Note: This is a research note supplementing the book Unscarcity, now available for purchase. These notes expand on concepts from the main text. Start here or get the book.

Global Employment Statistics 2025: The Numbers Behind the Labor Cliff

Here’s a riddle for you: What do you call a 4.6% unemployment rate when AI can already do 80% of what a computer programmer does, 65% of what a cashier does, and 100% of what a telemarketer does?

You call it the calm before the storm.

This isn’t a statistics dump. This is a crime scene report from the first wave of the Labor Cliff—the moment when AI (the Brain), robotics (the Body), and soon fusion energy (the Fuel) begin making human labor economically obsolete. The official numbers look fine. The underlying reality is anything but.

For deeper analysis, see our companion articles:

The 2025-2030 Labor Cliff — Full analysis of the automation wave
Musk’s Universal High Income — The case for abundance beyond UBI
AI Coding Revolution — How AI is transforming software development

Last updated: December 17, 2025

The Headline Numbers Look… Suspiciously Normal

Let’s start with what economists call “the data” and what I call “the greatest misdirection since ‘Iraq has WMDs.’”

Global Overview (ILO 2025)

Metric	Rate	The Spin
Global unemployment	4.9%	“Stable!”
Youth unemployment (men)	12.4%	“Always high”
Youth unemployment (women)	12.3%	“Making progress”
Global jobs gap	9%	“Down from 16% in 2004!”

See? Everything’s fine! Nothing to worry about! Go back to sleep!

Except—and this is a big except—these numbers measure whether people are looking for work. They don’t measure whether the work they find is meaningful, whether it pays a living wage, or whether their jobs will exist in five years. By those metrics, we’re not in a stable market. We’re standing on a trapdoor.

Source: ILO World Employment and Social Outlook: Trends 2025

United States: The Canary Just Fell Off Its Perch

November 2025 Employment Data

The Bureau of Labor Statistics dropped its latest numbers on December 16, 2025—and if you know how to read between the lines, they’re screaming.

Metric	Rate	What It Actually Means
Overall unemployment	4.6%	Highest in 4+ years
Adult men	4.2%	Up from 4.0%
Adult women	4.4%	Up from 4.2%
Teenagers (16-19)	13.5%+	The future is not working
Black workers	~7.8%	Still double the white rate
Hispanic workers	~5.7%	Rising faster than average
Labor force participation	62.3%	37.7% have given up

The economy shed 105,000 jobs in October and added only 64,000 in November. Healthcare (+46,000) and construction (+28,000) were the only bright spots. Manufacturing? Down another 5,000 jobs—continuing a multi-year hemorrhage.

Here’s the kicker: wage growth slowed to 3.5% year-over-year. In an economy where corporations are reporting record AI-driven productivity gains, workers are getting a smaller slice of a bigger pie. The machines are winning. The humans are being told to be grateful for crumbs.

Source: Bureau of Labor Statistics Employment Situation (November 2025), NPR

European Union (October 2025)

Region	Unemployment Rate	Youth Rate
Euro area	6.4%	14.8%
EU overall	6.0%	15.2%
Women (Euro area)	6.6%	—
Men (Euro area)	6.1%	—

That’s 13.35 million unemployed Europeans. Nearly one in six young people can’t find work. But sure, let’s keep pretending the system is working.

Source: Eurostat

Other Major Economies

Country	Unemployment Rate	Reality Check
Japan	2.6%	Aging population, not healthy economy
Germany	3.5%	Manufacturing in structural decline
United Kingdom	4.2%	Brexit hangover continues
China	4.6%	“Official” number (real youth unemployment hidden)
France	7.4%	Yellow vests weren’t wrong

The Global Extremes: A Tale of Two Economies

The Nightmare Scenarios

Country	Rate	Context
Eswatini	34.4%	One in three adults unemployed
South Africa	33.2%	Structural, not cyclical
Djibouti	25.9%	Strategic location, no jobs
Botswana	23.2%	Diamond-rich, job-poor
Gabon	20.1%	Oil curse in action

The Deceptive “Success” Stories

Country	Rate	What It Actually Means
Qatar	0.2%	Migrant workers don’t count
Cambodia	0.3%	Subsistence farming isn’t “unemployment”
Niger	0.4%	See above
Thailand	0.7%	Informal economy invisible
Laos	1.3%	Ditto

Low unemployment in poor countries usually means: “We stopped counting the desperate.”

2025: The Year of the Layoff

The Big Picture

Metric	Figure	Context
Total US layoffs (YTD)	1.1 million+	Most since COVID-19
Year-over-year increase	+65%	This isn’t cyclical
Top sector: Government	Tens of thousands	DOGE meets reality
Second sector: Tech	126,000-183,000	Depending on tracker

October 2025 saw the highest October layoff figure in 22 years. October is usually when companies hire for the holidays. Instead, they’re firing. The signal couldn’t be clearer.

Source: Challenger, Gray & Christmas via Fast Company

Tech Sector: Ground Zero

Different trackers, slightly different numbers, same story:

Tracker	Layoffs	Companies
Crunchbase	126,101+	—
TrueUp	182,963	626 layoffs
Layoffs.fyi	122,549	257 companies
Visual Capitalist	141,000+	—

The reason for the variation: some count only tech companies, others count tech jobs at any company. But every tracker agrees on the direction: up, up, up.

Key insight: While AI gets blamed for tech layoffs, it actually ranks sixth among stated reasons—behind “restructuring” and “economic conditions.” But here’s the thing: what do you think “restructuring” means when companies are simultaneously investing billions in AI? It means replacing humans with machines while using euphemisms.

Sources: TechCrunch, Crunchbase, Layoffs.fyi

The Corporate Hall of Shame (2025)

Company	Layoffs	% of Workforce	What They’re Spending On Instead
Intel	21,000+	~20%	AI chips
Microsoft	~9,000	<4%	Copilot, OpenAI
Amazon	14,000	—	AWS AI services
HP	4,000-6,000	—	“Efficiency”
Meta	4,200+	—	Llama, AI ads
Google	Hundreds	—	Gemini, Waymo
IBM	2,700-8,000	—	Watson successors

Notice a pattern? Every company laying off humans is simultaneously doubling down on AI. They’re not cutting costs; they’re substituting labor.

AI Job Displacement: The Elephant in Every Meeting Room

What’s Already Happened

Metric	Figure	Source
US jobs with 50%+ tasks automated	23.2 million (15.1% of employment)	SHRM
US jobs heavily using generative AI	12 million (7.8% of employment)	SHRM
Workers reporting direct AI displacement	14%	Multiple surveys
Tech layoffs explicitly citing AI	77,999	Layoffs.fyi

That 14% number should haunt you. It means roughly one in seven workers has already been displaced by AI—and we’re still in the early innings. GPT-4 is two years old. Claude 3.5 just arrived. The robots haven’t even started walking yet.

Source: SHRM Research

What’s Coming

Timeframe	Jobs Displaced	New Jobs Created	Net Change
By end of 2025	85 million	97 million	+12 million
By 2030	92 million	170 million	+78 million

“See!” say the optimists. “More jobs will be created than destroyed!”

Here’s the problem with that math: the jobs being destroyed aren’t the same as the jobs being created. A 55-year-old accountant who loses her job to AI isn’t going to become a “prompt engineer” or “human-AI collaboration specialist.” The people losing jobs and the people getting new ones are different populations.

It’s like telling a coal miner that solar panel installation jobs are growing. Technically true. Practically useless.

Source: World Economic Forum Future of Jobs Report 2025

The Most Exposed Occupations

Occupation	AI Exposure	Current Status
Computer programmers	~80%	Unemployment spiking
Customer service reps	80% by 2025	Mass replacement underway
Data entry clerks	7.5M jobs gone by 2027	Already hemorrhaging
Retail cashiers	65% automation risk	Self-checkout everywhere
Accountants & auditors	High	Copilots eating their lunch
Legal assistants	High	AI reading contracts faster
Telemarketers	Very high	Robots selling robots

The Safest Jobs (For Now)

Occupation	Why Protected
Air traffic controllers	Liability, regulation
Chief executives	Who’s going to fire themselves?
Radiologists	Liability (but AI reads better)
Clergy	Can’t automate meaning (yet)
Residential advisors	Human touch essential

Source: National University, Goldman Sachs

The Skills Gap: The Real Crisis No One’s Addressing

Here’s the statistic that should keep policymakers up at night:

New AI Job Requirements	Percentage
Require master’s degree	77%
Require doctoral degree	18%
Require bachelor’s or less	5%

Meanwhile, among American adults:

13% have a master’s degree
4% have a doctorate
23% have a bachelor’s only
60% have less than a bachelor’s degree

Do you see the problem? We’re creating jobs that 95% of the displaced workers cannot fill. The “just retrain” crowd might as well be saying “just become a different person.”

20 million US workers are expected to need retraining in the next three years. Current workforce development programs can handle maybe 2 million. The math doesn’t math.

The Demographic Disaster

Group	AI Anxiety Level	What’s Actually Happening
18-24 year-olds	129% more anxious than 65+	Big Tech cut new grad hiring 25%
Women in AI-exposed jobs	58.87 million	More exposed than men (48.62M)
Gen Z job seekers	49% say AI devalued their degree	They’re not wrong
Tech workers 20-30	Unemployment up ~3 points	Canaries, meet coal mine

Source: St. Louis Fed, Yale Budget Lab

The McKinsey Warning

McKinsey Global Institute—not exactly a radical outfit—projects that by 2030:

Scenario	Work Hours Automated	Americans Needing Career Changes
Without generative AI	21.5%	~8 million
With generative AI	29.5%	12 million

That’s 8 percentage points of additional automation just from generative AI. And the biggest shift? STEM professionals—the people we told to “learn to code”—face automation potential jumping from 14% to 30% of work hours.

The “safe” jobs aren’t safe anymore. The advice to “get a tech job” is about as useful now as “get a factory job” was in 1975.

Source: McKinsey Global Institute

Historical Context: Why This Time Is Different

Era	Disruption	Time to Adapt	What Workers Did
Industrial Revolution	Mechanization	~60 years	Moved to factories
Electrification	Factory automation	~40 years	Specialized skills
Computing	Digital transformation	~30 years	Learned computers
AI Era (2020s)	Cognitive automation	~10 years	???

Every previous disruption affected manual labor first, giving cognitive workers time to adapt. This one is eating cognitive labor first. The lawyers, accountants, programmers, and analysts are getting hit before the plumbers and electricians.

And the timeline is compressing. What took 60 years in the 1800s is happening in a decade now. AI capability doubles roughly every year. Humans don’t evolve that fast.

What These Numbers Really Mean

Let me translate the statistics into plain English:

“4.9% global unemployment” means: The system is stable by its own metrics—but its metrics are designed to hide instability.
“Youth unemployment at 12%+” means: An entire generation is being locked out of the economy before they start. This is how you get revolutions.
“1.1 million layoffs in 2025” means: Companies are restructuring for an AI-first future. The humans are being shown the door.
“77% of new AI jobs require master’s degrees” means: We’re creating a two-tier labor market—PhD holders and everyone else.
“14% of workers already displaced by AI” means: We’re not predicting the Labor Cliff. We’re already falling off it.

The numbers don’t lie. But they do whisper when they should be screaming.

The Unscarcity Perspective

In the Unscarcity framework, these statistics aren’t just data points—they’re early warnings of the Labor Cliff, the moment when human labor becomes economically optional for most tasks.

The solution isn’t more job training programs (though those help at the margins). The solution is recognizing that an economy built on human labor is becoming obsolete, and building new systems—like the Abundant Foundation and Impact Points—that decouple survival from employment.

We have a choice: distribute the gains from AI broadly, or concentrate them among the few who own the machines. The statistics above show which direction we’re currently heading.

The clock is ticking. The numbers don’t lie.

Live Data Sources

For real-time tracking:

US Employment: Bureau of Labor Statistics
EU Unemployment: Eurostat
Global Data: ILO ILOSTAT
Tech Layoffs: Layoffs.fyi | TrueUp Tracker
AI Impact Research: Yale Budget Lab

Sources and References

Official Government & International Organizations

Layoff Tracking

AI and Automation Research

News Coverage

This page is updated regularly as new data emerges. The Labor Cliff waits for no one.

"Global Employment Statistics 2025: The Numbers Behind the Labor Cliff"