The Neo humanoid robot promises the ultimate life without chores: a 5-foot-6, 66-pound personal assistant that folds your laundry, does the dishes, vacuums, and waters the plants on a perfect schedule. It represents the potential for massive value for people whose time is worth more than the cost, or those with mobility issues. It's available for pre-order now for $20,000 outright or $500 a month.However, there is a massive gap between the dream and the reality.The Problem with the Hype: When reporters were shown the robot, 100% of the complex tasks demonstrated—including loading the dishwasher and carrying things around—were remotely controlled by a human wearing a VR headset in another room (teleoperated). In the company's nearly 10-minute keynote video, only two scenes were labeled autonomous: opening a door (clumsily) and taking an empty, harmless cup from a hand. This strategy of selling the "dream" before the product is ready defines the "AI promise problem".The AI Hype Playbook: This approach is not a mistake; it's a deliberate strategy—a playbook perfected by tech giants like Tesla in the self-driving space. The goal is to get robots into real houses to gather the crucial asset that is currently missing: training data. Teaching an AI to operate inside a chaotic house is exponentially harder than teaching a car to drive, requiring billions of unique physical interactions (complex manipulation vectors).The Cost of Being an Early Adopter: When you buy Neo in these early stages, you are paying a premium price to be a high-risk, full-time beta tester and data provider. This involves a significant privacy trade-off, particularly through "expert mode," where remote human operators look through the robot's sensors directly into your home to teach it new tasks. While the company suggests they may blur faces and offer geo-fencing, this is the fundamental trade-off at the heart of the business model.Ultimately, this phenomenon forces consumers to recognize that early adopters are actively and expensively investing to become crucial primary data sources for an unfinished technological blueprint.--------------------------------------------------------------------------------Inspiration Credit: This analysis and discussion draw heavily upon the critical framework and initial observations laid out by Marques Brownlee, who first raised important questions about the Neo robot's capabilities and the emerging "AI promise problem".--------------------------------------------------------------------------------Inspiration Resource: @mkbhd