As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.

Companies are increasingly using multiple AI models for different tasks, optimizing performance by selecting the best model for each specific application.

Achieving sustainable scale in AI applications requires aligning models with real-world use cases and optimizing user behavior throughout the product design process.

The bottleneck in scaling AI systems can vary, but optimizing code design can help adapt workloads to the existing infrastructure, balancing resource demands effectively.

The last decade has shown that we found the right objective function for AI, which is simply to predict the next token, leading to a more beneficial form of intelligence.

As companies scale their AI applications, it's crucial to find the optimal balance across quality, cost, and speed to achieve sustainable growth.

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.

The real world is filled with signals that can be used to optimize AI systems, and we need to build systems that can adapt to these signals effectively.

We have developed a self-serve platform that allows developers to easily tune AI applications for speed and quality, facilitating rapid scaling and experimentation.