While swapping an API endpoint for a new AI model is trivial, the real barrier is the extensive QA and re-benching required. Each new model has qualitatively different outputs, necessitating a full product testing cycle to ensure it doesn't degrade user experience, creating high practical switching costs.

AI's inherent unpredictability necessitates new engineering practices. Developers must now build robust validation, monitoring, and fallback systems to manage incorrect outputs. Additionally, new security threats like prompt injection and excessive AI permissions demand carefully designed access controls.

The speed of AI development has created a paradoxical situation where the time to release a new model is shorter than the time required to conduct comprehensive, long-running tests on the previous version. This necessitates new evaluation frameworks, like a 'recall program' for API-based models.

AI product quality is highly dependent on infrastructure reliability, which is less stable than traditional cloud services. Jared Palmer's team at Vercel monitored key metrics like 'error-free sessions' in near real-time. This intense, data-driven approach is crucial for building a reliable agentic product, as inference providers frequently drop requests.

Unlike traditional software, AI products are evolving systems. The role of an AI PM shifts from defining fixed specifications to managing uncertainty, bias, and trust. The focus is on creating feedback loops for continuous improvement and establishing guardrails for model behavior post-launch.

An OpenAI employee warned that the pace of model development is so fast that any process, automation, or product built on a specific AI model today will likely become obsolete quickly. This necessitates a plan for continuous review and innovation to avoid relying on outdated technology.

Treating AI evaluation as a single, pre-launch check is a mistake. Model behavior drifts due to fine-tuning, infrastructure changes, and shifts in user queries. Production AI systems demand a continuous evaluation pipeline integrated into the deployment lifecycle to catch regressions and ensure ongoing reliability.

Mature AI applications are not static calls to a single large model. They are complex systems of many models that require a continuous "AI loop": tracing performance, identifying areas for improvement (cost, speed, accuracy), and constantly iterating by swapping models, fine-tuning, or refining prompts.

Unlike traditional software like SAP that operates predictably once configured, AI models are dynamic and can evolve, "hallucinate," or degrade in performance. HR teams must treat AI not as a static tool but as a system that requires ongoing monitoring and management, much like supervising a child.