Sushyam Nagallapati

I've noticed something changing in the way I write code. I still code, obviously. But more often now, part of the job looks like this: Explain what I want. Give an AI tool enough context. Let it generate something. Read the code carefully. Figure out what it misunderstood. Ask it to fix part of it. Run the tests. Then sometimes rewrite the important bits myself. And that made me wonder: Are developers slowly becoming managers of AI agents rather than people who primarily write code?

Thanks for taking the time to read. If you’ve worked on AI platforms, cloud infrastructure, or platform engineering, I’d love to hear how your architecture differs in the comments. In Part 4 of AI Infrastructure for Cloud Engineers, we looked at FinOps for AI and how GPU utilization, token consumption, model choice, and inference volume affect cost.

Thanks for taking the time to read. If you’ve worked with AI infrastructure or observability, I’d love to hear your experience in the comments. In Part 2 of AI Infrastructure for Cloud Engineers, we looked at how GPUs, scheduling, autoscaling, and model serving change the way AI workloads run on Kubernetes. Read Part 2: Running AI Workloads on Kubernetes: GPUs, Scheduling, Scaling, and Model Serving Getting an AI workload into production is only the beginning.