The Talking Points
A middle manager receives the company's official AI layoff script and realizes every firing for the past year had the same justification, word for word.
The email arrived at 6:47 AM on a Tuesday, which Karen Matsuda had learned was the universal corporate signal for bad news. Good news came at 10 AM with exclamation points. Bad news came before sunrise with clinical precision.
Subject: Updated Workforce Optimization Talking Points — Q1 2026 (CONFIDENTIAL)
She opened it with one eye, still holding her coffee, and scrolled past the corporate letterhead to the first bullet point.
"As part of our ongoing AI transformation strategy, we have made the difficult decision to optimize our workforce to better align with the capabilities of our intelligent automation platform."
Karen read it again. Then she opened the Q4 2025 version.
"As part of our ongoing AI transformation strategy, we have made the difficult decision to optimize our workforce to better align with the capabilities of our intelligent automation platform."
Identical. Word for word.
She opened Q3.
Same.
Q2.
Same.
She scrolled back to Q1 2025 — the first quarter Nexion Technologies had used the AI talking points. Before that, layoffs had been attributed to "market conditions" or "strategic realignment" or, in one memorable 2019 memo, "synergistic headcount rationalization." But since January 2025, every single workforce reduction at Nexion — all four rounds, 2,300 people — had been justified with the exact same paragraph about AI transformation.
Karen knew this because she had delivered the paragraph fourteen times. Fourteen one-on-one meetings where she sat across from someone whose job was ending, read the script from her laptop screen, and watched their face change.
She also knew something else: Nexion's "intelligent automation platform" was a Salesforce dashboard with three custom reports and a Zapier integration that Marcus from IT had built during a slow Friday. It had not replaced a single job. It could not replace a single job. It was, at best, capable of sending a Slack notification when a sales lead hadn't been contacted in 72 hours.
The 8 AM all-hands with the People Operations team confirmed what Karen already suspected.
"We're reducing the Customer Insights division by forty percent," said Diane Kessler, VP of People Ops, as though she were announcing the lunch menu. "Affected employees will be notified between 9 and 11. Karen, you have seven."
"Seven people?"
"Seven conversations. Here's the list."
Karen looked at the names. She recognized all of them. Rachel, who organized the office birthday celebrations. David, who had been at Nexion for eleven years. Priya, who had just gotten back from maternity leave three weeks ago.
"The AI talking points are attached to the calendar invites," Diane added. "Do not deviate from the script. Legal has approved this specific language."
"Diane, can I ask—" Karen hesitated. "What AI is replacing the Customer Insights team?"
Diane blinked. "The intelligent automation platform."
"The Salesforce dashboard?"
"The intelligent automation platform," Diane repeated, with the particular emphasis that meant the conversation was over. "Nine AM sharp, Karen. Conference room B."
Conference Room B had been redecorated since the last round. Someone had added a small succulent and a framed poster that read "Innovation is a Journey." Karen wondered if the poster had been purchased before or after the decision to fire seven people.
Rachel Okonkwo arrived first, precisely at nine, wearing a blazer that suggested she already knew.
"Hi, Rachel."
"Hi, Karen." Rachel sat down and folded her hands. "Is this about the restructuring?"
Karen opened her laptop. The script glowed on screen like a teleprompter.
"As part of our ongoing AI transformation strategy," Karen began, "we have made the difficult decision—"
"Stop." Rachel held up one hand. "Can you just tell me in normal words?"
Karen looked at the script. She looked at Rachel. She looked at the succulent, which was, she now noticed, fake.
"Your position is being eliminated."
"Because of AI?"
The honest answer was: because Nexion had missed its Q4 revenue target by 12% and the board wanted headcount reductions before the February earnings call. The AI had nothing to do with it. The AI couldn't do Rachel's job. Rachel's job was to fly to Des Moines and sit in a room with a regional grocery chain buyer and listen to them complain about shelf placement for three hours and then translate those complaints into a insights report that the sales team would ignore. No AI on Earth could replicate that particular combination of empathy, patience, and tolerance for passive aggression.
But the script said what the script said.
"As part of our AI transformation—"
"Karen." Rachel's voice was gentle. "I read the same article in Fortune last week. The one about AI-washing. Companies blaming AI for layoffs that have nothing to do with AI." She paused. "Is that what this is?"
Karen's finger hovered over the trackpad. On the screen, the next bullet point read: "We are committed to supporting affected employees through our comprehensive transition program, which includes access to AI skills training resources."
The AI skills training resources were three LinkedIn Learning links and a 15% discount code for a Coursera subscription that expired in 30 days.
"Rachel, I'm not allowed to—"
"I'm not asking you to get in trouble. I'm asking you to look me in the eye and tell me whether a machine is actually doing my job, or whether the board needed a story that would make the stock go up."
Karen closed the laptop.
"It's the second one," she said.
By 10:30, she had delivered the script four more times. She had not closed the laptop again. She had read every word as written, watched each person absorb the peculiar fiction that an algorithm had rendered them obsolete, and handed them the folder with the severance details and the Coursera discount.
David Chen, the eleven-year veteran, had cried. Not loudly — he had simply taken off his glasses, wiped his eyes with his sleeve, and asked Karen if the company's reference policy had changed. It hadn't. She told him he'd receive a strong reference, which was true, because David was excellent at his job, which had nothing to do with why he was losing it.
The fifth conversation was with Priya Sharma, and Karen had been dreading it since 6:47 AM.
Priya walked in carrying her laptop bag, her badge still clipped to her lanyard. Three weeks back from maternity leave. Karen had approved her leave extension personally, had written "Welcome back, Priya!" on the team Slack channel, had assured her that her position was secure.
"Please sit down, Priya."
Priya sat. She looked at the laptop, the folder, the fake succulent.
"Oh," she said.
Karen opened the laptop. The cursor blinked on the first line of the script. Outside the window, she could see Marcus from IT walking past, earbuds in, probably on his way to update the Salesforce dashboard that Nexion's quarterly investor letter described as "our proprietary AI-driven intelligence engine."
"As part of our ongoing AI transformation strategy," Karen read, and the words tasted like copper, "we have made the difficult decision to optimize our workforce—"
"Is it because I took leave?"
"No. No, Priya, this is about AI—"
"What AI? I ran the predictive analytics for the entire Midwest region. I built the models. I am the AI in this department."
Karen stared at the script. The script did not have an answer for this. The script had not anticipated that the person being fired for AI was, in fact, the closest thing the department had to AI.
"Legal has approved this specific language," Karen heard herself say, like a recording of someone she used to be.
At 11:15, Karen sat alone in Conference Room B. The fake succulent watched her from the corner. She had two conversations left, scheduled for after lunch, but she couldn't eat. She opened her phone and looked at Nexion's stock price. It was up 3.2% on the morning's announcement. The headline on TechCrunch read: "Nexion Technologies Announces Strategic AI-Driven Workforce Optimization, Stock Jumps."
She scrolled through the comments. Someone had written: "Smart move — every company needs to trim the fat that AI makes redundant." The person had 47 likes. Someone else wrote: "When will workers learn that fighting AI is like fighting the weather?" That one had 112 likes.
Karen opened the Fortune article Rachel had mentioned. She read the headline: "AI-washing and forever layoffs: Why companies keep cutting jobs, even amid rising profits."
The article cited a Forrester study: 55% of employers who laid off workers citing AI now said they regretted the decision. Most had rehired for the same positions within six months, often at higher salaries. The layoffs hadn't been about AI at all. They had been about the quarterly earnings call, the stock bump, the narrative that shareholders wanted to hear.
She looked at the talking points on her screen. She thought about the fourteen times she had read them. She thought about 2,300 people. She thought about the Salesforce dashboard.
Then she opened a new document and began to type.
The subject line read: "Re: Updated Workforce Optimization Talking Points — Q1 2026 (CONFIDENTIAL)"
She sent it to Diane at 11:42, cc'ing the Chief People Officer, the General Counsel, and — after a moment's hesitation — the CEO.
It read:
"Diane — I wanted to flag that the attached talking points are identical to the ones used in Q4 2025, Q3 2025, Q2 2025, and Q1 2025. Over the past year, we have used this exact script to terminate 2,300 employees, attributing each reduction to our 'intelligent automation platform.' For the record, the platform in question is a Salesforce dashboard with three custom reports and a Zapier integration. It has automated zero jobs. It cannot automate any of the jobs we attributed to it. I am raising this because Fortune published an investigation yesterday into exactly this practice, and multiple studies now show that companies using AI justifications for non-AI layoffs face significant legal and reputational risk. I recommend we update the talking points to reflect reality. — Karen Matsuda, Director, Customer Insights"
She stared at the sent confirmation for a long time.
Her calendar chimed. Two conversations left. 1 PM. Conference Room B.
Karen picked up the folder with the severance details, the Coursera discount, and the carefully crafted fiction that a machine had learned to do something no machine could do.
She walked toward Conference Room B, stopped, turned around, and walked out the front door instead.
The succulent didn't notice.
This story was inspired by recent reporting covered in my analysis of AI-washing layoffs and the corporate fiction of AI-driven workforce optimization.