Jesus' Coming Back

Twitter co-founder admits mistakes

Jack Dorsey has claimed responsibility for the layoffs at Twitter in the aftermath of Elon Musk’s takeover

Twitter co-founder and former CEO Jack Dorsey has apologized for the ongoing layoffs at the company, claiming he had grown it “too quickly.” The layoffs, which have already affected 50% of Twitter’s workforce, came after billionaire Elon Musk purchased the social media platform.

“Folks at Twitter past and present are strong and resilient. They will always find a way no matter how difficult the moment. I realize many are angry with me. I own the responsibility for why everyone is in this situation: I grew the company size too quickly. I apologize for that,” Dorsey said in a tweet, adding that he was “grateful for, and love, everyone who has ever worked on Twitter,” admitting that he did not expect such feelings to be mutual at “this moment or ever.”

The heartfelt apology from the CEO, who stepped down last year, followed media reports suggesting that “everyone” at Twitter have been blaming Dorsey for the post-takeover turmoil. “Jack is hated at Twitter,” an anonymous source told the New York Post. “They blame what happened with Elon taking over the company on Jack.”

“The company hadn’t grown because Jack was gallivanting around on his jet with these models. But nobody wrote about what a disaster the company was because everyone idolizes Dorsey and thinks he’s the next Steve Jobs,” the source added.

Twitter employed some 7,500 at the time of takeover. The layoffs were the only viable option, Musk explained, citing massive losses that the company has been experiencing.

“Regarding Twitter’s reduction in force, unfortunately there is no choice when the company is losing over $4M/day. Everyone exited was offered 3 months of severance, which is 50% more than legally required,” he tweeted on Friday.

According to a number of tweets by the social media platform’s employees, the layoffs primarily affected communications, content curation, human rights, and machine learning ethics teams, as well as some product and engineering departments.

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