The most powerful thing we've learned about using AI isn't a prompt or a technique. It's something much simpler: AI forces us to understand ourselves better. It acts as a mirror, reflecting back our ability to think clearly and communicate precisely.
Think about catching a ball. You know how to do it automatically, without conscious thought. Working with AI is about learning to explain these automatic processes.
You have to surface the shortcuts your brain takes, the patterns it sees, the previous information it’s drawing from, and the connections it makes instantly.
What we explain to every client is simple: to automate anything with AI, we first have to understand the details of the process. This applies to workflows, agents, data pipelines, every use case.
Often they come to us with a clear vision of what they want AI to do, but fuzzy understanding of how they currently do it. We have to slow down and map everything out. What are the decision points? What context are they drawing from? What assumptions are they making? Only then can we help the models replicate and eventually improve their process.
We've learned this lesson ourselves by mapping our own processes daily. The new reasoning models are transforming this work. They can make logical leaps that previous models couldn't, and still, they need us to define the game we're playing.
The problem isn't the capabilities of the AI—it's that you haven't yet learned to bridge the gap between your internal understanding and explicit communication.
This is where writing becomes crucial. Not writing prompts, but writing as a tool for clear thinking. When you write, you are forced to move your understanding from implicit to explicit. You have to name things that are nameless, structure ideas that are fluid, make concrete what is abstract.
The best AI users are clear thinkers. They are able to break down complex problems into smaller pieces, articulate their assumptions, and describe their processes. These aren't just useful skills for working with the LLMs, they're also the foundation of all good problem-solving.
Beyond problem-solving skills, the new technology is revealing something fundamental about human thought: we understand far more than we can explain.
Our challenge is learning to express what we actually mean.
This is where the real work happens. Each time you clarify your thinking enough to get AI to understand, you're not just solving the immediate problem. You're building a clearer model of your own thought process. You're getting better at the fundamental human skill of turning understanding into communication.
The future isn't about AI replacing human thought. It's about AI pushing us to become more thoughtful, more precise, more aware of our own mental processes. In trying to teach machines how to think like us, we're learning to think better ourselves.
This is the real opportunity: not just to make AI work for us, but to use it as a mirror for understanding our own minds more clearly. The better we get at this, the more powerful the partnership becomes.
Start there. Before you worry about prompts or parameters, practice explaining your thinking. Write out your process. Name your assumptions. Make the implicit explicit. This isn't just how you make technology work for you—it's how you improve your own thinking.
AI will keep progressing towards super intelligence. Those who can express their thinking clearly will advance with it.
