Some of the largest tech companies in the world have collectively fired more than 150,000 employees over the past few months. There are many reasons why this is necessary. Most of them boil down to the need to cut costs as global economic growth slows.
It’s unlikely that it will be, as the companies involved require money. Microsoft HTMLFT +2.1%, a company that is believed to have laid off approximately 10,000 employees, announced almost simultaneously that it will invest $10 billion into OpenAI, the creators and maintainers of viral app ChatGPT. The decision to invest $1 million per laid off employee in an AI company seems to have a business purpose. Google’s parent company Alphabet GOOGL+2%, GOOG +2% Google, announced that it plans to cut its global headcount by 12,000 people. This is a reduction of approximately 6%. Sundar Pichai, CEO of Google, has described AI as the most transformative technology ever. He stated that the strategy would be to “direct the capital and talent to our highest priorities” in the announcement. There is widespread belief that Google is developing its AI-powered solution to ChatGPT. This will be revealed soon.
Together, four of the largest tech companies, Meta, Alphabet and Amazon (+2.6%), have cut 50,000 jobs. Elon Musk, Twitter’s new boss, is reported to have fired half the employees of the company when he assumed control at the end last year.
What is the real reason for mass job cuts that have left thousands of people (80% in the US) without work? This is what 365 Data Science data experts tried to find out when they ran their own analysis on the figures.
Some of these findings weren’t surprising. It is well-known that tech companies, buoyed by record revenues, went on a hiring spree during and after the Covid-19 pandemic. As the competition for top talent raged, salaries rose to record levels and media was filled with stories about lavish perks. It’s no surprise that the median tenure of a laid-off employee is approximately two years. This could indicate that these cuts are a reversal of policies in hiring since the pandemic.
Surprisingly, the median experience level for those who were fired was 11.5 years. It’s possible that these workers are not all junior workers who have little experience and could be replaced quickly or even had their jobs automated. This could be because employees who have been around longer tend to get higher salaries. Businesses could reduce their costs by cutting them.
It is however interesting to see that 28 percent of all layoffs were in HR. This could be due to two reasons. First, if companies lay off employees, they will cut back on recruiting, which means that less HR staff is needed.
Another, but perhaps equally important reason is that automation is transforming HR. There are already platforms that automate routine tasks such as interviewing new hires and verifying their identities. It has been reported that Amazon and other companies have used AI to identify underperforming employees and then fire them.
We also learn how the affected roles differed from one company to another. At Microsoft and Meta, HR and talent sourcing were the most affected, while at Google and Twitter it was the software engineers who suffered the most.
365 Data Science data also showed that only 56 percent of those who were fired (56%) were women. This is concerning, as the tech industry has spent much time trying to correct the gender imbalance in the field. This doesn’t send a positive message to female potential hires. They will be faced with a lower pay gap and less chance of advancement to senior positions.
Another worrying statistic I found in the report was that only 10% of the laid-off have yet to list a new job on LinkedIn. It’s still too early to know if this will lead to long-term unemployment. Many may be simply looking for a job while they wait. Some may not even have updated their profiles. This statistic will be closely monitored over the next months to see if it becomes easier for skilled tech workers in moving between jobs. A large number of skilled tech workers may decide to go into freelance or self-employment.
Is it possible that tech giants have grown too fast and too far? Is it possible that AI and automation innovations have made it so that replacing people with machines is the fastest way to save money? It’s likely that it is a combination of both. Although automation has not been mentioned by any of these companies as a driving factor in the move, considering the job descriptions and the reading between the lines it’s easy to conclude that it is.