AI Can Technically Perform 12% of US Labor Market's Wage Value, MIT Simulation Finds

Researchers at MIT and Oak Ridge National Laboratory have built a simulation that models all 151 million American workers and their skills, then maps those skills against the capabilities of over 13,000 AI tools currently in production to see where the two overlap. The answer, according to their analysis: 11.7% of the US labor market's total wage value, or about $1.2 trillion, sits in tasks that AI systems can technically perform [PDF].

The researchers call this the Iceberg Index, and the name is deliberate. The visible AI disruption happening in tech jobs right now accounts for only 2.2% of labor market wage value. The remaining exposure lurks in cognitive and administrative work across finance, healthcare administration, and professional services, and unlike tech-sector disruption, it's spread across all fifty states rather than concentrated on the coasts.

Delaware and South Dakota show higher Iceberg Index values than California because their economies lean heavily on administrative and financial work. Ohio and Tennessee register modest tech-sector exposure but substantial hidden risk in the white-collar functions that support their manufacturing bases.

To validate the framework, the researchers compared their predictions against Anthropic's Economic Index tracking real-world AI usage from millions of Claude users. The two measures agreed on state categorizations 69% of the time, with particularly strong alignment at the extremes.

The Iceberg Index doesn't predict job losses or adoption timelines. It measures technical capability, the overlap between what AI can do and what occupations require. Traditional economic indicators like GDP and unemployment explain less than five percent of the variation in this skill-based exposure, which is partly why the researchers argue workforce planners need new metrics.

Read more of this story at Slashdot.