Redifning the career path for a 'World in Transition'
Sixteen years ago, during an internal training programme, I put three problems in front of a large group of colleagues and asked each of them to pick one.
The first was long and tedious. It needed a lot of brute-force computation, but the method was clear from the start. The second was a logic puzzle. The answer was measurable and there was obviously a method to reach it, though you had to find that method yourself. The third had no clear method and no single right answer.
Most people picked the first. A few picked the second. Only a handful picked the third.
I wrote about that experiment on my blog in 2010, in a two-part post on scaling up. My argument then was about managing uncertainty inside organisations. I still hold that argument. But the years since, and especially the last three, have convinced me that the more important story is about that handful of people who reached for Problem 3, and about what made them different.
We are trained to choose Problem 1
The choice was not surprising. Most of us spend our lives buying certainty. We accept the uncertainties we cannot control, such as health, weather and fate, with some equanimity. Where we do have some control, we work hard to remove uncertainty.
We go to college partly so that a degree can vouch for us. We join a company instead of starting one because someone else has already decided the business, the product, the technology and the process, and our job is to execute. A government career reduces uncertainty further, because in many positions the system asks for compliance with process far more than it asks for results.
There is nothing wrong with this. It is how societies function. But it means that by the time most people reach a workplace, they have spent twenty years being rewarded for choosing Problem 1. Exams have answer keys. Appraisals have rating scales. The habit of reaching for the problem with a clear method is not a personal weakness. It is what our education and our institutions have trained into us.
The machine for the many
In 2010 my conclusion was practical. If most people are uncomfortable deciding under uncertainty, then an organisation that wants to scale must reduce the uncertainty they face.
The small founding team of any venture is usually made up of restless, high-energy people who are at ease with ambiguity. They cannot run the operation at scale, and they rarely want to. Scale depends on large numbers of people who do their best work when the task is clearly defined.
So you map organisational goals down to team deliverables and then to individual deliverables. You write standard operating procedures and business rules for as many cases as you can. You build in exception reporting and escalation paths for the cases nobody anticipated. Without these, people behave like software with no error handling. They hang.
Chris Argyris made a similar point in Harvard Business Review, observing that reengineering worked best when jobs were rigorously specified rather than left for individuals to define. I also warned about the opposite failure: teams that follow the process so faithfully that they forget the result it was meant to produce.
All of that still stands. Systems, processes and SOPs carry the many through what is known. Every institution that has scaled, from a bank branch network to a national digital platform, has done it this way.
What changed: the machines took Problem 1
In 2010 I assumed that Problem 1 work would always need people. That assumption no longer holds.
Long computation with a known method is exactly what software has always done well. What is new is that AI systems now handle a growing share of Problem 2 as well. Give a capable model a well-posed puzzle, a contract to review against a checklist, a dataset to reconcile or code to write against a clear specification, and it will often find the method faster than most of us.
A well-written SOP was always a kind of program for people. Increasingly it is becoming a program for machines. The very work that organisations spent decades making predictable is the work that is easiest to automate, because it is predictable.
This is the uncomfortable part. The people who chose Problem 1 for their careers did nothing wrong, but the ground under that choice is shifting. And the scarce resource in any organisation is now the person who can face Problem 3, a situation with no method and no single right answer, and still move forward.
AI is useful there too, as a research assistant, a sparring partner and a fast drafter. But it does not decide which problem is worth solving, or own the consequences of a judgement made with incomplete information. That remains a human job.
What Problem 3 asks of a person
What carries a few people into Problem 3 is something no SOP can specify. In my experience it has three parts.
The first is critical thinking: the ability to question how the problem has been framed. Problem 3 situations rarely arrive labelled. They show up as a stalled project, a market nobody serves, or a regulation that works on paper and fails on the ground. The first act of problem solving is often to ask whether we are solving the right problem at all.
The second is the discipline of problem solving itself. Because there is no algorithm, you have to build one: break the mess into parts, test assumptions cheaply, accept partial answers and improve them. This is less glamorous than it sounds. Most of it is patience.
The third is an inventive mind, one that is willing to try an approach that has not been tried and is comfortable being wrong in public along the way. Most organisations punish visible failure far more than invisible inaction. People learn quickly that choosing Problem 1 is the safer career move.
None of these is rare in the sense of being a gift. Each can be trained and strengthened. What is rare is an environment that rewards them.
The advantage of having been elsewhere
There is a fourth quality that I have come to value more than any of these: exposure to diverse domains, and the ability to distil a lesson from one field and apply it to another.
Problem 3 in one domain is often Problem 1 in another. Someone has already solved something structurally similar. The difficulty is that they did it in a different industry, using different vocabulary, and nobody in your room has ever been there.
Taiichi Ohno built the pull system at Toyota by borrowing the logic of the American supermarket, which restocks shelves only as customers empty them. According to one history, he was applying the idea in his machine shop by 1948, years before he actually visited a US supermarket. Closer home, Dr G. Venkataswamy built Aravind Eye Hospital’s high-volume model of cataract surgery after studying how McDonald’s achieved scale. A fast-food chain taught an eye hospital how to restore sight to millions.
India’s digital public infrastructure has followed the same pattern. The idea that commerce could be opened up the way UPI opened up payments, with a shared protocol and many interoperable players instead of a few closed platforms, came from carrying a lesson across domains. It did not come from inside the industry it eventually changed.
David Epstein makes the broader case in his book Range: in complex, unpredictable fields, generalists who have sampled widely often outperform early specialists. That suits a world where AI is becoming the ultimate specialist.
The real job of the few
This is where my two arguments, sixteen years apart, meet.
It is tempting to read all this as a story of heroes and followers, with brilliant minds on one side and process-bound masses on the other. That reading is wrong, and it is dangerous.
The people who pick Problem 3 do not matter because they avoid process. They matter because they create it. Their real contribution is to turn Problem 3 into Problem 2, by finding the shape of the problem, and then into Problem 1, by writing down the method so that thousands of others can follow it without needing to be heroes.
Every SOP that works was once somebody’s Problem 3. Somebody faced the first failed transaction, the first angry customer or the first case the rules never anticipated, and worked out what to do. The SOP is the record of that thinking, written so that nobody has to repeat it.
Seen this way, the few and the many are not in competition. They are two halves of one system. An organisation with only Problem 3 people never scales. An organisation with only Problem 1 people stops working the moment the world changes, and in 2026 the world changes every quarter.
The turbocharger
Skills explain how someone solves Problem 3. They do not explain why anyone would choose it in the first place, or stay with it once it turns difficult.
Problem 3 is lonely for a long time. The early evidence is thin, the critics are confident, and every rational signal says retreat to safer ground. Many capable people with all the right skills turn back at exactly this point. The ones who keep going usually have three things that no training programme can install: commitment to the work, conviction that the direction is right, and a quiet faith that the thing is worth doing even if it fails.
I use the word quiet deliberately. The faith I mean is not the loud certainty of the pitch deck. It is closer to the steady belief that keeps someone working on a hard problem after the meeting ends and the audience has gone home.
When I wrote in 2010, I ended with a line from Bruce Barton, that nothing great has been achieved except by those who dared to believe something inside them was superior to circumstances. I would put it differently now. Skills are the engine. Commitment, conviction and that quiet faith are the turbocharger. An engine without one still runs, but it will not climb the steepest roads.
What institutions should do differently
If Problem 3 thinking is now the scarce resource, most organisations are set up to waste it. Five changes would help.
1. Hire for range, not only for depth. A candidate who has worked across sectors, or moved between government, markets and technology, is often screened out as unfocused. In a Problem 3 world, that breadth is the asset.
2. Give people Problem 3 work early, and protect them when it fails. Comfort with uncertainty is built through practice. If the first honest failure ends a career, nobody will try a second time.
3. Measure people on results, not just on compliance with process. An SOP is a tool for producing an outcome. People who improvise sensibly within agreed boundaries to reach that outcome should be rewarded, not written up.
4. Promote for judgement under uncertainty. Many organisations take their best technical expert from a low-ambiguity role and place them where ambiguity is the whole job. They often struggle, and the whole team suffers with them.
5. Let AI handle Problem 1, and redeploy the time. The productivity gained from automation should not simply become headcount savings. Some of it should buy capacity for people to work on problems that do not yet have a method.
The same applies to our education system. As long as our exams reward the fastest route to a known answer, we will keep producing graduates who instinctively pick Problem 1, just as machines are making that choice redundant.
Which problem would you pick?
When I first wrote about the experiment, a reader asked in the comments whether I would share the three problems. I never did. Perhaps that was the right instinct, because the specific problems never mattered. The choice did.
If I ran the same exercise today, I suspect the result would look much the same. Most people would still reach for Problem 1. The difference is that in 2010 that was a safe choice. In 2026 it means competing with a machine on the machine’s own ground.
Systems will carry the many through what is known, and they should. Institutions that scale have to be built that way. But the future of those institutions, and of the people in them, will be decided by the few who reach past the clear method for the problem that has none. They will bring with them sharp thinking, lessons borrowed from other fields, and enough quiet faith to keep going.
The good news is that the few do not have to stay few. The question is whether we will build organisations and schools that make room for them.
“You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.”
Steve Jobs, Stanford commencement address, 2005
This essay builds on two posts from May 2010: Scaling up: The Art of the Impossible, Part I and Part II.