You usually do not wake up one morning and decide, “Today I will shard a database.” Sharding tends to arrive after a slow, expensive warning cycle: a primary database that
The first few months of running large language models in production feel deceptively smooth. Demos land. Early users are impressed. Latency seems acceptable, and costs look manageable. Then the real
If you have ever stared at a slow dashboard query and thought, “We already calculated this yesterday, why are we doing it again?”, you are already circling the idea behind
If you have ever watched AI architectures stall after an impressive demo, you already know the pattern. The model worked. The architecture did not. Teams fixate on what the system
You feel it when the architecture review turns adversarial. One side wants stricter standards, tighter controls, and fewer degrees of freedom. The other wants autonomy, speed, and room to adapt
You usually feel the build vs buy question long before it shows up in a roadmap doc. A team hacks together internal platforms to unblock themselves. Six months later, half
You notice it first in the graphs. CPU spikes that look like a heart monitor. Latency is creeping up just enough to make your SRE instincts twitch. A few minutes
Most organizations don’t fail at AI-driven automation because the models are bad. They fail because the surrounding systems, data, and operating models were never designed to support probabilistic software at
If you have shipped an AI-powered system to production, you have likely lived this moment. The demo worked. Offline metrics looked solid. The model passed the evaluation. Then the incidents