Embodied AI Training Data Collection and Resale Platform (Resale Model)

📌 Background

With the explosion of humanoid robots and embodied AI in 2026, high-quality real-world operation data is extremely scarce. Industry estimates indicate that training a robot 'brain' close to human-level requires 10 billion hours of data, while global effective supply is only about 5 million hours, leaving a 200-fold gap. Wheel.ai (Guanglun Intelligence) recorded new orders of 550 million RMB in Q1 2026, exceeding its total for the whole of 2025. The resale rate for high-quality scenario data exceeded 10x, with collection-side hourly wages at 30 RMB and external selling prices ranging from 200 to 500 RMB per hour (according to media reports, independently unverified).

👤 Target Customers

Humanoid and embodied AI robot developers (large AI and hardware companies such as Agibot and Galbot)

💰 Revenue Streams

1) Generate revenue primarily through one-time sales of standardized datasets (such as embodied AI operation data), priced at approximately 200-500 RMB per hour (higher for real-machine collection); 2) Multiplying revenue by reselling the same standardized dataset to multiple clients, achieving a resale rate of over 10x; 3) Continuous support packages: Annual maintenance, upgrades, and operational guarantees for collection equipment and annotation pipelines, collected as an annual service fee.

🧮 Cost Structure

Primarily includes one-time data collection hardware/labor costs, data annotation and cleaning processing fees, and professional team salaries, with extremely low marginal distribution costs.

🛡️ Moat

First-mover advantage in accumulating massive exclusive real-world scenario manipulation datasets, a data iteration closed-loop formed through deep binding with leading robot clients, and a high-return financial model where data assets can be sold infinitely, establishing dual barriers on both the supply and client sides.

🔑 Keys to Success

• Pioneering integration with leading embodied AI clients to form a commercial closed-loop
• Mastering efficient, low-cost, large-scale collection pipelines and technologies

⚠️ Risks

• Robot mass production progress falling below expectations, leading to a collapse in data demand
• Data being circulated after purchase by a few clients, leading to technology export, subsequent copying, or substitution

🏢 Cases

• Wheel.ai: Founded in 2023, valuation over 15 billion RMB, Q1 2026 new orders of 550 million RMB, delivered over 1.5 million hours of data, with some data resold over 10x
• Mifeng Tech: Incubated by Agibot, adopting a 'body-less collection hardware + proprietary network' approach, targeting 10 million hours of production capacity in 2026, buying data 'as much as available'

📊 SWOT Analysis