Labor Economy Workers Less Worried Than Office Workers About Robots Taking Their Jobs

Despite high-profile demos from the likes of xAI and AWS and aggressive corporate roadmaps, PYMNTS Intelligence’s Wage to Wallet Index shows that workers who could be most exposed to automation are not acting as though humanoid robots are about to displace them at scale.

Even among Labor Economy workers, the consumer group made up of mostly hourly workers who make under $50,000 a year, a clear majority continue to believe their skills will remain relevant as technology evolves.

The Labor Economy depends on labor-intensive, task-based or contingent work such as gig work, freelancing, contracting and hourly wage roles, are often viewed as the most vulnerable to automation shocks. Non-Labor Economy workers, by contrast, are more likely to be salaried employees in stable roles with predictable pay, longer-term employment contracts and clearer career paths. If any segment of the workforce were going to show concern about the threats of robotics, it would be them. But the data suggests that shift has not yet happened.

Yet the data shows resilience alongside caution. According to PYMNTS Intelligence data, 65.3% of Labor Economy workers say they feel confident their skills will remain valuable as technology evolves, compared with 73.7% of non-Labor Economy workers. The gap reflects heightened exposure and uncertainty, not an expectation of near-term replacement.

Labor Economy Feels Vulnerable, but Not Replaced

The data paints a picture of caution rather than panic. About 32.1% of Labor Economy workers say they worry that new technologies may reduce the need for people in their position or lower demand for their skills, closely tracking the 31.8% reported by non-Labor Economy workers. When the question turns specifically to automation and robotics, 29.3% of Labor Economy workers say advances could reduce the need for the work they do, compared with 26.8% of non-Labor Economy workers, a modest but notable difference.

Where Labor Economy workers diverge more clearly is in lived experience. Roughly 33.6% say they have recently witnessed or heard about co-workers being laid off or freelancers in their field struggling, versus 28.5% of non-Labor Economy workers. Labor Economy workers are also slightly more likely to say they feel uncertain about the future of their organization, even though just over half of workers in both groups say layoffs or job cuts seem unlikely at their company.

Humanoid Robots Remain Far From Everyday Work

That measured response aligns closely with the current state of humanoid robotics. While companies like Tesla have positioned humanoids as a long-term solution to labor shortages, real-world deployment remains limited.

Tesla has repeatedly said it aims to become a mass-production humanoid robotics company, centered on its Optimus robot, but public demonstrations have underscored how early the technology still is. BGR has reported instances in which Optimus struggled with balance, coordination and basic task execution have reinforced skepticism about near-term scalability.

Scientific research offers a broader explanation for why humanoids have yet to move beyond demos. Embodied AI systems must operate in environments that are fundamentally unpredictable, requiring real-time perception, reasoning, and physical adaptation that today’s models still struggle to achieve. Tasks humans perform instinctively, such as handling unfamiliar objects or navigating cluttered spaces, remain disproportionately difficult for robots.

These limitations are especially relevant for the types of jobs Labor Economy workers often perform, which tend to involve variability, human interaction and context-dependent decision-making. Warehouses, kitchens, delivery routes and customer-facing environments are precisely the settings where humanoid robots face the greatest technical friction.

Embodied AI Progress Hasn’t Changed Worker Reality Yet

Northeastern University reports that even advanced humanoid systems struggle to generalize beyond controlled environments. Training a robot to succeed in one home or workspace does not guarantee success in another, a problem that continues to slow real-world adoption.

Even advances in Google DeepMind show that progress remains uneven. While robots are improving at specific tasks, reliability across diverse settings remains elusive. That gap between lab success and everyday usefulness helps explain why workers’ expectations have remained relatively stable.

Humanoid robots continue to fall short on efficiency, struggling with energy use, speed and reliability compared with both humans and task-specific automation. As PYMNTS has reported, these limitations make humanoids costly to deploy and difficult to justify at scale today, reinforcing why Labor Economy workers remain cautious.

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