At the late May Robotics Summit in Boston, industry stakeholders highlighted a stark contrast between promotional claims and the actual capabilities of humanoid robots. While companies like Figure AI, AgiBot, and Matrix Robotics showcase machines capable of running marathons, folding laundry, and performing hospitality duties, engineers emphasize that true general-purpose autonomy remains years away. Most deployed humanoids rely on teleoperation or are confined to highly specific, preprogrammed tasks rather than adapting to dynamic environments on command. Artificial intelligence is driving meaningful progress, particularly through vision-language-action models and world modeling systems. These AI architectures enable robots to interpret real-time visual data, follow natural language instructions, and predict physical outcomes, thereby improving dexterity and environmental awareness. Advances in tactile sensing have also brought robotic hands closer to human-like precision, allowing delicate object manipulation and safe interaction with humans. Despite these advancements, significant barriers to widespread deployment persist. The primary bottleneck is data scarcity. Scaling autonomous operations requires vast datasets of human movement and task execution, prompting firms to install extensive camera networks in domestic and industrial settings. Beyond data, safety and reliability present critical challenges. Unlike deterministic traditional automation, AI-driven humanoid systems are non-deterministic and function as black boxes, making their behavior difficult to predict. This unpredictability complicates the establishment of strict safety protocols necessary for human-robot coexistence, as engineers struggle to guarantee consistent performance in unstructured environments. Industry leaders acknowledge that current deployments, such as Hyundai’s Atlas trials and BMW’s AEON implementations, remain controlled experiments rather than commercial solutions. Developing robots that can safely navigate social and domestic spaces demands rigorous validation and fail-safes. Until AI systems achieve deterministic reliability and comprehensive data coverage, humanoid robotics will continue to evolve through incremental improvements in specific applications rather than transitioning to fully autonomous, multi-tasking generalists. The path forward requires sustained investment in sensor technology, data collection infrastructure, and predictive AI frameworks to bridge the gap between experimental prototypes and scalable, safe, everyday robots.