They teach what they learn and seek out other power users because both sharpen skill faster than solo practice
Chapter Progress: Early DraftDocendo discimus: by teaching, we learn.
Teach to clarify; observe to expand. Teaching converts tacit skill into explicit skill. When you explain your AI workflow to someone else, vague intuition becomes articulated process. Observation imports judgment patterns you would never invent alone: you see when another power user pauses, what they paste in, how they evaluate, when they switch models, and what they save. Both mechanisms accelerate faster than solo practice.
They teach because explaining forces clarity
When you explain an AI workflow to someone else, vague intuition has to become explicit. You have to name the task, the setup, the prompt, the model choice, the quality check, the failure modes, and the judgment call. The act of teaching exposes gaps you did not notice while working alone.
You can run a workflow you cannot fully explain. People do it on intuition constantly. What often stays locked is the harder work: evaluating each stage, knowing what counts as success at each step, automating the repeatable steps, and evolving the pipeline into something better. All of that draws on , which here means awareness of your own goals, expectations, and intent, so you can pull that out of your head and into context and instructions an AI can act on. Teaching forces that articulation into existence. The parts that are hard to put into words are the exact places where a clear account of your intent lets you move from running the workflow to handing it off and redesigning it.
Share one prompt with a colleague. Record a two-minute walkthrough. Write a one-page playbook. The person who teaches becomes clearer. The team becomes faster. The workflow becomes more robust because someone else has tested whether it can be repeated.
They learn near other power users because skill transfer is visual
Power users seek proximity to other power users. They join communities, compare workflows, watch screen shares, trade prompt libraries, and ask better questions than 'what prompt did you use?'
Better questions: 'What did you try before this worked?' 'What do you always check before trusting the output?' 'Where does this workflow usually fail, and how did you teach the system to catch it?' 'When the stakes are high, how do you push the AI toward the quality you need instead of taking the task back?' 'How do you decide when to switch models?' 'What did you stop doing that you used to think was necessary?' 'What is the wildest thing you have tried just to see if it would work?'
These questions surface the that no or tutorial can capture. The pause before the follow-up. The instinct to switch models on a specific kind of task. The decision to verify one claim and trust another. The habit of saving the failure mode alongside the successful prompt. These are the behaviors a power user builds to keep getting better at the work.
They ask questions that expose the invisible parts of a workflow
Most AI knowledge sharing focuses on the visible artifact: the prompt, the output, the tool. The invisible parts are where the expertise lives: the decision to use this model on this task, the choice to iterate three times instead of accepting the first response, the habit of verifying the second claim in every summary.
When you observe another power user, the moments that surprise you mark where your own practice has a gap, and the questions a learner asks that you cannot answer immediately mark where your own understanding has a gap. You do not have to hold all of this in your head. Dump the raw notes from a teaching session or an observation into an AI and ask it to interview you, find the gaps, and turn them into a short list of experiments. The system can do the noticing once you give it the material.
They keep discovering new workflows after solo practice slows down
Solo practice often hits diminishing returns. After a few months of intensive use, you tend to find your reliable workflows, identify your failure modes, and build a and a few saved standards. The rate of new discoveries can slow. This is where community and teaching add momentum that solo practice may not supply on its own.
A conversation with another power user can expose a workflow pattern you would never have invented alone. A teaching session can reveal that a step you thought was essential is unnecessary. A community thread can alert you to a model update that changes the reliability of a task you depend on.
Experimentation, teaching, and community work together rather than in a fixed order. Playing with the tool feeds the constant 'wouldn't it be cool if' that starts new projects. Teaching pushes your tacit process into words. Watching other power users imports judgment you would not invent alone. Each one feeds the others, and the point of the cycle is not to repeat it but to evolve the system: take what you learned and encode it into a better prompt, a standard, a rule, or a skill, then hand more of the work off so you can climb to the next problem. That is what keeps people in the learning game long after the first excitement fades and daily AI use becomes routine.