29 posts tagged with "engineering-leadership"

The new hire ships a feature on day three. Everyone on the team is impressed. Three weeks later, she introduces a bug that a senior engineer explains in five words: "We don't do it that way." She had no idea. Neither did the AI that wrote her code.

AI coding assistants have collapsed the time-to-first-commit for new engineers. But that speed hides a trade-off that most teams aren't tracking: the code-reading that used to slow down junior engineers was also the code-reading that taught them how the system actually works. Strip that away, and you get engineers who can ship features they don't understand into architectures they haven't internalized.

The problem isn't the tools. It's that we haven't updated onboarding to account for what AI now does — and what it no longer requires engineers to do themselves.

Here's an uncomfortable pattern: the AI feature your team is about to launch next quarter already died at your company two years ago. It shipped under a different name, with a different prompt, solving a vaguely different problem, and it got quietly decommissioned after six months of flat adoption. Nobody wrote it up. Nobody connected the dots. The leading indicators that would have saved this cycle were sitting in dashboards that got archived along with the feature.

Most engineering orgs are elaborate machines for remembering successes. Launches get retrospectives, blog posts, internal celebrations. The features that got killed — the ones with 12% weekly active users despite a polished demo, the ones whose unit economics inverted when token costs compounded across a longer-than-expected tool chain, the ones users learned to trust, lost trust in, and then routed around — generate almost no institutional memory. And the failure patterns embedded in those deaths are exactly the ones your planning process has no way to price in.

Three months after rolling out an AI coding assistant to their entire engineering team, a company noticed something disturbing: their code review pass rate had dropped 18%, their sprint velocity was up, but the number of production incidents had climbed. When they asked developers to explain a recent AI-generated module during a post-mortem, nobody in the room could. Not even the person who merged it.

This is the cognitive offloading trap. And it's not a failure of AI tools — it's a failure of how teams integrate them.

Most engineering teams that hire for LLM roles run roughly the same interview: two rounds of LeetCode, a system design question, maybe a quiz on transformer internals. They're assessing for the wrong things — and they know it. The candidates who ace those screens often struggle to ship working AI features, while the ones who stumble on binary search can build an eval suite from scratch and debug a hallucinating pipeline in an afternoon.

The skills that predict success in LLM engineering have almost no overlap with what traditional ML or software interviews test. Hiring managers who haven't updated their process are generating false negatives at a high rate — rejecting engineers who would succeed — while false positives walk in with solid LeetCode scores and no intuition for when a model is confidently wrong.

Three years ago, "AI team" meant a group of specialists tucked into a corner of the org chart, mostly invisible to product engineers. Today, a senior software engineer at a fintech company ships a fraud-scoring feature using a fine-tuned model on Monday, wires up a RAG pipeline for customer support on Wednesday, and debugs LLM latency on Friday. The specialists didn't go away—but the boundary between "AI work" and "product engineering" dissolved faster than almost anyone planned for.

Most teams responded by bolting new titles onto existing job descriptions and calling it done. That's the wrong answer, and the dysfunction shows up quickly: unclear ownership, duplicated tooling, and an ML platform team that spends half its time explaining why product teams can't just call the OpenAI API directly.

This post is about getting the structure right—not in the abstract, but for the actual stages of AI adoption most engineering organizations go through.