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.

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.

There's a significant gap between the current compute capabilities and what could be achieved with ten to a hundred times more compute, which would allow for much greater AI services.

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 paper revealed that increasing parameters, data, and compute leads to a smooth, consistent improvement in model performance, demonstrating that performance is more dependent on scale than on the algorithm used.

The scaling of models has worked consistently, and it's incredible that making these models bigger leads to predictable improvements in their performance.

Scaling from 10,000 GPUs to 100,000 GPUs introduces many issues, as rare occurrences can become catastrophic at larger scales, affecting infrastructure and failure rates.

We need to develop scaling science alongside our compute scaling efforts to ensure effective advancements in AI capabilities.

While scaling pre-training may have plateaued, training test time compute could open up a new paradigm for scaling laws, unlocking previously unimaginable capabilities.