Over the last year, many products added AI features.
Chat assistants, automatic summaries, classification, recommendations, drafting emails, generating documentation, and suggesting actions all look like product improvements from the outside.
In production, they also change the reliability surface. A feature can be available while producing inconsistent, delayed, expensive, unsafe, or low-confidence output. That means AI reliability is not only uptime; it is behavior, cost, latency, fallback design, and trust.
This MDX version is a temporary local archive created after Hashnode removed free GraphQL reads. Replace this body with the full exported article when the original content is available again.