Where are you using AI, or learning AI to implement, right now? If it's somewhere else, let me know in the comments

Regulation can feel like a brake, but most teams are not actually slowed down by rules. We are slowed down by uncertainty, unclear ownership, and the fear of making a decision that we will later regret. When we treat compliance as clarity, it becomes an accelerant. ------------- Context: Why AI Efforts Stall in the Messy Middle ------------- Many organizations begin AI adoption with energy. We run pilots, test tools, and create early wins. Then we hit the messy middle, where deployment meets reality. Questions stack up. Is this allowed? Who approves it? What data can we use? What happens if the model is wrong? Who is responsible if a customer complains? At this stage, it is common to blame regulation, especially when headlines make compliance sound complex. But when we look closely, many teams are stalled even without strict external requirements. They are stalled because nobody knows what the organization’s stance is. The risk is undefined, the owners are unclear, and the decision-making process is inconsistent. This confusion creates two predictable patterns. One is over-caution, where teams slow down and require too many approvals because they cannot tell what is safe. The other is shadow AI, where individuals adopt tools informally because the official path is too ambiguous or too slow. Neither pattern is what we want. Over-caution kills momentum. Shadow AI kills trust. Both are symptoms of the same underlying issue. Lack of clarity. Compliance, when approached well, is a method for creating that clarity. It forces us to name what we are doing, why we are doing it, what could go wrong, and who owns the outcome. That is not a burden. That is operational maturity. ------------- Insight 1: A Clear “Yes” and a Clear “No” Are Both Forms of Enablement ------------- Teams often interpret governance as restriction, but the most valuable part of governance is permission. When people do not know what is allowed, they default to either hesitation or improvisation.

In this video, I show you how to use Gemini's Canvas tool to transform your chats into web pages, quizzes, infographics, and more. Canvas is one of Gemini's best tools and if you're going to be using Gemini in 2026, this is the first tool you should master! Enjoy the video :)

We often talk about trust in AI as if it is an emotion we either have or do not have. But trust does not scale through feelings. Trust scales through systems, the visible structures that tell us what happened, why it happened, and what we can do when something goes wrong. ------------- Context: Why “Just Be More Careful” Is Failing ------------- As synthetic content becomes more common, many people respond with a familiar instruction: be more careful, double-check, trust your gut. That advice sounds reasonable, but it quietly shifts the entire burden of trust onto individuals. In practice, individuals are already overloaded. We are navigating faster communication, more channels, more content, and more urgent expectations. Adding constant verification as a personal responsibility does not create safety. It creates fatigue, suspicion, and inconsistent outcomes. The deeper issue is that the internet and our workplaces were built for a world where content carried implicit signals of authenticity. A photo implied a camera. A recording implied a person speaking. A screenshot implied a real interface. We are now in a world where those signals can be manufactured cheaply and convincingly. So the question becomes less about whether people can detect fakes, and more about whether our systems can support trust in the first place. When trust is treated as a personal talent, it becomes fragile. When trust is treated as an operational design problem, it becomes durable. ------------- Insight 1: Detection Is a Game We Cannot Win at Scale ------------- It is tempting to make trust a contest. Spot the fake. Find the glitch. Notice the strange shadow. Compare the audio cadence. This mindset feels empowering because it suggests that skill equals safety. But detection is inherently reactive. It assumes the content is already in circulation and now we need to catch what is wrong with it. As generation quality improves, the tells become fewer, subtler, and more context-dependent. Even if some people become excellent at detection, the average person will not have the time, tools, or attention to keep up.

Most people have tried ChatGPT. Very few people feel comfortable with it. They’ve typed a few prompts, maybe generated a paragraph or two, and seen that it can be impressive. But when they open it again later, they hesitate. They’re not quite sure where things live, what certain options do, or how to use it in a simple, practical way. So usage stays occasional, and occasional use never compounds. ---------- THE REAL PROBLEM ---------- The real problem isn’t that people don’t see the potential of AI. It’s that they don’t have a stable starting point. When an interface feels unfamiliar, every interaction carries a small mental cost. You have to think about where to click, what to try, and whether you’re even using the tool “correctly.” That friction is subtle, but constant. And constant friction eventually turns into quiet avoidance. Not because people dislike ChatGPT, but because it feels heavier than it should. ---------- TRYING AI VS USING AI ---------- Trying AI is driven by curiosity. You open ChatGPT, ask a random question, skim the response, and move on. It’s interesting, but disconnected from your real work and real problems. Using AI is different. You open ChatGPT because you have something specific you want to accomplish. You expect it to help. You refine the output with follow-ups, and you leave with something you can actually use. One creates moments of novelty.The other creates a habit. ---------- WHY BASELINES MATTER ---------- Before anyone can benefit from advanced workflows, frameworks, or automations, they need a shared baseline. They need to know where things are, what the main parts of the interface do, and how to perform simple actions without second-guessing themselves. These aren’t exciting skills. They don’t feel impressive. But they remove a huge amount of invisible friction. Without a baseline, every new guide feels harder than it should. With one, learning starts to stack. ---------- WHY CONFIDENCE COMES FIRST ---------- Most people assume confidence comes from mastering advanced features.