CEO Who Fired 80% of Staff Over AI Says the Gamble Paid Off

Two years ago, Suumit Shah, CEO of Indian e-commerce startup Dukaan, made global headlines after laying off nearly 80% of his employees. His reason was blunt: they refused to adopt artificial intelligence tools in their daily workflows.

At the time, the backlash was swift and severe. Critics accused Shah of replacing people with machines in the name of efficiency. Today, however, he claims the decision strengthened the company — and proved his thesis correct.

What Is News

Two years ago, Dukaan laid off roughly 80% of its workforce.

CEO Suumit Shah said the reason was employee resistance to AI adoption.

Dukaan deployed an AI chatbot that reportedly handled 85% of customer support queries.

The company claims revenue has grown and customer satisfaction has improved.

Shah maintains the company is now leaner and more efficient.

ormer employees have criticized the way the layoffs were handled.

The Original Decision

In 2024, Shah announced the mass layoffs publicly, framing them as a strategic pivot toward AI-driven operations.

According to Shah:

Customer support agents took over two hours on average to resolve tickets.

The AI chatbot could resolve most issues in under two minutes.

Operating costs dropped dramatically after automation

The move positioned Dukaan as an early and aggressive adopter of AI in core operations rather than as a supplemental tool.

What Is Analysis

Efficiency vs. Humanity

Shah’s experiment highlights a fundamental tension in the AI era:

Should companies prioritize productivity gains even if they displace large portions of their workforce?

From a purely operational perspective, the metrics Shah cites are compelling:

Faster response times

Lower labor costs

Higher margins

Focused human teams on high-value tasks

If those numbers hold, the decision appears financially rational.

However, the ethical and reputational consequences are more complicated.

Several former employees described the layoffs as abrupt and impersonal. Critics argue that technological transition should include reskilling rather than termination.

The question becomes whether AI adoption requires workforce replacement — or workforce transformation

Is Dukaan an Outlier?

Industry analysts caution against generalizing Dukaan’s case.

Dukaan is:

A tech-native startup

Focused heavily on customer support automation

Structurally adaptable

Larger enterprises with complex operations, regulatory constraints, and institutional inertia may not replicate this model easily.

Moreover, customer support is one of the most automatable functions in the AI economy. Other domains — strategy, creative work, leadership, compliance — may resist full automation longer.

A Signal for the Labor Market

Shah’s decision has become symbolic in the broader AI employment debate.

Tere are two dominant narratives:

AI as augmentation — tools that enhance workers.

AI as substitution — systems that replace workers.

Dukaan leans clearly toward substitution.

Yet even in Shah’s account, the company retained a smaller, more specialized team. Humans were not eliminated — they were concentrated.

This suggests a possible middle path:

Fewer employees, higher leverage, AI-heavy workflows.

That model, if scaled, implies structural changes to labor markets rather than total collapse.

The Risk of Overconfidence

One risk in celebrating Dukaan’s success is survivorship bias.

If AI tools underperform, hallucinate, or fail during scaling, companies could face service degradation or reputational damage.

Additionally, rapid workforce cuts can damage culture and brand perception.

Investors may reward short-term efficiency gains. Customers and talent markets may respond differently over time.

Visionary or Cautionary Tale?

Whether Shah is seen as a forward-thinking innovator or a warning sign depends on perspective:

To AI accelerationists, he acted decisively and early.

To labor advocates, he prioritized automation over people

To investors, the key metric is sustained performance.

The deeper issue is not Shah’s decision alone — it is whether more CEOs will follow.

If AI productivity tools continue improving, resistance to adoption may increasingly be seen as a strategic liability inside organizations.

The Broader Trend

Across industries, executives are quietly asking similar questions:

How many roles can AI replace?

How quickly should transitions happen?

Is retraining cheaper than rehiring?

Dukaan’s story represents one extreme answer.

Two years later, Shah insists he was right.

Whether history agrees will depend on whether AI-driven lean models prove sustainable — or whether they reveal hidden costs over time.

One thing is certain: the debate over AI and employment is no longer theoretical. It is already being tested in real companies.