‘AI is a tsunami for jobs’: Experts warn of mass white-collar cuts as calls grow for AI windfall tax

Alap Shah, a prominent figure behind the influential Citrini Research report, is urging governments to consider implementing taxes on artificial intelligence to mitigate the economic impact of widespread job losses. In a recent interview with Bloomberg TV, Shah, who serves as the Chief Investment Officer at Lotus Technology Management, highlighted the potential for artificial intelligence to significantly disrupt labor markets and consumption-oriented economies, particularly in the United States. Shah expressed grave concerns about the pace at which AI is evolving, stating, "The smarter AI gets, the more jobs it can replace." This warning comes on the heels of a weekend analysis from Citrini that outlined scenarios of AI-induced disruption, which contributed to a selloff in stocks related to delivery, payments, and software sectors. IMF Managing Director Kristalina Georgieva echoed these sentiments, describing AI as a "tsunami hitting the labor market." She noted that up to 40% of jobs worldwide, with 60% in advanced economies, could be affected in the coming years. Georgieva emphasized the need for policymakers to manage the risks associated with worker displacement and to enhance safety nets for those impacted. Shah advocates for a tax on incremental or windfall gains resulting from AI adoption, arguing that such measures could help offset the economic fallout from job displacement. He warned that rising unemployment could lead to decreased household consumption, ultimately hindering growth in advanced economies where consumer demand is crucial to GDP. The Citrini report presents a scenario where rapid advancements in machine intelligence could significantly increase productivity while also displacing a considerable number of white-collar jobs. Shah forecasts that up to 5% of white-collar positions could be eliminated within 18 months. This cycle of job losses could create a negative feedback loop, where companies reduce staff to protect profit margins, invest savings into AI, and further automate processes, thereby diminishing consumer demand. The market has already begun reacting to these warnings, with a notable decline in stocks of delivery platforms, payment companies, and certain software firms identified as particularly vulnerable. Technology stocks are experiencing volatility amid fears that AI could fundamentally alter business models across various sectors. Shah pointed out a dichotomy in the market, where companies in the semiconductor, data center, and foundational AI model sectors are likely to thrive, while industries reliant on intermediation, such as banking and insurance, face increased risks as automation reduces service fees and demand. Georgieva has stressed the urgency of addressing the challenges posed by AI, highlighting its unprecedented speed of transformation in how we work and interact. She noted that while overall employment may see an uptick in some areas, the gains are disproportionately in low-wage jobs, thus compressing opportunities for middle-income positions. Additionally, the impact of automation on entry-level jobs is a concern for recent graduates looking to enter the workforce. Vineet Nayar, founder of the Sampark Foundation, cautioned that the assumption that AI will automatically generate jobs is misguided, especially within India's IT sector. He emphasized the importance of fostering large-scale entrepreneurship to create employment opportunities, rather than relying solely on technological advancements. Nayar also raised concerns about data ownership and the risks associated with allowing global AI models to operate on Indian data, urging for a focus on developing domestic AI capabilities. As the discourse around AI evolves, governments must address the implications for fiscal policy. If AI-driven productivity increases are coupled with shrinking payrolls, the need for a restructured tax system becomes apparent. Shah's proposal to tax AI windfalls could serve as a framework for stabilizing labor-market disruptions before they lead to broader economic challenges.