Rangers, not narrow specialists

The most common question after a talk is not about tools. It goes roughly like this: which field should I pick so that I am safe in ten years. It is usually young people asking, and they want one concrete job title.

I have nothing to give them, and I say so out loud. Not out of modesty, but because the question is the wrong one. Safety today does not come from the field, it comes from how fast you can move to the next one. It took me a few changed fields to work that out, and none of those moves looked like a plan at the time.

Two honest ends of the same argument

On one side there are ten thousand hours. Malcolm Gladwell made that number famous in "Outliers", drawing on Anders Ericsson's research into deliberate practice. Ericsson himself later said the popular version flattened his work too much, but the idea stuck: depth comes from many hours in one narrow field.

On the other side there is David Epstein's "Range". His argument is simple. In clear, rule-bound domains like chess or golf, early narrow specialisation works beautifully. Where the rules are vague and keep moving, the people who win are the ones who tried many things and can carry a solution from one field into another. Working with AI is the second kind of environment, because the rules change every few months.

And here is the unpleasant part. AI is strongest exactly where a competence is narrow, well defined and repeatable. That is the job description of the classic narrow specialist. Not because that person is weak, but because their work is the clearest target on the board. I am not saying specialists stop being needed. I am saying specialisation on its own no longer makes anyone safe. Depth used to be both a tool and a shield. It is still a tool.

What a ranger is

Ranger is a word I made up for my talks: someone who moves across wide terrain instead of digging one deep well. What that person can do comes down to four things.

Pick the problem: not wait for a task, but see where it hurts and say what is worth solving first. Connect fields: bring a solution from logistics into HR, from games into training. Direct the machine: say what the result has to be, check what comes back, and say no when the answer is beautiful and wrong. And relearn without drama, because a new tool is not a threat, just another thing in the backpack.

Now the correction, without which all of that sounds far too pretty.

A ranger with no depth at all is just a person with opinions.

You need at least one field where you have worked long enough to feel it instantly when something is off. That feeling is the only real protection against a machine's well-phrased mistakes. Without it, AI looks right every time, and it is not.

Where I use this framework

When I talk to people about careers, I suggest measuring not years in one position but how many different problems they carried from start to finish. The number is often a lot smaller than the CV suggests.

For my own learning I keep a rule: one field where I dig deep, and several where I stay a beginner without any embarrassment. That is where most ideas come from. And I do not tell my own kids they need to learn to code. I tell them to learn to notice when something is broken, and to not be shy about saying it out loud. My daughter does this daily already, mostly about me.

A narrow specialist waits for the task. A ranger arrives with the problem.