Engineering Debt at Scale: Three Structural Failures in Production AI Systems
Most of what breaks AI systems in production has nothing to do with the model.
I spend a good chunk of my job reviewing code: production systems and open-source contributions as an engineer, plus the code behind systems and software papers as an academic peer reviewer. No matter the context, the pattern is the same: state-of-the-art math wrapped in software that can't survive a Tuesday afternoon of real traffic. Basic engineering discipline seems to evaporate the moment import torch shows up.
Based on hundreds of these reviews, the failures cluster into three recurring architectural flavors. Here's what they look like, why they happen, and the fixes that actually hold up under load.
Root Cause
What You'll See in Prod
Notebook-Driven State
Global model/cache objects, undetached tensors
OOM crashes after N requests
Happy-Path Networking
No timeouts, no circuit breakers, synchronized retries
Thread exhaustion, cascading outages
Dependency Anarchy
Unpinned transitive deps, implicit...
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