There's a mental model for working with AI that most people inherit from their experience with software: find the tool, learn how it works, use it to accomplish specific tasks. The mental model is tool-use, and it produces a certain kind of result. There's a different mental model that produces a different kind of result: management. Specifically, the kind of thoughtful management you'd apply to a capable but inexperienced hire who needs clear direction, good context, consistent feedback, and well-understood expectations to do their best work. These mental models produce genuinely different outcomes. Not because the tools are different, but because they shape how people interact with them in every session. ------------- Context ------------- The tool-use mental model tends to produce transactional interactions. You need something done. You open the tool. You describe what you need in the way that feels natural. You evaluate what comes back. You iterate until it's close enough. You move on. This works. It produces reasonable output. But it carries a specific set of limitations that become most visible when the work requires more than average output. The tool-use approach doesn't naturally lead to investing time in context, because context feels like overhead on a transactional interaction. It doesn't naturally lead to articulating quality standards clearly, because the assumption is that the tool will produce something and you'll adjust it. It doesn't naturally lead to diagnosing what went wrong when output misses the mark, because the instinct is to try a different prompt rather than identify the root cause. The management mental model produces different habits. A manager who wants good work from a new hire invests time in context upfront rather than treating it as overhead. A manager provides examples of what good looks like rather than leaving quality standards implicit. A manager who gets poor work diagnoses whether the problem was the brief, the capability, or the execution rather than just asking for a redo. These habits, applied to AI interactions, produce significantly different results over time.