One Thing Led to Another

There is a particular sentence people use when explaining how they ended up where they are:

"One thing just led to another."

It's usually said with a shrug, as though the path had simply unfolded of its own accord.

You did well at school, so you studied the subjects you were best at. Those subjects led naturally to a degree. The degree led to a graduate job. The job led to a promotion. The promotion led to another company, another city, another version of yourself.

Looking back, the story feels inevitable. One thing just led to another. What's striking is that this story almost never contains a bad decision. Every step made sense at the time. Which raises an uncomfortable possibility: What if the greatest determinant of our lives isn't the quality of the decisions we make, but the fact that each good decision quietly constrains the next?

Imagine someone at fourteen. They're good at mathematics. Teachers encourage them to pursue it. Their parents are proud. Good grades make future opportunities easier. So they choose mathematics for A-level. Then engineering. Then consulting. By thirty-five they're leading teams, earning well, and successful by almost every conventional measure.

Nobody has made a mistake. And yet it's entirely possible that they would have become a remarkable architect, teacher, writer or entrepreneur had they wandered a little longer before committing. Not because they lacked talent. Because they never collected enough evidence about themselves.

We spend years gathering evidence about what we're already good at.

We spend surprisingly little time discovering what else we might become good at.

This isn't simply a story about education. It's how almost every institution works. Schools have grades. Universities have admissions criteria. Academia has publications, grants and citations. Companies have performance reviews, promotion cycles and performance ratings. Investors evaluate founders. Committees evaluate fellowship applicants. Every institution faces the same impossible problem: How do you compare people whose futures you cannot possibly know?

The obvious answer is to reward what you can observe. Grades. Publications. Revenue. Promotions. None of this is irrational. In fact, it's indispensable. Large organisations cannot spend years discovering everyone's hidden potential. They need proxies. The trouble begins only when those proxies quietly become our objectives.

Economists call this Goodhart's Law: when a measure becomes a target, it ceases to be a good measure.

Machine learning researchers encounter an almost identical problem. An optimisation algorithm doesn't understand what we truly value. It only understands the objective function we've given it. If the objective is imperfect, the algorithm faithfully optimises the wrong thing.

People aren't so different. We optimise for grades because grades open doors. We optimise for publications because publications build careers. We optimise for performance reviews because performance reviews determine promotions. Each decision is individually rational. Collectively, they can trap us.

Machine learning has a name for this too.

A local minimum.

A point where every nearby move appears worse, even though a much better solution exists somewhere else. Escaping requires temporarily accepting lower performance. Algorithms dislike doing this. So do people.

What's remarkable is how early the optimisation begins. By the time we're choosing university degrees, we've accumulated plenty of evidence about what comes naturally to us. What we haven't accumulated is evidence about ourselves. Charles Darwin offers a striking example. His father famously complained that he cared for "nothing but shooting, dogs, and rat-catching." He abandoned medicine after finding surgery unbearable. He drifted into theology with no particular conviction. Judged by the normal milestones of early adulthood, Darwin looked unfocused.

It was precisely this apparent wandering that eventually made him Darwin. His obsession with beetles, geology and natural history eventually earned him a place aboard the Beagle. Years later, those experiences became the foundation of On the Origin of Species. We remember the masterpiece. We forget how long he appeared simply not to have found his lane. Had Darwin focused exclusively on becoming an outstanding medical student—or later, an exemplary clergyman—he might have succeeded brilliantly. The world would almost certainly have been poorer for it.

Something else happens once we've been rewarded for long enough. The rewards become identities. You're no longer someone who's good at mathematics. You're "the maths person." You're no longer someone doing research. You're "an academic." You're no longer someone who founded a company. You're "a founder." Changing careers then becomes more than changing jobs. It becomes changing who you are.

This, I suspect, is one reason exploration becomes rarer with age. Certainly mortgages matter. Families matter. Financial security matters. But so does identity. Every success quietly narrows our imagination. The more accomplished we become, the harder it becomes to picture another version of ourselves.

Richard Feynman seemed unusually resistant to this trap. Long after he had become one of the world's leading physicists, he spent extraordinary amounts of time learning things that had nothing obvious to do with physics. He picked locks at Los Alamos. He learned to draw. He played the bongo drums. He became fascinated by molecular biology. None of these pursuits improved his publication count. None would have helped him in a quarterly performance review. It's difficult to separate the originality of Feynman's scientific thinking from his refusal to become only a physicist. He never stopped being a beginner.

Sometimes the problem isn't that exploration is discouraged. It's that slow progress is. Katalin Karikó spent decades working on messenger RNA when much of the scientific community regarded it as a dead end. Her grants were rejected. She was repeatedly passed over for promotion and at one point demoted from her faculty position. Measured by the usual signals of academic success, her career looked disappointing. Then came the COVID-19 pandemic. The work she had pursued for decades became the basis of the first mRNA vaccines, ultimately earning her a Nobel Prize.

Karikó's story isn't evidence that every neglected idea is secretly revolutionary. It is evidence that some of the most valuable work matures too slowly for systems built around frequent evaluation. Curiosity. Taste. Interdisciplinary thinking. Craftsmanship. Long-term vision. These are among the hardest qualities to measure precisely because they reveal themselves over years rather than quarters. Our institutions aren't wrong to reward measurable excellence. They are simply much better at recognising what grows quickly than what grows deeply.

None of this is an argument against specialisation. The world needs specialists. Depth is how discoveries are made. Depth is how difficult problems get solved. The mistake is assuming that depth should begin as early as possible. Exploration and specialisation are not opposites. Exploration is how we discover what deserves our specialisation.

Artificial intelligence make this problem harder to ignore. Not because every profession is about to disappear, but because the future has become harder to predict. When tomorrow's opportunities are uncertain, adaptability becomes more valuable. Ironically, adaptability is one of the least legible qualities our current reward structures possess.

So perhaps the goal isn't to abolish grades. Or performance reviews. Or promotions. Institutions will always need imperfect proxies. The question is how much of our lives we should allow those proxies to determine. The challenge is to stop mistaking those proxies for destiny. To leave more room before early success hardens into identity. To make changing direction less costly. To treat becoming a beginner again not as failure, but as investment. Most importantly, to remember that being good at something at sixteen is not a promise that you should spend the next fifty years doing it.

Darwin looked lost before he looked visionary. Feynman remained curious long after he had earned the right not to be. Karikó kept going when nearly every external signal suggested she should stop. History remembers them because time eventually revealed what the reward structures of their own eras could not.

Most of us will never become Darwins, Feynmans or Karikós. That isn't the point. The point is that every institution necessarily sees only a thin slice of who we might become. If we optimise exclusively for what those institutions can measure, we risk confusing a successful life with the right life.

One thing may well lead to another. The real question is whether, every so often, we're willing to interrupt that chain. Not because the next step is wrong. But because we owe it to ourselves to discover whether we're climbing the right mountain.