AI can draft Linux commands, Kubernetes manifests, Ansible playbooks, and Terraform code. For DevOps engineer Naveed Ahmed, that raised an unsettling question: if a tool can do work he spent years learning, could relying on it weaken his skills—or make his experience obsolete? His account of asking his engineering manager offers a practical answer: use AI to get a first draft, but keep responsibility for checking whether it is right.
Why AI-generated infrastructure work made Ahmed uneasy
In a first-person essay, Ahmed describes using AI for technical tasks he had previously handled through his own command-line and infrastructure experience. He names Linux commands, Kubernetes YAML for StatefulSets and NetworkPolicies, Ansible playbooks, and Terraform for AWS infrastructure.
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The worry was not simply that AI could produce code. It was that accepting that code might mean losing the ability to do the work himself. Ahmed put the concern to his manager, Naveed Sanghera: “Will AI replace the actual skillset I’ve spent 10 years building?”
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The article is a personal account, not a published transcript. Its description of the conversation—and the quotations from Sanghera—are Ahmed’s report of a private exchange.
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What Ahmed says his manager told him
As quoted by Ahmed, Sanghera’s central advice was: “Use AI as a tool. Do not 100% rely on it.” He also urged Ahmed to check the output, understand what it proposed, and ask what could be improved.
Ahmed says this shifted his attention from whether AI could produce familiar syntax to whether an engineer could direct the work and judge its consequences. He uses an architect analogy: experience helps an engineer specify what is needed, assess whether a result fits its intended environment, and improve it. The analogy expresses Ahmed’s perspective; it does not establish that AI lacks technical capabilities or that engineering work has been replaced.
How Ahmed recommends reviewing AI output
Ahmed’s workflow treats generated work as a draft, not a production-ready answer. He describes four parts:
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Ask for a first draft. Use AI to generate a starting point for the task rather than treating its first response as the finished implementation.
- Review it against security and operational needs. Read the output and consider whether its choices suit the system where it will run.
- Verify it yourself. Ahmed stresses manual cross-checking, especially when incident pressure makes a plausible but incorrect suggestion costly.
- Look for missed edge cases and business rules. Consider what the generated answer may not know about the service or its constraints.
Ahmed illustrates that review with questions an engineer might ask:
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- Do the Kubernetes resource requests and limits fit the node pool?
- Could the change fail during a rolling node drain?
- How should a downstream HTTP 503 or timeout be handled?
- Is the operation stateful or idempotent?
These are examples from Ahmed’s essay, not a universal checklist validated by testing. Their value is in showing the kind of environment-specific reasoning he believes should accompany generated code.
What the story says about experience—and what it does not
Ahmed’s account reframes expertise as more than remembering commands or writing YAML by hand. In his telling, it also includes understanding resilience, blast radius, security, business context, and the operational effects of a change. That framing does not mean syntax knowledge no longer matters; it describes where he believes judgment remains important when AI can supply a draft.
The essay documents one engineer’s concerns and one manager’s reported advice. It does not provide independent evidence about DevOps job losses, productivity gains, or whether using AI causes skills to erode. It therefore cannot establish that DevOps careers are safe—or that AI has made engineers obsolete.
Read the account in context
Ahmed’s essay was published on DEV Community and identified there as originally published on his engineering blog. The page identifies Ahmed as a Lead DevOps Engineer at DigitalOcean and Sanghera as an Engineering Manager. The article is best read as a personal reflection on how one engineer chose to approach AI-assisted work, rather than as a general forecast for the profession.
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Read Ahmed’s account on DEV Community.
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