The Robotics Bottleneck: Why Humanoid Robots Aren't Replacing Humans Soon (eeko.systems)

Hype about an imminent humanoid-robot takeover clashes with hard physical and economic limits. Companies and banks project trillions and billions of units, but real-world constraints—high unit costs (Tesla Optimus ~ $120k–$150k), limited production capacity for specialized components (e.g., planetary roller screws made on scarce grinding machines from Japan/Europe), chip access, and weak battery endurance (prototypes often run 2–4 hours, not 22)—are throttling scale. Integration, not AI, is often the bottleneck: deployments require bespoke robot programming, PLC work, cell design, infrastructure changes and training, and 61% of executives report lacking internal capabilities. Trade tensions and supply-chain fragilities (rare earths, tariffs) further raise costs and risk. The implications for AI/ML and industry are practical: humanoids won’t scale like software or smartphones, VC timelines are misaligned with capital- and time-intensive robotics, and RaaS faces maintenance, liability and integration hurdles. Expect a gradual rollout: pilots in controlled sites (tens of thousands by 2025–27), broader industrial use with costs below ~$50k and hundreds of thousands of units by 2028–30, single-digit millions by 2030–35, and mass adoption only by 2040–50 as manufacturing, component supply, skills and integration ecosystems mature. Investors and practitioners should plan for a slower, infrastructure-heavy transition rather than a near-term labor-disruption shock.