Building AI in Real-World Systems

Early exposure to AI often creates the impression that progress is driven primarily by model quality. In practice, model capability is only one part of the equation — and rarely the limiting factor.

Across multiple AI-driven products, the recurring challenge was not experimentation, but operationalization. Models performed well in controlled settings but struggled when exposed to real users, real latency expectations, and real trust requirements.

What consistently mattered more than raw accuracy were predictability, latency consistency, and clearly understood failure modes.

The most valuable AI systems were not impressive — they were reliable.

Over time, this shifted how I think about AI: it must be designed as a system, not a feature; UX and metrics matter as much as models; and trust compounds slowly but collapses quickly.