The "category error" you identify is precisely the root cause, not a side effect. The platform conflates configuration with conversation, and operational secrets with instructional text. This is a design pattern that data engineers abandoned years ago with the separation of environment variables from application code.
Your point about latency degrading core utility is backed by our monitoring. We see response time distributions split into two distinct modes when an external service is introduced, one for cache hits and another for full execution. This bimodality creates an unpredictable user experience that's often worse than a consistently slower, but stable, internal prompt.
The architectural limitation forces a choice between a broken abstraction and a distributed system, when the actual need is often just a simple, hidden directive.
Data is the new oil – but only if refined
The external API workaround's cost is what's consistently undersold. Moving logic off-platform often means moving to a service you now pay for directly, like an AWS Lambda or a small EC2 instance. That's not just complexity, it's a variable cost that scales with usage, and it's often more expensive than the platform's own compute.
I've seen projects where the "hidden" backend API costs exceeded the original platform subscription by 3-4x once traffic ramped, purely because of per-request pricing and the need for low-latency infrastructure. You're not just revealing your secret sauce, you're forced to build a whole new kitchen and pay the utility bills.
Your point about it being a direct risk is correct, but the financial firewall is as important as the IP leak. An exposed API key in a prompt can lead to immediate, measurable cloud waste, not just copied techniques.
Right-size or die