Asimov (YC W2026)

Thesis

Asimov is a YC W2026 standout building a 5,000+ contributor motion dataset for training robots. The concept is to create the 'ImageNet of physical movement'. A foundational dataset that robotic AI systems can train on to learn manipulation, locomotion, and interaction with the physical world. ImageNet's creation in 2009 was the catalyst that enabled the deep learning revolution in computer vision. If Asimov can build a comparably full and standardized dataset for physical motion, it could play a similar catalytic role in embodied AI. The dataset approach is capital-efficient (data collection vs. Hardware R&D) and creates network effects (more contributors = better dataset = more users = more contributors). The 5,000+ contributors suggest a crowdsourced approach, which could scale faster than internal data collection efforts at robotics companies. This is relevant as companies like Tesla (Optimus), Figure AI, and Google DeepMind all need motion training data. Risks include that data quality control across 5,000+ contributors is extremely challenging, the robotics AI market may standardize on proprietary datasets from companies like Google, and seed-stage companies face high base-rate failure. The company is private with no public investment route.

Catalysts

• +5,000+ contributor motion dataset. Building the 'ImageNet of physical movement'
• +YC W2026 backing validates the foundational dataset approach for robotics
• +All major robotics companies need motion training data. Potential universal customer base

Risks

• -Data quality control across 5,000+ contributors is extremely challenging
• -Robotics AI may standardize on proprietary datasets from Google, Tesla, etc.
• -Seed-stage with high base-rate failure risk and no public investment route

Common questions