6 posts tagged with "career-development"

Everyone's talking about AI replacing individual contributors. Nobody's talking about what it does to engineering managers.

That asymmetry is interesting to me, because in my experience, EMs stand to gain more from AI than most ICs — or lose more ground if they ignore it. The difference isn't which tools you use. It's whether you use AI to reclaim the time that actually matters, or just use it to make your status updates look better.

Here's my honest accounting of what changed after a year of deliberately integrating AI into how I manage my team.

Two engineers. Two hundred clusters. Half a million nodes. Two million instances. Every year, two major Kubernetes version upgrades across the entire fleet — with zero incidents.

That's not a team of twenty. That's two people. And the reason it was possible isn't the tooling. It's the way we thought about the problem.

After years building Cloud Platform at a large e-commerce company and interviewing dozens of engineers for Platform roles, I've noticed a pattern. Most candidates who call themselves "DevOps" or "Cloud Operations" engineers are skilled, hardworking, and technically capable. But there's a fundamental difference in how they think — and that difference determines whether you're managing problems forever, or systematically eliminating them.

I grew up as a developer reading Martin Fowler and Kent Beck. The Agile Manifesto, the refactoring patterns, test-driven development — these weren't just methodologies I was handed. They were the lens through which I learned to think about software quality, team dynamics, and sustainable delivery.

Now I'm spending significant time with AI coding tools — Vibe Coding, Claude Code, spec-driven workflows — and a question keeps surfacing: do these principles still apply?

My answer, after a hands-on 50K-line project experiment, is yes. Not only do they apply — several of them become load-bearing pillars in an AI-augmented workflow.

There's a narrative spreading through the industry right now: AI is eliminating junior engineering roles, and early-career developers are the first casualties of the automation wave.

After years of interviewing candidates and leading engineering teams, I think this narrative is half right — and dangerously incomplete.

In early 2026, Anthropic published a case study: 16 Claude agents, working in parallel Docker containers, wrote 100,000 lines of Rust code in a few weeks — a C compiler that could successfully compile the Linux kernel. The API bill came to roughly $20,000. By almost any measure, it was an extraordinary result.

Then I mentioned it to a friend of mine, a CTO at a small startup. His response: "The best strategy right now is probably to wait."

That tension — between a genuine technical milestone and a seasoned engineer's skepticism — is what this post is really about.

Looking back at 20 years in platform engineering feels both humbling and exhilarating. From building RAD tools for web applications in 2000 to managing Kubernetes clusters with 2 million pods today, the journey has been one of continuous learning, adaptation, and growth. This is my story of building platforms at scale, and the lessons I've learned along the way.