Unlike the volatile LLM space, the world of physical AI—robotics and autonomous systems—diffuses more slowly and linearly. The inherent safety risks of operating in the real world prevent the explosive growth and subsequent crashes seen in software, creating a more stable development trajectory.

Unlike software distributed instantly through browsers, physical AI diffuses slowly across varied industries, geographies, and machines. This makes time and longevity critical factors. Customers need a stable, long-term partner, making it difficult for new, less-established startups to compete.

The current excitement for consumer humanoid robots mirrors the premature hype cycle of VR in the early 2010s. Robotics experts argue that practical, revenue-generating applications are not in the home but in specific industrial settings like warehouses and factories, where the technology is already commercially viable.

While AI computation improves exponentially, physical robot hardware evolves very slowly. A robot's hand is vastly inferior to a human's, which has millions of sensors and self-healing capabilities. This physical limitation is the primary barrier to creating AIs that can operate effectively in the real world.

Ken Goldberg quantifies the challenge: the text data used to train LLMs would take a human 100,000 years to read. Equivalent data for robot manipulation (vision-to-control signals) doesn't exist online and must be generated from scratch, explaining the slower progress in physical AI.

The gap between AI's potential and its real-world application is even greater in consumer hardware than in enterprise software. Incumbents like Apple iterate methodically, while challengers face multi-year manufacturing and distribution cycles, significantly delaying the adoption of advanced AI in consumer devices.

The billions invested in VR weren't a loss; they produced foundational technologies like SLAM, depth sensing, and spatial positioning. While VR gaming remains a niche, these innovations are now critical components accelerating the current boom in robotics and physical AI.

Top AI labs realize that progress in digital, keyboard-based AI is accelerating so vertically that it will soon saturate. The next major frontier for innovation and growth will be applying AI to the physical world: robotics, manufacturing, and industrialization.

Unlike software, consumer hardware has long development cycles. This means AI capabilities are advancing much faster than companies like Apple can integrate them into devices, creating a "capability overhang" where the hardware lags far behind the software's potential.