As we gather more data and build a larger platform, we can create more granular and personalized evaluations of AI models.

The more testing AI models undergo, the more reliable they become, similar to software development practices.

The more testing AI models undergo, the more reliable they become, similar to software development practices.

As AI evolves, we need to shift from static benchmarks to real-time evaluation systems that can adapt to user feedback.

The future of AI involves models that can provide ongoing insights about users, acting as continuous coaches rather than just responding to isolated queries.

We need to find a way to combine extended training for alignment with extended testing to ensure AI models are aligned in various situations, and to ensure the training process works effectively, we need models that are at or beyond the current frontier on certain topics.

As AI models improve, the expectation is that they will converge in quality and pricing, making it essential for startups to build software that leverages this intelligence effectively.

The current AI models are impressive on their own, but they need to be integrated into applications that enhance user engagement and retention.

When creating benchmarks for AI models, it's crucial to consider whether to prioritize distinguishing high-quality outputs or providing a continuous improvement path.