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A new note of caution is ringing through Silicon Valley and Wall Street alike: a fresh Goldman Sachs research note warns that rapid adoption of generative AI could spark another large round of workforce reductions as soon as 2026, even though investors have stopped rewarding pink-slip announcements with the same enthusiasm they showed during the pandemic era. The 42-page report models a half-percentage-point rise in global unemployment during the “AI transition period,” translating into roughly two million job losses worldwide. Goldman analysts argue that companies racing to embed large language models into customer service, compliance, and data-entry workflows will find payroll the easiest place to harvest quick cost savings. Tech, banking, retail, and media are flagged as the most exposed sectors, while health-care and skilled trades are likely to remain understaffed. Key findings • Up to 25 % of current business-support roles in developed economies could disappear by late 2026. • Firms that announce head-count reductions tied to AI now underperform the S&P 500 by an average of 3 % over the following quarter, a reversal from the 2022–2023 “layoff pop” phenomenon. • Productivity gains from AI are projected to lift U.S. GDP by 7 % over the next decade, but the benefits will accrue unevenly. Why investors are growing skeptical Goldman’s equity-strategy team notes that cost-cutting alone no longer satisfies shareholders; markets now demand a clear path to monetizing AI products as well. Companies that simultaneously slash jobs and roll out revenue-generating AI services—such as paid copilots or premium analytics—have avoided the post-announcement sell-off. What this means for workers 1. Upskilling pressure: Roles combining domain knowledge with AI-tool fluency (e.g., financial analysts who can engineer prompts) are commanding salary premiums. 2. Soft-skill dividend: Creative problem-solving and client rapport remain difficult to automate, bolstering demand for hybrid talent in consulting and sales. 3. Geographic rebalancing: Near-shoring of data-labeling and model-maintenance tasks is offsetting some white-collar cuts in high-cost cities. Policy responses on the table • Targeted tax incentives for firms that retrain, rather than release, employees displaced by automation. • Expansion of wage-insurance schemes to cushion mid-career transitions. • Fast-track accreditation for AI-adjacent certificate programs at community colleges and vocational institutes. Bottom line The next wave of AI deployment is no longer hypothetical; companies are already rewriting org charts in anticipation of 2026. For employers, the challenge is to convert efficiency savings into new revenue streams before investor patience wears thin. For workers, the mandate is clear: pair human insight with machine speed, or risk being left behind in AI’s widening wake.