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【告知】 現在発売中のミリタリー系専門雑誌 【PEACE COMBAT #073】に猫乃ここが登場してます🐈🍓# 巻中では@geartles さんを着用させていただいてます！✨️ ぜひ手に入れてねー💖

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?

2026 年前四个月，已有 49,135 个岗位因 AI 被裁，占全部裁员计划的 16%。 科技业是重灾区。年初至今科技行业裁员 85,411 人，同比增长 33%，创 2023 年以来同期最高。仅 4 月，AI 相关裁员就达 21,490 人，占当月总裁员的 26%。 Cognizant 裁 1.2 万至 1.5 万人，Cloudflare 裁约 20% 员工，Coinbase 裁 14%，Snap 裁 1,000 人。他们都把 AI 列为核心驱动因素。 但裁了这么多人，企业赚到钱了吗？ Gartner 对 350 家年营收超 10 亿美元的企业调查发现：80% 部署了 AI 的企业已实施裁员，但裁员率与投资回报率之间不存在任何正相关。 裁人多的公司，赚得并不比裁人少的多。 裁员创造的是预算空间，不是投资回报。 真正获得最高回报的企业，走的是一条相反的路：用 AI 放大员工产出，而非替代员工。Gartner 将这种模式称为「人类放大型企业」（human-amplified business）。 连 Sam Altman 自己都承认，存在「AI 洗白」现象：企业把本来就会进行的裁员，包装成 AI 驱动的结构性调整。 毕竟在这场游戏里，「我们正在拥抱 AI」比「我们真的靠 AI 赚到钱了」好讲得多。

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Gartner predicts machine-to-machine payments will become a $30 trillion market by 2030. Lightning Network is emerging as the payment layer for the agentic economy. With ~262 Bitcoin and global Lightning infrastructure, LQWD has built a full-circle stack for AI-driven payments.