Is "Fired Because of AI" True? The New Norm of Layoffs Created by "AI Washing"

Is "Fired Because of AI" True? The New Norm of Layoffs Created by "AI Washing"

"Layoffs Due to AI"—How True Is That Explanation?

Recently, the phrase "optimizing workforce because AI can automate tasks" has become a cliché in layoff announcements. Indeed, generative AI and automation tools are increasingly replacing parts of white-collar jobs, altering required skills and roles.


However, compared to the speed of this "change," the narratives accompanying corporate layoff announcements sometimes seem too polished. It's as if painful decisions are being beautified as "forward-looking reforms for the future."


This has led to the emergence of the term "AI washing." Similar to "greenwashing" in the environmental sector, it involves overstating AI's role to mask other issues (cost-cutting, declining performance, over-hiring).


What is AI Washing: It's More About "Explanation" Than "Technology"

The core of AI washing isn't that AI itself is a lie. The point is that citing AI as a reason is easier to explain and more appealing to the stock market.


Saying "we're reducing staff due to poor performance" is less appealing than "we're restructuring because AI is boosting productivity." It tends to be a more "investor-friendly" narrative for companies.


This dynamic is strengthened as expectations for AI rise. AI allows companies to talk about "future effects not yet fully realized," enabling them to assert "it will be necessary" even if current results are vague. As a result, layoffs are packaged as "strategic transformations" rather than "cleanup after failures."


By the Numbers: "Layoffs Due to AI" Are Increasing

On the other hand, the increase in "AI-named layoffs" is also confirmed by data. A report by Challenger, Gray & Christmas, known for U.S. outplacement and downsizing statistics, recorded 54,836 layoff plans citing "Artificial Intelligence" as a reason in 2025.


This number indicates (1) an increase in the frequency of companies citing AI in announcements, and (2) that this explanation has gained enough influence to drive public discourse.


However, it's important to note that this figure doesn't strictly measure the number of jobs actually replaced by AI. It's the scale of AI being cited as a "reason" in announcements, which is why the AI washing debate is valid. In other words, what's increasing is not only "replacement by AI" but also the "use of the word AI."


Case Study: What Happens When Companies Cite AI in Layoffs

A frequently cited example in reports is major tech companies.


For instance, Amazon has been advancing large-scale workforce reductions in the context of efficiency and organizational restructuring while strongly promoting AI utilization. The logic that as AI can handle more tasks, the "necessary job forms" change is understandable, but from the ground level, there's often a sense of unease as the organization shrinks first without clarity on "when, where, and which tasks AI will replace."


Similarly, Pinterest has been reported to be making significant layoffs while emphasizing its focus on AI. With such cases occurring in succession, questions arise about how much the explanation "AI increases productivity, so we reduce staff" is backed by an implementation roadmap.


Forrester's Warning: "Talking About AI Layoffs Without Mature AI"

Supporting the AI washing argument, research firms have also raised concerns. Forrester points out that some companies citing AI-related layoffs lack mature, validated AI applications capable of replacing the roles being cut.


This is a crucial point. Truly integrating AI into operations requires data preparation, governance, risk management, business design, operational structure, and training—there's a lot to be done. Merely introducing tools doesn't instantly make "people unnecessary"; in fact, during the transition period, the need for manpower might even increase.


The more one understands the reality of AI implementation, the more skepticism arises towards announcements of "reducing staff first." Misjudging the sequencing of AI implementation can lead to quality deterioration, increased incidents, worsened customer experience, and ultimately incur rehiring costs.


Social Media Reactions: A Common Feeling That "The Narrative Precedes AI"

There are three prominent types of reactions on social media regarding this topic.


1) The Theory That "The Story Changes to Match Earnings Week" (Suspicion of Investor-Oriented Staging)

Discussions shared on LinkedIn include observations like "layoffs coincide with earnings week" and "the message swings from 'AI changes jobs' to 'reducing bureaucracy' to 'reallocating around AI.'"


In essence, the view is that AI isn't the cause, but rather the most convenient explanation is retrofitted to the decision to lay off staff.


2) The Theory That "Overbetting on Immature AI Breaks the Ground" (Transition Period Accident Costs)

Comments on LinkedIn also express concerns like "it's not that AI replaced people, but management jumped on unprepared AI, reduced experienced staff, and replaced them with fragile tools, resulting in unstable products."


This is slightly different from "AI washing," focusing on failures caused by "excessive premature action" due to overconfidence in AI. However, the reliance on AI as an explanation for layoffs places it on the same ground.


3) The Theory That "AI Is an Excuse, the Real Cause Is Performance and Costs" (Traditional Restructuring with an AI Label)

Additionally, voices say "AI is an excuse to explain weak earnings" and "temporary cost cuts won't solve fundamental problems."


This type focuses more on the relationship between capital markets and management than AI itself. AI, being able to "speak of the future," is easily used conveniently.


4) Discussions in the Tech Community: "Is AI Replacing Jobs or Budgets?"

In tech communities like Hacker News, there's also discussion about the view that "AI doesn't directly take jobs, but budgets are allocated to AI investments (computing resources and development costs), reducing personnel expenses."


Here, the "replacement" is not seen as a simple human vs. AI issue but as a matter of resource allocation in management.


Checklist for Detecting "AI Layoffs"

Determining whether layoffs attributed to AI are genuine or a form of washing is difficult to discern 100% from the outside. However, at least the "strength of the explanation" can be measured from the following perspectives.

  1. Is the Targeted Work for Replacement Specific?: Which work processes, with which tools, are expected to improve which KPIs?

  2. Are the Preconditions for Implementation (Data, Governance, Operations) Discussed?: Replacement is unlikely without a foundation prior to AI.

  3. Is Redeployment and Retraining Included?: Not just reducing staff, but how to support skill transitions.

  4. Is It Overly Aligned with Short-Term Earnings Context?: Is the timing and narrative optimized for the "market"?

  5. Is There Mention of Quality and Risk?: AI replacement involves errors, accidents, and responsibility boundaries. Are these ignored?


Companies that withstand this checklist likely have a higher possibility of genuinely trying to restructure with AI. Conversely, if staff reductions precede with only abstract future narratives, there's ample room to suspect washing.


So What Can We Do?—To Avoid Being Overwhelmed by the "AI Narrative"

There are three major things workers can do.

  • Be Able to Explain Work in Terms of "Judgment" Rather Than "Procedure": Articulate exception handling, decision-making, and responsibility locations, which AI struggles with.

  • Become a "User" of AI: It's not "people who don't use AI" who are replaced, but "job forms that can't produce results with AI."

  • Read Signals from the Job Market: If the same company is increasing AI-related hiring while laying off, role shifts may be occurring.


On the other hand, what companies need is transparency. If they are to promote AI, they should speak about the realities of implementation (which takes time and costs) and the pains of the transition period (quality, risk, education). Otherwise, AI will be consumed as a "convenient excuse," continually eroding trust on the ground.


Conclusion: AI Can Be Both a Cause and an Excuse

Cases where AI-driven efficiency leads to layoffs will undoubtedly increase. However, at the same time, AI can easily be used as an explanation that resonates with investors, potentially masking the true circumstances of companies.


The question is not only "what can AI do," but also "who benefits and who bears the risk under the guise of AI." Misjudging this structure could lead organizations in the AI era to crumble not due to "technology" but due to "narrative."


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