AI / Wanderings 2026
Purpose, Curiosity and AI
Purpose gives work direction when passion fades. Curiosity can be practised, and AI makes the quality of our questions more valuable than ever.
AI has made answers cheap.
Give a machine a question and, within seconds, it will produce an answer. The answer may be useful, banal or confidently wrong, but the blank page is no longer blank.
This changes the value of human contribution. Knowing the answer still matters. Knowing which question deserves an answer matters more.
That brings me to two ideas I keep returning to: purpose and curiosity.
Purpose is more useful than passion. Curiosity is more useful than certainty. AI increases the value of both.
Passion is unreliable
“Follow your passion” sounds wonderful. It also places a lot of responsibility on a feeling.
Feelings change. Some mornings, I am passionate about an idea. By lunchtime, I am passionate about coffee. After three difficult meetings and a broken spreadsheet, the idea has mysteriously lost some of its sparkle.
Passion can give us energy, but it is a poor navigation system.
Purpose works differently. It gives direction even when the work becomes repetitive, uncomfortable or difficult. You can lose enthusiasm for a task and still understand why it needs doing.
A teacher may feel little passion for marking the twenty-seventh essay on a Sunday evening. The purpose remains: help a young person learn to think and communicate.
A founder may grow tired of chasing invoices, fixing bugs or explaining the same product for the hundredth time. The purpose remains: solve a real problem for people who care about it.
Purpose can be modest. You don’t need to save humanity before breakfast. Improving how your team works, helping a customer or making one part of a broken system less stupid can be enough.
Purpose answers a practical question:
Why is this worth continuing when I don’t feel like continuing?
Curiosity is a skill
We often describe curiosity as a personality trait. Some people appear naturally curious. Others seem happy to accept the first explanation and move on.
I think curiosity can be practised.
Curiosity begins when we pause before accepting the obvious answer. We look at something familiar and ask why it works that way. We notice an assumption. We test it. We follow an unexpected result instead of forcing it back into the original plan.
A curious person can say:
- I don’t understand this yet.
- What are we assuming?
- What evidence would change my mind?
- Who experiences this problem differently?
- What happens if we try the opposite?
- Why are we still doing it this way?
None of these questions requires genius. They require attention and a willingness to look slightly foolish for a few minutes.
That last part matters. Organisations regularly claim to value curiosity while rewarding certainty. The person with an immediate answer sounds competent. The person asking a basic question risks sounding uninformed.
And so everyone nods, the meeting ends, and the team spends six months solving the wrong problem very efficiently.
AI rewards better questions
AI can produce text, images, software, analysis and ideas at astonishing speed. Speed, however, doesn’t decide where to go.
Without purpose, AI helps us produce more things that nobody needs.
Without curiosity, it gives us faster versions of familiar answers.
A person who asks AI to “write a marketing strategy” will receive something that looks like a marketing strategy. It will contain audiences, channels, messages and enough headings to frighten a small child. It may still have no relationship with the customer, the product or the problem.
A curious person keeps going.
Why would the customer care?
Which assumption in this strategy is weakest?
What would make this product a bad choice?
How would a competitor attack this position?
What information is missing?
Where might the model be wrong?
AI becomes more useful when we treat its first answer as material to examine. We can challenge it, compare alternatives, expose assumptions and explore paths that would previously have taken days.
The prompt box is a rather unforgiving mirror. A vague question often reveals vague thinking.
Knowledge still matters
There is a tempting idea that AI makes knowledge unnecessary. We can look everything up, so why remember anything?
Because curiosity needs something to work with.
Knowledge helps us recognise when an answer feels wrong. Experience tells us which questions matter. Context lets us connect an idea from one field with a problem in another.
AI can offer ten options. A human still has to judge which option fits the situation, the people involved and the consequences of getting it wrong.
The goal cannot be to outsource thinking. We should use AI to extend the distance our thinking can travel.
This puts more responsibility on us, not less. We need enough understanding to question the output, enough humility to admit gaps and enough judgement to decide what happens next.
Purpose decides where curiosity goes
Curiosity without direction can become endless exploration. Interesting, enjoyable and occasionally useful, but endless.
Purpose gives curiosity somewhere to go.
If your purpose is to improve education, curiosity leads you towards questions about how children learn, why teachers burn out, what parents need and where technology helps or gets in the way.
If your purpose is to build a healthier company, curiosity leads you towards incentives, power, communication, customer behaviour and the awkward gap between the values on the wall and the decisions made in the room.
AI can help investigate each of those questions. It can summarise material, challenge an argument, simulate perspectives and suggest experiments.
Purpose decides which experiments are worth running.
What we should practise
We can build curiosity into everyday work.
Before asking AI for an answer, write down what you are trying to achieve and who should benefit.
Ask the machine to question your assumptions before it produces a solution.
Request several competing explanations rather than one polished response.
Look for evidence that would disprove the answer.
Take one useful observation out of the conversation and test it in the real world.
And occasionally close the laptop and speak to the person experiencing the problem. They have information the model does not.
The next generation will grow up with machines that can answer almost anything. Teaching them to produce more answers would be a strange use of their time.
They need to learn how to choose meaningful problems, ask better questions, examine what comes back and change their minds when the evidence demands it.
That starts with a purpose worth pursuing and the discipline to remain curious.
Pick one problem you care about. Write down why it matters. Then spend thirty minutes asking AI questions about it without requesting a finished solution. End by choosing one assumption to test with a real person.