The hype around humanoid robots like Tesla's Optimus and Figure AI has created a narrative of endless data hunger. But recent research suggests a different trajectory: massive initial data needs will peak around 2026-2028, then decline sharply. Studies on scaling laws and synthetic data show efficiency gains that reduce raw collection demands by 50-90%.

The core shift isn't about less data, but smarter data. Internal fleet learning at companies like Tesla means they generate terabytes internally, making external data sales less critical. The real bottleneck becomes curation—finding the 1% of footage that actually improves performance. Startups selling raw teleoperation data face obsolescence.

However, certain demands persist. The sim-to-real gap remains, requiring real-world data for fine-tuning. Safety validation for ISO standards is extensive, and human interaction cues are complex. The market will bifurcate: foundational training plateaus, but specialized domains like healthcare and edge-case handling will continue needing targeted data.