Continuously updating an AI's safety rules based on failures seen in a test set is a dangerous practice. This process effectively turns the test set into a training set, creating a model that appears safe on that specific test but may not generalize, masking the true rate of failure.

Insurers lack the historical loss data required to price novel AI risks. The solution is to use red teaming and systematic evaluations to create a large pool of "synthetic data" on how an AI product behaves and fails. This data on failure frequency and severity can be directly plugged into traditional actuarial models.

To address safety concerns of an end-to-end "black box" self-driving AI, NVIDIA runs it in parallel with a traditional, transparent software stack. A "safety policy evaluator" then decides which system to trust at any moment, providing a fallback to a more predictable system in uncertain scenarios.

Treating AI evaluation like a final exam is a mistake. For critical enterprise systems, evaluations should be embedded at every step of an agent's workflow (e.g., after planning, before action). This is akin to unit testing in classic software development and is essential for building trustworthy, production-ready agents.

AI's unpredictability requires more than just better models. Product teams must work with researchers on training data and specific evaluations for sensitive content. Simultaneously, the UI must clearly differentiate between original and AI-generated content to facilitate effective human oversight.

The critical challenge in AI development isn't just improving a model's raw accuracy but building a system that reliably learns from its mistakes. The gap between an 85% accurate prototype and a 99% production-ready system is bridged by an infrastructure that systematically captures and recycles errors into high-quality training data.

When selecting foundational models, engineering teams often prioritize "taste" and predictable failure patterns over raw performance. A model that fails slightly more often but in a consistent, understandable way is more valuable and easier to build robust systems around than a top-performer with erratic, hard-to-debug errors.

Fine-tuning an AI model is most effective when you use high-signal data. The best source for this is the set of difficult examples where your system consistently fails. The processes of error analysis and evaluation naturally curate this valuable dataset, making fine-tuning a logical and powerful next step after prompt engineering.

The current approach to AI safety involves identifying and patching specific failure modes (e.g., hallucinations, deception) as they emerge. This "leak by leak" approach fails to address the fundamental system dynamics, allowing overall pressure and risk to build continuously, leading to increasingly severe and sophisticated failures.