A new MIT study suggests that the economic impact of artificial intelligence may be far larger than what current adoption levels reveal.
Researchers from the Massachusetts Institute of Technology compared the task-level skills of production AI tools to human occupational skills across 923 jobs and 151 million workers.
Dubbed as Project Iceberg, the study covered 32,000 distinct skills in 3,000 counties to study AI’s potential impact on America’s $9.4 trillion labor market.
MIT researchers find that current AI systems have technical capability overlap with tasks representing 11.7% of the US labor market, representing $1.2 trillion in annual wages. While the study highlights that the figure reflects technical substitution potential and not a forecast of immediate displacement, researchers note that large firms are already adopting AI tools to trim headcount or freeze hiring.
“IBM reduced HR staff through AI automation, Salesforce froze hiring for non-technical roles, and McKinsey projects that 30% of financial tasks could be automated by 2030.”
Other firms that either plan or have reduced their workforce amid AI adoption include HP, Amazon and Salesforce. Challenger, Gray & Christmas said US employers announced 153,074 job cuts in October amid AI adoption, rising costs and cooling demand.
According to MIT researchers, the sectors with the highest exposure are finance, healthcare and professional services. They say these fields rely heavily on cognitive and administrative work such as analysis, documentation, compliance review, scheduling, claims processing, forecasting and internal reporting. Many of these tasks match closely with the capabilities of current AI tools, making them more vulnerable than industries defined by physical or manual labor.
Researchers are now calling on policymakers and leaders to prepare for the widespread adoption of AI in business.
“By using both traditional metrics and the Iceberg Index, policymakers gain a more complete picture of today’s economy and tomorrow’s transitions, enabling better alignment of training, infrastructure, and investment strategies with the realities of AI adoption.”
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