The Most Expensive Part of Fixing a Bug Isn't Actually Fixing It

We keep talking about developer productivity. But we're optimizing the wrong thing.

Over the past several years, I've worked closely with customer support teams across different companies. Different products. Different industries. Different stages of growth. And one thing has surprised me more than anything else. No matter how modern the engineering stack was, the process of handling bug reports looked almost identical. Companies invest heavily in making developers faster. AI writes code, reviews pull requests, generates tests, and explains unfamiliar codebases. Engineering teams are becoming dramatically more productive.

Yet one of the most common workflows in software development still begins with something as simple as:

"The app crashed."

What happens next is rarely efficient.

The bug isn't the bottleneck. Context is.

Someone reports a bug. Not a detailed bug report. Just a message.

"Notifications aren't working."

"I can't log in."

"The app crashed."

At that moment, it feels like engineering has work to do. But the more time I spent working with support teams, the more I realized that the bug itself isn't what slows companies down. It's everything that happens after. Support asks for screenshots. The customer can't remember exactly what happened, someone asks which app version they're using, engineering joins the conversation and asks for logs, product creates a task, QA asks whether anyone can reproduce the issue, someone copies the conversation into Jira, someone pastes screenshots into Slack, someone opens the monitoring dashboard to search for logs. By the time a developer finally starts looking at the issue, twenty or thirty minutes have already been spent—not solving the problem, but simply collecting enough context to understand it.

I've seen this workflow in startups and enterprise organizations alike. The tools change. The process rarely does.

Humans have become the integration layer

What surprised me most is that we've automated so much of software development—but not the handoff between teams. Support moves conversations into tickets, product moves tickets into development boards, engineers search monitoring platforms, QA reconstructs reproduction steps. Everyone spends time moving information instead of using it. The more I think about it, the more convinced I become that the real bottleneck isn't fixing bugs. It's reconstructing context. That's work nobody actually wants to do. And it's work that machines are remarkably good at. Imagine a customer simply writes:

"Notifications aren't working."

Within seconds, they receive a response confirming the issue is being investigated. Behind the scenes, the system automatically gathers logs, identifies the application version, searches for similar incidents, creates a ticket, and prepares a structured summary for engineering. By the time a developer opens the task, they already know what happened, where to look, and what information is available. No detective work. No copy-pasting between tools. No asking the same questions over and over again.

More bug reports can actually be a good sign

One consequence of this approach is something many teams initially see as a problem. Suddenly, more bugs get reported. At first glance, that feels like failure. In reality, it's often the opposite. When reporting an issue becomes effortless, people stop deciding whether something is "important enough" to mention. They simply report it. The number of tickets grows—but so does your visibility into what's actually happening inside your product. You're not creating more bugs. You're uncovering the ones that were already there. Personally, I'd rather know about every problem my users experience than proudly report that support tickets are down while issues quietly go unnoticed.

AI shouldn't replace support. It should remove invisible work.

I don't think the future of AI in customer support is about writing friendlier responses or replacing support agents. It's about removing the invisible work that happens between support, product, engineering, and QA. Because the goal isn't simply to answer customers faster. The goal is to make sure that when an engineer finally opens a bug report, they can start solving the problem immediately—instead of spending the first thirty minutes figuring out what actually happened. That's where I believe AI becomes truly valuable. Not when it behaves like another teammate. But when it quietly takes care of the work that people shouldn't have been doing in the first place. This is exactly the kind of workflow we've been exploring at BridgeApp: not just helping teams respond faster, but making sure every bug reaches engineering with the context already attached.