We imagine cybercrime as lines of code, zero-day exploits, and anonymous hackers hiding behind glowing monitors. We install antivirus software, enable two-factor authentication, and update our devices religiously. Yet the greatest vulnerability has never been our computers.

It has always been us.

Recently, I experienced this firsthand, not through a suspicious email or a fake banking app, but through what appeared to be a perfectly ordinary customer service conversation.

I was looking for a home internet provider. Everything seemed legitimate. The website directed me to a WhatsApp number. The representative introduced themselves professionally, explained the available packages, and asked for documents that sounded completely reasonable: my ID card, home address, even a photo of the property for installation planning.

None of that raised an alarm.

After all, internet providers often ask for identity verification. They need to know where the cable will be installed. They need to verify the customer. Every request fit neatly into a story that made sense.

That is precisely how social engineering works.

The scam does not begin with a lie. It begins with truth.

Then came one small request.

The representative told me that an activation PIN would soon arrive through WhatsApp and asked me to send it back once I received it.

Something felt off.

The conversation had been smooth enough that, for a brief moment, the request almost felt routine. But a quick search on GetContact revealed dozens of labels attached to the number: spam, fraud, scam.

I stopped.

I blocked the contact before sending the activation PIN.

Looking back, what fascinates me isn’t that someone tried to scam me. What fascinates me is how close the interaction felt to a legitimate business transaction.

There was no dramatic phishing page.

No broken English.

No prince from another country promising millions of dollars.

Just a conversation.

Social engineering succeeds because it exploits psychology rather than technology.

Kevin Mitnick, one of the world’s most famous former hackers, argued that people, not machines, are often the weakest link in security. Instead of breaking encryption, attackers manipulate trust, urgency, authority, and routine.

Humans evolved to cooperate.

Social engineers weaponize that instinct.

Psychologist Robert Cialdini described several principles of influence that repeatedly appear in scams: authority, commitment, reciprocity, social proof, scarcity, and liking. Most successful scams combine several of these at once.

In my case, authority came from the appearance of an official sales channel. Commitment came from the gradual escalation of requests: first an ID card, then an address, then property photos, and finally the activation PIN. Each previous step made the next request feel increasingly reasonable.

Nothing felt suspicious until the final piece.

Modern scams no longer rely on technical brilliance.

They rely on believable stories.

According to the FBI’s Internet Crime Complaint Center (IC3), billions of dollars are lost globally every year to online fraud, with social engineering remaining one of the primary attack methods. Verizon’s annual Data Breach Investigations Report has consistently shown that the human element is involved in the overwhelming majority of security incidents, whether through phishing, credential theft, or manipulation.

The trend is clear.

Attackers are investing less in writing sophisticated malware and more in understanding human behavior.

Artificial intelligence only accelerates this shift. Today, convincing emails, realistic voices, personalized messages, and fake customer service interactions can all be generated at scale. The technical barrier is dropping while psychological sophistication continues to rise.

The most unsettling realization from my experience wasn’t that I almost got scammed.

It was realizing that I had done everything most people would consider “correct.”

I found what looked like an official website.

I followed the provided contact.

I answered reasonable questions.

At no point did I think I was taking an unusual risk.

Security advice often tells us to avoid suspicious links or strange websites. But what happens when the environment itself appears legitimate? What happens when the attacker doesn’t ask for your password immediately, but patiently earns your trust over twenty minutes of conversation?

That’s where the real battlefield has shifted.

Not from computers to smartphones.

From technology to psychology.

Perhaps the future of cybersecurity isn’t primarily about stronger encryption or smarter firewalls.

Perhaps it is about cultivating skepticism without becoming cynical.

Knowing when a request is ordinaryand when one additional question changes everything.

Because the most advanced scam isn’t the one that breaks into your system.

It’s the one that politely asks you to open the door yourself.

Almost nothing disappears anymore.

You wake up, and your phone notes the moment your screen lights up.
You watch a video, and the platform measures how long you stayed.
You pause, rewind, skip, and those micro-decisions are quietly stored.
You send a message, and even if the words are encrypted, the metadata travels.
You apply for social assistance, and your life becomes a row in a database.

We live in an era where everyday existence leaves a trace.

Digital sociologists call this the age of digital society — a phase where the boundary between online and offline life has collapsed. The internet is no longer a place we “enter.” It is the environment we live inside.

And everything we do inside it produces data.

The United Nations now openly frames digital technology as the backbone of modern governance:
telemedicine, e-learning, digital welfare systems, AI-based poverty mapping, smart-city planning powered by big data.

The implication is simple, but profound:

Our lives are slowly entering a data factory.

Not only our jobs or financial transactions, but our pauses, likes, habits, moods, and routines.

From Humans to Metrics

This transformation has a name: datafication.

It is the process of turning human experience into numbers, scores, indicators, metrics, that can be compared, ranked, optimized.

What makes datafication powerful, and dangerous, is how ordinary it feels.

1. Consumption as behavioral insight

Shopping apps, ride-hailing platforms, food delivery services, and digital wallets don’t just process transactions. They interpret behavior.

They learn:

• when you tend to order food
• how sensitive you are to discounts
• which purchases are emotional, not rational
• how quickly you respond to notifications

This data isn’t archived for history.
It feeds predictive models, designed to answer one question:

What are you most likely to do next?

And how can that behavior be nudged, repeated, or monetized?

2. Work reduced to dashboards

Across offices worldwide , especially in remote work and the gig economy, performance is no longer judged primarily through human judgment.

It’s measured through dashboards.

• login hours
• task completion rates
• response times
• output per hour

Workers become graphs.
Productivity becomes a fluctuating line.

And context, fatigue, caregiving, emotional strain, rarely fits into a KPI.

3. The body as a dataset

Wearables and health apps now track:

• heart rate
• daily steps
• sleep cycles
• stress indicators

Some insurance companies are already experimenting with behavior-based premiums:
move more, pay less.

The body is no longer just biological.
It is informational.

Data as a New Form of Power

At a macro level, international institutions celebrate data-driven governance as more efficient and precise: better targeting of welfare, improved crisis prediction, smarter resource allocation.

But this shifts a deeper question into focus:

Who controls the data , and whose interests does it serve?

Several thinkers help frame this moment.

Shoshana Zuboff calls it surveillance capitalism:
human experience is harvested as raw material for prediction, behavioral control, and profit. Our actions are no longer expressions of self — they are economic assets.

David Lyon describes a surveillance society:
monitoring doesn’t only come from the state, but from corporations — and increasingly, from peers via social platforms.

Nick Couldry and Andreas Hepp propose data colonialism:
human data, especially from the Global South, is extracted, processed, and monetized by global platforms, echoing older colonial patterns of resource extraction.

In this framework, data is not neutral.
It is power.

When Numbers Decide Dignity

Popular culture often captures this reality more honestly than policy papers.

Black Mirror’s episode “Nosedive” imagines a world where social status is determined by a five-star rating system. Smiles, greetings, and social posts all feed an algorithm that ranks your worth.

Reality isn’t that far behind.

China’s social credit experiments drew global attention.
Elsewhere, the logic is softer, but just as consequential.

In Indonesia and much of Southeast Asia:

• credit scores determine financial access
• driver and passenger ratings decide livelihoods
• marketplace stars make or break small businesses
• content metrics define relevance and visibility

These numbers are not decorative.
They open doors, or close them.

Add dating apps with compatibility scores, gamified finance tools, habit trackers with streaks, and algorithmic visibility economies.

Life begins to feel like a game.

But the stakes are real.

Southeast Asia: Where the Impact Is Felt in the Body

Many theories about data power originate in the Global North.
But their effects are deeply embodied in places like Indonesia.

Digital welfare systems increase efficiency, but when data is wrong, people disappear from assistance without realizing it.

Local research highlights persistent challenges:
unequal access, weak privacy protections, and shifting social, religious, and political norms shaped by platforms.

In everyday life:

• ride-hailing drivers live and die by ratings
• small merchants depend on stars and reviews
• creators measure identity through views, watch time, and click-through rates

Slowly, people internalize the system’s logic.

“If my numbers are good, I am valuable.”

Light and Shadow

The benefits are real:

• more targeted public policy
• feedback loops for self-reflection
• data-informed planning that can reduce inequality

But the costs are equally real:

• humans reduced to homo numericus
• biased algorithms reinforcing old injustices
• self-worth tied to volatile metrics

One bad score.
One algorithmic drop.
One penalized credit record.

It doesn’t feel like a number.

It feels personal.

A Temporary Ending

The question is no longer whether our lives have become data.

They have.

The question now is whether this data helps humans live more meaningful lives , or quietly narrows the space in which life is allowed to matter.

In a world obsessed with measurement, perhaps the real challenge is not creating better metrics, but protecting spaces where human value does not need to be calculated.

Because when the watcher is not a compassionate God,
we risk becoming nothing more than exposed objects,
seen, measured, and stripped of dignity by numbers that never loved us back.

The alarm goes off at 5 a.m.
Not because we want to wake up — 
but because there is no real alternative.

Coffee is brewed half-consciously.
Notifications come first: work chats, pay-later promos, loan reminders.
Before the workday even begins, debt is already awake.

On the commute, the faces are familiar.
People with jobs.
People who are employed.
People who are never quite secure.

There are no chains.
No whips.
No guards.

And yet, something holds us firmly in place.

This is where an old term starts to feel uncomfortably alive again:
wage slavery.

1. Legally Free, Economically Cornered

Wage slavery does not mean slavery in the classical sense.
The term emerged in the 19th century, when societies shifted from independent artisans to factory laborers.

Legally: free.
Contractually: voluntary.
Economically: almost no alternatives.

Refuse the job on offer, and the consequence is not leisure or rest,
but poverty, hunger, or slow social exclusion.

Karl Marx called this alienation.
David Graeber later mocked its modern form as bullshit jobs.
Today, it survives as a meme shared half-jokingly, half-desperate:

“Why does working hard still feel like falling behind?”

2. Low Wages as the New Normal

In the 21st century, low wages rarely arrive with force.
They arrive with language.

Flexibility.
Competitiveness.
Global efficiency.

Short contracts are framed as freedom.
Outsourcing is called agility.
Labor unions are painted as relics slowing innovation.

On corporate spreadsheets, it all makes sense.
In workers’ bank accounts, it does not.

Across many advanced economies, real wages have stagnated for decades,
while productivity and corporate profits climbed steadily upward.

That gap does not explode.
It leaks — slowly.
And it nudges workers toward what seems like the only reasonable fix:

“If income isn’t enough, credit will fill the gap.”

3. Financialization: When Life Requires a Score

This is where technology enters the story — 
not as a villain, but as a facilitator.

Credit cards.
Buy-now-pay-later.
Microloans.
Personal finance apps.
Retail investing platforms.

All wrapped in smooth UX, calming colors, and promises of speed and simplicity.

Scholars call this the financialization of everyday life.
Daily survival is no longer supported by savings,
but by financial products.

In much of the Global South, research shows that financial inclusion often becomes credit-led consumption:
loans for food, rent, transport, and dignity — 
not for long-term asset building.

Technology makes it seamless.
Almost invisible.

What looks like empowerment often deepens dependency.

4. Debt Slavery: Working for the Past

Gradually, the meaning of work shifts.

Work is no longer about building the future.
It becomes about servicing the past.

Mortgages.
Car loans.
Student debt.
Credit cards.
Pay-later balances.

Paychecks arrive,
and vanish before they can be called “income.”

People stay in toxic jobs not out of loyalty,
but out of fear.

Fear of missing a payment.
Fear of a late fee.
Fear of one bad month collapsing everything.

David Graeber described debt as a moral obligation, not just an economic one.
Failing to pay feels like a personal failure,
even when the structure itself is designed to trap.

The system quietly teaches us:
if you’re struggling, it’s your fault.

5. The Illusion of Mobility

From the outside, it looks like progress.

A small home — on a mortgage.
A new car — on credit.
A powerful smartphone — on installments.

Social media fills with symbols of success.
LinkedIn overflows with hustle and resilience.

But structurally, life is governed by due dates.

As Mark Fisher once wrote:

“It is easier to imagine the end of the world than the end of capitalism.”

Today, it’s often easier to imagine endless work
than a life without debt.

6. The Locking Loop

The mechanism is brutally simple:

Low wages → no savings.
No savings → reliance on credit.
Rising debt → fear of job loss.
Fear → weaker bargaining power.

And when the system cracks, the solution offered is always the same:

More credit.

No violence is required.
The loop runs on logic, dashboards, and algorithms.

7. “Efficiency” as a Clean Word

For corporations:
low wages + flexibility + automation + consumer credit = efficiency.

For banks and fintechs:
bringing low-income workers into credit systems = market expansion.

For governments:
debt-driven consumption boosts GDP;
as long as default rates stay manageable, the system is considered healthy.

The cost of this efficiency does not appear in financial reports.
It shows up in human bodies and minds:

burnout,
anxiety,
fear of illness,
fear of stopping,
fear of rest.

8. The Human × Tech Question

The problem is not work.
And it is not technology.

The problem is a system where work is never enough.

The real question is not:

“Is this efficient?”

But:

efficient for whom — and exhausting for whom?

Modern wage slavery does not use chains.
It uses apps, scores, and notifications.

And because it feels normal,
it is harder to see,
and harder to escape.

Perhaps the task of Human × Tech today is not to build faster systems,
but to reopen space for human choice
inside structures that quietly remove it.

Because work should sustain life.
Not make us afraid to pause.