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Six months into 2025, over 50,000 tech workers have already been laid off, with giants like Microsoft, Google, Meta, IBM, PwC, and Chegg Inc. making headlines. While AI is often cited as the culprit, insiders say the reality is more complex—part efficiency, part financial recalibration.
Full story here: https://lnkd.in/d5G93Uu7
You Should Know:
1. Monitoring Layoff Trends with OSINT Tools
Track layoff announcements using open-source intelligence (OSINT) techniques:
Use Twitter API to scrape layoff-related keywords twint -s "tech layoffs OR AI layoffs OR Microsoft layoffs" --since 2025-01-01 -o layoffs.csv --csv
2. Analyzing Financial Reports for Layoff Indicators
Extract financial data to predict layoffs:
Use Python and yfinance to analyze stock trends
import yfinance as yf
msft = yf.Ticker("MSFT")
print(msft.history(period="6mo"))
3. Automating Job Posting Scraping
Compare layoffs with new job postings:
Use BeautifulSoup to scrape LinkedIn job listings
pip install bs4 requests
python -c "from bs4 import BeautifulSoup; import requests; html = requests.get('https://www.linkedin.com/jobs').text; soup = BeautifulSoup(html, 'html.parser'); print(soup.find_all('h3'))"
4. Detecting AI-Driven Workforce Changes
Check if AI tools are replacing roles:
Use NLP to analyze job descriptions for AI keywords
pip install nltk
python -c "import nltk; from collections import Counter; text = open('job_descriptions.txt').read(); tokens = nltk.word_tokenize(text); print(Counter(tokens)['AI'])"
- Windows & Linux System Monitoring for Layoff Preparations
Check for unusual IT activity before layoffs:
Windows: Check recent user logoffs (possible layoff prep) Get-EventLog -LogName Security -InstanceId 4647 -After (Get-Date).AddDays(-7)
Linux: Check for mass file access (HR preparing termination docs) sudo auditctl -w /hr/terminations/ -p rwa -k layoff_tracking
What Undercode Say:
The tech industry’s layoffs are not just about AI—they reflect financial restructuring, automation, and strategic shifts. By analyzing OSINT data, financial trends, and job postings, cybersecurity and IT professionals can anticipate workforce changes. Automation tools and scripting can help track these patterns, ensuring better preparedness.
Expected Output:
- A CSV file of layoff-related tweets (
layoffs.csv) - Stock performance data for tech companies
- Scraped job postings for trend analysis
- AI keyword frequency in job descriptions
- Windows/Linux logs indicating HR activity
Prediction:
By 2026, AI-driven efficiency will lead to more specialized roles, reducing mid-level tech jobs. Companies will increasingly rely on automated hiring/firing systems, requiring advanced monitoring tools to track workforce changes.
References:
Reported By: Mthomasson Ai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
