Key reasons from LinkedIn’s statement:
“We are adapting our organizational structures and streamlining our decision-making. We are continuing to invest in strategic priorities for our future and to ensure we continue to deliver value for our members and customers.”
In a summary of what we know:
1. The layoffs affected engineering, product, talent, and finance teams, with a majority of the affected employees coming from the engineering department.
2. This is the second round of job cuts that the company has made this year, following the layoff of 700 employees in May of 2023.
3. The layoffs are due to a company reorganization, with a note that in the last quarter, LinkedIn’s income increased by 5% year-over-year.
4. Early this month the company also released new AI products for candidate discovery and AI-powered coaching for its premium subscribers.
Without a clear statement on the reason for these layoffs, enterprise and tech media suspect that it’s due to AI initiatives to automate work previously done by employees.
In fact, the layoffs came after the company’s latest transparency report, published by their VP on Legal Digital Safety, mentioning efforts to improve automated processes to address LinkedIn community issues, with a key interest in fake accounts, spam, and malicious content. This transparency report identifies that 9.9% of fake accounts need to be revised manually, a process that is one of the company’s targets to automate.
We must note that this is not the first time Big Tech layoffs are speculated to be caused by automatization initiatives. In January, Forbes highlighted that because of the ongoing tech trends, it was tentative to relate AI as a cause. The feasibility of replacing the workforce with AI varies if the company’s foundation is technologically based, due to a main factor: data.
If it’s either to market and direct ads, like most social media platforms, or to automate employees’ tasks, it’s faster to train AI on a programmer’s code accessible from the server.
LinkedIn’s current job offers are oriented towards senior and management tech positions, with intense weighing on machine learning and data science engineers, signifying a shift from traditional “programming” careers and a significant leap into the future.
Written by Emily UlloaShare this: