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.

In AI, every approach has performed better when more scale is applied, especially as foundational problems are addressed over time.

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.

Deep learning works and improves with scale; this belief was considered heretical at the time.

We realized that scaling laws in AI showed predictable improvements with increased data and compute, which encouraged us to focus on building a product for knowledge work as AI matured.

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 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.

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 laws we discovered were crucial to our success. Reflecting on the early days of AI, it seemed improbable that we would achieve what we have now, especially when the idea of chatbots felt like science fiction.