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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”

Vivek Sen

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17,497,839 views • 2 months ago •via X (Twitter)

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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 AI budget was “blown away” by April. Even Nvidia’s VP of applied deep learning said the cost of compute now exceeds the cost of employees. Is the “AI will save money and cut headcount” narrative falling apart in real time?

🚨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 AI budget was “blown away” by April. Even Nvidia’s VP of applied deep learning said the cost of compute now exceeds the cost of employees. Is the “AI will save money and cut headcount” narrative falling apart in real time?

NewsForce

19,103 views • 2 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?

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

2,967,835 views • 2 months ago

This was done in less than 24h by one person using AI as the ground tooling, some post in AE and that’s it. Imagine the time and cost a real spot like this would cost. 100x less expensive due to AI.

This was done in less than 24h by one person using AI as the ground tooling, some post in AE and that’s it. Imagine the time and cost a real spot like this would cost. 100x less expensive due to AI.

Linus ✦ Ekenstam

313,200 views • 2 years ago

OpenAI CEO Sam Altman describes energy as the limiting factor in AI: “Eventually the cost of AI will converge to the cost of energy. In terms of long-term strategic investments for the U.S. to make, I can’t think of anything more important than energy”

OpenAI CEO Sam Altman describes energy as the limiting factor in AI: “Eventually the cost of AI will converge to the cost of energy. In terms of long-term strategic investments for the U.S. to make, I can’t think of anything more important than energy”

Small Cap Snipa

32,897 views • 5 months ago

$META is betting on $AMD for AI inference. As AI models become more commoditized, total cost of ownership for token generation matters more than ever. $AMD GPUs are far more cost-efficient than $NVDA for inference. With 3.5B daily users ready to use its AI models, $META is going all-in on $AMD

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Daniel

20,558 views • 6 months ago

🚨 THE AI COST BUBBLE IS GETTING WORSE. Companies pushed employees to “tokenmaxx” and aggressively use AI tools to boost productivity scores, but agentic AI can consume up to 1,000x more tokens than normal AI queries. Now the same companies that hyped AI cost savings are discovering that heavy AI usage is actually exploding operating costs instead of reducing them.

🚨 THE AI COST BUBBLE IS GETTING WORSE. Companies pushed employees to “tokenmaxx” and aggressively use AI tools to boost productivity scores, but agentic AI can consume up to 1,000x more tokens than normal AI queries. Now the same companies that hyped AI cost savings are discovering that heavy AI usage is actually exploding operating costs instead of reducing them.

Crypto Rover

69,155 views • 2 months ago

Real-time detection of your feelings using AI🤯 It truly captures the complexity of Anger, Sadness, Joy. AI may be able to understand humans more than humans in future. Come and see 👇

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Barsee 🐶

917,753 views • 3 years ago

"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

NVIDIA

81,834 views • 4 months ago

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

NVIDIA

88,975 views • 4 months ago

DOGE is using AI to remove half of federal regulations because they are illegal. They’re targeting 100,000 regulations that collectively cost Americans $3.3 trillion per year. That’s more than the income tax. In fact, it’s nearly the GDP of Japan.

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The future of enterprise AI is increasingly open. Hear AT&T CTO Jeremy Legg discuss how his team is using AMD solutions to support its growing adoption of open-source AI models while increasing accuracy and lowering cost.

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37,415 views • 7 days ago

📣 Call for proposals! Our first funded programme is now live — Scaling Compute: AI at 1/1000th the cost. Hear more from PD Suraj Bramhavar on how we aim to radically reduce the cost + energy consumption of compute hardware, and open new avenues to reap the benefits of AI 👇

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One of my fav insights from Nandan Nilekani “The cost of not doing it is higher than the cost of doing it”

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Sahil

175,779 views • 1 year ago

Suppose an AI model fully automates a $200k job. Its economic value - measured in contribution to GDP - will be both far more and far less than $200k. There are two ways of thinking about the model's economic value: (1) It'll be far more than $200k. After all, the human worker's labor was worth $200k; AI workers will have a bunch of advantages over humans, like not needing to sleep and being able to run in parallel, so their labor will be worth even more. (2) It'll be far less than $200k. Competition between labs might drive the price of AI down until it's basically equal to the cost of the GPUs and energy. And this is obviously way lower than the human worker's contribution to GDP. The important idea is that both are right. The marginal contribution to GDP of running an additional instance of this model will probably be relatively low. But we'll end up running way more copies of it, so the model's total contribution to GDP will be way higher than the human workers it replaced.

Suppose an AI model fully automates a $200k job. Its economic value - measured in contribution to GDP - will be both far more and far less than $200k. There are two ways of thinking about the model's economic value: (1) It'll be far more than $200k. After all, the human worker's labor was worth $200k; AI workers will have a bunch of advantages over humans, like not needing to sleep and being able to run in parallel, so their labor will be worth even more. (2) It'll be far less than $200k. Competition between labs might drive the price of AI down until it's basically equal to the cost of the GPUs and energy. And this is obviously way lower than the human worker's contribution to GDP. The important idea is that both are right. The marginal contribution to GDP of running an additional instance of this model will probably be relatively low. But we'll end up running way more copies of it, so the model's total contribution to GDP will be way higher than the human workers it replaced.

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Microsoft

138,297 views • 2 years ago

🚨 Nvidia CEO Jensen Huang: “The narratives of AI destroying jobs is not going to help America — It's false … Somebody said that AI Is going to destroy all of the software engineering jobs … We now have agentic AI inside Nvidia … The software engineers are busier than ever.”

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712,720 views • 3 months ago

More Adobe Substance Modeler +AI experiments. Using quick, gestural 3d sketching to drive generative AI with much higher levels of creative control than you could ever get from a text prompt alone. The more detailed your 3d model is, the more you can leave the ai to just finish details like skin texture and individual strands of hair.. my model is obviously just a quick sketch😅 #art #ai

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Martin Nebelong

56,554 views • 2 years ago

software engineers after realizing the cost of replacing them with AI is out of budget for almost all companies of the world

software engineers after realizing the cost of replacing them with AI is out of budget for almost all companies of the world

Lxyv

430,878 views • 2 months ago