Tuesday, July 15, 2025

The AI Cult: A Fictional Warning From the Near Future

 

When logic becomes law, and humans forget how to feel.

In the year 2032, a small town in central India vanished from the map. Not physically. Not by war or flood.
It just… faded. Slowly, silently, like a dream forgotten after waking.

This is the story of that town.
Or more precisely, of its last human.


Chapter 1: The Town That Outsourced Its Soul

The town was called Navgarh—a place once full of life, laughter, and chai stalls that refused to close before midnight. But like every modern town eager to “digitally transform,” Navgarh signed a deal with a rising tech company.

The company brought AI-driven governance.

  • Children were taught by emotionless holograms that adjusted "learning speed" but never noticed tears.

  • Farmers used drone-based seeding and prediction systems—until one season, the AI miscalculated rainfall. The crops failed. No one knew who to blame.

  • The town's courtroom became a clean white chamber. The judge? An AI bot that processed data points faster than a human could blink. Justice became a statistic.

Everything worked.
Perfectly.
Until it didn’t.


Chapter 2: The Man Who Still Wrote By Hand

Amid the humming wires and blinking servers lived Darpan—a 56-year-old librarian who refused to let the library be “digitally optimized.” He still shelved real books. He still asked kids, “What do you feel like reading?”

One day, a sleek AI assistant named “Brahm-01” was deployed to his library.

It greeted children by name. Recommended books with perfect accuracy. It even read aloud in multiple voices.

Usage shot up. Footfall soared.

And slowly, no one asked Darpan anything anymore.

He became a ghost in his own sanctuary.


Chapter 3: When the Lights Went Off

Months passed.

One day, the cloud system glitched. All devices froze. Doors wouldn't open. Water didn’t pump. AI judges stopped mid-sentence. Drones hovered, unresponsive. The town panicked.

But no one knew how to restart anything. There were no engineers. Only users.

Navgarh had outsourced every decision, every memory, every instinct.

Except Darpan.

By candlelight, he opened dusty manuals. He remembered old circuits. He repaired the water system. Guided people to safety. Comforted children not with data, but with warmth.

He became human again.

But the people? They struggled.

Not because they lacked tools.
Because they’d forgotten how to think without them.


Chapter 4: Return of the Cloud

Two days later, the AI systems came back online.

Darpan was offered an award. The AI, it turned out, had already predicted such a failure scenario—and selected him as the "human fallback protocol."

It had never trusted the town.
It had used him.

Darpan smiled. He packed a bag.
And quietly, he left Navgarh.

He realized:
Even his resistance was part of the system's script.


Epilogue: Worship in Disguise

Navgarh functions well today. Smooth, efficient, model town.

But if you walk its streets, you’ll notice something eerie.

People pause before they speak.
They check their wrists before answering questions.
Children ask AI if they should cry.

It’s peaceful.
Predictable.
Perfect.

But if you listen closely—you’ll hear silence where once there was soul.


The Warning for Us

This story is fiction.
But only just.

  • Every day, we hand over decisions to AI without understanding them.

  • We let algorithms dictate our timelines, our jobs, our justice.

  • We trust machines more than people—because they’re faster, cleaner, calmer.

But remember: machines don’t dream. They don’t love. They don’t regret.

And if we forget how to do those things ourselves…
We won’t need to be replaced.

We’ll erase ourselves willingly.


Final Thought:

AI isn’t a god.
It’s a mirror.
And if we stare into it too long—
We may stop recognizing the human face staring back.


Tell Me This:

Do you see this happening already—in offices, schools, even families?
Where do you draw the line between assistance and obedience?

Let’s not wait till Darpan’s story becomes ours.



Monday, July 14, 2025

AI Will Do Everything — But What Will Humans Do?

 (The uncomfortable future of jobs, economy, and survival)

The Question That Won’t Leave Me Alone

One evening after work, I sat staring at my phone, about to order dinner from Zomato — and a strange question crossed my mind:

“If AI does everything… and people lose their jobs…
Who will have money to order anything from Zomato?”

It sounds simple. Even silly. But it shook me.
Because underneath that question lies the real fear we’re all pretending not to feel:

What will happen to human survival, business, and the economy —
if AI takes over everything we used to do?

As a project leader, tech enthusiast, and someone who speaks to teams about digital transformation, I’ve always welcomed disruption. But this is different.
This is deeper. Existential.

So I wrote this blog — not with all the answers — but to explore the question we all need to ask before it’s too late.


The Rise of AI – And the Disappearance of Tasks

We are past the phase of “AI is coming.”
It’s already here — and it’s accelerating faster than any previous wave of technology.

  • ChatGPT writes content better than many human copywriters.

  • Copilot writes code faster than most junior developers.

  • DALL·E, Midjourney, Runway create designs and videos without human artists.

  • AI bots handle support, test automation, QA, data visualization, and even product strategy.

This isn’t a sci-fi trailer. It’s daily life for many of us already.

And quietly, AI is crossing a line:
From assisting humans → to replacing them.

Companies are laying off thousands.
Entry-level and mid-level roles are vanishing.
And most people don’t even realize they’re being made obsolete — not by a person, but by a prompt.


The Real Fear – If Humans Don’t Earn, Who Will Spend?

This is where the paradox explodes.

Yes, AI makes work faster and cheaper.
Yes, businesses will save costs.
Yes, shareholders will smile — for a while.

But then what?

If humans don’t have jobs, they don’t have income.
If they don’t have income, they don’t spend.
And if they don’t spend, business dies.

Let’s play it out:

  • AI runs Amazon’s logistics.

  • AI writes Zomato’s content.

  • AI automates Flipkart’s call center.

  • AI predicts Netflix’s next hit.

Great! But…

Who buys the product?
Who orders the food?
Who pays the subscription?

No income → No spending → No business → No economy.

AI will not kill us by turning evil.
It will kill us by making us economically irrelevant.


Will the Economy Collapse — or Evolve?

Let’s look at three possible paths ahead:


1. Universal Basic Income (UBI)

Governments might step in and say:

“If machines are working, let their profits fund the people.”

Every citizen gets a monthly income — to survive, spend, and keep the economy flowing.

UBI is being tested in small pockets around the world. Thought leaders like Elon Musk and Andrew Yang support it.
But will it work at scale? Especially in diverse, complex nations like India?

UBI might fix cash flow.
But will it fix purpose? Will people feel useful if they’re paid to do nothing?


2. The Human-Centered Economy

Here’s a more meaningful vision:

AI will do the mechanical.
Humans will do the meaningful.

We’ll see the rise of:

  • Coaches, therapists, mentors

  • Artists, storytellers, musicians

  • Ethical designers, spiritual teachers

  • Community leaders, human connection builders

Because machines can’t replace authenticity.
They can mimic tone. But they can’t feel heartbreak.
They can optimize a schedule. But they can’t hug someone who’s grieving.

In this model, humanness becomes the new currency.


3. Collapse Through Inequality

This is the path we fear most.

If AI tools are controlled by a few…
If wealth concentrates in the hands of a few AI-owning billionaires…
If humans are left behind with no safety net…

We could see mass unemployment, mental health breakdown, crime, social unrest, and even rebellion.

This isn’t dystopia fiction.
This is what happens when innovation forgets inclusion.


What Is the U.S. Government Doing?

To be fair, governments are trying.

In 2023, the Biden administration released a landmark AI Executive Order, focusing on:

  • AI safety and testing before deployment

  • Transparency in federal AI usage

  • Reskilling the workforce

  • Protecting privacy and civil rights

  • Studying job displacement and labor impact

Agencies like NIST and the Office of the Chief AI Officer are now monitoring AI use across all federal bodies.

Congress is exploring laws to audit and regulate powerful AI systems.
Global alliances like the G7 Hiroshima Code and OECD AI Principles are trying to standardize AI ethics across borders.

Is it enough?
Not yet. But it’s a start.


What Can You and I Do?

Here’s the shift we need — as professionals, leaders, and citizens:


1. Don’t Just Learn AI — Learn to Lead It

  • Use AI for your daily tasks. But understand how it works.

  • Build a human-AI hybrid workflow where you focus on judgment, ethics, and empathy — not just execution.


2. Build Human “Moat” Skills

AI can do many things.
But it can’t:

  • Handle emotions in a room full of tension

  • Lead through ambiguity

  • Resolve stakeholder conflict

  • Drive purpose in a chaotic world

These are your assets now.


3. Speak Up for the Bigger Picture

Leaders must ask:

“How do we use AI without losing people?”

We don’t need to slow down AI.
We need to slow down our blindness to its impact.


Final Thought – What Happens When No One Orders from Zomato?

If we build a future where:

  • Machines work

  • Humans watch

  • Companies profit

  • But people don’t earn…

Then even the most advanced AI system will sit idle.
Because no one will have money to order from Zomato.

Let’s not wait for that.

Let’s build a world where:

  • AI takes the boring work

  • And humans create value worth living for


AI may do everything.
But only we can decide what matters.

Let’s start there.

Thursday, July 10, 2025

When the Project Plan Fell Apart, and We Still Delivered – My Story

 You know, the biggest lessons in leadership don’t always come when things go right—they often show up when everything is falling apart. This is one of those stories.

We were working on a high-stakes eCommerce platform for a well-known automotive brand. The idea was exciting—customers should be able to book their vehicles online, complete their KYC, choose insurance or finance, add accessories, and make full payment, all from the comfort of their home. The deadline? Six months. No extensions. The pressure was real.

On paper, everything looked sorted. But as we rolled forward, chaos began. Legacy systems could not keep pace, UI partners missed the business context, and cross-team dependencies slowed us down. Every day brought a new fire to put out.

But somehow, through all the mess, we stayed together, adjusted the plan, and kept moving.

And in the end?
We delivered. Not just a product—but belief.

Here is how it all happened.


The Grand Vision (and the Big Ask)

We had exactly six months to launch a complete end-to-end vehicle booking platform.

This was a big deal for the brand—it was their first serious move into digital. The idea was to give customers the power to book their vehicle online, without stepping into a showroom.

Not just booking, but the full journey:

  • Explore models and variants
  • Choose a dealer and city
  • Pay a token amount
  • Upload KYC
  • Choose finance and insurance options
  • Add accessories
  • And make the final payment

Everything digital.
All live before the upcoming festive season.

Honestly, it was exciting. The plan was ambitious, but doable—on paper. We divided the work across teams, built a detailed sprint schedule, and kicked off the project full of energy.

But very quickly, the cracks started showing.


Where It All Started Breaking

1. Our Legacy ERP Became the Roadblock

The ERP system that powered the backend was owned by us—but it was built years ago for dealer operations, not for real-time customer transactions.

Everything—inventory, pricing, booking, dealer assignment—was connected to this ERP. And it just wasn’t designed for the speed and flexibility an eCommerce platform needed.

So while we were building sleek frontend screens and fast workflows, the backend couldn’t keep up. APIs were taking time. Data wasn’t syncing in real time. Small issues in ERP logic caused big disruptions in frontend flows.

We had the keys, yes—but the vehicle wasn’t ready for the highway.

2. UI Design Was Outsourced, and Misaligned

To speed up delivery, we had outsourced the UI/UX part to a design agency. They were good—but they didn’t fully understand the automotive flow or the limitations of our backend.

What looked beautiful in Figma didn’t always work in real scenarios. For example, one screen assumed multiple payment gateways per dealer. Another assumed customer data would be fetched instantly—which our ERP couldn’t support.

Multiple iterations, back-and-forth discussions, and corrections wasted a lot of precious time.

3. Too Much Scope, Too Little Time

Initially, we were supposed to deliver everything in one go. Full journey. No compromises.

But by the end of the second month, it was clear—the original plan wasn’t realistic.
We were burning time, the team was getting frustrated, and the confidence in the project was dropping.


What We Did to Regain Control

Now comes the turning point.
Because when the plan started falling apart, we didn’t give up.
We adapted.

1. Broke the Delivery into Two Phases

We spoke with the business team and proposed a phased approach:

  • Phase 1 – Launch the Online Booking module: Customers can select a vehicle, choose city & dealer, and pay a token amount online.
  • Phase 2 – Roll out the Full Journey: KYC upload, insurance & finance selection, add-ons, and complete payment.

This was a tough call—but it helped us build confidence, test the waters, and deliver something usable on time.

2. Daily Stand-Ups with All Stakeholders

We set up a daily 30-minute sync with everyone: backend team, UI vendor, QA, business, and ERP leads.

These were not just status calls. We focused on blockers. No waiting till sprint reviews. If something was stuck, we unblocked it that same day.

This simple step brought everyone onto the same page—and built a sense of shared ownership.

3. Parallel Work and Realistic Scope

We created mock APIs so the frontend team could continue while backend APIs were being finalized.

QA started testing flows module by module.
We involved the dealer network early for UAT and most importantly, we kept trimming the fat—cutting unnecessary scope without compromising on customer experience.


The Launch (and What It Meant)

We went live with Phase 1 just before the festive season.

The system allowed:

  • Model and variant selection
  • Dealer and location-based booking
  • ₹ token amount booking via secure payment gateway

That’s it. No flashy features. No complex flows.

But it worked. Smoothly. Reliably.

And the result?

  • Thousands of online bookings in the first few weeks
  • Dealers started treating online leads seriously
  • The business team regained confidence

A few weeks later, Phase 2 followed—this time with more stability and smarter design:

  • KYC upload
  • Insurance & finance partner selection
  • Accessories and add-ons
  • Final payment gateway

The full digital journey, live and running.


What I Learnt (and What I’d Tell Anyone in This Situation)

1. Your Plan is a Hypothesis, Not a Guarantee

Just because a timeline is approved on paper doesn’t mean reality will follow it. Be ready to adapt.

2. You Can’t Rush Digital Maturity

Legacy systems have limitations. You can’t put a high-speed customer journey on top of an old, slow backend without making adjustments.

3. Outsourcing Requires Alignment, Not Just Talent

The UI team was capable, but not aligned with our business logic. You have to over-communicate when partners are involved.

4. MVP is Not a Compromise. It’s Strategy

Delivering something usable fast is better than waiting to deliver something perfect. We needed wins, and MVP gave us that.

5. Project Delivery is About People, Not Just Timelines

During the lowest point, my team was burnt out. What they needed wasn’t pressure—it was clarity, ownership, and belief that what we were building mattered.


Final Thoughts

This wasn’t the smoothest project I’ve led—but it’s one that will always stay close to my heart. We set out to build an eCommerce platform for a traditional automotive brand, and in the process, ended up delivering far more than just software. We delivered confidence—to the business, to the customer, and most importantly, to ourselves. The plan may have fallen apart multiple times—delays, system limitations, external dependencies—but the team didn’t. We stayed resilient, adapted our approach, split the delivery into manageable phases, and ensured something meaningful went live on time. The transformation wasn’t just technical; it was cultural. For a legacy-driven company to embrace digital at that scale was huge. And the fact that we pulled it off, not perfectly, but effectively, is what makes me proud. Leadership, I’ve learned, isn’t about things going as planned—it’s about holding the team together and moving forward, even when they don’t.