The original responsible scaling policy committed to ensuring models reach a sufficient level of robustness to jailbreaks to mitigate risks effectively.

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

The responsible scaling policy is Anthropic's plan to ensure that deployments are safe, outlining thresholds for new risks associated with more capable models.

I think it's very unlikely that the scaling laws will just stop. If they do, it might be due to not having the right architecture for the task.

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

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.

If the old scaling laws are beginning to lose their edge, we need to consider what comes next for AI development.

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

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.