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🤖 AI turned out to be too expensive to replace humans Microsoft has started limiting employee access to Anthropic models and revoking Claude Code licenses. Uber is also cutting AI spending: the technology is becoming too expensive and failing to justify expectations. According to an NVIDIA executive, some teams... show more

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61,994 次观看 • 2 个月前 •via X (Twitter)

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🚨 THE AI BUBBLE IS STARTING TO CRACK. In the last 30 days: – Microsoft cut Claude Code licenses – Uber blew through its AI budget in 4 months – Uber's COO questioned AI spending – Fortune 20 companies started slashing token usage – One firm burned $500M on Claude in a month – H200 rental prices collapsed from $7/hr to $4/hr The people actually paying the AI bills are pulling back. Wall Street is still pricing perfection.

🚨 THE AI BUBBLE IS STARTING TO CRACK. In the last 30 days: – Microsoft cut Claude Code licenses – Uber blew through its AI budget in 4 months – Uber's COO questioned AI spending – Fortune 20 companies started slashing token usage – One firm burned $500M on Claude in a month – H200 rental prices collapsed from $7/hr to $4/hr The people actually paying the AI bills are pulling back. Wall Street is still pricing perfection.

Crypto Rover

535,282 次观看 • 1 个月前

College grads are spending $100,000+ to have a higher unemployment rate than [ checks notes ] -- everyone, including non-college graduates. This is because of AI. 1. It is simpler to give your existing employees AI tools than to bother them with mentorship. 2. It's simpler to automate the repetitive work that executives dump on recent graduates. #ISWIS The Vice President JD Vance JD Vance and I had this discussion two weeks ago in DC. We need to keep an eye on this, because young American, college-educated workers are more expensive than BOTH AI and offshoring.

College grads are spending $100,000+ to have a higher unemployment rate than [ checks notes ] -- everyone, including non-college graduates. This is because of AI. 1. It is simpler to give your existing employees AI tools than to bother them with mentorship. 2. It's simpler to automate the repetitive work that executives dump on recent graduates. #ISWIS The Vice President JD Vance JD Vance and I had this discussion two weeks ago in DC. We need to keep an eye on this, because young American, college-educated workers are more expensive than BOTH AI and offshoring.

@jason

92,563 次观看 • 11 个月前

CHRIS CAMILLO ON WHY AMAZON $AMZN IS HIS BIGGEST AI BET Chris Camillo's thesis: the AI efficiency wave. Every company in the world gets massively more efficient over the next few years as they adopt AI. The biggest beneficiary of that wave is undoubtedly Amazon. Why: they spent 20 years building a global logistics infrastructure that is now poised to get dramatically cheaper to run through AI and robotics. On top of that: - One of 3-4 companies spending hundreds of billions on AI infrastructure - Top 3 global digital ad company - "People think they're spending too much. I think they're spending too little." "Everything I could think of at Amazon right now is a positive long term."

CHRIS CAMILLO ON WHY AMAZON $AMZN IS HIS BIGGEST AI BET Chris Camillo's thesis: the AI efficiency wave. Every company in the world gets massively more efficient over the next few years as they adopt AI. The biggest beneficiary of that wave is undoubtedly Amazon. Why: they spent 20 years building a global logistics infrastructure that is now poised to get dramatically cheaper to run through AI and robotics. On top of that: - One of 3-4 companies spending hundreds of billions on AI infrastructure - Top 3 global digital ad company - "People think they're spending too much. I think they're spending too little." "Everything I could think of at Amazon right now is a positive long term."

WOLF

45,256 次观看 • 3 个月前

Corporations are spending more on AI than on the workers it replaced. They could’ve just paid workers but instead are paying more to replace us. That’s how much these tech billionaires hate humanity. Their endgame is total control and domination of humanity unless we stop them!

Corporations are spending more on AI than on the workers it replaced. They could’ve just paid workers but instead are paying more to replace us. That’s how much these tech billionaires hate humanity. Their endgame is total control and domination of humanity unless we stop them!

Power to the People ☭🕊

15,267 次观看 • 16 天前

$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

92,960 次观看 • 17 天前

🚨 THE WORLD’S CENTRAL BANKERS ARE STARTING TO PANIC ABOUT AI. Not because AI is failing. Because the entire AI boom is now being built on debt, leverage, shadow banking, and financial structures that are starting to look disturbingly similar to 2008. The BIS just warned that an “AI bust” could trigger serious global financial instability. And once you look at the numbers, the concern starts making sense very quickly. Amazon, Microsoft, Meta, Google and Oracle are expected to spend more than $1 TRILLION on AI capex between 2025 and 2026 alone. For 2026 itself, hyperscaler AI spending is tracking around $750 BILLION. That is a 77% jump from already record levels last year. The problem? A massive part of this expansion is being financed through debt. Morgan Stanley estimates hyperscalers and AI joint ventures alone could generate $250–300 BILLION in debt issuance by 2026. AI-focused companies already secured at least $200 BILLION through debt financing in 2025. And some companies are now literally borrowing money using Nvidia GPUs as collateral. That is where things start getting dangerous. CoreWeave, one of the biggest AI infrastructure companies, now has liabilities above $21 BILLION after pioneering GPU-backed debt financing. Its entire structure depends on Nvidia chips holding value long enough for the debt to get repaid. But GPUs depreciate extremely fast. Every new Nvidia generation immediately weakens the value of the previous one. Which means billions in loans are now backed by hardware that can lose relevance before the debt even matures. And according to the BIS, the financing structures around AI are becoming increasingly opaque. The same assets may be pledged multiple times across different financing vehicles. That is exactly the type of circular leverage that made the 2008 system so fragile. At the same time, the actual economic returns from AI still remain largely unproven. Goldman Sachs found “basically zero” economy-wide productivity gains from AI in 2025 despite hundreds of billions already being spent. Only 1% of S&P 500 companies were even able to quantify any earnings impact from AI. Yet markets are behaving like the returns are already guaranteed. Now combine this with: • Global debt at a record $353 TRILLION • Inflation jumping back to 4.2% • Private credit stress already appearing • AI chip shortages • Data center bottlenecks • And AI valuations approaching dot-com bubble extremes according to the IMF, ECB, BIS and Bank of England. This is why central banks are suddenly sounding alarmed. Because if hyperscalers ever slow AI spending, the debt chain behind the entire boom starts getting tested immediately. And right now, the global financial system is more leveraged than ever.

🚨 THE WORLD’S CENTRAL BANKERS ARE STARTING TO PANIC ABOUT AI. Not because AI is failing. Because the entire AI boom is now being built on debt, leverage, shadow banking, and financial structures that are starting to look disturbingly similar to 2008. The BIS just warned that an “AI bust” could trigger serious global financial instability. And once you look at the numbers, the concern starts making sense very quickly. Amazon, Microsoft, Meta, Google and Oracle are expected to spend more than $1 TRILLION on AI capex between 2025 and 2026 alone. For 2026 itself, hyperscaler AI spending is tracking around $750 BILLION. That is a 77% jump from already record levels last year. The problem? A massive part of this expansion is being financed through debt. Morgan Stanley estimates hyperscalers and AI joint ventures alone could generate $250–300 BILLION in debt issuance by 2026. AI-focused companies already secured at least $200 BILLION through debt financing in 2025. And some companies are now literally borrowing money using Nvidia GPUs as collateral. That is where things start getting dangerous. CoreWeave, one of the biggest AI infrastructure companies, now has liabilities above $21 BILLION after pioneering GPU-backed debt financing. Its entire structure depends on Nvidia chips holding value long enough for the debt to get repaid. But GPUs depreciate extremely fast. Every new Nvidia generation immediately weakens the value of the previous one. Which means billions in loans are now backed by hardware that can lose relevance before the debt even matures. And according to the BIS, the financing structures around AI are becoming increasingly opaque. The same assets may be pledged multiple times across different financing vehicles. That is exactly the type of circular leverage that made the 2008 system so fragile. At the same time, the actual economic returns from AI still remain largely unproven. Goldman Sachs found “basically zero” economy-wide productivity gains from AI in 2025 despite hundreds of billions already being spent. Only 1% of S&P 500 companies were even able to quantify any earnings impact from AI. Yet markets are behaving like the returns are already guaranteed. Now combine this with: • Global debt at a record $353 TRILLION • Inflation jumping back to 4.2% • Private credit stress already appearing • AI chip shortages • Data center bottlenecks • And AI valuations approaching dot-com bubble extremes according to the IMF, ECB, BIS and Bank of England. This is why central banks are suddenly sounding alarmed. Because if hyperscalers ever slow AI spending, the debt chain behind the entire boom starts getting tested immediately. And right now, the global financial system is more leveraged than ever.

Crypto Rover

86,404 次观看 • 26 天前

Elon Musk just pulled off the biggest AI power grab of 2026. Tesla is capping every employee at $200 a week on AI spending starting Monday, July 6. Media's celebrating it as cost control. But what Elon actually built is an expense policy that redirects his own engineering workforce off Claude and onto Grok, while every competitor gets throttled by internal procurement rules. Here's what happened: Tesla spent the last six months pushing engineers to use AI as aggressively as possible. Leadership built an internal platform called Bottle Rocket that gave employees access to Claude, GPT, Gemini, Grok, and Cursor. They gamified adoption by ranking engineers on internal leaderboards by how many AI tokens they consumed. The strategy worked. Software engineers started burning THOUSANDS of dollars a week on Claude and Cursor. Then the invoices arrived and Tesla panicked. But they didn't pull the standard cost-control response... The loophole: The $200 weekly cap does not apply to beta products from xAI. Grok is completely exempt from the cap. Anthropic's Claude, OpenAI's GPT, and Google's Gemini all get throttled at the same $200 line. Four Tesla engineers told Electrek that internal usage overwhelmingly favors Claude over Grok. That preference is about to become financially punishing overnight. The genius part: This quarter SpaceX is closing a $60 billion all-stock acquisition of Anysphere, the parent company of Cursor. The moment that deal closes, Cursor's Composer coding model falls under the same Musk-controlled ecosystem, and any Tesla engineer choosing between a capped Claude session and an uncapped Composer session will pay a financial penalty for using the tool they actually prefer. By exempting only his own products from the cap, Elon is using Tesla shareholder money to build market share for xAI without ever having to disclose that is what he is doing. Because on paper, it is cost control. Now zoom out to what this signals for the wider AI narrative: Uber capped employees at $1,500 a month after burning $3.4 billion in four months. Meta introduced spending caps. Amazon and Walmart pushed staff toward cheaper models. Microsoft canceled Claude Code licenses across 100,000 engineers. Every Fortune 500 that pushed heavy AI adoption in 2025 is now rationing it in 2026. Meanwhile Nvidia is trading at a $5 trillion market cap. That entire valuation assumes enterprise AI consumption is about to explode across the economy. But every company actually deploying AI at scale is telling their own engineers to slow down. One of these narratives is lying. Goldman Sachs still forecasts a 24x increase in token consumption by 2030. Gartner says total enterprise AI costs will keep climbing because agents consume exponentially more tokens per task. Jensen Huang keeps repeating that 100 AI agents will work alongside every employee. And now the CEO of the most agentic company on the planet just told his own engineers they cannot spend more than $200 a week on the tools those agents need to run. Retail investors buying Nvidia and Palantir today are betting enterprise AI adoption compounds without limit. The CEOs deploying AI inside those same enterprises are betting the exact opposite, in writing, by internal memo. Thoughts?

Elon Musk just pulled off the biggest AI power grab of 2026. Tesla is capping every employee at $200 a week on AI spending starting Monday, July 6. Media's celebrating it as cost control. But what Elon actually built is an expense policy that redirects his own engineering workforce off Claude and onto Grok, while every competitor gets throttled by internal procurement rules. Here's what happened: Tesla spent the last six months pushing engineers to use AI as aggressively as possible. Leadership built an internal platform called Bottle Rocket that gave employees access to Claude, GPT, Gemini, Grok, and Cursor. They gamified adoption by ranking engineers on internal leaderboards by how many AI tokens they consumed. The strategy worked. Software engineers started burning THOUSANDS of dollars a week on Claude and Cursor. Then the invoices arrived and Tesla panicked. But they didn't pull the standard cost-control response... The loophole: The $200 weekly cap does not apply to beta products from xAI. Grok is completely exempt from the cap. Anthropic's Claude, OpenAI's GPT, and Google's Gemini all get throttled at the same $200 line. Four Tesla engineers told Electrek that internal usage overwhelmingly favors Claude over Grok. That preference is about to become financially punishing overnight. The genius part: This quarter SpaceX is closing a $60 billion all-stock acquisition of Anysphere, the parent company of Cursor. The moment that deal closes, Cursor's Composer coding model falls under the same Musk-controlled ecosystem, and any Tesla engineer choosing between a capped Claude session and an uncapped Composer session will pay a financial penalty for using the tool they actually prefer. By exempting only his own products from the cap, Elon is using Tesla shareholder money to build market share for xAI without ever having to disclose that is what he is doing. Because on paper, it is cost control. Now zoom out to what this signals for the wider AI narrative: Uber capped employees at $1,500 a month after burning $3.4 billion in four months. Meta introduced spending caps. Amazon and Walmart pushed staff toward cheaper models. Microsoft canceled Claude Code licenses across 100,000 engineers. Every Fortune 500 that pushed heavy AI adoption in 2025 is now rationing it in 2026. Meanwhile Nvidia is trading at a $5 trillion market cap. That entire valuation assumes enterprise AI consumption is about to explode across the economy. But every company actually deploying AI at scale is telling their own engineers to slow down. One of these narratives is lying. Goldman Sachs still forecasts a 24x increase in token consumption by 2030. Gartner says total enterprise AI costs will keep climbing because agents consume exponentially more tokens per task. Jensen Huang keeps repeating that 100 AI agents will work alongside every employee. And now the CEO of the most agentic company on the planet just told his own engineers they cannot spend more than $200 a week on the tools those agents need to run. Retail investors buying Nvidia and Palantir today are betting enterprise AI adoption compounds without limit. The CEOs deploying AI inside those same enterprises are betting the exact opposite, in writing, by internal memo. Thoughts?

Ricardo

188,553 次观看 • 20 天前

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 • 1 个月前

The markets might be the last place left where humans can make money Michael Sikand thinks getting rich in the AI era only happens one way and that’s through trading. "If you're going to be living on universal basic income, you're probably gonna be spending more time researching stocks with AI and trading."

The markets might be the last place left where humans can make money Michael Sikand thinks getting rich in the AI era only happens one way and that’s through trading. "If you're going to be living on universal basic income, you're probably gonna be spending more time researching stocks with AI and trading."

EllioTrades Clips

53,428 次观看 • 2 个月前

Extraordinary that Jim Chalmers, Chris Bowen, and Anthony Albanese are going to WAR against Carbon🌱 (???) which is the fundamental building block of life. And they have a defence budget to match which is the spending on renewable energy. About $9 trillion according to AI. It is an extraordinary amount of money being wasted on a war that does not exist anywhere except in Labor's UN-driven fantasy world. FOLLOW The Spectator Australia 🥰

Extraordinary that Jim Chalmers, Chris Bowen, and Anthony Albanese are going to WAR against Carbon🌱 (???) which is the fundamental building block of life. And they have a defence budget to match which is the spending on renewable energy. About $9 trillion according to AI. It is an extraordinary amount of money being wasted on a war that does not exist anywhere except in Labor's UN-driven fantasy world. FOLLOW The Spectator Australia 🥰

Alexandra Marshall

14,215 次观看 • 1 年前

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

SouthernValue

31,196 次观看 • 2 个月前

🚨 MICHAEL BURRY MAY HAVE JUST SPOTTED THE DEBT RISK HIDING BEHIND THE AI BOOM APOLLO IS REPORTEDLY RAISING $38 BILLION TO FINANCE GOOGLE AI CHIPS FOR ANTHROPIC. MEANWHILE, ANTHROPIC CONTINUES TO ABSORB MASSIVE AMOUNTS OF CAPITAL ACROSS THE TECH ECOSYSTEM. SOME INVESTORS ARE STARTING TO QUESTION WHETHER AI GROWTH IS BECOMING TOO DEPENDENT ON LEVERAGE AND CONCENTRATED FUNDING. POTENTIAL WARNING SIGN 👀

🚨 MICHAEL BURRY MAY HAVE JUST SPOTTED THE DEBT RISK HIDING BEHIND THE AI BOOM APOLLO IS REPORTEDLY RAISING $38 BILLION TO FINANCE GOOGLE AI CHIPS FOR ANTHROPIC. MEANWHILE, ANTHROPIC CONTINUES TO ABSORB MASSIVE AMOUNTS OF CAPITAL ACROSS THE TECH ECOSYSTEM. SOME INVESTORS ARE STARTING TO QUESTION WHETHER AI GROWTH IS BECOMING TOO DEPENDENT ON LEVERAGE AND CONCENTRATED FUNDING. POTENTIAL WARNING SIGN 👀

Bitcoin PulseX

35,337 次观看 • 1 个月前

UPDATE: Nasdaq futures tumbled more than 2% on June 23, 2026, as tech stocks led a sharp market sell-off. Investors grew wary amid rising expectations for Federal Reserve rate hikes, with markets now pricing in up to 50 basis points of increases by year-end. This shift hit technology and AI-related companies especially hard, since their valuations are sensitive to higher borrowing costs and expensive infrastructure spending. Subscribe to TruthTrumpPost for more videos covering global affairs.

UPDATE: Nasdaq futures tumbled more than 2% on June 23, 2026, as tech stocks led a sharp market sell-off. Investors grew wary amid rising expectations for Federal Reserve rate hikes, with markets now pricing in up to 50 basis points of increases by year-end. This shift hit technology and AI-related companies especially hard, since their valuations are sensitive to higher borrowing costs and expensive infrastructure spending. Subscribe to TruthTrumpPost for more videos covering global affairs.

Donald J Trump Posts TruthSocial

88,498 次观看 • 1 个月前

NVIDIA CEO: “CHINA IS MOVING INCREDIBLY FAST IN AI” Jensen Huang said China is accelerating AI on all fronts - infrastructure, models, and real-world applications: "China is fast to both invent a new technology, but also to integrate the technology, to apply the technology. And because there's so much competition in China, everybody wants to build a great company and wants to succeed, a lot of great capabilities come out. And at the foundation of all of that is China's education system has produced some of the world's finest AI researchers - about 50% of the world's AI researchers are based here in China." Source:

NVIDIA CEO: “CHINA IS MOVING INCREDIBLY FAST IN AI” Jensen Huang said China is accelerating AI on all fronts - infrastructure, models, and real-world applications: "China is fast to both invent a new technology, but also to integrate the technology, to apply the technology. And because there's so much competition in China, everybody wants to build a great company and wants to succeed, a lot of great capabilities come out. And at the foundation of all of that is China's education system has produced some of the world's finest AI researchers - about 50% of the world's AI researchers are based here in China." Source:

Mario Nawfal

1,018,768 次观看 • 1 年前

Steve Wozniak built the first Apple computer in 1976. 50 years later he walked onto a graduation stage and said this: “you all have AI. actual intelligence.” “I was at a company where the engineers figured out how to make a brain. it takes 9 months.” the AI industry is spending trillions to build what humans make for free.

Steve Wozniak built the first Apple computer in 1976. 50 years later he walked onto a graduation stage and said this: “you all have AI. actual intelligence.” “I was at a company where the engineers figured out how to make a brain. it takes 9 months.” the AI industry is spending trillions to build what humans make for free.

Damian Player

10,110 次观看 • 2 个月前

When asked about whether AI stocks are priced too high, Jeremy Siegel said: "I think the biggest risk in AI investing is not whether it will work or not, but can it be done more cheaply?” Firstly, Siegel is a professor at the Wharton School of Business and has been analyzing markets for decades. His point is AI will work and transform the economy—there’s no doubt in this. The real risk is whether tech companies are spending too much ($1 trillion) on data centers to power AI. He pointed to a historical example: During the dot-com boom in the late 1990s, telecom companies laid thousands of miles of fiber optic cables across the country. They spent BILLIONS doing this. Then engineers discovered multiplexing—a way to send a thousand times more data through the same cables. Suddenly, all that infrastructure spending was unnecessary, contributing to the inevitable crash. Siegel is suggesting something similar could happen with AI. What if someone figures out how to run AI much more efficiently? The technology might work perfectly, but the current approach could be massively overbuilt. PS - Siegel covers this in more detail on his interview with CNBC. If you'd like to watch this to learn: - The 3 reasons why AI will transform the world just like the internet did in 90s - Why the biggest risk right now is if it can run more efficiently - How to position yourself when the inevitable crash happens RT and comment "SIEGEL" and I'll DM it to you immediately.

When asked about whether AI stocks are priced too high, Jeremy Siegel said: "I think the biggest risk in AI investing is not whether it will work or not, but can it be done more cheaply?” Firstly, Siegel is a professor at the Wharton School of Business and has been analyzing markets for decades. His point is AI will work and transform the economy—there’s no doubt in this. The real risk is whether tech companies are spending too much ($1 trillion) on data centers to power AI. He pointed to a historical example: During the dot-com boom in the late 1990s, telecom companies laid thousands of miles of fiber optic cables across the country. They spent BILLIONS doing this. Then engineers discovered multiplexing—a way to send a thousand times more data through the same cables. Suddenly, all that infrastructure spending was unnecessary, contributing to the inevitable crash. Siegel is suggesting something similar could happen with AI. What if someone figures out how to run AI much more efficiently? The technology might work perfectly, but the current approach could be massively overbuilt. PS - Siegel covers this in more detail on his interview with CNBC. If you'd like to watch this to learn: - The 3 reasons why AI will transform the world just like the internet did in 90s - Why the biggest risk right now is if it can run more efficiently - How to position yourself when the inevitable crash happens RT and comment "SIEGEL" and I'll DM it to you immediately.

Felix Prehn 🐶

88,068 次观看 • 7 个月前

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.

Shay Boloor

261,700 次观看 • 2 个月前

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

404,947 次观看 • 2 天前

Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th of what OpenAI, Microsoft, or Google typically use. Open-Sourcing the Innovation: In a bold move, the developers open-sourced their AI models, making both the code and methodology freely available to the public, significantly reducing the competitive edge of proprietary models like OpenAI’s. Impact on AI Industry: The release of high-quality free alternatives disrupts the market, potentially devaluing OpenAI’s models and forcing American AI companies to rethink their strategies. China’s Rapid AI Innovation: The success of DeepSeq underscores China’s growing self-sufficiency in AI and its ability to leapfrog hardware limitations through advanced software and algorithmic breakthroughs.

Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th of what OpenAI, Microsoft, or Google typically use. Open-Sourcing the Innovation: In a bold move, the developers open-sourced their AI models, making both the code and methodology freely available to the public, significantly reducing the competitive edge of proprietary models like OpenAI’s. Impact on AI Industry: The release of high-quality free alternatives disrupts the market, potentially devaluing OpenAI’s models and forcing American AI companies to rethink their strategies. China’s Rapid AI Innovation: The success of DeepSeq underscores China’s growing self-sufficiency in AI and its ability to leapfrog hardware limitations through advanced software and algorithmic breakthroughs.

Johannes Maria

24,512 次观看 • 1 年前