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The Fuzzy Work

2020-2025: the nebulous shift of the tech industry

Between March 11, 2020, and April 16, 2025, tech companies around the globe conducted a series of layoffs, impacting people working in many different industries. However, job cuts don’t always mean business failure; Layoffs in the past five years are often a symptom of macroeconomic shifts. Post-COVID normalization, the end of easy money, and the rise of AI all converged to reshape what tech work looks like.

This essay explores data shared on Layoffs FYI using interactive charts.

In the original dataset, each row represents a layoff announcement in the news. For this analysis, I rolled up the announcement data by company. I also simplified the classification of the funding stage of a company. See Behind The Scenes for details.

The most impacted industries

The chart below compares the number of companies that announced layoff events and the number of jobs eliminated in each industry.

Companies

industry

Jobs Eliminated

Note that the definition of an industry can be ambiguous.

“Other” included companies like Microsoft, Miro, Oracle, and Asana.

“Infrastructure” refers to information networking companies like Cisco, VMware, New Relic, Docker, and BitTorrent.

Google and Meta are classified as “Consumer”, whereas Apple is in “Hardware”, and Amazon is in “Retail”, although they all have businesses in different industries.

Startups disappear quietly.
Big Tech cuts loudly.

The chart below compares the number of companies and the number of jobs eliminated at different funding stages.

The top 3 made up nearly 10% of the entire job loss.

Publicly traded companies are often large employers, and they could also eliminate the largest number of jobs in times of uncertainty.

In the past 5 years, Amazon eliminated 27,840 jobs, making it the top for job loss, followed by Meta, which eliminated 24,600 jobs, and Intel, 16,115.

1 in 3 companies that announced a 100%-layoff is at an early funding stage.

Early-stage companies are more susceptible to social-economical shifts and, hence, more likely to announce layoffs or even go bankrupt, but they tend to have much less impact on the total job loss. Of 78 companies that announced 100% layoffs, presumably bankrupted, 25 are at an early funding stage.

Companies

stage

Jobs Eliminated

"Unknown" stage companies included many foreign companies such as SoundCloud, ByteDance, and Huawei, as well as US companies like Everlane.

How industry and funding stage cross-compare

Although public companies make the headlines, the rise and fall of startups is more indicative of innovation trends.

Assuming “100% layoff” as bankruptcy, the number of bankrupt companies took up about 4% of the entire set. While 4% is negligible, bankruptcy alone could be an indicator to understand the industry shift.

The chart below provides options to observe the layoff impact based on companies, jobs, or bankruptcy.

Choose which measure to visualize

Public

Private

Unknown

Early Stage

Mid Stage

Late Stage

Retail

9

Consumer

4

Hardware

0

Other

2

Transportation

5

Infrastructure

1

Sales

1

Healthcare

10

Food

8

Education

5

Finance

4

Travel

4

Fitness

0

Energy

1

Manufacturing

0

Real Estate

1

HR

0

Construction

1

Support

2

Recruiting

2

Media

4

Marketing

5

Crypto

1

Data

1

Legal

0

Aerospace

2

Security

1

Logistics

2

Product

2

AI

0

78

80% of job loss came from 20% of companies

The layoff pattern follows the 80-20 rule: The top 20% of companies are responsible for 80% of job elimination.

Specifically, of 1891 companies in the dataset, the top 378 companies eliminated 576,529 jobs, out of the total 707,544 job loss.

The job elimination per company ranges from 4 to 27,840.

You can use the filters below to play with the data and see more patterns.

Filter selected industries:
Filter data range:
Observe data distribution:

Stage
Count

Public
18

Private
7

Unknown
1

Early Stage
4

Mid Stage
9

Late Stage
2

200jobs14,708jobs

Closing thoughts

In the 2010s, being a tech company meant rapid growth, high valuation, modern culture, and investor hype. But in the 2020s, that label carries more scrutiny: platform monopolies, information silos, fake news… and existential questions about AI, data, and power.

The ground beneath the industry is shifting—and with it, so are the stories we tell about work, security, and progress. We can’t predict what the next five years will look like, but one thing is clear: we’ll need to keep adapting—our skills, our assumptions, and our expectations.

The charts you just explored show trends and numbers, but behind every datapoint is a decision, a departure, a disruption.

So what did you feel as you moved through this data? What do you take away from all this?

For details on how this project came into being, read Behind The Scenes of this project.

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