The Bottleneck Has Moved

When code generation was the constraint, technical skill was the differentiator. Developers who could implement faster, debug quicker, and architect more elegantly commanded premium value. Business understanding was nice to have, a soft skill that complemented the hard skills that actually mattered.

AI code generation is dissolving this hierarchy.

The developers I see thriving aren’t the ones who code fastest. They’re the ones who understand what to build, why it matters, and what tradeoffs are acceptable. Technical execution is increasingly commoditized. Business judgment is not.

What Actually Made Me Better

I became a better developer as I became more business-minded. Not because I learned new languages or frameworks, but because I developed a different relationship with the work.

Understanding value and customer alignment changed how I approached every decision:

• I stopped building the wrong thing well—the most expensive mistake in software
• I could evaluate tradeoffs against actual value, not abstract “best practices”
• I knew when “good enough” was actually good enough
• I understood the cost of delay—shipping imperfect beats perfecting endlessly

Code knowledge tells you how. Business understanding tells you what, why, and whether. AI is getting remarkably good at how. It has no grasp of why.

The Bifurcation

The middle is hollowing out. Two paths are emerging, and they’re diverging, not converging.

The Broad-and-Human Path

This path centers on value judgment, customer alignment, tradeoff navigation, domain expertise, and system thinking. It requires broader context and serves fewer people directly. Becoming more human means developing empathy, judgment, relationships, and context that machines cannot replicate.

The Deep-and-Mechanical Path

This path leads toward R&D, algorithms, performance optimization, novel architectures, security research, and low-level systems. It demands narrower focus and extreme depth. Becoming more mechanical means precision, relentless optimization, and working at the frontier where AI assistance runs out.

For those drawn to this path: the bar rises dramatically. Knowing a language well becomes pushing the boundaries of what’s computationally possible. Implementing algorithms becomes inventing them. Using frameworks becomes building them. Following security practices becomes discovering vulnerabilities and designing novel defenses. You compete globally for positions that require genuine innovation, building the substrates that AI and others build products on. This path demands excellence that few can sustain, but for those who can, it remains valuable precisely because it’s rare.

The Vanishing Middle

Between these paths, roles are losing leverage: the coder who implements specs without questioning them, the integration specialist whose value was knowing API quirks, the framework expert whose depth was a single ecosystem, the ticket-taker who translates Jira stories into pull requests.

These roles don’t vanish overnight. But the leverage shifts. One business-aligned architect with AI assistance can accomplish what previously required a team. Both paths are valid, and neither is easy. But the space between them is compressing.

A Warning

Tool-orientation without value-orientation is increasingly precarious. AI is a better tool-operator than most humans. It doesn’t tire, doesn’t context-switch, doesn’t forget syntax. If your value proposition is “I can use the tools,” you’re competing on terrain where AI has structural advantages.

This isn’t new wisdom. Tool-orientation has always caused friction when divorced from value and alignment. The developers who thrived before AI were already the ones who understood the business context of their work. AI just accelerates the consequences.

The Atrophy Concern (and Why It’s Familiar)

There’s a darker worry beneath the surface: what happens to our skills as we rely on AI?

Right now, senior engineers with decades of hard-won intuition can leverage AI as a force multiplier. They know when AI is confidently wrong. They have the architectural judgment to evaluate generated code. They built debugging instincts from years of suffering through problems manually.

But every time AI handles something, you get a little worse at handling it yourself. Skills require practice to maintain. Outsource the practice and the skill decays. If AI stalls or regresses, will we still have the competence to engineer without it, or even to continue using it at our current level?

This concern is real, but it’s also not new.

Could you build a radio if you needed to? Could you manufacture a car, synthesize medicine, or grow enough food to feed yourself for a month? Civilization is dependency. Specialization is the trade. We gave up self-sufficiency for leverage a long time ago.

Every technological layer creates the same pattern: a new capability emerges, early adopters with pre-existing skills use it as force multiplier, the next generation learns with the tool rather than before it, and the underlying skill becomes specialized knowledge held by few. We don’t mourn that most people can’t forge steel or build semiconductors. We accept that specialists exist and the rest of us build on their work.

AI is another layer in this stack. Some skills will atrophy; that’s the trade. The question isn’t whether we’ll lose capabilities, because we will. The question is whether you’re positioning yourself to provide value at the new layer, or holding onto skills being absorbed into the substrate.

The people who thrived weren’t the ones who could build radios. They were the ones who understood what to do with radios.

The Learning Inversion

This brings us to a question that sounds new but isn’t: how do people develop judgment without grinding through the middle?

The honest answer is that the old path was never as necessary as we pretended. We learned the hard way, spending years on syntax, framework quirks, and theoretical foundations before we were trusted with real decisions. Much of that time was waste. We learned “computer science” when we needed to learn “this job.” We studied theory for hypothetical problems while the actual problems sat waiting.

This was always a trade-versus-theory problem. Traditional education and career paths optimized for theoretical completeness, not practical judgment. “Learn the fundamentals first, apply later” sounds rigorous, but it mostly meant years of gatekeeping before you got to do the work that actually built intuition.

AI doesn’t just hollow out the middle. It offers a way through.

When coding takes a fraction of the time, you can redirect that effort toward what actually matters: the domain, the users, the constraints, the tradeoffs. You still need to understand security, architecture, and system thinking. But you can acquire that knowledge in context, while solving real problems, rather than stockpiling it in advance for scenarios that may never come.

Focus on this job, this problem, this domain. Generalist knowledge accumulates naturally from solving diverse real problems. It doesn’t require years of abstract preparation.

I say this as someone who took the long path. The grind taught me things, but it also taught me how much of it was unnecessary. That perspective is exactly what lets me tell you to skip what we went through. We know which parts mattered because we suffered through the parts that didn’t.

The middle was always a holding pattern, not a destination. AI just makes that visible.

Choosing the Broad Path

If you’re drawn toward the human side of this bifurcation, the answer isn’t to learn more tools or chase the latest framework. The answer is older than AI.

Market yourself, not just your skills. Skills are inputs. Value is output. Organizations don’t need people who can code; they need people who can solve problems that matter. Position yourself around the problems you solve, not the tools you use.

Become value-oriented, not tool-oriented. Every technical decision exists in a business context. What are we trying to achieve? For whom? What does success look like? What’s the cost of being wrong? These questions matter more than implementation elegance.

This is the human work that AI cannot do. It requires empathy, judgment, relationship-building, and context that spans conversations, projects, and years. The middle is not a resting place; it’s a transition zone, and it’s narrowing. The tools will keep getting better. The question is whether you’re wielding them toward value, or being replaced by someone who is.