2026 Archives - UnemploymentData.com

The U.S. Bureau of Labor Statistics (BLS) released its February
employment / unemployment report on March 6^th,2026.

Employment / Unemployment

Seasonally Adjusted U-3 was 4.4% up from 4.3%
Unadjusted U-3 was 4.7%
Unadjusted U-6 was 8.3%
Labor Force Participation was 62.0%
Unadjusted Employment rose from 156.723 million to 157.286 million
30,000 of the Job losses are temporary due to a strike
ADP is reporting 63,000 Jobs added in February, saying “Hiring jumped in February, delivering the best showing for job gains since November 2025″
Next Update: April 3rd, 2026

Summary:

The BLS adjusted many of its numbers this month. According to the BLS “January 2026 estimates were revised to incorporate updated population controls.” January’s UnAdjusted U-6 was increased from 8.7% to 8.8%, UnAdjusted U3 was increased from 4.6% to 4.7%, but the Seasonally Adjusted U3 remained the same at 4.3%. The changes were based on the adjustment of the Civilian population from 274.982 million to 274.676 million.

According to the Commissioner of the U.S. Bureau of Labor Statistics:

“Total nonfarm payroll employment edged down by 92,000 in February, and the unemployment rate changed little at 4.4 percent, the U.S. Bureau of Labor Statistics reported today. Employment in health care decreased, reflecting strike activity. Employment in information and federal government continued to trend down… Both the unemployment rate, at 4.4 percent, and the number of unemployed people, at 7.6 million, changed little in February.”

You can read the full BLS report here.

As usual, they are talking about “Seasonally Adjusted Jobs,” and there was a major strike of Healthcare workers in California and Hawaii that accounted for 30,000 of those lost jobs, which will be back shortly.

Looking at the Unadjusted Establishment Survey report we see…
Originally, the BLS reported employment of 156.714 million for January, which they adjusted slightly to 156.723 million this month.

They are currently reporting 157.286 million jobs for February which is actually an increase of 572,000 jobs based on their original numbers. The LFPR was originally 62.5% in January, but January’s numbers were adjusted down to 62.1% this month. February is said to be 62.0%.

AI Is an Easy Scapegoat for Layoffs

Several major companies across tech, finance, logistics, and consulting have publicly cited AI or “AI-driven efficiencies” as justification for layoffs. The most explicit examples include Block, Amazon, Salesforce, Accenture, and dozens more identified in aggregated reports. Some CEOs and analysts argue many firms are using AI as a scapegoat for deeper business problems.

Block cut ~40% of its workforce (~4,000 jobs). CEO Jack Dorsey said AI “enables a significantly smaller team to do more and do it better.” But they were really inefficient to start with.

Amazon blamed AI for a 14,000-job reduction. Meta cited AI investments while cutting jobs; critics say the real cause was overexpansion. Google said layoffs linked to “AI-driven restructuring,” though OpenAI’s Sam Altman says AI is not the real driver. According to CNBC, many firms “significantly overhired” and are now using AI as a public-facing justification for layoffs.

Bottom line: Although AI is enabling some productivity gains and role consolidation, especially in customer support, basic coding, content production, and back-office workflows. But, announced “AI-related layoffs” are still a small share of total layoffs, and Macro productivity data doesn’t yet show a clear, AI-driven step-change. Many firms appear to be using AI as an excuse to impress investors, signal “modernization,” or cover overhiring and weak demand rather than reporting actual savings.

On Wednesday, the European Central Bank (“ECB”) contradicted the AI job loss narrative by saying:
“Companies that make significant use of AI are about 4% more likely to take on additional staff. In other words, AI-intensive firms tend, on average, to hire rather than fire. Much the same can be said of investment in AI: firms that invest in AI are nearly 2% more likely to hire additional staff than those that don’t.”