The combination of classical simulation and neural networks will provide the necessary power to advance the next generation of robotics systems.

There is an ongoing debate in the AI community about whether scaling laws will continue to hold or if we are approaching bottlenecks in model performance.

The scaling of compute resources will vary significantly depending on the problem, with some requiring up to 10,000 times more compute than others, which allows for more capable models.

Scaling and training very large neural networks has proven effective, as demonstrated by our training of a neural network 60 times larger than previous models using 16,000 CPU cores.

The scaling laws in AI are still valid, but as we scale, challenges such as data management and system complexity become more pronounced, necessitating innovative solutions.

The scaling law in AI suggests that as models grow larger, they will require significantly more computational resources, making them more capital intensive to develop.

By training models that learn like the human brain, we aim to generate entire worlds and overcome the limitations of large language models.

There is ongoing debate in the AI community about whether we have reached the limits of scaling laws, as the latest models have become larger and more expensive, yet their capabilities seem to be plateauing.

Instead of just scaling up model size during training, researchers are likely to focus on scaling the compute available for the model's Chain of Thought.