Artificial intelligence drives cost cuts, job layoffs and policy debates

A McKinsey report warns that AI inference costs are becoming a major enterprise expense, with token‑pricing dropping but overall spend rising as models move from pilot to production. The analysis highlights 13 technological levers—model optimization, specialized chips, optical networking, and energy efficiency—to lower per‑token costs, and notes that major cloud providers plan to invest over $700 billion in AI infrastructure by 2026. In the United States, AI‑linked layoffs surged in 2026, with 97,006 jobs cut in May—a 16 % rise from April—and 38,579 of those attributed directly to AI, representing 40 % of total dismissals that month. Since the start of the year, AI has been cited in 87,714 layoffs, affecting roughly 150,000 workers in the tech sector. China’s Communist Party school is sparking a debate on universal basic income, reduced work hours and stronger welfare as AI promises massive productivity gains, raising questions about wealth distribution. Corporate governance experts note that AI is reshaping board‑level decision‑making, from capital allocation to anti‑money‑laundering and fraud detection, prompting new regulatory expectations under the EU AI Act and Italy’s 2025 AI law. Researchers such as Ines El Gataa highlight systemic bias in AI models trained on predominantly Western data, warning of “compute divide” where a few nations control the hardware and data needed to build large models. Google has launched a suite of free, one‑hour AI courses on its Cloud Skills Boost platform, covering generative AI, large language models, responsible AI, and image diffusion. More than 200 economists, including several Nobel laureates, have called for immediate policy action to ensure AI complements rather than replaces human labour. Meanwhile, Meta announced a $50 billion investment in a new data‑center campus in Louisiana, and the EU is drafting age‑verification rules for social‑media access.