The era of scaling pre-training may not be completely over, but reasoning now appears to offer the most potential for maximizing gains from scaling compute.

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.

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

The scaling laws observed in AI suggest that larger models trained longer lead to more compression and intelligence, which is supported by the philosophical grounding of these concepts.

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.

The scaling laws became a foundational principle for AI development after being popularized by researchers, including those at OpenAI and Google DeepMind, who emphasized that it's not just about making models larger, but also about training them on sufficient data.

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.

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

The influential paper on scaling laws for neural language models published in January 2020 established that increasing model size, data, and compute leads to consistent improvements in performance, emphasizing the importance of scale over algorithm.