AI Coding Tools Are Making Senior Devs Obsolete. Good.

In 2023, Stripe had a candidate fail a live coding screen because they couldn't reverse a linked list without Copilot. Six months later, that same candidate's team shipped a payments reconciliation feature that handled $2M in daily transaction volume. First try. No rollbacks.

That broke something in my brain.

Here's the thing: we spent 20 years building a hiring pipeline designed to filter for people who can hold abstract syntax trees in their heads. Leetcode. Whiteboard recursion. "Design Twitter in 45 minutes." The entire apparatus was built on one assumption: that raw implementation ability was the scarce resource.

It's not anymore.

What's Actually Changing (Not The Part You're Thinking)

Everyone's having the wrong argument. The debate is "will AI replace developers" and that's a distraction. The real shift is that the bottleneck moved.

For 15 years, the constraint was writing correct code fast. That's gone. Cursor with Claude 3.5 Sonnet writes a Redis-backed rate limiter in 90 seconds. A clean one. With tests.

The new constraint is knowing what to build, why it's the right abstraction, and what will kill you in 18 months when traffic is 40x and you're doing a PagerDuty postmortem at 2am.

That's a different skill. Most people calling themselves senior engineers don't have it.

The War Story Nobody Wants To Tell

We had an outage in late 2023. Three hours, approximately $400k in processing fees we couldn't recover. Root cause: a developer used GitHub Copilot to generate a database migration that looked completely correct. Passed code review. Passed CI. Added a non-null column to a Postgres table with 800M rows without a default value.

The AI didn't know we were running Postgres 13 with a specific RDS configuration that made that migration strategy a table lock. The reviewer didn't check because the code looked right. The tests didn't catch it because we don't test infrastructure timing in CI.

Don't do this. The AI is not thinking about your infra constraints. It's pattern-matching against every Stack Overflow answer ever written, including the bad ones.

Who Gets Hurt And Who Gets Rich

Mid-level engineers who coasted on implementation speed are in trouble. I've seen this kill production careers. If your entire value proposition is "I write clean React faster than the other guy," that's not a moat anymore. Copilot writes cleaner React than most humans I've hired.

But the engineers who understand distributed systems failure modes? Who know why you don't put business logic in database triggers? Who can read a Datadog flame graph and immediately know which service call is the problem? Those people are printing money right now.

The floor for shipping code dropped to zero. The ceiling for architectural judgment went through the roof.

What This Means For How We Hire

I stopped running Leetcode screens six months ago. I run production incident simulations now. I give candidates a Datadog dashboard from a real (anonymized) outage and ask them to walk me through what happened. I give them a Linear ticket with a vague requirement and ask what questions they'd ask before writing a single line.

I've rejected people who could implement a perfect LRU cache in 20 minutes but couldn't articulate why we'd want one in the first place.

The engineers who are going to matter in 2026 are the ones who use AI as a multiplier on good judgment, not a substitute for having any.