Image from Agora (2009) – Reason vs dogma, science vs power, open inquiry vs ideological control

A personal reflection

When Being Hired Meant Becoming Someone

When I was younger, being hired by a large organization felt like crossing a line.
You were inside.

Inside a system.
Inside a structure.
Inside a future that seemed already written.

A contract meant security.
A role meant identity.
A promotion meant progress.

For many years, this model worked. It gave discipline, exposure, and a professional grammar that still matters today.

But looking at the world now (through the lens of Artificial Intelligence) I would not give the same advice to someone starting today.

Because being hired is no longer the same thing as building a career.

Employment Is a Transaction, a Career Is a Construction

Organizations hire you for a function:

• to fill a gap
• to execute a process
• to operate inside a predefined logic

This has always been true.
AI simply makes it more visible.

Tasks can be decomposed, automated, reassigned, optimized.
Roles become modular.
People become interchangeable faster than before.

A career, instead, is not defined by your current role.
It is defined by what you are becoming capable of doing over time.

You are not your job description.
You are the trajectory of what you learn to handle.

The Multinational Still Teaches You – But It No Longer Guides You

Large organizations still offer real value:

• exposure to complexity
• access to scale
• structured learning
• international environments
• operational discipline

They remain powerful training grounds.
But they no longer design your path for you.

AI accelerates this:

• expertise is embedded in systems
• decisions are supported by models
• junior layers shrink
• middle layers flatten
• specialization is constantly challenged

You can spend ten years being productive without becoming more strategically relevant.

The trap is growing inside a role without growing beyond it.

A career exists only beyond the role.

Experience Means Something Different Now

Once, experience meant:
“I have done this many times.”

Today, AI can:

• write code
• summarize documents
• generate reports
• simulate scenarios
• optimize workflows

Repetition loses value.

What matters now:

• understanding systems, not just tasks
• knowing why, not only how
• judging, not just executing
• integrating humans and machines

The new form of experience is being trusted with decisions, not actions.

Careers now grow in judgment, not in repetition.

Where Automation Stops, Responsibility Begins

Every profession is discovering its boundary with automation.

The question is not:
“Will AI replace me?”

It is:
“Which part of my work carries responsibility?”

Responsibility means:

• someone answers for the outcome
• someone explains the decision
• someone takes ethical and social risk

This is where leadership, governance, interpretation, and legitimacy remain.

If you want a career, aim where accountability cannot be delegated.

Trailblazing Environments vs Follower Environments

There is a deep difference between being hired where the future is built and where the future is sold.

In trailblazing environments, you work close to where:

• technology is invented
• standards are shaped
• products are defined
• risks are taken

You learn how decisions are made and why systems exist.

In follower environments, you mainly:

• deploy existing solutions
• adapt global strategies
• meet forecasts
• optimize what is already known

Both create jobs.
But they produce different professional identities.

One teaches: “I help create what comes next.”
The other: “I help distribute what already exists.”

AI amplifies this gap.

Careers increasingly depend not on using tools well, but on understanding their implications.

From Open Systems to AI Platforms

Early technologies were about cooperation:

• Open Systems
• TCP/IP
• interoperability
• standards
• protocols

Different systems could talk to each other.
Technology was collective architecture.

Today’s dominant technologies focus on:

• engagement
• prediction
• personalization
• attention
• behavioral data

The logic has shifted:
from connecting machines
to shaping human behavior.

Technology moved from infrastructure to influence.

This changes what kind of professionals are produced.

You can optimize feeds and tune models without understanding:

• who controls the systems
• what incentives shape them
• what consequences they create
• where responsibility lies

A job can live inside this logic.
A career must understand it.

What a Career Now Really Is

In the age of AI, a career is:

• the long-term pattern of your adaptations
• your position between humans and machines
• your capacity to take responsibility where automation ends

Getting hired is an event.
Building a career is an act of authorship.

A Conclusion to the New Generations

You are entering a world where tools change faster than institutions, roles change faster than degrees, and machines will know more facts than you ever will.

Do not build your identity on what you execute.
Build it on what you understand.

Do not define yourself by the role you are given.
Define yourself by the responsibility you can assume.

Go where things are being shaped, not only where they are sold.
Learn how systems work, not just how interfaces look.
Understand not only what technology does, but what it does to society.

A job can be assigned.
A career must be designed.

If you only adapt to the tools of your time, you will remain useful.
If you understand the logic of your time, you will remain relevant.

And that difference will decide not only how you work,
but who you become.