EXPLYT TEAM
02.10.2025
3 MINUTES
It seems like just yesterday, the biggest debate in the IT community was about the best framework. Today, our feeds are filled with existential dread: "Will artificial intelligence replace us?" And at first glance, there are reasons for concern. We see neural networks generating code from text descriptions, passing interviews, and solving junior-level tasks. Tools like GitHub Copilot have evolved from exotic novelties to everyday realities for millions.
The fear is understandable: if a machine can write code, why do you need a person who does the same thing, only slower and more expensively? But this question is based on a fundamental misconception. It assumes that a programmer's entire job is the mechanical act of writing lines of code. Let's break down why that's not the case and what the developer of tomorrow will actually look like.
Modern LLMs have indeed learned to generate syntactically correct code that often even runs. But there's a vast gap between "it runs" and "it works correctly." Code generated and left unsupervised is a ticking time bomb. It's often suboptimal, ignores project architectural patterns, contains vulnerabilities, and most importantly, fails to account for edge cases.
Writing a feature is just the tip of the iceberg—20% of the task at best. The other 80% involves testing, debugging, integration, refactoring, and maintenance. Modern AI code generators almost completely ignore this submerged part of the iceberg. They produce a "semi-finished product" that requires even more expertise to refine than writing the code from scratch.
This is where the main shift is happening. The value of the future developer will be determined not by their typing speed, but by their ability to manage the entire development lifecycle. The role is shifting from "implementer" to a combination of "system architect," "quality engineer," and "technical lead."
The architect-validator doesn't write all the code themselves; they assign tasks to an AI assistant. Then, they conduct a rigorous inspection of the generated solution:
Their primary tools are no longer just the keyboard and IDE, but also powerful analysis, testing, and debugging instruments.
This new paradigm requires appropriate tools. Simple code completion that generates code in a vacuum is powerless here. What's needed are intelligent agents that take on the most complex part of the routine: ensuring quality.
This is precisely the problem Explyt solves. Our AI agent isn't designed to just write code. Its main strength lies in automating that submerged part of the iceberg:
We give developers the "superpowers" for validation and control, turning them into the architect-validators of the future.
So, will AI replace programmers? No. But it will draw a clear line between artisans who simply translate specs into code and engineers who build reliable and efficient systems. The former are indeed at risk of becoming obsolete. The latter, however, will become even more valuable.
By delegating routine tasks to a machine, engineers can focus on what matters most: architecture, system design, and strategic technical decisions. Their work will become more creative, complex, and, consequently, more highly compensated. AI is not a threat; it's the most powerful upgrade our profession has seen in decades. The key is to use it correctly.


