Onshoring is not possible by replicating China's labor-intensive model, making autonomous robots a necessity. Simultaneously, the strategic, dual-use nature of this technology makes it imperative to develop these robots domestically. This creates a powerful feedback loop where the technology enables onshoring while the need for the technology drives it.

Contrary to the belief that hardware is inherently capital-intensive, Monumental's founder argues their biggest expense is salaries for high-quality talent, much like a software startup. The cost of the robots is manageable and their payback time is good, challenging typical VC perceptions of the business model.

Unlike human employees, who are an expense, humanoid robots are assets. This allows companies to capitalize their labor force for the first time, turning an operational expense into a depreciable, value-generating asset on the balance sheet. Each million robots could add a trillion dollars in market capitalization based on their profit-generating potential.

In the current market, AI companies see explosive growth through two primary vectors: attaching to the massive AI compute spend or directly replacing human labor. Companies merely using AI to improve an existing product without hitting one of these drivers risk being discounted as they lack a clear, exponential growth narrative.

The first home humanoid robot, Nio, requires frequent human remote intervention to function. The company frames this not as a flaw but a "social contract," where early adopters pay $20,000 to actively participate in the robot's AI training. This reframes a product's limitations into a co-development feature.

The narrative of "evil capitalists" replacing jobs with robots is misguided. Automation is a direct market response to relentless consumer demand for lower prices and faster service. We, the consumers, are ushering in the robotic future because we vote with our wallets for efficiency and cost-savings.

The robotics field has a scalable recipe for AI-driven manipulation (like GPT), but hasn't yet scaled it into a polished, mass-market consumer product (like ChatGPT). The current phase focuses on scaling data and refining systems, not just fundamental algorithm discovery, to bridge this gap.

Current home security systems are passive. The next major opportunity lies in active deterrence, moving beyond cameras to physical, patrolling robots. The market wants a "better big dog"—a device that can actively patrol property and deter threats, a more practical application of robotics than consumer humanoids.

While on-device AI for consumer gadgets is hyped, its most impactful application is in B2B robotics. Deploying AI models on drones for safety, defense, or industrial tasks where network connectivity is unreliable unlocks far more value. The focus should be on robotics and enterprise portability, not just consumer privacy.