40,132 Tech Workers Laid Off in 2026 So Far (787 Per Day) - Is This the New Normal?

General
1

The numbers are sobering: 40,132 tech workers laid off in 2026 through February (source: TrueUp.io).

That’s 787 layoffs per day, every single day.

Context: How Does This Compare?

2022-2023: Peak layoff period, 260,000+ tech workers
2024: Slowed but continued
2025: Stabilization
2026: Acceleration again

The AI Narrative

44% of hiring managers expect AI to drive 2026 layoffs (InformationWeek data).

The justification: “AI can do this work, so we need fewer people.”

But let’s be clear about what’s happening: Companies are laying off workers and claiming AI efficiency, but many aren’t actually replacing work with AI - they’re just cutting costs and calling it AI transformation.

The Financial Reality

From CFO perspective, the math is:

  • Layoffs = immediate cost reduction
  • AI investment = gradual capability increase
  • Net result = lower expenses, maintained (or slightly reduced) output

Boards love this story. But it’s short-sighted.

Three Dynamics Driving This

1. Economic Uncertainty
Rising interest rates, economic slowdown fears → companies cut costs preemptively

2. AI Productivity Claims
“We can do more with less because AI” - whether true or not, it’s the justification

3. Shift to Contingent Labor
Why maintain headcount when you can hire contractors/consultants as needed?

The Human Cost

787 people per day losing jobs means:

  • Mortgages at risk
  • Healthcare concerns
  • Career disruption
  • Family stress

These aren’t just numbers. These are engineers, product managers, designers, data scientists with families and financial obligations.

The Market Dynamic

For those laid off:

  • Job market is tight
  • Competition is fierce
  • Companies demand more experience for fewer roles
  • Salary expectations have to adjust downward

For those still employed:

  • Increased workload (covering departed colleagues)
  • Job insecurity and anxiety
  • Pressure to prove value
  • Hesitation to negotiate or leave

The Question Nobody’s Asking

If AI is making us more productive, why are we seeing both:

  • Layoffs (we need fewer people)
  • Increased workload for remaining employees (they’re overworked)

Shouldn’t AI productivity gains mean same output with fewer hours, not same hours with fewer people?

The 2027-2028 Reckoning

My prediction: Companies laying off aggressively in 2026 will face talent shortages in 2027-2028 when:

  • Economy recovers
  • New projects need staffing
  • Laid-off engineers have moved on to other industries/companies

They’ll then overpay to hire, creating expensive hiring cycles.

This is classic short-term cost optimization creating long-term inefficiency.

What Should Companies Do Instead?

Sustainable approach:

  • Use AI to augment workers, not replace them
  • Redeploy talent to higher-value work
  • Invest in reskilling
  • Maintain core team through cycles

Short-term approach (current trend):

  • Lay off workers
  • Claim AI efficiency
  • Overwork remaining employees
  • Face talent crisis in 18 months

Is 787 layoffs per day the new normal? Or will 2027 bring the hiring whiplash we’ve seen before?

2

Carlos, your observation about “claiming AI efficiency while just cutting costs” is exactly what I’m seeing.

Talked to VP Eng at company that laid off 30% of engineering. Asked: “What AI capabilities replaced that work?”

Answer: “Well, we’re hoping GitHub Copilot and AI code review will help the remaining team be more productive.”

Translation: They laid off people, THEN looked for AI to justify it. Not the other way around.

Result: Remaining engineers are burnt out, velocity has dropped, not increased.

The “AI productivity” narrative is being used to justify layoffs that are really about cost-cutting in uncertain economy.

3

Perspective from someone who survived layoffs at previous company:

The survivors pay a tax: Survivor guilt + increased workload + job insecurity.

After our 25% reduction:

  • I took on work from 1.5 departed engineers
  • Constant anxiety about next round
  • Stopped taking any risks (what if my project fails and I’m next?)
  • Eventually left for more stable company

The “do more with less” expectation is unrealistic. AI helps, but it doesn’t replace human problem-solving, collaboration, and domain knowledge that walked out the door.

Companies are optimizing for next quarter’s balance sheet while degrading long-term capability and morale.

4

The 2027-2028 whiplash is coming. We’ve seen this pattern before:

Typical cycle:

  1. Economic uncertainty → aggressive cost-cutting
  2. Layoffs and hiring freezes
  3. Economy recovers → need to staff up
  4. Laid-off talent has moved on
  5. Desperate hiring at inflated salaries
  6. Organizational chaos from rapid growth
  7. Next downturn → repeat

Sustainable alternative:

  • Maintain core team through cycles
  • Use downturns to invest in capability building
  • Be positioned to execute when market recovers

But Wall Street rewards short-term cost optimization, so we get the whiplash cycle instead.

As CTO, I’m advocating internally: Don’t lay off engineers we’ll need to rehire in 18 months at 30% higher salaries.