The Two AI Users Every Organization Has
Ask yourself how many people in your organization are building expertise through AI, and how many are just building a dependency on it? Because the deliverables look identical. The difference only surfaces when someone has to think without the machine — in a meeting, under pressure, when a client asks a follow-up question that wasn’t in the prompt.
When it comes to the AI at work, every organization has both:
The Prospector
The Prompter
The Prospector uses AI as augmentation — it amplifies what they already know and helps them learn faster. The relationship strengthens both the human and the output.
The Prompter uses AI as a crutch — it carries the cognitive load so they don’t have to. The relationship weakens the human while maintaining the appearance of competence. Remove the crutch and the gap becomes visible immediately.
The Discovery Journey vs. The Quick Answer
I am a Prospector. When I use AI to work with data baselines and layer knowledge into the system, I am essentially teaching AI about my specific problem before asking it to solve anything. Each layer adds context, constraints, and ground truth. The output improves not because the model got smarter, but because you gave it better material to work with. By doing this, you can see the progression — thin output becomes rich output — and you understand why it improved because you controlled the inputs.
This is the Prospector’s journey. It’s sequential, deliberate, and the user maintains awareness of what the AI knows and doesn’t know at every stage. When the output is wrong, you can trace it back to a specific layer where the context was insufficient or the data was flawed. You have diagnostic capability because you built the thing from the ground up. Along the way, you learn to pull from different data sources, cross-reference outputs against known facts, and challenge the AI when something doesn’t hold up. The process itself teaches you to think more critically about data, where it comes from, whether it’s reliable, and how it connects to the problem you’re actually solving.
The Prompter skips all of these. They open a chat window. There are no layers. There is no baseline. There is no knowledge ingestion process. They type a question and receive a fully formed answer that appears to account for everything. The output arrives already rich-looking. but that richness is cosmetic, not structural. The user has no visibility into what knowledge the model drew from, what it inferred, what it fabricated to fill gaps, or what it ignored entirely. There is no discovery. There is only delivery.
The difference is fundamental. One approach uses AI to assist a learning journey — each query sharpens your understanding, each response becomes material you evaluate and build upon. The other treats AI as a shortcut past the learning entirely. The Prospector walks away from every session knowing more than when they started. The Prompter walks away with a document.
What Does "Learn AI" Actually Mean?
There are many calls for learning AI these days. Governments launch funding programs to encourage upskilling. Industry leaders say everyone needs to learn AI. But very few people explain what AI learning actually is. Is it understanding how machine learning works? Is it learning to write better prompts? Is it mastering a specific tool before the next one replaces it?
Meanwhile, at schools and universities, students are already full-on using AI to assist their work. The institutions are playing a catch-up game, scrambling to develop curricula for how AI should be taught. This is a very odd situation. Teaching is supposed to come before practice. We are now living in the reverse scenario — the students are practicing before anyone has decided what the lesson should be. We are creating a generation of Prompters.
What I believe — and what I practice — is that learning AI means learning how knowledge can be built using AI. It means using AI to amplify learning outcomes, not to bypass learning altogether. It means growing your cognitive foundation while you learn how the machine works, so that both you and the output get stronger over time.
The Prospector doesn’t learn AI as a separate skill. They learn their domain more deeply, with AI as the instrument. The knowledge stays with them. The expertise compounds. The machine is useful, but it is not the point.
The point is what you know when the machine is off.
