When David Cote took over Honeywell in 2002, the company was recovering from the dot-com collapse and 9/11 downturn. Layoffs were everywhere. The board expected Cote to follow the industry playbook.
Instead, he challenged the entire logic.
“Layoffs give short-term relief but long-term damage. You lose capability, you lose trust, and you lose the people you need when the recovery comes.”
Rather than firing thousands, Honeywell:
- aggressively cut non-people costs
- improved working capital
- pushed for cross-training
- invested in R&D during the downturn
- fixed internal processes, not headcount
What happened next?
📈 From 2002 to 2016, Honeywell’s market value grew from $20B to over $90B.
💰 Earnings rose 5x.
📉 Competitors that relied on layoffs took years to recover capacity.
Extensive research has shown he was right about layoffs. They’re damaging to companies – especially when done with cost cutting purpose.
So, why do companies keep doing them?
Honeywell Chairman and CEO David Cote addressing public
Further reads:
- Worried about layoffs? How to avoid career mistakes that increase your layoff risk
- Your most powerful defense against layoff. Build a personal brand that makes your career layoff-proof
💼 A Brief History of Layoffs in Tech
The 2001 Dot-Com Implosion
It started at the turn of the millennium. At Yahoo’s headquarters, a banner reads:
“The World Will Be Digital — Everything Else Is Dead.” This was more than to be just a slogan.
The late 1990s were a gold rush — It feels like a giant casino where every table is paying out. Packets of venture capital checks are flying into companies with “.com” in their names but no revenues. Engineers jump companies every six months for a better stock option. Startups rent entire floors, install pool tables, and order sushi lunches for interns.
During 1999, a website with 100 daily users had a $500M valuation. Over 300 dot-com companies had gone public in USA in a single year. Market caps worth billions were backed by… nothing. The Nasdaq Composite index rose nearly sevenfold between 1995 and its peak in March 2000.
And then the music stopped at the party. The capital began to dry up. Businesses lacked sustainable, profitable business plan. Within weeks, IPOs were cancelled. Then the big fellas fell:
- Cisco laid off 8,500 employees (8%) — its first ever.
- HP cut 7,000 jobs.
- IBM cut 10,000.
- Dell, Intel, Sun Microsystems — all slashed thousands.
Within a year, over 1,00,000 tech workers lost their jobs in the US alone.
Every day became a ritual:
- People came to work.
- Their access badges didn’t work.
- A cardboard box and a security escort waited inside.
The world saw the first modern mass tech layoff, and the template was set. Overnight, layoff announcements became corporate strategy:
- “Rightsizing.”
- “Restructuring.”
- “Operational realignment.”
- “Focusing on core priorities.”
These phrases were invented in 2001 — and still haunt the industry today.
The 2008 Global Financial Crisis
Fast forward to 2008. The subprime mortgage collapse triggered another wave. This time, they were about financial discipline.
Even profitable companies cut aggressively to signal “prudence” to Wall Street.
- Microsoft laid off 5,000 employees, its first major job cut in history.
- HP axed 24,000 jobs in one go — nearly 8% of its workforce.
- IBM, Yahoo, and Dell all followed.
Meanwhile, India’s IT sector (TCS, Infosys, Wipro) weathered the storm better, thanks to their outsourcing model. Instead of large layoffs, they froze salaries and reskilled staff.
But something subtle changed: investors began rewarding companies that cut staff. The market started to equate layoffs with discipline. What began as a survival tool started morphing into a “management ritual.”
2015–2019: The Era of Hypergrowth — “Hire Fast, Fire Later”
The 2010s brought a tech boom unlike any before. Cloud computing, mobile apps, and venture capital money flooded Silicon Valley.
Amazon, Google, and Meta hired tens of thousands each year. Startups doubled headcount every 12 months, chasing growth at all costs.
In India, the startup wave exploded too — Flipkart, Paytm, and Ola turned into unicorns almost overnight. Engineering grads saw campus placements as golden tickets.
Behind the scenes, employee cost as % of revenue was creeping upward. But no one cared; money was cheap, and growth masked inefficiency.
2020: The Pandemic Paradox
When COVID-19 hit, fear struck instantly. Tech firms froze hiring; Zoom and Slack surged, while travel and mobility apps bled.
Then came the twist: the world went digital overnight.
- Amazon hired over 500,000 employees in 2020 alone.
- Indian IT majors added thousands to handle new digital transformation projects.
For two years, it felt like a new industrial revolution. But by 2022, the demand frenzy cooled. Inflation soared, and central banks tightened rates. Suddenly, the very companies that scaled up were bloated. The pendulum swung back hard.
2023–2025: The AI-Driven Wave 🤖
The 2023–2025 period marked the first-ever global layoff cycle driven not by recession… but by technology itself.
Unlike 2001 (internet bubble) or 2008 (financial crash), this wave was triggered by the hyper-acceleration of AI, automation, and over-hiring during the pandemic boom.
By mid-2023, tech giants began adjusting their workforce to an “AI-first operating model.” Companies suddenly realized that:
- Back-office operations could run lean with RPA + AI
- Customer support could be automated
- Content creation no longer needed large teams
- QA, testing and documentation roles could be reduced
- Coding productivity jumped 20-40% with AI copilots
- Sales and marketing workflows became semi-automated
What unfolded next
2023:
Google, Meta, Amazon, Microsoft, Salesforce — almost all cut 5–12% of staff, citing “AI efficiencies” and “role redundancy.”
2024:
The second wave arrived. Companies realized AI wasn’t just reducing workload — it was restructuring entire departments. HR, marketing, design, finance ops, and support teams faced the biggest hit.
2025:
The largest wave yet.
India saw mass layoffs led by TCS, Infosys, Wipro, Tech Mahindra — collectively eliminating over 100,000 roles as projects turned AI-assisted and automation-heavy.
US tech firms followed suit, using phrases like:
- “AI-driven restructuring”
- “Recalibrating talent mix”
- “Efficiency through intelligent automation”
This was no longer cost cutting — it was business model rewiring.
Categorizing Tech Layoffs by Scale and Strategic Driver
To make sense of the patterns, we grouped layoffs by severity and what typically triggers them. Think of this as a helpful guide rather than a strict formula — real-world cases can always break the rules.
- Small scale (<4%) are strategic fine-tuning, not distress signals.
- These often accompany efficiency through automation, project reshuffles, or pyramid optimization in service firms.
- Example: TCS and Infosys 2025 layoffs fall here — trimming fat.
- Medium scale (5–9%) are margin defense maneuvers.
- Companies like Amazon or Google use these to recalibrate after over-hiring periods (post-pandemic).
- Typically follow a “year of slower growth” or “stock price correction.”
- Large scale (10–14%) indicate strategic shifts.
- Meta’s 13% cut in 2022 came with its “Year of Efficiency” reset.
- Microsoft’s 14% in 2014 was about killing the failed Nokia phone venture.
- Severe (>15%) are crisis-driven and usually short-term survival tactics.
- Airbnb’s 25% layoff during COVID is the perfect example — revenue dropped >80% overnight.
| Layoff Scale (% of Workforce) | Typical Driver / Intent | Description | Examples |
|---|---|---|---|
| <4% | Operational Efficiency / Workforce Optimization | Minor trims to flatten org structures, reduce redundancy, or rebalance talent pyramid. Usually done during “business as usual.” Often targeted to operational efficiency. | 🏢 Amazon (2025 – 4%), TCS (2025 – 2%), Infosys (2025 – 2%), Accenture (2025 – 2.5%) |
| 5–9% | Profit Margin Protection / Cost Rationalization | Moderate-scale layoffs to protect operating margins during slower revenue growth, or after a hiring surge. | 🏢 Google (2023 – 6%), Microsoft (2023 – 5%), IBM (2009 – 6%) |
| 10–14% | Restructuring / Business Model Realignment | Indicates a deeper change — divestiture, or failure of a product line. Typically driven by strategic reorientation (e.g., from hardware to cloud). | 🏢 Microsoft (2014 – 14%), Meta (2022 – 13%), Intel (2016 – 11%) |
| >15% | Major Disruption / Overexpansion Correction | Response to an external shock (pandemic, bubble burst, demand collapse). Usually involves exiting geographies or product lines. | 🏢 Airbnb (2020 – 25%) |
What Insights From Research Reveals?
1️⃣ Productivity Drops 12–40%
Across large studies from Harvard, MIT, and Wharton, the pattern is consistent:
- Remaining employees become risk-averse
- Collaboration drops sharply
- Innovation pipelines slow for 12–24 months
- High performers begin quietly looking elsewhere
A Harvard study found:
Productivity drops by an average of 12% after layoffs — even when only 1–3% of staff is cut.
Another study (Wharton, 2018) found:
When layoffs exceed 10%, productivity drops nearly 40% for 6–12 months.
Why?
Because layoffs create a fear culture — people stop experimenting, avoid mistakes, and protect their own turf.
2️⃣ Voluntary Exits Rise 15–25%
Layoffs rarely end with the people who were fired.
Multiple HR analytics reports show:
- Survivor syndrome triggers anxiety
- Employees rethink loyalty
- Top talent leaves because “stability is broken”
McKinsey found:
Voluntary attrition rises 15–25% within 3 months of a layoff.
This creates a “double loss”:
You lose the people you fired and the people you wanted to keep eventually exit.
3️⃣ Share Prices Usually Rise in the Short Term… Then Reverse
This is where the myth of “layoffs = success” was born.
Short-term:
Stock prices almost always increase because:
- Markets believe costs will shrink
- Analysts reward “decisive leadership”
- Investors interpret layoffs as a sign of discipline
But research across 4,000 US firms shows:
Companies that made layoffs underperform the market by 2 years later on revenue growth, innovation output, and shareholder value.
Because layoffs fix symptoms, not strategy.
4️⃣ Revenue per Employee Often Improves
The oldest trick in financial storytelling:
Cut 5,000 employees → Revenue stays flat → “Revenue per employee increases” → Stock goes up.
But this doesn’t signal efficiency.
It signals scarcity.
Research shows:
- RPE improves for 2–4 quarters
- Then stagnates or declines because fewer employees = fewer projects, slower execution, weaker sales
This “efficiency illusion” is why many tech companies repeat the layoff cycle every 2–3 years.
5️⃣ Employee Cost as % of Revenue Barely Changes
After examining data from Microsoft, Amazon, Meta, IBM, Cisco, Infosys, TCS, and HCL:
Employee cost as % of revenue usually falls only 1–2 percentage points after layoffs — not enough to materially change profitability.
Why so small?
Because:
- Severance costs spike
- Hiring resumes after 6–18 months
- Contractors replace full-time staff at higher hourly rates
- Productivity loss offsets cost savings
This is surprising, but true.
6️⃣ Long-Term Effects: A Slow Erosion of Culture & Trust
Companies like Google and Meta saw Glassdoor ratings fall sharply post-layoffs despite strong financials..
Research from MIT Sloan and Stanford shows:
- Trust in leadership falls 30–50%
- Employee engagement drops 20–40%
- Employer brand takes 12–36 months to recover
- Top performers avoid companies known for frequent layoffs
Firms rarely measure this damage — but it impacts every future quarter.
So… Do Layoffs Actually Work?
Here’s the uncomfortable truth:
Layoffs work only when the company is fundamentally unhealthy — not when the company is mismanaged.
✔️ When Layoffs Make Sense
(And historically produce positive outcomes)
1️⃣ Business Model Disruption — When a Core Revenue Engine Has Collapsed
Example triggers:
- A product line becomes obsolete
- A market disappears
- A disruptive technology replaces your value proposition
Real-world:
- Nokia (2014) exiting mobile
- Airbnb (2020) during global travel shutdown
- Print media downsizing in the 2010s
📌 In these scenarios, the company is resizing to match a permanently smaller opportunity.
2️⃣ Strategic Exit — Leaving a Non-Core or Unprofitable Business
A company discontinuing a failed or non-strategic business unit must reduce staff.
Real-world:
- Microsoft cutting Nokia phone division (2014)
- IBM reducing hardware teams to focus on cloud (2009–2012)
📌 Here, layoffs align the workforce with the new direction.
3️⃣ Large-Scale M&A Integration
When two companies merge, roles overlap.
Examples:
- Sales teams
- HR, Finance
- Support functions
- Operations
📌 Reducing redundancy improves efficiency without impacting capacity.
4️⃣ Severe Financial Distress or Survival Mode
If a company is running out of cash → layoffs are a survival mechanism, not a strategy.
Examples:
- Startups facing funding winter
- Companies with negative cash flow and shrinking runway
📌 Cash preservation > growth.
❌ When Layoffs Do NOT Make Sense
(And usually cause long-term damage)
1️⃣ When Done to “Please Investors” or Chase Short-Term Margins
This is the most common and the most harmful reason.
Research shows:
- 70% of layoffs conducted for “cost optimization” fail to improve profitability after 12–18 months.
- Revenue per employee often drops because capacity is lost.
- Stock price bumps fade within two quarters.
📌 Cutting talent rarely creates efficiency — it mostly creates burnout.
2️⃣ When Layoffs Target Workers Instead of Fixing Systems
Many organizations:
- Have inefficient processes
- Duplicate workflows
- Legacy bureaucracy
- Poor automation
But instead of fixing systems, they cut people.
📌 This worsens inefficiency and increases errors.
3️⃣When Layoffs Follow Aggressive Over-Hiring Cycles
If the company over-hired during a boom (common in tech), it follows course correction.
Effects:
- Loss of institutional knowledge
- Lower morale
- Higher voluntary turnover
- Fragmented teams
📌 Over-hiring is a leadership failure, but employees pay the price.
4️⃣When AI Adoption Is Used as a Pretext
Companies cutting in anticipation of future automation — not current need — often regret it.
Because:
- AI efficiency gains take time
- AI adoption isn’t uniform across orgs
- Companies underestimate reskilling needs
📌 Premature cuts create capability gaps.
🧩 So Why Do Layoffs Continue?
Because the system rewards them.
Even though:
- Productivity drops
- Best employees leave
- Innovation stalls
- Culture erodes
- Morale collapses
- Customers get worse service
- Long-term growth slows down
Layoffs continue because:
👉 Leaders are incentivized for short-term gains.
👉 Markets reward visible cost cuts.
👉 Psychology rewards action over strategy.
👉 Culture celebrates efficiency over resilience.
👉 AI gives an acceptable excuse.
In short:
Companies don’t keep layoffs because they work.
They keep doing them because they appear to work.
Further reads:
https://hbr.org/2024/10/research-the-long-term-costs-of-layoffs
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