New Measure Reveals AI’s Impact on Labor Market: Early Evidence

The integration of artificial intelligence (AI) into the workforce is one of the most significant developments in recent years. A new measure has been introduced to quantify the impact of AI on job displacement, termed “observed exposure.” This method combines both theoretical AI capabilities and actual usage in workplaces. Emphasis is placed on automated, work-related applications rather than augmentative uses.

Key Findings on AI’s Labor Market Impact

• AI’s actual application remains limited compared to its theoretical capabilities.
• Occupations with higher observed exposure are anticipated to experience slower growth from 2024 to 2034, as outlined by the Bureau of Labor Statistics (BLS).
• Workers in highly exposed jobs tend to be older, more educated, and better compensated.
• No significant increase in unemployment for these workers has been noted since late 2022, although younger workers show signs of decreased hiring in exposed fields.

Framework for Assessing AI’s Effects

Past attempts to assess job vulnerability have often fallen short. For instance, earlier research suggested that a significant portion of jobs were at risk of offshoring, yet many of these jobs continued to grow in availability. The new framework addresses this gap by focusing not only on theoretical AI capabilities but also on how these capabilities translate into real-world applications.

Understanding Observed Exposure

The observed exposure measure evaluates tasks most likely to be influenced by AI by combining data from multiple sources:

• The O*NET database, which categorizes tasks across various occupations.
• Usage data from the Anthropic Economic Index, tracking real-world application of AI technologies.
• Exposure estimates based on research that assesses task feasibility via AI.

This measure helps identify the extent of occupational exposure to AI and how this translates into economic changes over time.

Job Categories Most Affected by AI

According to the observed exposure framework:

• Computer Programmers lead with a 75% exposure level.
• Customer Service Representatives follow, showing significant automation potential.
• Data Entry Keyers also demonstrate high levels of exposure at 67%.

Future Employment Projections and Worker Characteristics

Data from the BLS forecasts employment changes across sectors from 2024 to 2034. Preliminary analysis indicates that jobs with greater exposure to AI are projected to grow at a weaker rate. Specifically, for every 10% increase in exposure, the BLS predicts a 0.6% decrease in job growth.

Demographic analyses reveal striking contrasts between workers in high exposure roles and those in low exposure roles. The more exposed workers are typically:

• 16% more likely to be female.
• Engaged in higher-paying roles, earning 47% more than less exposed counterparts.
• More educated, with a higher percentage holding advanced degrees.

Initial Employment Trends and Young Workers

Initial findings indicate no significant increase in unemployment for those in the highest exposure jobs. However, a slowdown in hiring for younger workers aged 22-25 has been observed. The monthly job finding rate within exposed occupations has shown a notable decline after the introduction of new AI technologies.

Conclusion

This new methodology sheds light on the labor market effects of AI, particularly in identifying occupations vulnerable to job displacement. The ongoing evaluation of usage data and employment patterns will provide essential insights into the evolving landscape of work influenced by AI technologies.

As the impact of AI continues to unfold, this framework aims to enhance our understanding of economic disruption, ensuring that timely and relevant data informs future analyses.