Manpreet Chadha @bitochadha - Twitter Profile

Manpreet Chadha @bitochadha

7 days ago

Can you buy SpaceX shares from UAE? Step-by-step guide https://t.co/Pm9cvUQXW7

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Manpreet Chadha @bitochadha

7 days ago

From Hormuz to Fujairah: UAE now key to Gulf's Plan B https://t.co/h9yd5ttI2X

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Manpreet Chadha @bitochadha

9 days ago

India to phase out mAadhaar as new app takes over https://t.co/WpVhseOFMx

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Manpreet Chadha @bitochadha

9 days ago

Abu Dhabi rent freeze to affect old or new tenants? https://t.co/TKZTHuGasM

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Who to follow

Sanjeev Dhiman

@lifedhiman

Telecom & IT Consultant🧑🏻‍💻| Commercial | Pre Sales | Business Development | I’m real, and I hope some of my followers are too. I apologize in advance!!!!

Neeraj

@nerajsays

gud peeson rest u find

bitochadha retweeted

16 days ago

"लोग AC पर 20-30 हजार खर्च कर रहे हैं... ये भाईसाहब ने छत पर जो किया, उससे 15 डिग्री ठंडक फ्री में!😱

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bitochadha retweeted

Ricardo

@Ric_RTP

18 days 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?