Here’s A $32 Million Bet That Robots Don’t Need A Billion Dollars Of Real-World Data

The future of physical AI may depend on how efficiently we can train it. A new Forbes article highlights Antioch’s $32M Series A and its approach to using simulated data to accelerate robot training.

We’re excited to support the Antioch team as they build the infrastructure needed to make physical AI development faster, more scalable, and more accessible. Real-world data will always matter, but simulation can help companies get more from the data they do collect, creating a more efficient path toward deploying intelligent systems in the physical world.

Read the full article in Forbes