IEEE names physical AI top tech megatrend through 2030

The IEEE's Technology Megatrends 2030 report, examined through an interview with Dejan Milojicic—chair of the IEEE Future Directions Committee Industry Advisory Board and an HPE Fellow at Hewlett Packard Labs—positions physical AI and robotics as the technology area most likely to see major advances over the next four years. The broader report evaluates 30 breakthrough technologies across five core areas: artificial intelligence, energy, health and biotechnology, space technology, and physical AI, assessing how they could reshape industries and human life by 2030.

The acceleration in physical AI potential stems primarily from advances in AI itself, which Milojicic identifies as the primary driver enabling many other breakthroughs. AI is making cybersecurity much more effective, reliable, and trustworthy, while also improving energy efficiency—though energy consumption remains a major challenge at scale. In robotics specifically, AI enables new collaborative algorithms. At the same time, non-AI advances in materials and sensors continue, with AI increasingly supporting these areas by accelerating design space exploration. The report highlights human-AI interaction as a key enabler of physical AI development. Rather than relying solely on voice commands or natural language processing, effective interaction will increasingly combine video recognition with cognitive capabilities layered on top—such as anticipation—that allow machines to better understand human intent and support human movement. Milojicic notes that sensors on the human body could further accelerate interaction, though brain-computer interfaces remain in an early phase and are unlikely to be practical in the near term.

When asked which robotics sectors are likely to achieve widespread commercial deployment first, Milojicic points to constrained use cases in safe environments as accelerators for near-term development. Autonomous robots were initially applied at airports and in manufacturing within very constrained spaces and limited options; over time, more degrees of freedom were added as behaviors, risks, and opportunities became better understood. Today, robotics is advancing at a rapid pace, with military use cases among the most promising applications, both driven by strong demand and substantial funding levels. However, power delivery remains the largest hurdle for broader commercial use, except in manufacturing environments where robots can remain continuously connected to a power source. For wider deployment beyond factories, power is described as a "hard limitation."

Balancing AI flexibility with industrial safety is a central challenge. Milojicic acknowledges this as an "extremely well-posed question that reflects exactly what is going on." With deployment experience, developers and operators will gain trust and remove obvious safety hazards; as trust increases, more autonomous operations will be permitted. Improved human-AI interfaces will enable stronger human-in-the-loop oversight, reducing the risk of disruption from unexpected events. On cybersecurity, Milojicic foresees that security exploits will be the initial vulnerability. While AI can be used both to find and prevent exploits, the industry will eventually reach a point where only complex and costly exploits remain. A prominent target is power supplies; therefore, backup solutions will be required to provide continuous supply. He notes that while such solutions existed in the past—generators provided uninterruptible services to hospitals—the power demand and dependency of AI factories is now surpassing these previous cases.

Regarding employment, Milojicic believes both job augmentation and job displacement will occur. "Humanity, especially the workforce, will have to adjust, as they did during every previous technology revolution." This current transition is developing the fastest and could have the broadest impact. Blue-collar jobs have been historically affected; white-collar roles are increasingly targeted. Any profession that could be automated using AI is possibly a target. Companies are reporting almost daily restructuring—whether by replacing roles with AI, repositioning for future opportunities, or redirecting resources toward building AI infrastructure. However, Milojicic suggests relief may come when the race for AI infrastructure reduces the cost of services—citing tokens used in large language models as an example—further reducing the need for layoffs. Both employees and employers continue to evaluate which jobs will disappear, emerge, or transform.