Simplice Fosso opened Slack (CRM) in March and saw a green checkmark next to his team’s name: “
automation.” It was small, just an icon and a single word. But it meant his role as Head of Security Operations at a major consulting firm was gone.
For months, he’d watched as his employer developed and tested a machine-learning system that could detect and sort security threats — his team’s function — until the digital system was as accurate as the human one. This meant no more 2 a.m. pages, but Fosso was wary. At first, leadership spoke of “upskilling analysts to oversee AI output.” Soon, the language shifted to “efficiency gains.”
“Between December and January, I oscillated between relief and self-doubt,” he said in an interview. “I told family and close friends this was a wake-up call to pivot, while privately wrestling with frustration and a bruised ego.”
Then came the layoff.
What happened to Fosso is happening to knowledge workers across the U.S., from entry-level to management, from tech-forward companies like Accenture (ACN) to more staid corners of Corporate America. The larger waves of layoffs make the news — Microsoft (MSFT) cutting software engineers, Duolingo (DUOL) replacing bilingual contract writers, Walmart (WMT) cutting its technology team just last week.
Many more don’t make headlines. They live inside calendar invites, Slack channels gone suddenly silent, group chats that turn to gallows humor, and remote happy hours once the axe finally falls. They’re in job listings that never get posted because the roles no longer exist.
And as the losses accumulate, a kind of ambient fear is settling in. White-collar jobs that until very recently offered a comfortable middle- or even upper-middle-class living are quietly disappearing, from copywriters and communications specialists to web designers and software developers. Even some CEOs and venture capitalists fear losing their jobs to AI.
Unlike past waves of automation, these changes are happening not on factory floors but in the land of glass-walled conference rooms and standing desks, places where your brain, degree, and ability to navigate the organization count most. That’s why they feel so different. Sekoul Krastev, a behavioral scientist and managing director of The Decision Lab, said AI-related job losses feel much more disturbing.
“It feels like you’re basically defunct — that you’re being replaced by something better than you in a way that you can’t achieve,” Krastev said. The speed of the AI wave washing across the corporate landscape makes the shift even more unsettling, as security gives way to uncertainty. “It’s a lot more difficult to compete with something evolving so quickly that you can’t predict,” he said. You’re being pitted against something that isn’t another human.”
People also feel a deep moral aversion to AI, Krastev said, which makes the already significant pain of layoffs and job cuts worse. When you’re being replaced with another person, you may feel as if your specific employer no longer needs you. When you’re replaced with AI, on the other hand, you may feel a sense of disgust in a much larger sense, one that’s more global and transcendent than personal.
That deeper disgust Krastev describes — the sense of being discarded not just by an employer, but by the system itself — is something Anne Glaberson felt viscerally.
A 20-year tech industry veteran and senior engineering manager at GoDaddy (GDDY), Glaberson was proud of how she had helped turn around her department, which covered payments and analytics for the web hosting and small-business services provider. Her team’s performance was strong, key metrics were up, and supervisors had publicly praised her work. Then she was laid off.
“You think you’re doing a good job,” she said. “So you think it won’t affect you. But it did.”
What stung even more was the pattern she saw in who else was cut, mostly people over 40, and more women than men, she said. The reorg left men in their 30s in charge of the remaining team. And it came as a shock. She’d first heard about Airo, GoDaddy’s AI-based offering, about six months before, but its functions weren’t the same as her department’s. The only warning was a Zoom invite.
“I was Slack messaging my direct supervisor,” she recalled. “And I said to him, ‘I just got pulled into a call with the president, am I getting laid off?’ And he responded to me, ‘Let me check.’ Because he didn’t know about it.”
The lesson, she said, is two-fold. Your job duties don’t need to be specifically replaced by AI for such cuts to affect you, because businesses are pulling money out of existing initiatives and reallocating it toward AI. By extension, it’s not just a wheat and chaff thing either. Even strong performers can get cut.
“Although there was shock and hurt, there was also a little bit of relief,” Glaberson said. “The day before, I was in 13 meetings. I was trying so hard to keep up with everything and make things right. Working weekends, every day until midnight. I wasn’t resentful — that was my choice — but the pace was becoming a problem.”
Like many laid-off workers, she didn’t pause for long. Within days, she was back on the hamster wheel, applying for new jobs in what she described as an “absolutely flooded” market. But something felt off. “I knew what I had been making,” she said. “And the salaries being offered were… a recipe for poverty and misery. I realized I needed to pivot.”
Glaberson has since founded her own startup with an AI angle, part of a larger pattern heard again and again. People who have been affected by AI job cuts are leaning into AI as a response. Simplice Fosso, the head of security at a major consulting firm, has since retrained in an AI-focused analytics program at Harvard.
Mark Quinn, now senior director of AI Operations at Pearl, is another example of that pivot. After being laid off from a healthcare tech startup, he turned to the very tools that disrupted his career — and found a way forward.
“We spent four or five months building a bespoke model to solve a hard problem in a vertical,” Quinn said of his former role in healthcare tech. “I fed that prompt to GPT-4 and 30 seconds later it spat out something good enough.”
Two months later, the company embraced the fact that the model could replace most of the agents Quinn had been hired to help ramp up. He was laid off not long after.
In the aftermath, Quinn built what he called “Job Hunt GPT,” a personal tool to help him rewrite his resume, identify keywords in job descriptions, and prepare for interviews. “I was learning how to actually use these tools while building it,” he said. “It was a learning journey that turned into a way back in.”
That project helped him land his current job at Pearl, where he now leads human-in-the-loop AI design and internal prompting workflows. “We’re teaching people to stop treating AI like a search engine,” he said. “It’s not a box. It’s a collaborator.” People should think of working with AI as a chance to work with a fellow expert, he added.
Quinn now uses AI not just for work, but for every aspect of his life: optimizing travel plans, finding movies to watch and dinner recipes to make based on what’s in his fridge, even helping him come up with ideas for bedtime stories for his children.
His enthusiasm is infectious. At the same time, he doesn’t sugarcoat the human cost of AI adoption. His own layoff was difficult, and he knows others’ are, too.
His advice now? “Surf’s up.” If your company isn’t talking about AI every single day, it should be, he said, and the more human workers lean into AI collaboration, the better off they’ll be, personally and professionally.
Still, the ambient fear lingers. Even the engineers quietly building the systems that lead to layoffs feel disturbed.
A data scientist working on Fortune 500 automation projects, speaking on condition of anonymity, recalled the moment when the stakes became personal. A few years ago, they spent a week listening to customer service complaint calls. The data helped them build a model that would eventually replace dozens of jobs.
Not long after, they got a takeout delivery. “Well-spoken, professional. Didn’t seem like a gig worker,” they said. Then they saw the name of the delivery person — and recognized from a list of people they’d helped lay off.
“I had the full list of names being eliminated,” they said. “It was bundled into training data. I couldn’t be 100% certain. But I put two and two together.”
They paused. “That wasn’t a good evening.”
“Once you’ve had DoorDash (DASH) delivered by someone whose job you helped eliminate… feels bad, man.”
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