Most backend systems eventually grow a hidden workflow engine. It rarely starts that way. It usually begins with a simple cron job that wakes up every ten minutes, queries the database for pending records, processes them, and updates a status column. It seems simple enough, until production happens. A downstream API times out. A worker crashes halfway through processing. A deployment abruptly kills the process. A customer asks why their job is stuck....

For decades, software engineering had a fairly predictable constraint. Writing software was expensive. Every feature required engineers, every system required implementation, and every new capability required time, effort, and people. That constraint shaped how we built software. Today, it is disappearing. AI can generate code faster than most teams can review it, and that changes something fundamental about the job. The Old Bottleneck For most of software history, engineering organizations optimized for output....

Walk into any AI infrastructure discussion and you’ll hear the same question: What’s your GPU utilization? It’s become the infrastructure equivalent of asking a web service for its CPU utilization. The assumption is simple: higher utilization is better. After all, GPUs are expensive, and a cluster running at 90% utilization sounds far more impressive than one running at 50%. For a long time, I believed that too. Then I spent more time working on GPU scheduling and multi-tenant AI workloads....