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“Customer trust has to come first—especially as companies adopt powerful generative AI,” said Leah Perry, VP – Legal, Chief Privacy Officer & Global Head of Public Policy at Box, at The Hill’s AI in America Summit. She explained that Box built its AI governance framework early, ensuring security, privacy,...

11,174 次观看 • 8 个月前 •via X (Twitter)

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60% of companies expect to be transformed by AI within two years. But here's the reality: most are still bolting AI onto legacy workflows instead of reimagining how work gets done. That's not transformation—that's just expensive automation. Our latest research reveals what separates AI leaders from laggards. The companies winning with AI aren't just adopting new tools—they're redesigning how their business creates value from the ground up. The challenge: Most organizations are nowhere near putting AI at the center of their strategy. They're missing the exponential gains that come from true AI-first thinking. The solution: A complete playbook for IT leaders and CIOs ready to move at the speed of AI. 🎯 5 Core Principles of AI-First Companies: 1️⃣ AI as a capability expander — Enable work that wasn't possible before, not just faster versions of old tasks 2️⃣ Human-AI partnership — Augment human creativity and decision-making rather than replacing it 3️⃣ AI-native design — Build systems that seamlessly integrate thousands of AI agents working behind the scenes 4️⃣ Trust & governance foundation — Remember: "AI agents can't keep a secret"—never rely on AI to maintain core security models. 5️⃣ Data as strategic fuel — Transform unstructured content into your competitive advantage The companies implementing these principles today are building insurmountable competitive advantages. They're not just working faster—they're working in entirely new ways. Ready to lead your industry's AI transformation? 📖 Get the complete framework Want to explore the benefits? Get the full details here:

Box

128,566 次观看 • 1 年前

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

2,970,717 次观看 • 2 个月前