I can type at more than 100 words per minute.
For years, that felt like an advantage.
Faster typing meant faster coding.
Faster coding meant more output.
More output meant more productivity.
Then AI arrived.
Suddenly, the bottleneck wasn’t typing.
The bottleneck became understanding.
Understanding the problem.
Understanding the constraints.
Understanding the trade-offs.
Understanding the system.
It forced me to ask a deeper question:
Was my job ever to write code?
Or was my job to build systems?
The distinction matters more than most people realize.
Because AI is forcing every profession to confront the same question.
Not:
“Can AI do my job?”
But:
“What is my job, really?”
The confusion we’ve lived with for years
Most people describe their jobs through tasks.
A developer writes code.
A manager runs meetings.
A designer creates screens.
A lawyer writes contracts.
A consultant builds presentations.
But organizations do not hire people because tasks need to happen.
Organizations hire people because outcomes need to happen.
The tasks were simply the mechanism through which those outcomes were achieved.
For decades, we treated the mechanism as the job itself.
AI is exposing that mistake.
Every role has two layers
The first is the Task Layer.
What you do every day.
The activities.
The motions.
The visible work.
The second is the Purpose Layer.
Why the role exists in the first place.
The outcome the organization is trying to create.
The value it expects to receive.
For years, these two layers were tightly coupled.
Today they are separating.
And that separation is creating anxiety across every profession.
The developer example
A developer’s task may be to write code.
But the purpose of the role is not coding.
The purpose is to build reliable systems that solve meaningful problems.
Code is simply one mechanism.
Historically, it was the primary mechanism.
Now it is becoming one of many.
The moment you realize this, the AI conversation changes.
If your identity is tied to writing code, AI feels threatening.
If your identity is tied to building systems, AI becomes leverage.
Because the purpose survives even when the mechanism changes.
The pattern repeats everywhere
Consider a few examples.
Product Managers
Task:
- Writing requirements
- Managing backlogs
- Running ceremonies
Purpose:
- Creating customer outcomes
- Aligning priorities
- Reducing uncertainty
Designers
Task:
- Creating interfaces
- Producing prototypes
- Designing screens
Purpose:
- Improving human interaction
- Reducing friction
- Making complexity usable
Managers
Task:
- Running meetings
- Approving decisions
- Reviewing performance
Purpose:
- Creating clarity
- Aligning people
- Enabling execution
Executives
Task:
- Reviewing reports
- Attending meetings
- Monitoring dashboards
Purpose:
- Allocating resources
- Setting direction
- Managing trade-offs
The task changes.
The purpose evolves.
But the responsibility remains.
Why organizations are struggling
There is a contradiction hidden inside most organizations.
They hire people for outcomes.
But they manage them through tasks.
Performance reviews measure activity.
Job descriptions describe responsibilities.
KPIs track outputs.
Yet what leaders actually care about is impact.
AI is exposing this gap.
Because when machines begin performing tasks more efficiently, organizations are forced to rediscover what they were actually hiring people for.
The new divide
The AI era will not separate technical people from non-technical people.
It will separate two different mindsets.
People who understand tasks.
And people who understand purpose.
One group asks:
“What work do I perform?”
The other asks:
“What outcome am I responsible for?”
The second question becomes increasingly valuable.
Because tasks can be automated.
Responsibility cannot.
The hidden opportunity
Much of the fear surrounding AI comes from viewing work through the Task Layer.
If your professional identity is built around a specific activity, automation feels like replacement.
But if your identity is built around outcomes, automation becomes amplification.
The people who benefit most from AI will not be those who protect the old mechanisms.
They will be those who understand the purpose deeply enough to redesign the mechanisms entirely.
The System Layer Test
If AI could perform 90% of your daily activities tomorrow—
What value would still remain uniquely yours?
If that question is difficult to answer,
you may understand the tasks of your job better than its purpose.
Why this matters now
Every major technological shift changes how work is done.
The AI shift is different.
It is forcing us to reconsider why work exists.
For the first time in decades, many professions are being asked to separate activity from purpose.
That is uncomfortable.
But it is also clarifying.
Because the future belongs to people who can move beyond execution and understand the systems, decisions, trade-offs, and outcomes that execution was always meant to serve.
Closing thought
Technology changes the tools.
Markets change the priorities.
Organizations change the structures.
AI changes the mechanisms.
But meaningful work has never been defined by the task itself.
It has always been defined by the value created on the other side of it.
The developer who only sees code will struggle to understand the future.
The developer who sees systems will help build it.
The manager who sees meetings will be overwhelmed by change.
The manager who sees alignment will remain indispensable.
The organizations that thrive will not be the ones that protect existing tasks.
They will be the ones that reconnect every role to its underlying purpose.
Because tasks evolve.
Purpose evolves.
Responsibility remains.
—
Next Issue: The Human Override Problem — What Happens When People Stop Questioning AI Systems
—Majid Nisar The System Layer
Thinking clearly about products, software, and leadership — by examining the systems beneath them.
Read the full issue on LinkedIn →
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