This article applies scaling laws and unit economics to data collection in robotics, arguing that raw data volume isn’t the key metric. It breaks robot data into observational, interventional, and deployment streams, then shows how novelty and diversity drive model performance per dollar.

• Goldberg estimates reaching frontier robot performance via teleoperation alone would take 100,000 human-years, exposing the scaling limits of manual data collection.
• Repeating the same training scenario more than 4 times adds almost no value, and beyond 16 repeats it can actively hurt model performance.
• "Free" deployment telemetry is deceptively low-value because production environments are low-variance and produce repetitive, low-entropy data.
• The real lever for capital efficiency is pricing data by novelty and cross-domain diversity rather than raw volume, balancing spend across observational, teleop, and deployment sources.

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• South Korea is redirecting its AI strategy and government funding toward "Physical AI" (robotics, smart manufacturing, embodied agents) rather than pure software.
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