Most expensive incidents are not expensive because of the fix. They are expensive because of the wrong theories that came first.

A service starts misbehaving. Three teams join a call. Within ten minutes there are four hypotheses on the table: a recent deploy, a noisy neighbour, a database hiccup, a downstream timeout. Each one sounds plausible. Each one points to a different owner.

The next two hours are not spent fixing anything. They are spent ruling out theories that were never the cause.

The expensive part of an incident is rarely the repair. It is the time spent being wrong with confidence.

Misdiagnosis is a decision problem

MTTR charts treat incidents as one number. In practice, that number hides several decisions stacked on top of each other.

What is the actual symptom. Which subsystem is involved. Which deploy is suspicious. Which dependency changed. Which engineer should be paged. Which rollback to attempt first.

Each of these is a guess made under pressure with partial signals. A wrong guess at any step extends the incident, sends the wrong team in, and burns trust in the tooling that suggested it.

The deeper cost is not the minutes added. It is the muscle memory teams build around uncertainty. They start assuming every incident is partly a mystery. They over-page, over-rollback, over-communicate, and under-decide.

Why teams form wrong theories

Wrong theories do not come from carelessness. They come from reconstructing a live system out of fragments.

A trace shows one journey. A log shows one moment. A dashboard shows one angle. An on-call engineer brings memory of how this service used to behave six months ago. None of it is the system. All of it is interpretation.

When the picture is assembled from fragments, the first plausible story wins. Often it is wrong, and the team commits to it before the contradicting signal arrives.

This is not a tooling shortage. It is a layer shortage. Teams need to see what actually ran, which paths were active, which dependencies were touched, and what changed — not as a puzzle to assemble, but as the starting point of the conversation.

The shift

Better incident response is not about more alerts or more channels. It is about shortening the distance between the symptom and a behaviour-backed answer.

When the first thing on the call is what the system actually did across services, APIs, and flows — instead of what each engineer thinks it did — the wrong-theory tax drops fast. Decisions get made on evidence, not advocacy.

This is the gap CodeKarma is built to close: bringing real production behaviour into the moment teams are forming theories, so fewer of them need to be unlearned later.

Production never lies. Most of the cost of an incident comes from the time spent not listening to it.