Imagine it’s your first week mentoring a new junior engineer. They’re sharp, enthusiastic, and incredibly fast at writing boilerplate code or spinning up draft configs. But here’s the thing: you wouldn’t give them root access to production and hope for the best.
That’s exactly how we should think about AI in engineering and DevOps today. It’s powerful, helpful, and accelerates work — but it doesn’t replace human judgment. AI is your new intern: useful for drafting, dangerous if left unsupervised.
The Promise: AI as an Accelerant
AI excels at speed. Need a Terraform module scaffolded? A Kubernetes Deployment YAML? A quick test script? AI can generate these in seconds, saving hours of manual work.
Instead of starting from a blank page, engineers now get a head start — a sketch to refine. This reduces cognitive load and frees humans to focus on the higher-order work: architecture, optimization, and problem-solving.
Think of it as an endless supply of boilerplate code that never complains, never tires, and always delivers something. The catch? Like a junior engineer, it may not fully understand context.
The Risk: Lack of Context and Judgment
Here’s where the danger lies: AI doesn’t know your environment, policies, or hidden pitfalls.
A YAML file might look right, but it could include deprecated APIs, insecure defaults, or resource settings that cause a meltdown under load. A Terraform script might spin up resources — but in the wrong region, with the wrong IAM permissions, or at a cost that surprises you on the next bill.
It’s the same as giving a junior engineer full autonomy: they’ll produce something that compiles, but whether it’s secure, efficient, or aligned with business needs is another question entirely.
The Human Role: Review and Guardrails
AI shifts our role. Instead of writing everything from scratch, engineers become reviewers and decision-makers.
Some best practices:
- Validate outputs with linters, policy-as-code tools, and CI pipelines.
- Treat AI as draft-only — no AI-generated config should ever go straight to production.
- Document workflows so teams know when and how to use AI effectively.
- Pair AI with guardrails like OPA, security scanners, and automated tests.
The goal is to amplify human judgment, not replace it.
The Philosophy: Tools vs. Judgment
This is where the philosophical side comes in. Tools accelerate; judgment directs.
A junior engineer’s value isn’t measured by how many lines of code they produce, but by how effectively they solve problems under guidance. Similarly, AI’s value isn’t in the sheer volume of configs it spits out, but in how it reduces toil and frees humans to think strategically.
The danger comes when speed tempts us into skipping thought. Fast isn’t always right.
Practical Takeaways
Here’s how to position AI in your workflow:
- Treat it like a junior engineer — helpful, but in need of supervision.
- Use it to draft, not deploy.
- Automate review gates so nothing AI-generated goes live without checks.
- Encourage experimentation, but set clear boundaries.
The winning teams won’t be those that “replace” engineers with AI, but those that combine human judgment with AI acceleration.
Closing Thought
AI isn’t here to take your job — it’s here to sit beside you, like an enthusiastic intern with endless energy. But just like any intern, it needs leadership.
The future belongs to engineers and teams who know how to guide AI, review its work, and apply wisdom where automation stops. The trick isn’t to let AI drive — it’s to keep your hands firmly on the wheel, while letting it push you forward.
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