The engineering perspective on production-grade AI

AI cleared the adoption hurdle in 2026. Reliability may be the harder problem.At NVIDIA’s GTC 2026 keynote, CEO Jensen Huang said the “inflection point of inference has arrived,” pointing to a new phase where the cost and reliability of running AI continuously matters as much as building the models themselves.The shift is already showing up inside production systems. Companies are watching AI budgets climb, model failures reach customers and roughly half of AI-generated code still breaking in production after passing QA. Ridhima Mahajan, a senior software engineer with more than a decade of experience in decision pipelines and backend architecture, sees many of today’s AI problems as familiar engineering challenges with new vocabulary.What has changed now that the industry is calling it AI?The model has moved from being a specialized component to becoming a dependency that other services build around.In older machine learning applications, a model might score or classify something with