The physical AI industry is no longer in the fundamental research stage. It has entered a crucial "advanced engineering" phase between R&D and mass production. The focus is now on solving the subcomponent and reliability problems required to productionize existing technologies.

While centralized AI data centers ("NeoCloud") are booming, the larger, long-term growth market is "far-edge" AI. This refers to AI embedded in physical devices operating independently of the cloud. This sector, spanning countless industries from automotive to retail, is still in its infancy and represents a vast, untapped opportunity.

According to a partner at Radical Ventures, the frontier for AI startups is expanding beyond software ('bits') into the physical world ('atoms'). The next wave of high-impact AI companies will tackle complex challenges in sectors like energy, critical minerals, and manufacturing.

The AI revolution isn't just about software. For the first time in years, venture capital is flowing into hardware like specialized semis and even into energy generation, because power is the core bottleneck for all AI progress.

For decades, hardware startups failed because building the necessary bespoke software was too difficult and expensive. The rise of general-purpose AI provides a powerful, adaptable software layer "out of the box." This dramatically lowers the barrier to scaling for hardware-intensive businesses like robotics and drones, making them more attractive for creative financing.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

Successful AI models will be small, specialized ones that run efficiently on consumer CPUs at the edge (laptops, phones). This leverages existing hardware (e.g., Apple's M-series chips) and avoids costly cloud GPUs, creating a strategic advantage for companies like Apple.

The prohibitive cost of building physical AI is collapsing. Affordable, powerful GPUs and application-specific integrated circuits (ASICs) are enabling consumers and hobbyists to create sophisticated, task-specific robots at home, moving AI out of the cloud and into tangible, customizable consumer electronics.

The rise of agent orchestration using specialized, open-source models will drive demand for custom ASICs. Jerry Murdock argues that putting a model on a dedicated chip will be far cheaper and more tunable for specific workloads than using expensive, general-purpose GPUs like Nvidia's, spurring a hardware shift.