"The current approach to measuring AI's labour market impact relies on 'exposure' scores: calculations based on how many of a job's tasks AI can theoretically perform."
"University of Chicago economist Alex Imas argues these metrics are fundamentally inadequate. According to MIT Technology Review, exposure alone may be insufficient for predicting which jobs actually vanish."
"When AI makes a service dramatically cheaper, the employment outcome depends entirely on whether lower prices generate enough new demand to offset the efficiency gains."
"This is the gap that matters most. And it is largely unaddressed in the current analysis of AI's impact on the labor market."
The impact of AI on jobs is often assessed through exposure scores, which measure how many tasks AI can perform. However, this method is inadequate for predicting job displacement. Price elasticity, which measures demand shifts when costs decrease, is a more critical factor. If AI significantly reduces service costs and demand increases, employment may rise. Conversely, if demand remains unchanged, job losses may occur. Reliable data on price elasticity is largely missing, creating a significant gap in understanding AI's labor market effects.
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