Quiet Quitting: The First Wave

It was last year’s buzz term, “quiet quitting.” It did not mean quitting one’s job — it meant quitting on the culture of working more than necessary. Employees clung to their job title, did the bare minimum, and protected their personal time.

For others, it was a survival technique in the climate of:

• Burnout from working too many hours.
• Being undervalued by their employers.
• The pandemic causing individuals to reassess what work ought to look like in their lives.
• Quiet quitting was a form of soft protest. Rather than quitting, individuals checked out — emotionally disengaged while still receiving paychecks.

 Step in “Resenteeism”

Now we’re seeing the rise of something a little different — resenteeism. This is when employees do stay in their jobs, but they’re not just disengaged; they’re actively unhappy about it.

Imagine showing up every day, feeling trapped, resentful, and vocal (even if passively) about your dissatisfaction. You’re there in body, but your energy is negative.

Resenteeism is fueled by factors like:

• Economic duress — inflation, debt, and fewer opportunities make individuals feel they can’t quit, even when they despise their job.
• Toxic cultures — micromanaging, no recognition, or discriminatory pay instigate resentment.
• Uncertainty — layoffs and unstable markets hold people back in jobs they’d otherwise leave.

Lame Quitting vs. Resenteeism

• Quiet Quitting: A survival tactic. Maintains mental well-being by establishing boundaries.
• Resenteeism: A pressure cooker. People stay, but resentment seeps and brews.

Quiet quitting was withdrawal. Resenteeism is bitterness. Weak quitting is passive resignation; resenteeism is active discontent.

The Human Factor

Resenteeism isn’t so much about people — it resonates across teams and organizations:

• An unhappy employee can demotivate others, spirits sag.
• Customers sense the tension when interacting with disengaged employees.
• Managers are most likely to churn over as discontentment goes viral.
• It’s like having someone come to a family meal who clearly doesn’t want to be there  they change the whole vibe.
• For the employees themselves, resenteeism exhausts them. Rising every morning to show up for work to a location you don’t want to be at, with no choice but to go, can contribute to depression, anxiety, and even physical sickness.

Why This Matters Now

We are living in a time of economic and cultural transformation:

• Job insecurity and inflation cause people to “stick it out.”
• Social media normalizes complaining about dissatisfaction in the workplace publicly.
• Smaller generations crave purposeful employment, flexibility, as much of the workplace lags behind.
• This cocktail of stress makes resenteeism look like the next destination in the office revolution after quiet quitting.

 How Businesses Should Respond

• Listen, Not Punish
  Addressing workers as “negative” won’t fly. Employers need to hear the whys of frustration.
• Address Pay and Fairness
  All too frequently resentment stems from being overworked, underpaid, or unfairly treated. Transparency and fair policies can make a huge difference.
• Invest in Culture
  Humans accept long hours if they feel valued, supported, and respected. Toxicity more than workload is likely the real issue.
• Career Pathways
  Employees who are left without career development opportunities are more likely to resent work. Small steps toward development can limit frustration.
• Mental Health Support
  Supplying support and placing dialogue around burnout and discontent assist in keeping quiet quitting from spilling over into resenteeism.

The Future of Work Attitudes

• Increased resenteeism will occur if fiscal stresses persist, but it highlights inappropriate management practices as well.
• Companies that prosper by offering flexibility, incentives, and fair treatment  will retain and attract the best and brightest.
• Employees, especially Gen Z, are less afraid of griping about poisonous workplaces. They may grit it out for a little while but that resentment is something that businesses can’t afford to ignore.

 Bottom Line

Quiet quitting was all about rebating to survive. Resenteeism is all about being present but resentful and trapped. It’s noisier, more infectious, and perhaps even more poisonous  to workers and organizations as well.

Companies have a choice: deny resenteeism and let it gnaw at culture from the inside out, or confront it with empathy, equity, and actual change.

Because in the end, employees don’t only want a paycheck they want to feel valued, respected, and empowered to succeed.

The Magic of Personalization

• Expertly implemented personalization is about as close to magic as it gets.
• Netflix suggesting the ideal thing to watch on a rainy evening.
• Spotify creating a playlist according to the way you’re feeling.
• An online store telling you precisely the shoes you’ve been looking for.
• Personalization brings sales, loyalty, and engagement to businesses. For consumers, it feels like being heard — like the company “knows” them.
• But. To tailor, companies need information. And the more information they amass, the greater the number of customers who wonder: “How much do they know about me? And what are they doing with it?”

The Privacy Dilemma

Consumers today are more privacy-aware than ever before. Leaks of private information, spygates, and covert tracking have broken down faith. Nowadays, many wonder:

1. Am I losing too much of my own life for convenience?
2. What if my data gets sold or used illegally?
3. Do I actually have a voice and a veto?

For businesses, it’s a paradox: what they use to build a better customer experience (data) is the same that can destroy trust when abused.

The Balancing Act: Principles That Work

1. Transparency is the New Currency
   Humans will provide data — if they understand what they’re getting and why. Informing them “We utilize your location to suggest offers in the region” is honorable. Sneaking it in is eerie.
2. Consent, Not Coercion
   Companies need to shift from “opt-out” to “opt-in.” Allow individuals to select the degree of customization with which they are comfortable. Control creates confidence.
3. Minimalism Matters
   Collect only what you need, not everything you can. And if an app from a coffee shop requires access to your microphone, alarm bells start ringing.
4. Data as a Fair Trade
   Customers insist: “If you are collecting my data, what do I receive in return?” The answer has to be open value — better terms, better service, genuine convenience.
5. Privacy by Design
   Instead of adding privacy features after the fact, design systems where customer information is anonymized, encrypted, or processed on device so it never exits the customer’s phone.

 Examples in the Wild

• Apple positions itself the “privacy-first” company — showing users clearly what data apps gather. That transparency has become second nature to it.
• Spotify Wrapped shows that data can be enjoyable to interact with, giving consumers information about themselves while securing loyalty.
• Shoppers such as Amazoners tread this tightrope every day: recommendations are helpful, but often the comprehensiveness of their understanding feels oppressive.
• These moments make one think that personalization isn’t the privacy killer — but it has a lot to do with how the data relationship is framed.

 The Human Side

Consider a friendship: When your friend commemorates your birthday and favorite dish, it’s lovely and affectionate. But when they tracked your every step but never said anything to you, it would be suffocating.

The same is true for business: respect, not control, is what makes personalization feel good. When brands respect boundaries, customers lean in. When they cross boundaries, customers pull back — or worse, rebel in public.

• Zero-Party Data
  Instead of stalking to track, companies will more and more simply say: “Ask us what you like.” Trust is established by people voluntarily sharing.
• AI + Privacy Together
  Federated learning and edge AI technology allow companies to personalize without sucking raw personal data to a central point.
• Regulation as Guardrails
  GDPR and CCPA are merely the beginning. More governments will mandate that companies prove they’re protecting people’s privacy.
• Customer-Led Control
  Soon, people will have personal data wallets with them — decide what to share, with whom, and for how long. Brands will have to earn it, not assume it.

Bottom Line

• It’s not a tech issue — it’s an issue of trust.
• If personalization is seen as empowerment, then customers embrace it.
• But if it’s viewed as exploitation, customers abandon it.
• The winners will be businesses that work with data as to borrow, not harvest, with respect.
• In short: personalization must be a service, and not an espionage game. That is how companies make money from data as not profits alone, but long-term relationships.

 What Are Digital Twins?

A digital twin is a mirror replica — an imitation of something actual. It could be:

• A factory, where the machines, conveyor belts, and power meters are replicated digitally.
• A city, where traffic flow, water pipes, and electricity grids are simulated in real time.
• Even an organ of your own body, where your heart might have a twin that doctors can utilize to experiment with treatments before they ever touch your body.
• The brilliance of a digital twin is that it is tied back to real-world data. All sensors provide real-time data into the model, so it is not merely a snapshot replica, but a living simulation.

Why Businesses and Governments Care

Decision-making is always a risk: “What if we produce more?” “What if the traffic flows change?” “What if we cut emissions in this way?”

Digital twins enable business leaders to try out decisions in simulations first, before they are real. It’s a crystal ball, but data-driven, not intuition.

Examples:

• Factories: Predict when machinery fails, cutting downtime in millions.
• Cities: Simulate climate change flood risk to predict where new housing must be built.
• Retail: Rebuild customer behavior in virtual shops before reconfiguring physical store layouts.

The Benefits: Why They Feel Like the Future

• Risk Reduction
  You can try out safely in virtual space before putting money in the physical space.
• Efficiency & Cost Savings
  Companies can optimize supply chains, energy usage, and production schedules to perfection.
• Faster Innovation
  Want to test a new car model? Instead of making prototypes, you can crash-test and test thousands of virtual ones overnight.
• Sustainability
  Digital twins have the potential to reduce waste — fewer physical prototypes, better energy planning, efficient city infrastructure.

 The Challenges & Human Limits

There’s also a downside:

• Data Dependency
  The accuracy of a digital twin is a function of what it’s given. Poor data or skewed data equals poor results — and poor decisions at scale.
• Complexity & Accessibility
  Developing a digital twin of a city or factory needs state-of-the-art technology and know-how. Poor and poor nations are likely to fall behind.
• Over-Reliance on Simulation
  The twin can be used by the leader to over-rely upon it and overlook that human behavior is not predictable. A city simulation can forecast traffic patterns, but not precisely how humans will likely alter behavior overnight in a crisis scenario.
• Privacy & Ethics
  If a city’s digital twin has people’s movement data, whose is it? May it become a surveillance tool rather than smart planning?

The Human Side of the Story

There are two different workers, let’s say.

A factory maintenance engineer whose job previously involved fixing machines when they broke. With digital twins, she gets a warning instead, so her job is less reactive, more strategic. Her job is more intelligent and safer.

A city dweller learns that local authorities are tracking real-time mobility patterns to feed into a digital twin. He wonders: am I being part of the solution, or part of an observation mechanism?

Digital twins are emancipating but unsettling — people feel more watched and protected, but also more controlled and regulated.

 Are They the Future of Decision-Making?

All the indications are positive — digital twins are gaining traction in sectors like aerospace, energy, construction, healthcare, and urban planning. Digital twins allow CEOs to transition from responding to being ahead, from “What happened?” to “What will happen if.”

But — they will not replace human judgment. The future will resemble partnerships:

• Digital twins provide data-driven information and simulations.
• Humans provide context, ethics, empathy, and imagination.
• The danger is that digital twins will not make the decisions for us, but that we will rely too heavily on the model and lose the messy, uncertain, deeply human quality of life.

Bottom Line

In fact, digital twins are already going to form the basis of business, city, even personal health decision-making. They work because they reduce risk, save money, and enable new opportunities.

But the human problem will be:

• Guaranteeing that everyone has equality and access (so corporations or rich nations aren’t just stealing the wealth).
• Maintaining privacy and agency.
• Keeping in mind no model can ever capture the human factor.
• In short: digital twins can guide us, but not substitute us.

The Emergence of the AI “Co-Founder”

Startups these days start with two or three friends sharing talents: one knows tech, one knows money, someone else knows marketing. But now think that rather than having a human co-founder, you had an AI agent as your co-founder — working 24/7, analyzing data, creating websites, haggling prices, or even creating pitch decks to present to investors.

Already, some founders are trying out autonomous AI agents that can:

• Scout for business opportunities.
• Automate customer service.
• Program code or create prototypes.
• Simulate forecasting market changes.

It is no longer science fiction to say: an AI may assist in launching, running, and scaling a business.

 Where AI May Beat Humans

• Speed & Scale
  An AI never sleeps. It can run 100 marketing campaigns during the night or review ten years of financial data within a few minutes. As far as execution speed is concerned, humans have no chance.
• Bias Reduction (with caveats)
  Humans tend to allow emotion, ego, or personal prejudice to interfere with judgment. AI — properly trained — bases decisions on logic and data rather than pride or fear.
• Cost Efficiency
  A startup with an AI “co-founder” may require fewer staff in the initial stages, reducing payroll expenses but continuing to perform at professional levels.
• Knowledge Breadth
  An AI is capable of “knowing” law, programming, accounting, and design all at the same time — something no human can achieve.

 But Here’s the Catch: Humanity Still Matters

Being a business isn’t all about spreadsheets and plans. It’s also about vision, trust, empathy, and creativity — aspects where humans still excel.

• Emotional Intelligence
  Investors don’t finance an idea; they finance individuals. Employees don’t execute a plan; they execute leaders. AI can’t motivate, inspire, or console in the same manner.
• Ethics & Responsibility
  Who is held accountable when an AI makes a dangerous choice? Humans continue to have the legal and moral responsibility — courts don’t have “AI CEOs” as entities.
• Creativity & Intuition
  Many of the greatest innovations in business resulted from gut feelings or acts of imagination. AI can recombine historical patterns but has trouble with revolutionary uniqueness.
• Relationship Building
  Partnerships, deals, and local goodwill are founded on human trust. AI can compose an email, but it can’t laugh, shake hands, or create lifelong loyalty.

The Hybrid Future: Human + AI Teams

The probable future is not AI replacing founders but AI complementing them. Consider an AI co-founder as:

• The “super-analyst” who does the grunt work.
• The “always-on partner” who never grumps.
• The “data-driven conscience” that holds humans accountable.
• While that happens, humans offer:
• The imagination and narratives that draw in investors.
• The emotional cement that binds the team together.
• The moral compass that holds the business accountable.

In this blended model, firms can operate leaner, smarter, and quicker, yet still require human leadership at the center.

The Human Side of the Question

Envision a young Lagos entrepreneur with a fantastic idea but a limited amount of money. With an AI agent managing logistics, fundraising tactics, and international reach, she now competes with Silicon Valley players.

Or envision a mid-stage founder who leverages AI to validate 50 product concepts in a night, allowing him to spend mornings coaching employees and afternoons pitching investors.

For employees, however, the news is bittersweet: AI co-founders can eliminate some early marketing, legal, or admin hires. That’s fewer entry-level positions, but perhaps more space for higher-value creative and strategic ones.

Bottom Line

• Do AI co-founders make better companies? Yes, in some respects — but not in the respects that really count.
• They’ll beat us at efficiency, accuracy, and sheer scope.
• But no matter how powerful they are, they can’t substitute for vision, empathy, trust, and ethics — the beat of what makes a business excel.
• The entrepreneurial future is not about the human or AI choice. It’s about building collaborations between human creativity and machine consciousness. The successful companies will be those that approach AI as the ultimate collaborator, not a boss or a menace.

Setting the Scene: A Double-Edged Sword

Third-world nations have long relied on industries of sweatshops — textiles in Bangladesh, call centres in the Philippines, or manufacturing in Vietnam — as stepping stones to wealth. Such workaday employment is not glamorous, but it pays millions of individuals secure incomes, mobility, and respect.

Enter artificial intelligence automation: robots in the assembly plant, customer service agents replaced by chatbots, AI accounting software for bookkeeping, logistics, and even diagnosing medical conditions. To developing countries, this is a threat and an opportunity.

 The Threat: Disruption of Existing Jobs

• Manufacturing Jobs in Jeopardy
  Asian or African plants became a magnet for global firms because of low labor. But if devices can assemble things better in the U.S. or Europe, why offshoring? This would be counter to the cost benefit of low-wage nations.
• Service Sector Vulnerability
  Customer service, data entry, and even accounting or legal work are already being automated. Countries like India or the Philippines, which built huge outsourcing industries, may see jobs vanish.
• Widening Inequality
  Least likely to retain their jobs are low-skilled workers. Unless retrained, this could exacerbate inequality in developing nations — a few technology elites thrive, while millions of low-skilled workers are left behind.

 The Opportunity: Leapfrogging with AI

But here’s the other side. Just like some developing nations skipped landlines and went directly to mobile phones, AI can help them skip industrial development phases.

• Empowering Small Businesses
  Translation, design, accounting, marketing AI tools are now free or even on a shoestring budget. This levels the playing field for small entrepreneurs — a Kenyan tailor, an Indian farmer.
• Agriculture Revolution
  In the majority of developing nations, farming continues to be the primary source of employment. Weather forecasting AI-based technology, soil analysis, and logistics supply chains could make farmers more efficient, boost yields, and reduce waste.
• New Industries Forming
  As AI continues to grow, entirely new industries — from drone delivery to telemedicine — could create new jobs that have yet to be invented, providing opportunity for young professionals in developing nations to create rather than merely imitate.

The Human Side: Choices That Matter

• Governments must decide: Do they invest in reskilling workers, or stick with dying industries?
• Businesses must decide: Do they automate just for cost savings, or build models that still have human work where it is necessary?
• Workers have no promise: Some will be forced to shift from monotonous work to work that demands imagination, problem-solving, and human connection — sectors that AI is still not able to crack.

The shift won’t come easily. A factory worker in Dhaka who loses his job to a robot isn’t going to become a software engineer overnight. The gap between displacement and opportunity is where most societies will find it hardest.

Looking Ahead

AI-driven automation in developing economies will not be a simple story of job loss. Instead, it will:

• Kill some jobs (especially low-skill, repetitive ones),
• Transform others (farming, medicine, logistics), and
• Create new ones (digital services, local innovation, AI maintenance).

The question is if developing nations will adopt the forward-looking approach of embracing AI as a growth accelerator, or get caught in the painful stage of disruption without building cushions of protection.

 Bottom Line

AI is not destiny. It’s a tool. For the developing world, it might undermine decades of effort by wiping out history industries, or it could bring a new path to prosperity by empowering workers, entrepreneurs, and communities to surge ahead.

The decision is in the hands of policy, education, and leadership — but foremost, whether societies consider AI as a replacement for humans or an addition to humans.