I Manage 50+ Engineers. Here’s What’s Actually Changing.

Six months ago, I started tracking something unusual.

Every week, I asked my engineering leads the same question: "Which skills saved you time this week? Which ones felt useless?"

The patterns that emerged surprised me. Not because AI was changing things—everyone expected that. What surprised me was what was changing and what wasn’t.

The Skills That Are Dying

Let me be direct. These skills are losing value fast:

Memorizing syntax. I watched a senior developer spend 20 minutes trying to remember the exact parameters for a PHP function. A junior developer next to him typed it into Claude and had working code in 30 seconds. The senior developer wasn’t slower because he was worse. He was slower because he was using the wrong tool for the job.

Framework-specific expertise. "5 years of React experience" used to mean something. Now it means you know patterns that AI can generate in seconds. The value has shifted from knowing React to knowing when React is the right choice—and when it isn’t.

Writing boilerplate code. This one died quietly. Nobody at WisdmLabs writes CRUD operations by hand anymore. We specify what we need, AI generates it, we review it. The boring parts got automated.

“Years of experience with X.” This credential is becoming meaningless. I’ve seen developers with 2 years of experience outperform 10-year veterans because they learned to work with AI instead of competing against it.

The Skills That Are Thriving

Here’s what I’m seeing in my best performers:

Problem decomposition. Knowing which functions to build matters more than knowing how to build them. The developers who excel break problems into small, testable pieces before touching any code. AI handles the pieces. Humans handle the architecture.

Architectural judgment. AI can’t see trade-offs that span multiple systems. It doesn’t know that your payment provider has a 3-second timeout that will break if you add that extra validation step. It doesn’t know your team has no Redis experience and that caching solution will become a maintenance nightmare. This judgment comes from battle scars, not training data.

Codebase intuition. Every codebase has secrets. The function that looks simple but breaks everything if you change it. The config file that three different services depend on. The workaround from 2019 that nobody documented. This institutional knowledge lives in people’s heads, and AI can’t access it.

User empathy. AI builds what you ask for. It doesn’t build what users actually need. The gap between those two things is where great engineers live. At WisdmLabs, our best developers spend more time talking to users than writing code. AI made that trade-off easier.

Technical leadership. Deciding when to ship versus when to refactor. Knowing which tech debt to accept and which to fight. Understanding when "good enough" is actually good enough. These decisions require context that no model can provide.

This Isn’t New

Look at history:

• 1990s: Assembly knowledge gave way to high-level languages
• 2000s: Server admin skills gave way to cloud platforms
• 2010s: jQuery expertise gave way to modern frameworks
• 2020s: Framework mastery is giving way to AI-assisted development
• 2025+: Coding ability is giving way to engineering judgment

Abstraction always wins. AI is just the next abstraction layer.

The assembly programmers who complained about C were right that something was lost. They were wrong that it mattered. The value moved up the stack. It’s moving up the stack again.

What I Tell My Engineers

I’ve had versions of this conversation dozens of times in the past year. Here’s the advice that seems to stick:

Shift from implementation to orchestration. Stop thinking “I write React components.” Start thinking “I architect solutions and direct AI to implement them.” The job title might stay the same. The job itself is different.

Double down on what AI can’t do. Deep domain expertise in your company’s context. Stakeholder relationships. Translating technical decisions for non-technical people. Product intuition. These skills are getting more valuable, not less.

Become the bridge. The most valuable engineers aren’t the best coders anymore. They’re the best translators. Business problems to technical solutions. Technical constraints to business language. User pain to product requirements. This translation work is exploding in value.

Build AI fluency, not AI dependency. Know what AI is good at: patterns, boilerplate, translations, explanations. Know what AI is bad at: novel architecture, business context, long-term thinking, your specific codebase. Learn to verify AI output efficiently. Trust but verify.

The Real Shift

The engineers who will thrive aren’t the ones who resist AI. They’re the ones who use it to amplify what was always valuable about them.

I’ve been writing code for 18 years. The first 15 years, my value came from knowing things other people didn’t know. The last 3 years, my value comes from understanding things AI can’t understand.

That’s the shift. Knowledge to understanding. Implementation to judgment. Coding to engineering.

The title of “engineer” is finally starting to match the job.

What skill on the “thriving” list are you investing in most? I’m genuinely curious how other teams are navigating this.