Companies are blaming AI for job cuts. Critics say it's a 'good excuse' (www.cnbc.com)

Companies across the U.S. and Europe have publicly cited AI-driven efficiency as a reason for recent layoffs—high-profile examples include Accenture’s reskilling-focused restructuring, Lufthansa’s plan to cut 4,000 roles by 2030, Salesforce’s 4,000 customer-support cuts (which it says resulted from its Agentforce AI), and Klarna shrinking from 5,500 to about 3,000 employees amid an AI push. Critics and researchers warn many firms are using “AI” as a convenient cover for tougher business choices: pandemic-era overhiring, cost-cutting pressures, or organizational missteps. Oxford’s Fabian Stephany calls this “scapegoating,” arguing that positioning cuts as AI-driven helps firms appear innovative while obscuring other causes and fueling worker fear. But emerging labor research suggests automation-driven displacement remains limited so far. A Yale Budget Lab analysis (Nov 2022–Jul 2025) found little occupational upheaval since ChatGPT’s launch, and New York Fed work shows AI adoption rose sharply (e.g., service firms using AI 25%→40%) yet very few firms report layoffs attributable to AI; many instead use AI to retrain staff (35%) or hire (11%). The takeaway for AI/ML practitioners and policymakers: monitor firm-level AI deployment versus public narratives, prioritize transparent reskilling pathways, and measure real labor impacts with rigorous data to distinguish genuine automation effects from managerial or macroeconomic adjustments.