The 108,000 Question

In January 2026 alone, 108,000 tech jobs disappeared. Not because the work stopped needing to be done—it just stopped needing humans to do it.

AI wrote the code. AI reviewed the code. AI generated the tests, the documentation, the deployment scripts. What was left for human engineers?

The uncomfortable answer: mostly coordination. And that's where most engineers are completely unprepared.

The Skills That Matter Are Changing

For twenty years, the engineering career ladder rewarded:

→Deep technical specialization
→Writing complex code from scratch
→Knowing the codebase inside and out
→Solving hard algorithmic problems

AI now does all of this better. Faster. Cheaper.

But here's what AI can't do—and what leading engineers in 2026 need to master:

Asking the right questions. AI can write code, but it can't decide what code should be written. That requires understanding business context, user needs, trade-offs that can't be quantified.

Making judgment calls in ambiguity. When the data doesn't tell you what to do, when stakeholders disagree, when there's no clear right answer—that's human territory.

Building alignment across humans. Getting diverse people to agree on direction, to commit to shared goals, to work through conflict—these are leadership skills that AI can support but never replace.

Coaching other humans to grow. Mentoring, developing talent, helping people navigate career decisions—fundamentally relational work.

The Middle Management Collapse

Gartner predicts that organizations are flattening. The traditional middle manager—whose main job was passing information up and decisions down—is becoming obsolete.

Why? Because AI does information transmission perfectly. Status updates? AI can generate them. Decision documentation? AI can synthesize them. Performance reporting? AI can analyze the data.

What's left for humans is actual leadership: creating vision, developing people, making judgment calls that require human context.

This is why we're seeing the largest leadership transition in tech history. The engineers who thrive won't be the ones who write the best code—they'll be the ones who lead the best teams, guided by AI.

The New Leadership Required

The engineering leaders who'll succeed in the AI era need to shift their identity from "doer" to "enabler."

This means:

From writing code to writing context. Your job isn't to produce code—it's to produce understanding. What problem are we solving? Why does it matter? What constraints do we have?

From solving problems to framing problems. AI is incredible at solving well-defined problems. But defining the problem correctly—that requires human judgment about what's valuable, what's urgent, what's worth the trade-offs.

From technical review to strategic review. Instead of "does this code look good?" ask "are we building the right thing?" The technical question is increasingly answered by AI. The strategic question is uniquely human.

From managing tasks to developing people. When AI handles execution, human managers become coaches. Their value shifts from "did the work get done?" to "is the team growing?"

The Hexaco Angle

Here's what the personality research shows: these new leadership requirements map directly to specific traits.

High Openness to Experience becomes crucial—the ability to think creatively about what to build, not just how to build it.

High Emotionality (in the HEXACO sense of experiencing emotions strongly and being attuned to others) becomes an asset, not a weakness. Leadership is fundamentally relational.

High Honesty-Humility matters more than ever. When AI can do the technical work, the human contribution is trust, authenticity, and genuine care for people.

The engineers who adapt will be those whose personality profiles support this shift—while those optimized purely for technical execution will find the ground shifting beneath their feet.

What This Means for Your Team

If you're leading engineers today, here are the questions you should be asking:

What percentage of your team's work could AI actually do? Be honest. If it's above 50% for any individual contributor role, you need to think about role evolution.

Are you developing leaders or performers? The engineers who only know how to execute will struggle. The ones who know how to enable others to execute will thrive.

Can your team articulate what makes their work valuable beyond what AI could do? If they can't answer this, they're already behind.

Are you investing in the human skills that AI can't replace? Coaching, relationship-building, strategic thinking, creative problem framing—these are the investments that pay off when AI handles the rest.

The Bottom Line

AI isn't coming for engineering jobs. It's coming for the parts of engineering jobs that were always less than human—the repetitive execution, the mechanical translation of requirements to code, the information processing that was never what made the work meaningful.

What remains is the work that was always harder: deciding what to build, leading the people who build it, and navigating the ambiguity that no algorithm can resolve.

The engineers who embrace this shift—who become leaders in the fullest sense—will find themselves more valuable than ever. The ones who cling to the old definition of "technical excellence" will discover it's been commoditized.

The question isn't whether AI changes engineering. The question is whether you're willing to change with it.

This article is part of the Leadership Unfiltered series on engineering team dynamics. For more insights on building high-performing teams in the AI era, explore LU Teams.