AI is often described as disruptive because it is new. In reality, it feels disruptive because it refuses to operate inside ambiguity we have quietly relied on for years. When AI struggles, it is rarely because the task is too complex. It is because the work was never clearly defined in the first place. ------------- Context ------------- Most organizations run on a mix of formal processes and informal understanding. Some work is documented, standardized, and repeatable. Much more work lives in habits, conversations, and “the way we usually do things.” Humans are remarkably good at navigating this ambiguity. We fill in gaps without noticing. We infer intent. We compensate for missing steps. We rely on experience and social cues to keep things moving. AI does none of that naturally. It needs clarity. Inputs, rules, definitions, boundaries. When those are missing, AI does not quietly adapt. It fails visibly. That failure is uncomfortable, but it is also diagnostic. AI is showing us where work has always depended on tribal knowledge rather than shared understanding. ------------- The Hidden Dependence on Tacit Knowledge ------------- Tacit knowledge is what people know but rarely write down. It includes how to prioritize when everything is urgent. Which requests can wait. Who really needs to be looped in. What “good enough” means in different contexts. These judgments are learned over time, often through mistakes. Because tacit knowledge works, it feels efficient. Writing it down feels unnecessary. Until someone new joins. Or until work scales. Or until we ask AI to help. When AI enters the picture, tacit knowledge becomes a bottleneck. The system asks questions humans never had to articulate. What counts as complete? Which exception matters? When do we escalate? AI exposes how much of our work relies on shared assumptions rather than shared definitions. ------------- Why Informality Has Been Carrying More Weight Than We Admit ------------- Informal work has always absorbed complexity.

Most entrepreneurs aren’t stuck because they’re lazy or incapable. They’re stuck because they’re waiting. Quietly. Waiting for more clarity, better timing, more confidence, or for things to settle down. As long as you’re waiting, your potential and your business stays parked. Progress in business doesn’t come from more preparation. It comes from decisions. Every time you explain why you’re not moving yet, you hand control to something outside yourself: the market, the economy, your schedule, your past results. None of those are coming to build the business for you. The people who actually break through don’t feel ready. They move while uncertain. They don’t wait for perfect conditions... they adapt to the conditions they’re in. They don’t wait for permission, because no one is handing it out. Most people keep their effort conditional. “I’ll go all in when things calm down.” “I’ll commit once I feel more confident.” “I’ll start after this next thing.” And months (sometimes years) pass. Not because the idea wasn’t good, but because the conditions were never removed. So here’s something actionable for the week ahead: Pick one decision you’ve been delaying because you wanted more clarity. Make it by the end of the week...imperfectly. Then take the first uncomfortable action that follows from that decision. No optimizing. No overthinking. Just movement. Clarity shows up after action, not before it. Drop in the comments: What’s the one decision you’re done waiting on this week?

We've just dropped a few of our guides from inside the AI Advantage Club here inside Skool! Inside the Classroom area, you'll find a new section titled "Guides" and inside you'll find 3 of our step-by-step guides we create twice a week for the AI Advantage Club community. Every month we'll add a new guide to help you implement AI in your life/business. Check out the Guide section inside the classroom!

In this video, I break down the week's happenings in AI including Clawdbot (Moltbot), a ton of new upgrades to the Claude ecosystem, new techniques and workflows people are using to create short films with AI, and more. Enjoy!

The loudest fear around AI has always been job loss. But the quieter, more accurate shift is happening somewhere else. AI is not removing work, it is removing the space between work, and that is changing how roles feel, how value is created, and how people experience their day. ------------- Context ------------- Most modern jobs are not made up of one continuous task. They are made up of transitions. Moving from a meeting to notes. From notes to action items. From action items to follow-ups. From information to decisions. From one system to another. For years, these transitions have been the invisible glue of work. They are rarely written into job descriptions, but they consume enormous time and cognitive energy. People become the connectors, translators, reminders, and memory holders that keep organizations moving. AI is now stepping directly into those gaps. It summarizes conversations, drafts follow-ups, organizes tasks, routes requests, and preserves context across tools. The work still exists, but the friction between steps is shrinking fast. That is why this shift feels unsettling. When transitions disappear, the shape of work changes. And when the shape of work changes, identity and value can feel suddenly unclear. ------------- Why Transitions Have Always Carried Hidden Value ------------- Transitions may look like overhead, but they have always been where judgment lives. Deciding what matters from a meeting. Interpreting tone in a message. Knowing who needs to be looped in. Choosing when to escalate and when to wait. These are not mechanical steps. They are human sensemaking. Because this work is informal, it often goes unrecognized. It sits between roles. It rewards experience more than expertise. And it creates a sense of indispensability for the people who quietly manage it well. When AI absorbs parts of this transition work, it can feel like value is being taken away. But what is actually happening is exposure. The work was always there. It just was never named.