I analyzed 153,000 Tech Layoffs to see if AI is actually stealing our jobs.

There is a growing trend of executives blaming "AI automation" for recent tech layoffs. Analysts call it "AI-washing"—using the AI hype cycle as a scapegoat to mask internal financial issues or over-hiring.

I was tired of the think-pieces, so I wanted to look at the raw data.

I grabbed a dataset tracking 153,522 tech layoffs across 35 major companies (Jan 2025 - Feb 2026) and dropped it into my BI tool, NeoShift BI.

Here is what the data actually says.

The Reality of AI-Driven Layoffs

• 14.15% were explicitly tagged as being driven by AI automation.

• 31.77% were partially attributed to it.

• 54.08% had no relation to AI.

Nearly half of all workforce reductions in this dataset involved AI in some capacity. It is not just a scapegoat; it is a measurable factor.

Where the money is moving

While legacy departments are shrinking, the dataset also tracks open headcount. Hiring for roles like ML Engineers and Cloud Security Engineers is surging, with average salaries hovering near the $200k mark. The jobs aren't disappearing; they are transitioning.

Play with the data yourself

I didn't want to just post static screenshots.

I actually used this dataset to test a new feature I shipped today for NeoShift BI: Public Dashboards. You don't need to log in or create an account. You can click the link below to interact with the live charts, hover over the data points yourself:

👉 Layoffs/AI Impact

Building the Dashboard (Product Update)

If you are curious about how the visualizations were made, I recently unified the AI generation UI in NeoShift.

I literally just uploaded the raw Kaggle CSV, and the AI handled the SQL joins and charting was easily done by describing what I wanted to see. If you work with messy datasets or need to build client portals without paying per-user licensing fees, come try the Open Beta.

What dataset should I visualize next? Let me know in the comments.

Comments

[Tim Rattigan]

Great data analysis. The "AI-washing" angle is exactly right - companies using AI as a scapegoat for financial corrections they needed to make anyway.

But there's another side to this that your data doesn't capture: what happens to the individual worker who actually tries to USE AI effectively at work? There's a short film making the rounds that nails this - a guy uses AI for a routine quarterly task, delivers better results faster, and gets fired for it.

youtu.be/O5FFkHUdKyE

Whether AI is "really" causing layoffs or just being used as an excuse, the worker gets caught in the crossfire either way.