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’s really going on with office politics (and how do you decode it)? Reading the hidden system at work so your effort actually counts Framing the real question Most of us bump into  office politics  and think, “Ugh, drama.” But what’s  really  going on is a hidden system of power, relationships, and incentives that sits underneath job titles and org charts. If you learn to read that system—office politics, workplace politics, company politics—you stop being blindsided and start understanding why certain ideas fly while others quietly die. The deeper question is this:  how can you decode that system without becoming fake or manipulative? In this post, we’ll walk through how office politics actually work, how to spot the invisible rules in your environment, and how to navigate them in a way that still feels like  you . Seeing the hidden system behind office politics The loud version of office politics is gossip, cliques, and power plays. The real version i...

Why do people who ask better questions improve faster, even with less information? How sharper curiosity quietly outperforms sheer data and hustle Big Picture Snapshot People who ask  better questions  turn every interaction, mistake, or data point into a learning engine. Instead of hoarding information, they clarify what actually matters, surface hidden assumptions, and get precise feedback quickly. That’s why, over time, great questioners outpace others who may have more experience or data: they’re constantly tightening the loop between action, reflection, and adjustment. Think of better questions as a kind of mental debugging tool—small, sharp prompts that reveal where to focus next so improvement compounds. Two people sit in the same meeting, hear the same facts, and walk out with completely different trajectories. One shrugs and waits for more information. The other asks a sharp, better question that changes what everyone does next. That gap isn’t about talent or access—i...