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.

The performance of AI models improves significantly with reinforcement learning, and OpenAI aims to scale this approach to enhance reasoning capabilities further, moving towards a future where reinforcement learning will dominate AI development and potentially lead to significant advancements in capabilities.

When we started OpenAI, I was convinced that deep learning was genuinely working and that it improved with scale. This realization was crucial because it seemed like others weren't recognizing its potential.

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

To maximize improvement in AI, we need to scale existing techniques to their limits while also investing in future innovations that could lead to significant advancements.

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.

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

The future of AI research should focus on understanding the scaling laws and capabilities of reinforcement learning models.

The scaling laws in AI research are being explored alongside advancements in reasoning models, which could lead to significant breakthroughs in various fields.