It made a single answer cost 1,000x more compute, then handed that answer to 100x more people. Anyone still budgeting AI around "inference gets cheap" is planning for a curve that already snapped.

Karl Mehta

54,717 次观看 • 2 个月前

Went to a new kind of movie today. One that cost $400,000 in AI compute to make. It was the San Francisco premiere. If you hadn’t told me it was largely done with AI I wouldn’t have known. The takeaway after listening to Higgsfield AI 🧩’s CTO introduce it is that a competent movie can now be made for half a million dollars. Where they used to cost $100 million or more. Here is the first few minutes of “Hell Grind” and that intro from Yerzat Dulat. We are far from seeing the ultimate use of AI in movies. As a touch point of where AI for creative people is this seems like an important time marker and one I will look back on frequently for years.

Went to a new kind of movie today. One that cost $400,000 in AI compute to make. It was the San Francisco premiere. If you hadn’t told me it was largely done with AI I wouldn’t have known. The takeaway after listening to Higgsfield AI 🧩’s CTO introduce it is that a competent movie can now be made for half a million dollars. Where they used to cost $100 million or more. Here is the first few minutes of “Hell Grind” and that intro from Yerzat Dulat. We are far from seeing the ultimate use of AI in movies. As a touch point of where AI for creative people is this seems like an important time marker and one I will look back on frequently for years.

Robert Scoble

87,324 次观看 • 3 个月前

Brad Gerstner said the quiet part out loud when it comes to ROI on AI spending and what bears are missing about adoption As of today, the big impact and ROI from AI has been on the cost side of the equation, which has come primarily through headcount reductions and efficiencies Brad believes that we are on the cusp of breakthroughs that will change the revenue side of the equation as well Once that takes place, AI platforms such as Anthropic become mission critical for its customers, where you cannot rip it out of your enterprise even if you wanted to Jensen Huang has famously said that all new chip production design at NVIDIA is now happening with the help of AI. In some sense, we already have the futuristic recursive loop of "the machine building the machine." Brad goes on to predict that if Anthropic can end 2026 at a $100B run-rate, there is very real possibility that they will 3-5x again to $300-500 billion in revenue We have never seen anything like this in history

Brad Gerstner said the quiet part out loud when it comes to ROI on AI spending and what bears are missing about adoption As of today, the big impact and ROI from AI has been on the cost side of the equation, which has come primarily through headcount reductions and efficiencies Brad believes that we are on the cusp of breakthroughs that will change the revenue side of the equation as well Once that takes place, AI platforms such as Anthropic become mission critical for its customers, where you cannot rip it out of your enterprise even if you wanted to Jensen Huang has famously said that all new chip production design at NVIDIA is now happening with the help of AI. In some sense, we already have the futuristic recursive loop of "the machine building the machine." Brad goes on to predict that if Anthropic can end 2026 at a $100B run-rate, there is very real possibility that they will 3-5x again to $300-500 billion in revenue We have never seen anything like this in history

Boring_Business

37,319 次观看 • 2 个月前

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Patrick OShaughnessy

616,953 次观看 • 3 个月前

🚨 NOW: In a major blow to the "experts," President Trump reveals US customers of American Electric Power will get $16 BILLION in electricity cost offsets by AI data centers — and more is coming "$16 BILLION. Think of that!" 👏🏻 He's singlehandedly blocking AI data centers from jacking up rates!

🚨 NOW: In a major blow to the "experts," President Trump reveals US customers of American Electric Power will get $16 BILLION in electricity cost offsets by AI data centers — and more is coming "$16 BILLION. Think of that!" 👏🏻 He's singlehandedly blocking AI data centers from jacking up rates!

Eric Daugherty

125,476 次观看 • 1 个月前

Dylan explains why the next wave of private equity looks nothing like the last one. "If we're looking at these companies that are AI rollups, let's take an existing company and completely destroy its cost structure. Nuke its cost structure by just making it efficient with AI." "Private equity companies buy a company, and right now they just squeeze the rag, discard it, and make the American populace screwed." "What new people started to do is the AI private equity strategy. Instead of trying to wring it dry, they're modernizing all the systems. Oh, you use Excel for your databases? Okay, let's move to standard cloud. Spend a lot of money up front." "AI makes that front load much more severe. You spike up on spend a lot for the one time, and then you spike down a lot and your cost efficiency is way better. AI cold calling, AI invoicing, AI accounting, none of it is really at critical mass. But we're so close."

Dylan explains why the next wave of private equity looks nothing like the last one. "If we're looking at these companies that are AI rollups, let's take an existing company and completely destroy its cost structure. Nuke its cost structure by just making it efficient with AI." "Private equity companies buy a company, and right now they just squeeze the rag, discard it, and make the American populace screwed." "What new people started to do is the AI private equity strategy. Instead of trying to wring it dry, they're modernizing all the systems. Oh, you use Excel for your databases? Okay, let's move to standard cloud. Spend a lot of money up front." "AI makes that front load much more severe. You spike up on spend a lot for the one time, and then you spike down a lot and your cost efficiency is way better. AI cold calling, AI invoicing, AI accounting, none of it is really at critical mass. But we're so close."

SemiAnalysis

72,852 次观看 • 24 天前