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AI 'Too Dangerous to Release' Gets Full Rollout in Weeks—The Core Contradiction Behind Anthropic's Mythos Policy Reversal
Anthropic announced plans to deploy Mythos-class AI to all customers within weeks—the same model it deemed too dangerous for public release due to cybersecurity risks just weeks prior. We examine the contradiction, the reasoning, and what it means for AI governance.
Read Article »Does 'You are an expert' lower AI accuracy? The pitfalls of prompt design revealed by USC research
Research from the University of Southern California (USC) reveals that giving AI an 'expert' role in prompts can underperform baseline performance in tasks requiring accuracy like math and coding. Learn why expert personas can be counterproductive and how to design effective prompts.
Read Article »Pizza Hut's AI delivery system triggered a 16 billion yen lawsuit—how 'forced implementation' caused on-site collapse
Pizza Hut franchisees filed a lawsuit claiming over $100 million in damages from forced implementation of AI delivery management system 'Dragontail.' Analyzing structural problems from another major corporate AI failure following Uber and Starbucks.
Read Article »Is AI Investment Really Effective? Lessons from Uber and Starbucks' AI Investment Failures
Uber's COO states difficulty justifying AI costs, while Starbucks discontinues its AI inventory management system after just 9 months. Learn key points for AI implementation from these major corporate failure cases.
Read Article »Are Autonomous AI Agents Really Useful? 3 Limitations Revealed by Technical Structure and Realistic Compromises
Autonomous AI agents, highlighted by the latest 'Claude Cowork,' are gaining attention. This article structurally unravels the '3 technical limitations' behind this boom and explains the design philosophy of 'semi-automated lines (human-in-the-loop)' to achieve real results in the field.
Read Article »2026 AI Investment Judgment: Why 73% of Companies Are Disappointed with ROI and the Structure of '74:20 Profit Concentration' Explained
As of 2026, the AI adoption frenzy has ended, and companies face a 'judgment phase' demanding strict ROI (return on investment). Based on multiple latest survey reports, this article explains in simple terms the current state of companies disappointed with AI investment, the structure of '74:20 profit concentration' described as Pareto's law for the AI era, and the 'organizational learning speed' that determines winners and losers.
Read Article »Can Claude for Excel and Cowork Really Automate Spreadsheets? A Comprehensive Guide to Accuracy, Risks, and Best Practices
A detailed explanation of spreadsheet automation using Anthropic's Claude for Excel and Cowork, covering formula entry methods, risk of errors, and correct usage based on the latest information.
Read Article »The New Trap Created by AI's Evolution — 'Undetectable Hallucinations' Are Spreading Through Society
As AI quality improves, hallucinations (false information generation) are becoming more sophisticated and harder to detect. We explore why confident misinformation emerges, its mechanism and social risks, and practical steps you can take right now.
Read Article »"AI for Efficiency" Was a Lie? The Real Reason Zuckerberg Admitted to IT Industry Layoffs
Meta CEO Mark Zuckerberg discussed the relationship between AI infrastructure investment and headcount reductions at an internal company meeting. At Meta, securing funding for AI investment was a more direct factor than "AI efficiency." Meanwhile, across the industry, AI investment expansion and organizational restructuring are proceeding simultaneously, and each company's circumstances are not uniform. This article carefully examines the reality of 2026 IT industry layoffs based on primary sources.
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