The Layoff Epidemic: Why We Keep Firing Our Way Out of Bad Planning

In 2009’s Up in the Air, George Clooney plays Ryan Bingham, a consultant who flies around the country to fire people for companies that don’t want to do it themselves. His job exists because leaders treat mass layoffs as an inevitable part of running a business, like winter storms that roll through every few years.

Fifteen years later, the numbers say this isn’t just a tech problem or a recession quirk. It’s an operating model.

From the tech side alone, layoffs.fyi’s tracker shows:

2022: 165,269 tech employees laid off across 1,064 companies
2023: 264,220 tech employees laid off across 1,193 companies
2024: 152,922 tech employees laid off across 551 companies
2025 (so far): 120,124 tech employees laid off across 237 companies

And that’s just tech.

In the US federal government, the Department of Government Efficiency (DOGE) has become a kind of real-world “firing consultancy,” driving at least 71,981 layoffs and 182,528 total departures from federal agencies in 2025 alone, part of a broader plan that could affect about 12% of the 2.4 million-person civil service.

We’re not looking at a series of one-off emergencies. We’re looking at a repeatable pattern: when forecasting and headcount planning are weak, layoffs become the blunt instrument that cleans up the mess.

My argument is simple:

Layoffs are usually presented as unfortunate but inevitable. In reality, most mass job cuts are the overdue bill from sloppy headcount planning and weak forecasting, not fate.

And if that’s true, we can do something about it.

What I Saw From the Inside

In 2019, I was running a recruiting team of about 30 people.

Over the year, I did what I thought good operators were supposed to do:

I forecasted expected demand for the next year
I adjusted the team through managed backfills and restructuring
I worked the numbers down to around 21 recruiters, matching the hiring plan I believed was realistic

So when the company announced a broader restructure, I assumed my team would be fine.

We weren’t.

Despite a year of adjusting to the forecast, I still had to cut 3 more roles.

That hurt. But what shocked me more was what happened in teams that hadn’t been managing to a forecast:

Some orgs were told to cut 50%+ of their team
People who had joined in the last year, who had moved their families across the country, found out in a 15–20 minute meeting that the job they relocated for no longer existed

Sitting in those notification meetings changed how I think about headcount planning.

Before that, workforce planning was an optimization problem: “How do we hit next year’s plan with the right number of recruiters, engineers, sales reps?”

After that, it became an ethical problem: “How do we stop creating avoidable harm by over-hiring into roles that are likely to disappear in the next downturn or strategy shift?”

That experience is a big part of why I’ve spent the last few years building a thesis and practice I call Operational Headcount Planning.

The Layoff Curve: What the Data Actually Shows

Let’s look at the tech numbers again, because they tell a very specific story:

2022: 165k people laid off, 1,064 companies
2023: 264k laid off, 1,193 companies
2024: 153k laid off, 551 companies
2025 (year-to-date): 120k laid off, 237 companies

Rough translation:

2022–2023: The great over-correction Tech companies that over-hired during the low interest-rate, “growth at all costs” phase swung hard in the opposite direction. Headcount became the fastest way to show “discipline.”

2024: Fewer companies, still big cuts The number of companies doing layoffs dropped by more than half vs. 2023, but the total still cleared 150k. That suggests repeated cuts at the same firms and targeted reshaping around AI, automation, and margin targets.

2025 (so far): Layoffs as ongoing hygiene With more than 120k tech layoffs and counting, cuts have become a recurring maintenance tool. Many companies are not “correcting a bubble” anymore; they’re tuning the model by firing people instead of tuning their planning.

Now add the public sector.

The DOGE “layoff tracker” on layoffs.fyi and reporting from multiple outlets suggest that by mid-2025:

Around 300,000 federal civil servants have been announced or targeted for layoff
Nearly 200,000 federal workers have already left their jobs through firings, forced resignations, or buyouts
In some agencies, 60–80% cuts are on the table

You can debate the politics all day, but mechanically, it’s the same pattern:

Leadership sets an aggressive cost or size target
There is no credible, living headcount and capacity model
The “plan” becomes: cut now, re-learn capabilities later

The throughline: Layoffs are being used as a primary adjustment mechanism in systems that are not built to see risk early.

How Layoffs Actually Happen Inside Companies

From the outside, layoffs look like a single day: emails, calendar invites, Slack gone, laptops returned.

Inside, the process usually looks something like this:

1. Target setting

The board and CEO agree on a headline number:

“We need to reduce operating expenses by 15%”
“We need to regain 5 points of operating margin”
“We need to take out $X million in run-rate costs”

Finance translates that into headcount equivalents:

“If we assume average fully-loaded cost of $Y per head, that implies Z roles”

At this point, the discussion is mostly about money, not capacity or risk.

2. Allocation of pain

Leadership decides which parts of the org will contribute what share of the cut.

In well-run orgs, this is anchored in a portfolio view: what’s core, what’s experimental, what’s underperforming.
In many orgs, it’s a mix of politics, “fat trimming” narratives, and a rough sense of who has “too many people.”

Teams that have not been doing any proactive headcount planning are often handed blunt numbers:

“Your function needs to be 20% smaller by next quarter.”

3. Selection mechanics

Once teams know their target, they have to decide who actually goes.

Criteria often include:

Role criticality and org uniqueness
Performance history
Location and pay level
Legal constraints (works councils, protected classes, visa status)
Optics (DEI impact, public-facing teams)

Here’s the uncomfortable part: the data foundations are often weak.

Org charts are stale. Role definitions are fuzzy. Workload and capacity data lives in separate tools no one reconciles.

So you get some combination of:

Genuine optimization
Last-minute political horse-trading
“We’re not sure what this team does, so we can’t cut them too deep” decisions

4. Execution

This is the part Up in the Air dramatizes: a consultant (or HR, or a cross-functional team) delivers the message and manages the emotional fallout. Clooney’s character is literally a “corporate downsizer” who travels around the country firing people on behalf of employers.

In real life, this might look like:

Standardized scripts for managers
Pre-written FAQs and talking points
Coordinated meeting blocks, security steps, and systems lockouts
Outplacement vendors and “transition support”

All of that machinery sits on top of the planning failures that made the layoff necessary.

5. Post-layoff “planning”

In theory, the organization re-forecasts:

New org charts and span-of-control
Reduced capacity vs demand
Updated growth plans

In practice, exhausted leaders often say some version of:

“We’ll figure it out once the dust settles.”

So the cycle resets with a worse planning baseline than before.

The Leading Indicators You Can See Coming

Mass layoffs almost never “come out of nowhere.” Inside the company, the warning lights are usually blinking for months.

You can think about indicators at three levels.

A. Organizational signals

Sudden hiring freezes except for “business critical” roles
Travel and discretionary spend cut, with strong language around “belt-tightening”
Multiple “strategic reviews” or “portfolio prioritizations” with no clear follow-through
Reorgs that move boxes around but never address the underlying capacity model

None of these guarantee a layoff, but they say: we’re trying to buy time.

B. Planning and data signals

This is where Operational Headcount Planning lives.

Flags that should worry you:

Forecasts consistently missing on the same side (over-forecasting growth and under-forecasting risk)
Headcount continuing to grow while revenue, volume, or backlog is flat or declining
High vacancies plus slower fills, but no change to the approved plan
Productivity or utilization declining, but headcount decisions still anchored to last year’s budget, not this year’s reality

If you’re a CHRO or CFO and you see this pattern for 2–3 quarters, you don’t have a comms problem. You have a risk problem.

C. Market and balance sheet signals

On the macro side:

Rising cost of capital and refinancing risk
Big customer churn or delayed large deals
Sector-wide corrections (what we saw in tech 2022–2023, and now in parts of federal government via DOGE-driven cuts)

Taken together, these signals tell you: “If we don’t adjust hiring and redeployment now, we’re going to be talking about layoffs in 6–18 months.”

Why Headcount Planning Fails (and Layoffs Follow)

If you trace big layoff events backward, you tend to find the same root causes.

1. Static budgets in a dynamic system

Annual planning treats headcount like concrete:

We “lock” a hiring plan in Q4
We argue a bit about contingency
Then we run the year as if the plan is reality

When demand, product roadmap, or strategy shifts, headcount lags by 6–12 months. By then, the gap between actual need and actual payroll is huge.

2. Fragmented ownership

Finance owns the dollars
HR owns the process
Business leaders own the outcomes

Nobody owns the system that ties demand, supply, and cost together.

So decisions ping-pong:

Ops says, “We need more people.”
Finance says, “We’re over budget.”
HR says, “The process is slow.”

No one is on the hook for a single view of headcount as capital.

3. Point-in-time reporting, not rolling forecasts

Most organizations have:

An HRIS snapshot
An approved req list
Some spreadsheets in Finance
Maybe a headcount dashboard

What they don’t have is:

A rolling, 6–18 month forecast that reconciles demand, capacity, and cost, updated weekly or monthly.

Without that, risk accumulates invisibly until a big cut is the only thing loud enough to matter.

4. Ethics outsourced to comms

The ethical conversation often happens last:

“What severance can we afford?”
“How do we make this sound humane?”
“How do we manage PR risk?”

By that point, the real ethical decisions (over-hiring, ignoring warning indicators, punting on hard tradeoffs) are months in the rearview mirror.

What Operational Headcount Planning Actually Is

Operational Headcount Planning (OHP) is my attempt to fix this at the system level.

In plain language:

OHP is a rolling, cross-functional process that connects demand signals, workforce supply, and financial constraints into a single headcount and capacity forecast, refreshed on a weekly or monthly cadence.

The building blocks:

A golden roster
Demand-to-capacity models
Scenario bands and rules of engagement
Ethical guardrails baked into the model

When you run this as a discipline, you still make hard calls. You may still run a layoff in extreme scenarios.

But the shape of those decisions changes:

Corrections become earlier and smaller
Fewer people relocate for roles that will evaporate in the next correction
You use consultants for specialized advisory work, not to read scripts to hundreds of people whose jobs were always going to vanish

In other words: the system stops generating avoidable layoffs as its primary adjustment mechanism.

From Up in the Air to “Put Us Out of Business”

Back to Up in the Air for a moment.

Ryan Bingham exists because companies want to separate themselves from the emotional and ethical cost of firing people. His firm is literally paid to absorb the psychological blast radius the planning system created.

In the federal government, DOGE has become a macro-version of the same idea: a centralized unit that handles the messy business of mass reductions in force across agencies.

The thesis behind Operational Headcount Planning is not “make layoffs feel nicer.” It’s much more aggressive:

Design planning systems so that the Ryan Binghams of the world have far less work to do.

If we can:

Make headcount decisions explicit and probabilistic
Tie them tightly to demand and strategy
Force earlier action on risk
And treat ethics as a design constraint, not a press release

…then the demand for large-scale “downsize my entire function” consulting should go down.

I’m very comfortable saying the quiet part out loud:

The goal is to put at least one category of consultant out of business.

Not because consultants are bad, but because relying on them to do repetitive mass layoffs is a symptom of a broken operating model.

A 12–18 Month Plan to Reduce Your Layoff Risk

If you’re a CFO, CHRO, or COO, here’s what the next 12–18 months could look like if you take this seriously.

1. Stand up a single headcount and capacity model

Within 60 days:

Build a golden roster: approved, planned, and actual FTE, plus comp, location, and manager.
For your three most critical functions, add basic capacity assumptions: utilization, ramp time, and productivity.

Don’t wait for perfection. Ship version 0.9 and iterate.

2. Map demand to headcount

Within 90 days:

For each major function, choose one or two demand drivers (pipeline, tickets, units, backlog).
Define simple rules: “Every X units of demand sustained over Y months requires Z incremental FTE.”

This doesn’t need to be a PhD thesis. It just needs to be explicit.

3. Create scenario bands and pre-agreed levers

Within 6 months:

Define what mild / moderate / severe downturns look like for your business.

For each, write down the exact sequence of actions you’ll take before layoffs:

Slow or freeze hiring
Reduce contractors and agencies
Pause non-critical projects
Redeploy people to higher-need areas
Offer voluntary exits

Only at the end of that chain should you reach “involuntary layoffs.”

4. Bake ethics into the procedure, not the apology

Within 12 months:

Set minimum standards for notice, severance, and support for anyone affected.
Build diversity, equity, and location checks into your scenario modeling.
Require a brief written case for why layoffs are necessary in any scenario, and what alternatives were evaluated earlier in the curve.

This doesn’t make layoffs painless. It makes them less frequent, more honest, and less chaotic.

The Real Question

The layoff epidemic won’t end because we invent a nicer script or a more compassionate Zoom protocol.

It ends when companies get as serious about headcount as capital as they are about cash, inventory, and infrastructure.

When you treat layoffs as an unavoidable act of God, you design a system that keeps producing them. When you treat them as a planning failure, you’re forced to build an operating model that uses smaller, earlier, less harmful corrections instead.

That’s the work I’m focused on with Operational Headcount Planning: taking the tools supply chain and operations have used for decades, and applying them to headcount so we stop treating people like a shock absorber for forecasting mistakes.

If we do this right, we won’t just reduce the number of Ryan Binghams in the world. We’ll reduce the number of people who have to sit in that room, holding a termination packet, wondering why no one saw this coming sooner.