ai ate my yaml

Overview

I used to mass-produce Kubernetes manifests all day. Mass producing of YAML was my job security. Then one day, a developer typed a single prompt into Claude and got a perfect Deployment with HPA, PDB, and anti-affinity rules in 10 seconds.

That YAML took me 30 minutes to write. AI ate it in 10 seconds.

This isn't a sci-fi story. It's happening right now. This post is an honest look at what's changing for DevOps Engineers and how to stay relevant when AI is coming for your manifests.

Facing Reality

DevOps Tasks AI Is Already Replacing

Let's be honest. AI is already performing a significant portion of DevOps work faster and more accurately than humans.

Task AreaAI Replacement RiskCurrent Status
YAML/Manifest WritingHighCopilot, Claude instantly generate Kubernetes manifests, Terraform HCL
TroubleshootingMedium-HighAI analyzes error logs and suggests solutions
DocumentationHighAuto-generates Runbooks and READMEs
Monitoring SetupMediumGenerates Prometheus rules, Grafana dashboard JSON
CI/CD PipelinesMediumAuto-writes GitHub Actions, GitLab CI workflows
Security ScanningMedium-HighAnalyzes vulnerabilities and suggests fix code
# This used to take 30 minutes
kubectl get pods -o yaml > pod.yaml
vim pod.yaml  # manual editing

# Now it's just one prompt to AI
"Create a Kubernetes deployment with 3 replicas,
resource limits, liveness probe, and anti-affinity rules"

Reality in Numbers

This is reality. Denying or avoiding it won't change anything.

It's Not Just DevOps

This isn't a DevOps-only phenomenon. It's hitting software engineers across the board. US programmer employment dropped 27.5% between 2023 and 2025 6. Big Tech entry-level hiring fell 25% year-over-year 7.

Juniors are hit the hardest. According to the Federal Reserve Bank of New York, CS grads now have a 6.1% unemployment rate—higher than philosophy majors (3.2%) 8. 70% of hiring managers believe AI can do intern-level work, and 57% trust AI output more than junior developers' work 7.

Seniors are relatively safer. "AI doesn't make the tough calls on architecture, compliance, or security."

Areas AI Struggles to Replace

That said, not everything is being replaced. There are clear areas where AI still falls short.

1. Architecture Decision-Making

"Should this service use EKS or ECS?"

AI can list the pros and cons of both services, but it cannot make holistic decisions considering your team's capabilities, existing infrastructure, cost structure, security requirements, migration costs, and 3-year expansion plans.

2. Judgment in Incident Response

It's Friday at 6 PM, and an unknown incident occurs in production.

Quick judgment and communication in these situations remain a human domain.

3. Cross-Team Collaboration and Persuasion

Dev Team: "The deployment pipeline is too slow, speed it up"
Security Team: "We need to add vulnerability scanning steps"
Management: "Cut infrastructure costs by 30%"

Satisfying all three requirements simultaneously while persuading each team and finding common ground—this is a uniquely human capability that AI cannot replace.

4. Understanding Legacy Systems

Undocumented Jenkins pipelines from 2015, Ansible scripts written by someone who left the company, 10-year-old on-premises systems. AI is powerless without context. Understanding organizational history and tacit knowledge remains a competitive advantage.

Career Strategy

Short-Term Strategy (1-2 Years)

1. Embrace AI as a Tool

Accept AI as a tool, not a competitor. Integrate Claude, ChatGPT, and GitHub Copilot into your daily work.

Even Linus Torvalds, known for his uncompromising stance on code quality, has shifted his perspective. In 2024, he dismissed 90% of AI as hype and refused to let AI touch the Linux kernel. But by January 2026, he released AudioNoise on GitHub, openly acknowledging AI assistance for the Python visualizer portion 4. At Open Source Summit Korea 2025, he stated that while "vibe coding" may be horrible for maintenance, it's a great way for new people to get excited about computers 5.

New workflow with AI:

  1. Generate drafts with AI (Terraform, YAML, Scripts)
  2. Human reviews and adds context
  3. Generate test cases with AI
  4. Human decides on production deployment

One DevOps Engineer who uses AI well is more productive than three who don't.

2. From T-Shaped to Pi-Shaped

Traditional T-Shape:
        DevOps (deep)
           |
-----------+-----------
Broad tech knowledge (shallow)

New Pi-Shape:
    DevOps    +    Business Domain
       |               |
-------+---------------+-------
      Broad tech knowledge

DevOps skills alone aren't enough. Go deep into one of FinOps, Security, Data Engineering, or business domain knowledge.

3. Strengthen Communication Skills

The value of coding ability is declining, but the value of these skills is rising:

Mid-to-Long-Term Strategy (3-5 Years)

1. Expand into Platform Engineering

DevOps automated repetitive ops tasks through CI/CD pipelines, IaC, and configuration management. Platform Engineering takes this further by productizing that automation—treating internal tools as products with developers as customers. It requires deep understanding of organizational workflows, team dynamics, and developer pain points—context that AI cannot acquire on its own. Building Internal Developer Platforms (IDP) and improving Developer Experience (DX) are roles AI struggles to replace.

DevOps Engineer
      ↓
Platform Engineer
      ↓
- Build IDPs like Backstage, Port
- Design Golden Paths
- Define developer productivity metrics
- Operate self-service platforms

2. AI/ML Infrastructure Specialist

While AI is replacing many jobs, paradoxically, more people are needed to operate AI infrastructure.

3. Deepen SRE Expertise

Evolve from simple operations to designing system reliability.

Practical Advice

Anxiety Is Natural

Honestly, I'm anxious too. Five years ago, writing Kubernetes YAML well was a competitive advantage. Now AI does it better. But if you're consumed by anxiety, you can't adapt to change.

Things You Can Do Right Now

# Actions you can take starting today
□ Actively adopt AI tools (Claude, Copilot) in your work
□ Document one technical decision this week
□ Keep communication logs during the next incident response
□ Deep dive into one business domain knowledge area in your current team
□ Read one article about Platform Engineering

Pitfalls to Avoid

  1. Complacency thinking "AI has limitations, I'll be fine": Don't underestimate AI's pace of advancement.
  2. Collecting certifications: Even with CKA and AWS SA Pro, AI knows more. Real problem-solving experience matters more than certifications.
  3. Chasing new tools only: ArgoCD, Flux, Crossplane... Learning new tools is important, but fundamental problem-solving ability matters more.
  4. Struggling alone: Join communities and talk with people who share the same concerns.

Conclusion

DevOps Engineers in the AI era are undoubtedly going through difficult times. Skills that were once valuable are rapidly declining in value, and the job market is shrinking.

But this is both a crisis and an opportunity. Those who use AI as a tool while focusing on areas AI struggles to replace—architecture decision-making, organizational collaboration, business understanding, incident response judgment—will be recognized for even greater value.

In the end, it's not the strongest species that survives, but the one most adaptable to change.

"The future belongs to those who learn more skills and combine them in creative ways." — Robert Greene

References