Is Mass Layoff Due to AI? Exploring the Truth Behind Major Tech Companies' Layoffs: A New Dawn in the AI Era?

At the end of October 2025, Amazon announced a reduction of approximately 14,000 people worldwide. Education tech company Chegg announced a 45% workforce reduction, Salesforce planned to cut 4,000 customer support positions, and logistics giant UPS revealed a net reduction of 48,000 people since last year. The term "AI" and "machine learning" prominently featured in all announcements, sparking discussions on social media about AI beginning to take over jobs. However, the numbers indicate a more complex reality. This article calmly organizes the movements of each company, changes on the ground, reactions on social media, and practical strategies workers should adopt now.

1) What Happened: A Brief Timeline

• October 28 (local time): Amazon confirmed plans to cut approximately 14,000 positions, citing "efficiency" and "organizational restructuring," along with the advancement of AI utilization.

• October 27: Online education company **Chegg announced a 45%** reduction, explaining that the spread of generative AI forced a review of its existing model.

• Last month: Salesforce announced a reduction of 4,000 customer support positions. The top management stated that "AI agents are handling" the tasks.

• End of October: UPS revealed a reduction of 48,000 people since last year. The president had previously hinted at the impact of demand fluctuations and optimization through machine learning.

Note: The above is a summary of the numbers and points mentioned in official explanations and reports by each company. AI is treated as one of the multiple factors, not the "sole reason."

2) Is "AI as the Main Cause" a Misunderstanding or Half-True?

Mass layoffs by companies always involve multiple factors. In the context of fall 2025, the following four points overlap.

1. Macro Environment: With high interest rates and capital costs, profit margins and cash generation are prioritized.

2. Post-Pandemic Adjustment: Normalization of workforce from past years' over-hiring (anticipating demand surge)..

3. Increase in "Productivity per Unit of Workforce" due to AI: More scenarios where the same workload can be handled by fewer people.

4. Investment Portfolio Reallocation: Job restructuring during the process of shifting resources from legacy businesses to AI/automation pipelines.

In other words, while there are indeed situations where "AI is the trigger," it is also undeniable that AI is being used as a **"rationalization explanatory variable"** in the optimization problem with cash generation and investor expectations.

3) "Job Redesign" Happening on the Ground

Areas prone to replacement and areas being expanded are being divided as follows.

• Customer Support: Initial responses are being handled with high accuracy by AI agents. Humans focus on escalation (complex/high-value cases).

• Operations/Logistics: Machine learning is constantly running for demand forecasting, route optimization, and shift design. The field shifts to exception handling and safety management.

• Back Office: Repetitive tasks in accounting, legal, and recruitment are automated. Humans focus on review/decision-making and governance.

• Data/Product: New roles are increasing, such as prompt design, evaluation metric design, data maintenance, and AI safety verification.

The keyword is **"Redesign" rather than "Replacement."** A design competition is starting to achieve the same KPIs with different workflows.

4) Quick Notes on the Four Companies (Megatrends to Aid Understanding of This Issue)

• Amazon: Accelerating AI implementation across fulfillment, CS, advertising, and cloud. The focus of reductions is on eliminating redundancies and integrating processes.

• Chegg: The penetration of generative AI into the learning Q&A model has hit hard. A shift to AI-based new pricing/value design is urgent.

• Salesforce: CRM's automatic response and proposal functions shorten the pre-sales process. "Human involvement" is being repositioned to high-difficulty areas.

• UPS: Seasonality of demand and smoothing through ML. Exploring the optimal point between automation investment and labor costs.

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5) Social Media Reactions: Three Temperature Zones

(A) Caution/Backlash (X, Reddit)

• "AI is directly cutting jobs"—Pessimism especially in CS and back office initial response roles.

• "Profits to shareholders, pain to workers"—The debate over profit distribution reignites.

• "AI implementation is an 'excuse'"—The real aim is stock prices and cost reduction, some say sarcastically.

(B) Cautious Realism (LinkedIn)

• "Teams that changed their business design with Human × AI may resume hiring"—Sharing specific examples of skill retraining.

• "Machines handle initial responses, humans handle exceptions"—The narrative suggests rebuilding careers based on role division.

(C) Frontier Optimism (Tech Community)

• "New AI operational roles (evaluation, auditing, guardrail design) are growing"

• "The more senior the talent, the more leverage AI provides"—Good examples of small elite teams are spreading.

In summary, discussions on social media are shifting from **"total employment" to "changes in job content". The ability to redesign careers

is becoming the watershed.

6) Jobs Most Affected and "Remaining/Growing" Skills

Growing Cross-Functional Skills

• Prompt/Workflow Design (Designing branches that leave human decision-making)

• Evaluation/Audit (Not just accuracy, but responsibility, safety, explainability)

• Data Understanding (Metadata, origin, quality definition)

• Consensus Building/Communication (Role division design based on AI)
  
  
  

7) Suggestions for Companies: Giving "Narrative" to Numbers

Layoffs affect not only the short-term P/L but also the long-term hiring brand. If AI is used as a reason,

1. 1) How each process was redesigned, 2) Where human involvement is being repositioned, 3) Opportunities for skill retraining, should be shown with specific examples. Only then will trust be restored internally and externally.
   
   
   

8) Outlook for 2026: Three Scenarios

• Soft Landing: Productivity improvements from AI adoption boost profit margins, leading to selective hiring resumption.

• K-Shaped Recovery: Only companies and individuals quick to adapt to AI grow, leading to polarization.

• Backlash from Over-Automation: Decline in quality/customer experience leads to a **"reversion to humans"**. Evaluation and audit roles expand.

The path chosen depends on the quality of redesign and the depth of retraining.##HTML_TAG_