Architects or Tenants: Modern AI Stacks Are Being Built on Rented Foundations

The text discusses the challenges engineers face when relying on AI inference vendors whose models change without notice, leading to production failures and debugging difficulties. It highlights the frustration developers experience due to AI-generated code issues and the lack of visibility into the systems they depend on. The problem compounds as more developers integrate AI tools into their workflows, causing issues like degraded model performance to affect code quality and architectural integrity. The text advocates for a model-agnostic architecture that includes normalization layers, provider-aware routing logic, continuous evaluation, and model version pinning to minimize lock-in risks and ensure resilience. It suggests that teams should incrementally build abstraction layers to manage inference quality and address lock-in while maintaining the flexibility to switch providers without extensive rewrites.