The importance of trust in AI systems is growing, as users need assurance that these systems will behave reliably and consistently.

Trust in AI systems is becoming more critical than raw performance, as users need to feel confident in the reliability and consistency of these systems.

Trust in AI requires more than just improved accuracy metrics; it involves understanding the nuances of human trust and ensuring that AI technology is developed with user needs in mind.

The behavior of AI systems is not just about what they produce but also how they produce it, which can significantly impact user trust.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

We need to manage transparency and control while using AI, ensuring users can trust the AI's actions on their behalf.

Building trust with enterprise customers is crucial for an AI code review company, which involves progressively adding value, consistently delivering on promises, and ensuring best practices for security.

Enterprises need to define what reliability means for their AI systems, starting with the ability to detect changes in behavior over time.

The main issue preventing people from gaining value from AI systems isn't performance optimization; it's the ability to confidently trust these systems.