Behind the Meta scale AI deal: why more data Isn’t always better for physical AI

When Meta shocked the industry with its $14.3 billion investment in Scale AI, the reaction was swift. Within days, major customers (including Google, Microsoft, and OpenAI) began distancing themselves from a platform now partially aligned with one of their chief rivals.

Yet, the real story runs deeper: in the scramble to amass more data, too many AI leaders still assume that volume alone guarantees performance. But in domains like robotics, computer vision, or AR - that demand spatial intelligence - that equation is breaking down. If your data can't accurately reflect the complexity of physical environments, then more is not just meaningless; it can be dangerous.

In Physical AI, fidelity beats volume

Current AI models have predominantly been built and trained on vast datasets of text and 2D imagery scraped from the internet. But Physical AI requires a different approach. A warehouse robot or surgical assistant ...