AI in Recruiting: Understanding How Expertise Shapes the Impact of Automation on Jobs

In today’s rapidly evolving workplace, the influence of artificial intelligence (AI) on job roles and employment is a topic that commands increasing attention. While many discussions focus on automation’s ability to replace human tasks, the story is far more nuanced. Drawing on insights from leading economist David Autor of MIT’s Stone Center on Inequality and Shaping Work, this article explores the complex relationship between AI in recruiting, job expertise, and labor market outcomes.

Autor’s research, conducted alongside Neil Thompson and presented in their working paper “Expertise,” reveals that simply measuring a job’s exposure to automation is not enough to predict how technology will reshape work. Instead, the key lies in understanding how AI affects the nature of expertise within a job — whether it raises or lowers the level of specialized skill required. This distinction has significant implications for wages, employment, and the future of work itself.

Reframing Automation: Beyond Quantity to Quality of Tasks

When we consider automation and AI in recruiting, the conversation often centers on how many tasks a machine can perform compared to a human. However, as Autor points out, it is not just about the quantity of tasks that change but the quality — specifically, the level of expertise those tasks demand.

Automation can either remove routine, inexpert tasks or eliminate highly specialized, expert activities. The former tends to make a job more expert by freeing workers from mundane duties, allowing them to focus on complex, value-added work. Conversely, if automation replaces the core expert tasks, the job becomes less specialized, potentially lowering the wage premium associated with that role.

How Removing or Adding Tasks Changes Expertise

Autor’s analysis of four years of labor data shows a fascinating pattern. Some jobs become more expert because they shed inexpert tasks or add new expert tasks. Others become less expert due to losing expert tasks or gaining more routine work.

This shift in expertise drives wages in predictable ways:

• More expert jobs: Tend to see wage increases because they require higher specialization.
• Less expert jobs: Tend to experience wage decreases as they become more accessible and less specialized.

However, the impact on employment is often the opposite of what one might expect:

• Jobs becoming less expert: Typically see an increase in employment as more people can perform the simplified tasks.
• Jobs becoming more expert: Often face stagnant or reduced employment due to the higher skill barriers.

Real-World Examples: From Taxi Drivers to Proofreaders

To ground these concepts, consider the disruption in taxi and chauffeur driving caused by ride-sharing platforms like Lyft and Uber. Taxi driving was once a specialized job with a limited number of incumbents. The advent of ride-sharing technology introduced a less expert version of the job, opening it up to many more people and increasing overall employment in that category. This democratization of access, however, created competition that incumbent taxi drivers often resisted.

Another illustrative example is the evolution of proofreading. Traditionally, proofreading involved mechanical checking tasks such as spelling, formatting, and verifying citations—tasks that were routine and could be automated. Today, these mechanical tasks are largely handled by technology, meaning proofreaders now focus on the more expert task of improving writing quality, which requires nuanced judgment and skill.

As a result, proofreaders today earn higher wages relative to other occupations compared to decades ago. However, fewer people are employed as proofreaders overall, partly because the automated tools reduce the demand for routine proofreading and partly because the job now demands a higher level of expertise.

Expertise and Task Composition: The Totality of Work

Understanding how AI in recruiting and automation impact jobs requires a holistic view of the tasks that comprise a role. Autor emphasizes the importance of distinguishing between:

• Specialized expert work: The core skills and knowledge that define the job’s value.
• Supporting or routine tasks: The more mechanical, inexpert activities that can often be automated.

Technology’s effect depends on whether it increases the expertise required—raising wages but making jobs harder to perform—or removes expertise—lowering wages but expanding who can participate.

The Complex Role of AI in Reshaping Work and Expertise

The rise of AI adds a compelling layer to this dynamic. Unlike earlier forms of automation that replaced routine physical or cognitive tasks, AI has the potential to change who can perform certain types of work by altering the expertise required.

For example, AI tools might enable more people to engage in coding, medical services, legal work, kitchen design, or skill repair—professions traditionally requiring specialized training and experience. This democratization can create new opportunities for individuals who previously lacked access to these roles.

However, this shift also introduces real competition for incumbent experts. Those who have spent years mastering a specialized skill may find that AI lowers barriers to entry, leading to concerns about job security and wage pressures.

In this context, the impact of AI in recruiting is double-edged. It can broaden the talent pool by enabling more people to perform higher-skilled work, but it can also disrupt existing career trajectories and wage structures.

Balancing Opportunity and Competition

This duality underscores the importance of thinking carefully about how AI and automation technologies are integrated into the workforce. Policymakers, employers, and workers alike need to consider not just who is exposed to these technologies but how they transform what skills are needed and who can perform the work.

For workers, this means adapting to new forms of expertise and possibly acquiring new skills to remain competitive. For employers, it involves rethinking job design and recruitment strategies to leverage the expanded talent pool effectively. And for policymakers, it requires crafting education and labor policies that support skill development and manage the transitions created by evolving job expertise.

Implications for AI in Recruiting

When it comes to AI in recruiting, understanding the nuances of expertise is crucial. AI tools can help identify candidates who possess the specialized skills needed for more expert jobs, but they can also widen access to roles that have become less expert and more accessible due to automation.

Recruiters must therefore use AI not simply to automate selection processes but to thoughtfully assess the changing nature of expertise within roles. This includes recognizing when a job’s core skills have shifted due to technology and adapting recruitment criteria accordingly.

Moreover, AI can help organizations identify potential in candidates who may not have traditional credentials but can perform expert tasks with the assistance of technology—expanding diversity and inclusion in the workforce.

Conclusion: Rethinking Expertise in the Age of AI

The impact of AI in recruiting and the broader labor market is far from straightforward. As David Autor’s research highlights, the key lies in understanding how automation changes the expertise required in jobs. Whether AI makes a job more or less expert has profound effects on wages, employment, and who can participate in the workforce.

Jobs that become more expert tend to offer higher pay but fewer opportunities, while those that become less expert open doors to more workers but often with lower wages. AI’s transformative potential lies in its ability to reshape expertise itself, creating new opportunities and challenges alike.

As we move forward, it is essential to think beyond simple measures of automation exposure. Instead, we must focus on how AI changes what workers need to know and who can do the work. This approach will help guide effective policies, business strategies, and individual career decisions in the evolving world of work.

For those interested in exploring this topic further, the full working paper, "Expertise", is available through the MIT Stone Center on Inequality and Shaping Work.