I spend a lot of my day job thinking about systems that outlast whatever framework or tool is trendy this year, so this article's framing clicked with me right away.
The core idea: we've been obsessing over which model to pick, but the harder (and more durable) problem is what you build around it. The orchestration, monitoring, fallback logic, and integration layer is what actually determines whether an AI feature survives contact with real users and real edge cases. Models will keep changing. Your serving infrastructure is what has to keep working.
This matches what I've seen moving from traditional web dev into AI-adjacent projects. Swapping a model is often the easy part. The hard part is everything around it staying stable while that swap happens.
If you're on a team evaluating AI tools right now, worth asking: are we picking a model, or are we building something that can survive several models over the next few years?
Article: https://www.technologyreview.com/2026/10/22/1146109/designing-an-ai-serving-harness-built-to-last/ #AI #SoftwareEngineering #EnterpriseTech