No Two Days With AI Have Been the Same
Working with AI across constantly changing tasks has shown me that human judgment matters most when unrelated domains need to be connected.
I have been working with AI for a long time, and recently I noticed something strange: I cannot remember a single day when I used it for exactly the same task as the day before.
My problems keep changing.
Sometimes I use AI while building a project. Sometimes it helps me prepare for a negotiation, investigate an unfamiliar subject, structure a decision, or find a way through a problem I have never encountered before.
As the number and variety of these tasks grow, I become better at finding ways to delegate parts of them to AI.
That, to me, is a much more important skill than mastering one tool or learning one perfect workflow.
AI can make repetition more efficient
I see many people who technically work with AI but remain stuck in the same intellectual loop. They keep designing an architecture, refining a business plan, searching for an ICP, or learning how to automate one narrow task.
They may be busy, but they are no longer moving.
Once they find something that works, they stay there. AI becomes a more efficient way to repeat what they already know instead of a way to explore what they could do next.
My experience has been almost the opposite.
Every new task forces me to understand a different problem, find the limits of what the model can do, and decide which parts of the work I can actually delegate.
This is not an easy or passive process.
Serious AI work is mentally demanding
Working seriously with AI requires an enormous amount of mental energy. I have to formulate what I mean, read the response carefully, notice what is missing, correct the direction, and make the final decision.
AI does not remove me from the process. It keeps pulling me deeper into it.
And after doing this for a long time, I feel that my mind works better.
I do not mean that AI has somehow made me automatically smarter. I mean that I spend much more time actively formulating ideas, evaluating answers, comparing possibilities, and turning vague thoughts into explicit decisions.
It is like communicating every day with an extremely capable technology that still needs you to know where the conversation should go.
Access to knowledge is not judgment
Every AI system eventually reaches the edge of its understanding of the problem.
If I ask it to work on a question in biology, it may remain inside the frame of biology. It will not necessarily recognise that an idea from physics, business, psychology, or an experience from ordinary life could change the answer.
I can bring that connection into the conversation.
I can take something I learned in one situation and apply it somewhere that appears completely unrelated. Cognitive scientists call part of this analogical reasoning: recognising a useful relationship in one domain and transferring it into another.
This ability becomes especially valuable when working with AI.
A model can give me access to knowledge across many fields. But access to knowledge is not the same as knowing which fields should be connected at a particular moment.
That still depends on the person directing the work.
Expertise may be becoming combinatorial
Perhaps this is how expertise is changing.
It is no longer only about spending your entire life inside one narrow domain. AI allows one person to work meaningfully across a much wider range of subjects. But the value does not come from pretending to be an expert in everything.
It comes from combining what you know.
The more varied problems I encounter, the more material I have for making those combinations. A lesson from one project can influence a negotiation. An observation from life can change a product decision. An idea from one scientific field can provide a new way to understand a completely different system.
AI can help examine each piece.
The human contribution is often the moment when several pieces that did not seem related suddenly become one useful idea.
This is why I do not want to become good at using AI for only one task. I want to keep giving it different problems. I want to keep finding its limits, improving the way I think, and discovering which parts of my experience can be brought into the next decision.
The more work I delegate to AI, the more clearly I see what cannot be delegated: knowing when two seemingly unrelated things belong together.
Further reading
- Analogical reasoning, MIT Open Encyclopedia of Cognitive Science.
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