Feb. 27, 2026
14 minutes read
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A few weeks ago, I wrote that the “death of coding” is a distraction. I stand by that. What we are witnessing is not the disappearance of software engineering, but the exposure of what it was always meant to be.
For decades, our industry used syntax fluency as a proxy for competence. If someone could manage memory in C++, write complex boilerplate without referencing documentation, or implement intricate patterns from memory, we labeled them “senior developer“. We rewarded speed at the keyboard. We equated familiarity with APIs to architectural wisdom.
That equation no longer holds.
Generative AI has reduced the marginal cost of producing syntactically correct code to near zero. With tools that can draft entire modules in seconds, the friction that once defined technical craftsmanship has largely disappeared. What remains is something far more fundamental: judgment.
The conversation should not be about whether coding is dying. It should be about what engineering actually is, not about typing speed.
When code generation becomes instantaneous, the bottleneck shifts. It shifts decisively from production to validation.
In the past, writing code consumed the majority of a developer’s time. Debugging and review were essential, but they followed creation. Now, creation is trivial. Verification is the constraint.
I see this daily. An AI assistant produces a plausible block of code. It compiles. It even passes superficial tests. Yet embedded within it might be:
The engineer’s responsibility has therefore intensified. The task is no longer to write every line manually. The task is to interrogate what has been produced. Does it align with system constraints? Does it respect invariants? Does it scale? Does it degrade safely?
Verification is not proofreading. It is systems thinking applied under compressed timelines.
The individual who thrives in this environment is not the fastest typist. It is the one who can examine a generated implementation and immediately identify where reality will diverge from expectation.
That skill cannot be automated away easily because it is rooted in experience with failure.
If generating code is effectively free, generating bad systems becomes cheaper than ever.
In a pre-AI workflow, friction limited damage. Boilerplate took time. Integrations required manual wiring. Decisions carried an implicit cost because they demanded effort. Today, that friction is gone. It is easier than ever to assemble a tangled mass of services, abstractions, and dependencies that appear functional in isolation but collapse under complexity.
When AI can produce any pattern on request, discipline becomes the differentiator. Architectural clarity is no longer optional; it is the only mechanism preventing organizational chaos.
I have observed teams that treat generative tools as accelerants without adjusting their standards. The result is velocity without direction. Codebases grow faster, but cohesion deteriorates. Interfaces blur. Ownership becomes ambiguous. Technical debt compounds invisibly until the system resists change.
In this context, architecture is not a document. It is a constraint system. It defines:
Without these constraints, AI assistance amplifies disorder. With them, it amplifies productivity.
This is why seniority must be redefined. The most valuable engineer is not the one who can implement a pattern from memory, but the one who understands why a pattern should or should not exist within a given system.
There is an uncomfortable truth embedded in this transition. Much of what we previously celebrated as expertise was memorization.
Knowing the precise incantation for a framework configuration was once impressive because the cost of retrieving that knowledge was high. Documentation was scattered. Tooling was immature. The search was imperfect. Memory filled the gap.
That gap has closed.
When an assistant can generate the correct configuration file, including edge-case flags, in seconds, memorization loses its strategic value. Syntax becomes table stakes.
This does not trivialize programming. It clarifies it.
Engineering was never about recalling function signatures. It was about managing constraints. We simply conflated the two because syntax consumed our attention.
Now that the mechanical aspect is automated, the cognitive layer is exposed. The engineer must reason about trade-offs:
These decisions were always present. They were simply overshadowed by the manual effort of implementation.
AI has not diminished the profession. It has removed the camouflage.
If code can be generated with AI, what remains uniquely human?
The ability to decompose ambiguity.
Business requirements rarely arrive as precise technical specifications. They are narratives. They contain implicit constraints, unstated assumptions, and competing objectives. Translating them into atomic, testable components is the essence of engineering.
Prompting an AI effectively requires structured thought. If the problem is vague, the output will be vague. If constraints are contradictory, the system will optimize for whichever pattern appears most statistically probable, not necessarily most correct.
This elevates problem decomposition to a primary competency. It requires the engineer to:
In this paradigm, the prompt becomes a design artifact. It encodes intent. It reflects the clarity of thought applied before generation occurs.
An engineer who cannot break down a complex requirement into coherent sub-problems will struggle, regardless of tool sophistication.
Conversely, someone who can model the problem space precisely will leverage AI as a multiplier.
There is another dimension that demands attention: trust.
AI-generated code often looks correct. It follows conventions. It reads cleanly. That aesthetic alignment can create false confidence.
However, plausibility is not correctness.
Generated code may include outdated dependencies, insecure defaults, or subtle vulnerabilities. It may reference APIs that exist in similar but incompatible forms. It may silently ignore edge conditions.
In a high-stakes system—financial infrastructure, healthcare platforms, critical data pipelines—such errors are not cosmetic. They carry material risk.
Therefore, verification must include:
The engineer’s role expands into that of a guardian. AI may draft the structure, but the human must certify its integrity.
This reinforces a broader shift: credibility in engineering will increasingly derive from demonstrated judgment rather than demonstrated fluency.
This transition not only affects individual contributors. It reshapes engineering leadership.
Managers and technical leaders must reconsider how they evaluate performance. If productivity metrics still revolve around lines of code written or tickets closed, incentives will distort behavior. Engineers will optimize for output rather than coherence.
Instead, leadership must emphasize:
Furthermore, onboarding changes. Junior developers entering the field with AI assistance may reach functional productivity quickly. However, without deliberate mentorship, they may bypass the experiential learning that builds intuition about failure.
Leaders must create environments where verification skills are cultivated intentionally. Code reviews become educational forums. Design discussions become explicit explorations of trade-offs rather than implicit decisions buried in implementation.
The future engineer is not self-sufficient in isolation. They are effective within a well-defined system of constraints and shared standards.
This is not a contradiction of the profession. It is a maturation. Hiring will be one of the clearest indicators of whether organizations understand this shift.
For years, interview loops have over-indexed on algorithm puzzles and syntax-heavy exercises. Candidates were evaluated on their ability to reconstruct known solutions under time pressure. That method measured recall and pattern familiarity more than architectural reasoning.
In a world where generative AI can produce a near-optimal algorithmic implementation on demand, those filters lose signal.
If I were designing an engineering interview process today, I would place greater weight on how candidates think rather than what they can type unaided.
Specifically, I would evaluate:
Instead of asking someone to implement a data structure from scratch, I would present them with an AI-generated solution and ask them to critique it. Where are the hidden risks? What assumptions does it make? How would it behave under stress? What monitoring would they add?
This approach tests engineering judgment directly.
Moreover, hiring managers must distinguish between tool proficiency and conceptual depth. An engineer who knows how to prompt effectively but lacks architectural intuition will eventually produce fragile systems at high velocity. Conversely, someone with strong systems thinking can adapt to new tools quickly because their competence is rooted in principles, not mechanics.
The market will increasingly reward the latter.
The title “Senior Engineer” has often been ambiguous. Sometimes it meant tenure. Sometimes it meant familiarity with a codebase. Sometimes it meant the ability to ship features independently.
In the verification era, seniority should imply something more precise: accountability for system coherence.
A senior engineer must be able to:
This is less about individual heroics and more about stewardship.
When AI accelerates implementation, the cost of poor oversight increases. A junior engineer with a powerful assistant can introduce structural flaws at scale if guardrails are weak. Senior engineers must therefore act as constraint designers, not just contributors.
This reframes mentorship as well. Teaching syntax is no longer the primary developmental task. Teaching reasoning is.
There is also a human dimension to this transition. Many engineers built their professional identity around mastery of complexity at the syntactic level. The ability to manipulate low-level constructs or navigate obscure framework internals conferred status.
When those skills become automated, it can feel destabilizing.
However, I believe this discomfort reveals an opportunity. The core of engineering has always been problem-solving under constraint. If one’s identity is anchored in that capability rather than in specific tools, the shift becomes liberating rather than threatening.
The engineer is no longer constrained by the speed of manual production. Cognitive energy can be redirected toward:
In that sense, the profession becomes more aligned with its stated purpose: translating complex human goals into reliable technical systems.
Despite these opportunities, there is a genuine risk if organizations misunderstand what is happening.
If leadership interprets AI-assisted development purely as a cost-reduction mechanism, they may reduce investment in architectural oversight, peer review, and long-term planning. They may assume that faster output equates to greater efficiency.
That assumption can be costly. Without deliberate reinforcement of standards, the acceleration of code production can outpace an organization’s ability to maintain coherence. Incidents increase. Refactoring becomes continuous rather than periodic. Teams spend more time managing entropy than delivering value.
The solution is not to resist AI. It is to adapt governance models accordingly. Verification must be institutionalized. Architectural review must be prioritized. Design documentation must be explicit rather than implicit.
In short, discipline must scale alongside capability.
When I say the “death of coding” is a distraction, I mean that it frames the wrong variable.
Coding, understood narrowly as manual syntax production, was always an implementation detail. It was visible, measurable, and easy to evaluate, so we treated it as central.
But the enduring value of engineering lies elsewhere:
AI alters the cost structure of implementation. It does not eliminate the need for clarity, judgment, or accountability. If anything, it magnifies them.
Viewed historically, this transition feels less like disruption and more like correction. Engineering was never meant to be a typing contest. It was meant to be the disciplined application of logic to complex systems under constraint. We simply tolerated inefficiencies for decades because the tools required it.
Now that those inefficiencies are diminishing, the profession is converging toward its core. The coder whose value was defined primarily by syntax fluency may indeed face uncertainty. But the engineer who understands intent, constraints, and systems thinking will find their relevance strengthened.
The job is not disappearing. It is shedding a layer of friction that obscured its essence. And in that clarity, the expectations rise.
There is also a structural consequence that deserves attention: the economics of software delivery are changing.
When implementation costs decrease, experimentation increases, and teams can prototype multiple approaches before committing. Features can be validated through thinner slices of functionality. Internal tooling can be developed with lower opportunity cost.
However, the reduction in implementation friction does not eliminate the cost of poor decisions. In fact, it can amplify them.
If a flawed architectural direction is chosen, AI assistance can accelerate its propagation. The system becomes more deeply entrenched before corrective feedback arrives. The cost curve shifts from linear to exponential because errors scale faster.
Therefore, organizations must recalibrate their investment distribution. Fewer resources may be required for manual construction, but more attention must be directed toward:
The strategic advantage will not belong to those who generate the most code. It will belong to those who generate the most coherent systems.
The implications extend into education and early-career development.
If students rely on generative tools from the beginning, they may never experience the friction that previously forced deep understanding. That friction, while inefficient, often served as a teacher. Debugging manual mistakes built intuition about state, memory, concurrency, and failure.
With AI abstracting much of that surface complexity, educators must be deliberate about preserving conceptual rigor. Curricula should emphasize:
The goal is not to prevent the use of AI in the software development lifecycle. It is to ensure that foundational reasoning develops alongside it.
Otherwise, the industry risks producing developers who can assemble solutions but cannot diagnose them.
Another question inevitably arises: if AI contributes to the code, who is accountable? The answer remains unchanged. The engineer is.
Accountability cannot be outsourced to probability. Tools do not bear responsibility. Organizations do not deploy “AI-generated systems.” They deploy systems that humans choose to ship. The decision to accept, modify, or reject generated output is an engineering judgment.
This reinforces the need for transparency in the process. Teams should be explicit about:
Public discourse often frames this transition as a competition between humans and machines. That framing misses the point. The relevant comparison is not human alone versus AI alone. It is human with structured judgment versus human without it.
AI amplifies capacity. It does not replace accountability. It extends reach. It does not define direction. An engineer who lacks clarity will produce confusion more efficiently. An engineer with disciplined reasoning will produce structured systems more efficiently.
The differentiator remains cognitive rigor.
When I describe the “death of coding” as a distraction, I do so because it centers the conversation on an artifact rather than on a discipline. Coding, in its narrowest sense, was always a means. Engineering is the end.
As generative tools reduce the cost of implementation, the profession is being re-centered on its essential functions: defining constraints, modeling systems, verifying correctness, and safeguarding integrity. This is not a contraction of opportunity. It is an elevation of expectation.
The engineer who embraces verification, architectural clarity, and disciplined problem decomposition will find that their role has not diminished. It has sharpened. The job is not vanishing. It is finally becoming what it was always supposed to be.
As the Vice President of Sales, Michael leads revenue growth initiatives in the US and LATAM markets. He focuses on three core pillars to drive success: fostering continuous improvement within our sales team and ensuring they consistently have the necessary skills and resources to exceed targets; creating and optimizing processes to maximize efficiency and effectiveness throughout the sales cycle; consolidating tools and technologies, streamlining our lead generation capabilities to improve our market reach and conversion rates.
As the Vice President of Sales, Michael leads revenue growth initiatives in the US and LATAM markets. He focuses on three core pillars to drive success: fostering continuous improvement within our sales team and ensuring they consistently have the necessary skills and resources to exceed targets; creating and optimizing processes to maximize efficiency and effectiveness throughout the sales cycle; consolidating tools and technologies, streamlining our lead generation capabilities to improve our market reach and conversion rates.
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