Encord explores biometric sensors to generate dense training data for physical AI robots.
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The bottleneck choking physical AI development isn't processing power anymore—it's training data. Encord, a data annotation startup, is now experimenting with brain wave sensors and electrical muscle signals to generate the densely labeled manipulation data that humanoid robots desperately need to learn real-world tasks. Working with German robotics startup Zander Labs, the company estimates that richly annotated physical training data is worth 100 times more than basic video footage, yet costs 20 times more to produce. That gap has created a new industry. As robotics companies race to scale humanoid platforms—Boston Dynamics, Tesla, Figure AI all pushing harder—the constraint isn't their models' capacity to learn; it's the sheer scarcity of high-quality behavioral data. Encord's bet is that biometric signals—the electrical noise your brain and muscles produce during movement—can be captured and translated into precise annotations of intent and action that video alone cannot capture. If they're right, brain waves become a tool for manufacturing training data at scale, unlocking the next generation of physical AI the same way internet data unlocked the last generation of language models.
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Multiverse Computing, a Spanish AI model compression startup, is raising €500m in Series C funding at a €2bn valuation to scale its CompactifAI technology. The round is co-led by Forgepoint Capital, Bullhound Capital, and BNP Paribas's Solar Impulse Venture Fund, with backing from the European Innovation Council and other institutional investors. The funding will help Multiverse expand its compressed model library, accelerate R&D, and grow operations in Asia, the Middle East, and North America as it targets €200m in annual recurring revenue by 2026.
TechCrunch AI 4 hours ago
Encord, a data tooling company, is experimenting with brain wave sensors and muscle electrical signals to improve physical training data for humanoid robots, working with German startup Zander Labs. The company estimates that densely annotated physical training data is worth 100 times as much as basic video data but costs 20 times more to produce. The scarcity and high cost of real-world manipulation data has become a significant bottleneck for robotics companies developing physical AI models, creating a new business around manufacturing training data that doesn't exist at scale.
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TLDRocket
27 July 2026
The bottleneck choking physical AI development isn't processing power anymore—it's training data. Encord, a data annotation startup, is now experimenting with brain wave sensors and electrical muscle signals to generate the densely labeled manipulation data that humanoid robots desperately need to learn real-world tasks. Working with German robotics startup Zander Labs, the company estimates that richly annotated physical training data is worth 100 times more than basic video footage, yet costs 20 times more to produce. That gap has created a new industry. As robotics companies race to scale humanoid platforms—Boston Dynamics, Tesla, Figure AI all pushing harder—the constraint isn't their models' capacity to learn; it's the sheer scarcity of high-quality behavioral data. Encord's bet is that biometric signals—the electrical noise your brain and muscles produce during movement—can be captured and translated into precise annotations of intent and action that video alone cannot capture. If they're right, brain waves become a tool for manufacturing training data at scale, unlocking the next generation of physical AI the same way internet data unlocked the last generation of language models.
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