As research, data, and engineering talent dries up, employees are shifting to AI roles, which are rapidly in demand.
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A greater proportion of job changers are changing careers. According to Revelio Labs, approximately 38.5% of workers who change jobs change jobs, up from 35% in 2019.
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AI and data center technician roles have seen the fastest growth in inflow from other roles since 2023, with each increasing in the number of openings.
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Opportunities outside of AI are decreasing, such as research, data analysis, and engineering roles, which are among the most common sources of career changes to AI.
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For most of the past few years, the labor market has been in a low-hiring, low-firing mode. Although the number of job openings has declined, overall employment levels have remained stable despite the impact of AI and growing concerns about economic recession. Nevertheless, workers are taking precautions. This week we look at what types of career transitions are happening more frequently, following reports that more workers are looking to pursue more “AI-friendly” careers.
Many workers are changing careers, not just jobs.
First, let’s take a look at the trends in the job change rate in recent years. Using Revelio Labs’ employee data and role classification, we found that a higher percentage of job transitions involved switching from one role type to another.
Put another way, in 2023, more than 63% of job changers were in the same job category (e.g., engineering to engineering, finance to finance, etc.). By 2025, that number will drop to around 61.5%, indicating that job changers are increasingly moving into different occupations. This trend shows a divergence from changes in the overall job change rate, and it is known that the job change rate is strongly cyclical.
Rapid increase in job seekers for AI roles
It’s no surprise, as some of the fastest-growing roles due to increased job turnover are closely related to AI. If you move down a few levels in Revelio Labs’ role classification, you’ll see that the fastest growing role as a destination for career changers is AI Project Coordinator. This is the person who manages the development and implementation of artificial intelligence projects and ensures alignment with organizational goals. Also included in the top five are AI Engineer and Data Center Technician, two more roles that are proliferating amid the AI boom, as shown below.
Jobs where workers change jobs to AI
Once you’ve identified the roles that are most appealing to career changers, your next question is: Which roles are most often left behind when coordinating AI projects or pursuing a career in AI engineering? The most common sources are academic, research, and data analysis, with manufacturing engineering not far behind. All of these roles include many transferable skills and activities that overlap strongly with the fields of AI project coordination and AI engineering.
For example, computational modeling and data analysis activities are strongly connected to AI project coordination and AI engineering roles, as well as research and data analysis roles. Further down the list, you’ll see that moving into an AI career is also relatively common in consulting and marketing roles. Both professions are seeing massive adoption of AI.
To better understand these transitions, we explore the pushes and pulls felt by people in the above professions as they consider transitioning to AI-driven roles. While the number of job openings for AI roles (AI project coordinator and AI engineer combined) increased by 33% and data center technician job openings by 28% last year compared to two years ago, job openings for the roles most commonly impacting AI careers, such as academic researcher (-8%), data analyst (-15%), and research scientist (-25%), all decreased. This helps explain why people in these roles are increasingly turning to AI roles, where opportunities abound.
What AI leaders are saying about their work now
These changes are occurring despite much public debate about whether AI will eliminate jobs in the first place, and even the most prominent voices in AI have recently weakened their warnings.
OpenAI’s Sam Altman recently said he was “pretty wrong” about the short-term impact of the technology, admitting that “we thought the impact of eliminating white-collar, entry-level jobs to date would be greater than what is actually happening.”
Anthropic’s Dario Amodei once warned that AI could eliminate half of white-collar jobs, but he has since reframed automation as increasing output rather than destroying jobs. “If you automate 90% of the work, everyone will be doing 10% of the work, and those 10% will expand to become 100% of the work done by humans,” he said. Goldman Sachs CEO David Solomon, who never accepted the “jobs apocalypse,” points to a century of precedent: “The United States has a long track record of creating new jobs in response to disruption.”
The Revelio data more closely matches this newly measured view. What we’re seeing is that workers are not being pushed out of the workforce, but rather being repositioned within the workforce, moving across career lines to where demand is strong.
Are career changes due to AI occurring within companies?
Are employees simply transitioning to new roles, or are companies also changing at the same time? Next, we examine how much of this career change to AI is occurring within companies rather than between companies. We found that around 18% is done within the same ultimate parent company.
Meanwhile, the overall rate of changing jobs within the company is much higher at around 28%. This means that workers who move into AI roles are less likely to do so internally than those who make more extensive career changes.
What this means for businesses
Given the highly technical nature of AI jobs, it’s no surprise that the rate of transition into AI roles within companies is low. But for businesses, it also represents a clear opportunity. Retain and better deploy existing talent by creating stronger internal routes to in-demand roles. As we have shown, employees at companies with high internal hiring rates also tend to report higher levels of satisfaction.
Additionally, we found that lateral career opportunities are more than twice as important as compensation when predicting employee retention. Future research will take a closer look at the landscape of internal career transitions and what it reveals about how companies can better support mobility from within.
