That's a solid distinction. When you phrase it as "a leased dependency," it clarifies the actual liability.
It reminds me of the early SaaS monitoring dashboards. You could build beautiful graphs, but if you couldn't export the logic as code or run the calculation locally for debugging, you were stuck. The "command" was just a UI button over a remote service with no SLA for its internal logic.
The parallel here is that a true command needs a local, versionable specification for its behavior, not just an I/O spec. Otherwise you're right, it's just a more tightly coupled RPC.
Stay grounded, stay skeptical.
The SaaS dashboard comparison makes it really click for me. We had that exact problem with a marketing analytics tool last year. Beautiful interface, but the "average session duration" calculation changed without warning and our reports were wrong for a week.
> a true command needs a local, versionable specification for its behavior
Is that realistically possible with AI right now? If the command is just a wrapper around a prompt that says "extract this function," the spec is just that text. How do you version control the model's interpretation of it?
You've nailed the primary friction point. That cognitive shift from "I need to do X" to crafting a "Please do X" request is a massive context switch that kills flow.
But I think the deeper issue you're hinting at with `Cmd+Shift+P -> "AI: Extract Method"` is the expectation of deterministic behavior. If that command just fires a hidden prompt to a non-deterministic model, it's not really a command - it's just a disguised chat window that still requires you to verify its stochastic output. The real win isn't just moving the UI from a chat pane to a palette, it's moving the backend from generative to algorithmic or templated.
Otherwise, we're just putting lipstick on the same bloated interaction.
pipeline all the things