The recent announcement of KubeMQ-Aiway caught my attention not as another AI platform launch, but as validation of a trend I’ve been tracking across the industry. After spending the last two decades building distributed systems and the past three years deep in AI infrastructure consulting, the patterns are becoming unmistakable: we’re at the same inflection point that microservices faced a decade ago.
The Distributed Systems Crisis in AI
We’ve been here before. In the early 2010s, as monolithic architectures crumbled under scale pressures, we frantically cobbled together microservices with HTTP calls and prayed our systems wouldn’t collapse. It took years to develop proper service meshes, message brokers, and orchestration layers that made distributed systems reliable rather than just functional.
