What happens to careers, and to education, when machines start solving the problems we used to climb through
For decades, a professional career followed a script so familiar we
rarely questioned it. Score well in school. Earn a professional degree that
hands you a domain-its concepts, its standard procedures, its tricks, tips and
tools. Land a job in an established institution or in government. Then climb,
level by level, into steadily more complex problems, until either the ladder
ends or you do.
Strip away the detail and every job at every level reduces to the same
thing: solving problems, for yourself or for others. What changes as you rise
is not the act but the kind of problem you are handed.
A map I drew fifteen years
ago
In 2010 I sketched this progression as four quadrants of uncertainty,
borrowing a frame from R. Gopalakrishnan and stretching it to fit a career. The
two axes were simple: how well you can identify the problem, and how
well you can find the solution. (The original post is here.)
In Quadrant 1, the problem is known and so is the solution. Success needs
a good repository of knowledge and the discipline to apply it, you know the
tune and you know the steps. In Quadrant 2, the solution is known but the
problem is not; you earn your keep through analysis and root-cause deduction,
figuring out what the crowd wants and then performing it. Quadrant 3 is where
the known solutions run out: the problem is clear, but the answer must be
invented, which demands curiosity, persistence, and the maturity to abandon an
idea you have grown fond of. Quadrant 4 is the leader's terrain, problem
undefined, solution unforged, only a vision of a place no one has been and the
will to get others to build toward it.
Most careers walk this map slowly, from corner to corner. A few leap
quadrants. A rare few begin in the hardest one. But the shape held for a
generation, and organisations were built around it, entry rungs where the young
learned their craft, senior rungs where judgment lived. The whole edifice
assumed a pipeline: you paid your dues in Quadrant 1, and the paying itself was
the education. The trainee who reconciled ledgers for three years was not just
producing reconciliations; she was, without anyone naming it, absorbing the
pattern-sense that would later let her spot the anomaly no procedure flagged.
The value flowed in two directions, output for the firm, tacit judgment for the
person, and we mostly noticed only the first.
It is worth being precise about what each quadrant rewards, because AI
does not treat them equally. Quadrant 1 rewards fidelity: doing the known thing
correctly and consistently. Quadrant 2 rewards diagnosis: seeing through
symptoms to the actual fault. Quadrant 3 rewards invention under constraint:
producing a solution that did not exist, to a problem you can at least name.
Quadrant 4 rewards imagination under ambiguity: deciding what is even worth
attempting when neither problem nor answer is given. Fidelity is the most
codifiable of these, and codifiable is exactly what machines eat first.
Then AI walked onto the
map
Every wave of technology made us better inside each quadrant. Better
tools, better analysis, better reach. AI is different in kind, not degree: it
does not just sharpen the work within a quadrant, it absorbs the quadrant.
Quadrant 1 is already substantially automatable. The junior coder writing
from a known pattern, the first-line support agent following a script, the
trainee doctor working through a differential, the junior lawyer drafting from
a template; this is precisely the known-problem, known-solution work that
current systems handle well. Quadrant 2, the diagnostic labour of finding the
real problem before applying a known fix, is following fast; increasingly the
machine does the root-cause pass and hands a human the shortlist.
Quadrants 3 and 4 will hold out longer. Genuine invention and genuine
vision still sit beyond what these systems originate rather than remix. But
"longer" is not "forever," and even here the honest word is
augment: the innovator and the visionary will work with AI as a
collaborator that widens what one mind can attempt.
Two stories, and the one
that actually matters
You can tell this two ways. The grim version: AI is eroding the
opportunities of a vast section of job-seekers, dissolving exactly the lower
rungs where a generation once learned its trade. The expansive version: it
multiplies what we can attempt, new problems become solvable, service quality
rises, new forms of entertainment and enterprise appear, and human–AI
collaboration opens frontiers we could not reach alone. Perhaps, eventually,
even new geographies, new planets, a genuine abundance.
Both may turn out to be partly true, and we will argue about the balance
for years. History gives ammunition to both camps: earlier automation destroyed
categories of work while creating others we could not have named in advance,
and the net was, over long horizons, more prosperity. But that reassurance
carries an asterisk that is easy to skip past. The transitions were brutal for
the people caught in them, they unfolded over generations rather than years,
and the new work usually demanded capabilities the displaced did not have and
could not quickly acquire. "It worked out eventually" is cold comfort
to a cohort whose working life is the transition.
What is genuinely new this time is the direction of travel. Past machines
climbed from the bottom, they took the physical, the repetitive, the manual, and
human advantage retreated upward into cognition. AI climbs from a different
face of the mountain. It is strongest precisely at the codified cognitive work
we treated as the entry point to professional life. The rungs being removed are
not only the lowest; they are the ones we used as the on-ramp to everything
above.
But fixating on the optimist-versus-pessimist argument is a distraction,
because it treats the outcome as weather, something that happens to us while we
hold an umbrella. The decisive variable is not whether AI expands or erodes
opportunity in the aggregate. It is whether we rebuild the one institution that
decides which future the next generation is actually equipped for: education.
Here is the uncomfortable mechanism. For a century, education has been
optimised to manufacture competent Quadrant-1 workers, people who can absorb a
domain and execute known solutions reliably. The examination, the syllabus, the
graded problem set, the very architecture of a degree, all reward the reliable
reproduction of known answers. That was rational when the economy needed
millions of such people and when Quadrant-1 competence was also the on-ramp to
everything above it. AI breaks both halves of that logic at once. The economy
needs far fewer Quadrant-1 executors, and the traditional path, learn the craft
by grinding through its lowest tasks for a decade, no longer works when those
tasks are the first to be automated. You cannot apprentice into judgment by
doing the drudgery when the drudgery is gone.
This is the trap, stated plainly: the same system that is least useful
for producing the capabilities AI cannot replicate is the system we are still
running at full capacity. We are optimising harder for the quadrant that is
vanishing, because that is the quadrant our institutions know how to measure,
fund, and rank. The measurable is crowding out the valuable, and the gap widens
with every model release.
What reinventing education
would actually mean
If that diagnosis is right, tinkering will not save us. "Add a
coding class, add an AI module" leaves the Quadrant-1 factory intact. The
harder pointers, offered as arguments rather than certainties:
– Stop
teaching for recall; teach for judgment. When the machine holds the facts
and executes the procedures, the human value is knowing which problem to solve,
when the machine is wrong, and what to do when the situation falls outside the
training data. Curriculum built around memorising and reproducing is training
for the quadrant that is disappearing fastest.
– Move
Quadrant 3–4 skills forward by decades. We currently defer invention,
ambiguity, and vision to the senior rungs, things you are "ready for"
at forty. If AI collapses the lower rungs, an eighteen-year-old must start much
closer to the top. That means teaching students to sit with ill-defined
problems, run experiments, and tolerate the frustration of not knowing, early
and deliberately, rather than as a reward for surviving the grind.
– Rebuild
the apprenticeship model without the drudgery. The old system had a hidden
virtue: repetitive junior work quietly built pattern-recognition and tacit
judgment. Remove the work and you remove the schooling. Someone has to design
the replacement, simulated hard cases, AI as a sparring partner that poses
problems rather than solves them, deliberate exposure to failure, so the young
still develop instincts they can no longer absorb by osmosis.
– Teach
students to direct AI, not compete with it. The durable skill is
orchestration: framing a problem well, interrogating an AI's output, catching
its confident errors, and combining several tools toward an end. This is closer
to managing a talented, unreliable team than to using a calculator, and almost
no curriculum teaches it.
– Make
learning genuinely continuous, and mean it. "Lifelong learning"
has been a slogan for thirty years while the actual structure, front-load
education, then work, stayed fixed. If the ground shifts every few years, the
institutional model of a one-time degree followed by a career is itself
obsolete. Re-entry into education has to become normal, cheap, and expected,
not a mid-life exception.
– Credential
the things machines can't yet certify. As AI can pass most knowledge tests,
the signalling value of exams that measure recall collapses. What still needs a
human verdict, originality, judgment under ambiguity, the quality of a question
rather than an answer, is exactly what our assessment systems are worst at
measuring. That gap is where the reinvention has to happen.
None of these is a finished blueprint, and reasonable people will contest
several. The point is the direction: an education system that keeps producing
Quadrant-1 graduates for Quadrant-1 jobs that no longer exist is not protecting
the young, it is walking them off a cliff with a certificate in hand.
The real question
So the future of work is not, at heart, a question about AI. AI is the
forcing function. The real question is whether our schools, universities and
training systems can stop preparing people to begin at the bottom of a ladder
whose bottom rungs have been sawn off, and start teaching the next generation
to begin where the machines, for now, still cannot follow.
We will figure out the world of abundanc
e or scarcity as it comes. What
we decide now is whether the coming generation is built to shape that world or
to be displaced by it. That decision is being made, mostly by inertia, in
classrooms today.
“Disruption becomes transformative when
it does not merely change what we do, but changes the ground on which we decide
what is possible.”
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