OpenAI’s Sora was the creepiest app on your phone — now it’s shutting down

Sora's shutdown reinforces a lesson we've learned repeatedly: generative AI models, however technically impressive, don't automatically translate into sustainable products. For data engineering teams, this signals that we shouldn't rush infrastructure investments around every new foundation model release. The real cost isn't the model itself—it's the supporting data pipeline, feature engineering, and observability layer. When products shutter, those investments evaporate. I'd recommend adopting a modular approach to AI tooling: build abstractions that let you swap underlying models without rewriting core infrastructure. Use managed APIs initially rather than self-hosting models that might lose support. More importantly, this validates the importance of product-market fit validation before scaling data infrastructure. Don't provision massive feature stores or real-time pipelines for speculative AI features. The industry tendency to chase generative AI hype has left many data teams managing technical debt from abandoned experiments. Focus instead on proven use cases where data infrastructure investments have clear ROI.