VCs are betting billions on AI’s next wave, so why is OpenAI killing Sora?

OpenAI's decision to discontinue Sora signals a crucial reality for data teams: not every AI capability translates into sustainable products. When we see VCs betting billions on generative AI infrastructure, we tend to assume every feature survives. This shutdown suggests otherwise. For data engineering teams, the implication is clear: don't architect your pipelines around cutting-edge AI products expecting indefinite support. Instead, focus on building flexible ingestion and feature engineering layers that can swap underlying models without major refactoring. The broader trend here is that AI infrastructure consolidation is happening faster than product proliferation. This means our data platforms need modularity and API abstraction rather than tight coupling to specific model outputs. My recommendation: audit your ML serving patterns now. If you're deeply integrated with proprietary model APIs, create abstraction layers that allow quick pivots. The vendors winning long-term are those solving infrastructure and compute—not necessarily those producing the flashiest consumer features. Build your data foundations accordingly.