The news that AI can now summarize incident channels, analyze unfamiliar code, and even generate pull requests during production outages is not surprising. It is the logical next step in a trajectory we have been tracking closely, from Unlock ChatGPT for Work: A Practical Guide to Getting Started to the more ambitious idea of Bridging Retrieval and Action: A New Approach to AI Tasks. The tooling is maturing, and the operational wins are real. But as Craig Risi's reporting makes clear, the harder truth is that the most painful parts of incident response have never been about the mechanical work of reading logs or spotting a bad commit. They are about judgment under pressure, context that is never fully written down, and the human cost of being paged at 3 a.m. with a production service on fire.
Here is our honest take: AI is about to make you faster at the *what*, but it will not tell you the *why* with any more confidence than your most senior engineer can. AI's ability to diagnose and suggest remediation is genuinely useful for the routine, the repetitive, and the purely code-adjacent. But production incidents are rarely purely code-adjacent. They are tangled in recent deploys, infrastructure drift, customer behavior spikes, or a forgotten cron job that ran an hour ago. An AI that summarizes the channel is giving you a cleaner version of the same incomplete picture you already had. It is not giving you the tacit knowledge of the person who built that service two years ago and left. That is not a failure of the technology; it is a boundary of the medium. So when we read about AI generating a pull request to fix a bug, we see a helpful draft, not a solution. The solution still requires a human to decide whether the fix matches the intent, not just the test suite.
What does this mean for you, practically? It means your incident response process will change, but not in the way the hype suggests. You will spend less time on the initial triage and more time on the questions that matter: *Is this the real problem or a symptom? What else changed that the model cannot see?* This is where we would point you to the deeper question of Explore the Future: When AI Designs Its Own Hardware. If we are willing to let AI design its own silicon, we are implicitly trusting it to make trade-offs that have no obvious right answer. Incident response is no different. The trade-off between a quick rollback and a targeted fix, between alerting the on-call team and waiting for more data, these are judgment calls that require an understanding of business risk, not just system state. An AI can present the trade-off, but it cannot own the accountability. That is still on you.
The specific consequence to watch is not whether AI can solve incidents, but whether your team starts to trust it more than their own instincts. That is a real failure mode. The tool is there to reduce noise, not to replace the hard, messy, and often slow work of building shared understanding. So take the summaries, take the code suggestions, but keep the human in the loop for the decision. The moment you outsource the judgment is the moment you lose the very expertise you need to ask the next question. The hard problems are still yours. That is not a limitation to solve; it is the job.