

Building AI Systems You Can Trust
284 snips May 23, 2025
Scott Clark, Cofounder and CEO of Distributional, and Matt Bornstein, a Partner at a16z, discuss the pivotal role of trust in AI systems, moving beyond just performance metrics. They delve into the hidden complexities of generative AI behaviors and the critical need for robust evaluation frameworks. Topics include the pitfalls of traditional testing methods, the rise of 'shadow AI,' and practical strategies for scaling AI from prototypes to real-world applications. Their insights shed light on managing reliability and addressing the challenges of enterprise AI adoption.
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Trust Over Performance
- Trust in AI systems matters more than just optimizing performance metrics.
- Undesired behaviors can be masked when only focusing on performance evals.
AI System Behavioral Complexity
- AI systems are non-deterministic and non-stationary, leading to unpredictable behaviors.
- Complexity grows as systems involve multiple interconnected AI components.
Trust and Verify AI Systems
- Enterprises must trust AI models and continuously verify their reliability.
- Testing provides mechanisms to verify AI behavior as models and environments evolve.