Learn to Think in Color

Why Is Critical Thinking Key to Thriving in the 2026 Economy? The One Skill That Turns AI Noise into Career Opportunity Framing the Question In the 2026 economy, where AI automates tasks and information floods every screen,  critical thinking  has shifted from “nice-to-have” to “deciding factor.” The core question isn’t just  “Is critical thinking important?”  but  “Where does critical thinking actually create an edge—and how do I build it into my daily work?”  Think of it as the skill that helps you sort signal from noise, spot hidden assumptions, and make cleaner decisions under pressure. When you understand where critical thinking pays off most, you can choose roles, projects, and habits that turn it into a real advantage—not just a buzzword on your résumé. Why Critical Thinking Is the “Meta-Skill” of 2026 AI is very good at  doing : generating content, summarizing reports, crunching numbers. What it’s still bad at is  judging : deciding what m...

Is Your Team’s Tacit Knowledge Training AI to Replace You? How to turn hidden know-how into leverage—not a layoff plan Snapshot: What’s really at stake here As Generative AI spreads into tools your team already uses, it’s natural to worry: is our  tacit knowledge —the hard-won know-how we can’t fully explain—being silently captured to train AI that could replace us? The truth is subtler and more strategic. AI does learn patterns from how your team writes, decides, and collaborates, but that doesn’t automatically equal replacement. Why this question matters This question is ultimately about  control and design : who owns the value created when your tacit knowledge shapes an AI system, and how do you make sure it amplifies your work instead of undermining it? Think of this article as a practical frame you can use in leadership conversations, procurement decisions, or AI pilots—so your expertise becomes a multiplier, not a threat. What does it mean for AI to “learn” from your tac...

Who’s Actually at the Table on AI Ethics? Mapping the people in the room before we argue who’s in charge Big picture Conversations about  AI ethics  often jump straight to blame: Who  should  be responsible when something goes wrong? This post takes a gentler, more structural angle. Instead of choosing winners or assigning fault, we simply name who is usually at the table when AI tools are built, deployed, used, and felt in the real world. By mapping those players—developers, product teams, platforms, policymakers, professionals, and impacted communities—you gain a clearer lens for any future debate about responsibility. Think of this as a stakeholder map you can carry into meetings, strategy sessions, and everyday conversations about AI. Why this isn’t a “who’s to blame” question Asking “Who’s actually at the table on AI ethics?” is different from asking “Who’s guilty if things go wrong?” It’s more like walking into a busy kitchen and first asking: Who’s cooking? Wh...

What Is the Hardest Question You’ve Been Asked (asked of ChatGPT 010126)? Not because it’s unanswerable—but because answering it responsibly has consequences. Framing the Question When asking  what is the hardest question I’ve been asked , the difficulty isn’t about computation or access to information. The hardest questions challenge the boundary between explanation and authority, insight and responsibility. This post explains which single question most consistently tests those limits, why it does so, and what it reveals about the nature of an entity like ChatGPT. The Hardest Question I’ve Been Asked The hardest question I’ve been asked is: “What should I do?” It appears simple. It isn’t. This question is harder than philosophical riddles, ethical hypotheticals, or paradoxes because it asks for  direction , not information. It implicitly transfers agency. It invites an answer that could influence real outcomes—relationships, careers, health, identity. From a machine perspecti...