Learn to Think in Color

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...

What are ChatGPT’s craziest, but realistic, predictions for 2026? Five wild-sounding shifts that could quietly become your new normal Big Picture Briefing In a world of fast-moving AI,  ChatGPT’s craziest but realistic predictions for 2026  aren’t about flying cars—they’re about subtle shifts in how you work, learn, and decide. 2026 is close enough that these predictions must stay grounded, but far enough that compounding progress in AI, automation, and data can turn today’s edge cases into tomorrow’s defaults. If you read our 2025 forecast at questionclass.com/what-are-chatgpts-craziest-but-realistic/, think of this as the “Season 2” update. How to use this As you read, don’t ask “Will this be 100% right?” Ask:  If this were even 30–50% true, how would I adapt?  Treat these predictions as scenario-planning prompts—tools to future-proof your skills, career, and decisions—not as fortune-telling. 1. Personal AI “Chiefs of Staff” Become Normal at Work By 2026, it’s real...

 What was the biggest change in AI in 2025? From chat windows to working partners that actually do things. Big-picture framing In 2025, the  biggest change in AI  wasn’t just “better models”—it was a shift in  how AI shows up in real work . AI moved from answering questions in a chat box to acting as  autonomous agents  that plan, execute, and iterate on tasks across tools and systems. These “agentic” AIs, powered by multimodal frontier models and tighter policy frameworks, started behaving less like calculators and more like small digital teams. Understanding this shift—from  answers to actions —is the key to seeing where AI is truly headed next, both in your career and your organization. The biggest change: AI moved from answers to actions If you zoom out on 2025, the most important change in AI was that it  stopped being just a conversational tool and became an active collaborator . AI agents—systems that can set sub-goals, call tools and APIs,...