When I look at what I do during an average working day, I write less code than I used to. I’m not working less; I’m working on different things. And that is a shift people talk about far less often.
Will AI replace developers?
It’s often said that AI will replace programmers. From what I see in my day-to-day work, that’s only half the story. AI can generate a large portion of the code, but it can make mistakes—and sometimes those errors lurk within output that looks completely sound. You still need someone who knows how to evaluate what is produced.
To me, this transition mirrors the shift from assembly to high-level languages. Back then, developers feared losing control, yet they simply stopped managing raw machine instructions and began designing more complex systems. It’s an analogy, not a prophecy, but it captures the trajectory of my work: less manual writing, more design, verification, and high-level decision-making.
From mock-up to application
Take a concrete example: implementing mock-ups. Translating a visual concept into a responsive, functioning application takes time and meticulous attention to detail. In complex scenarios, delivering the desired user interface means striking the right balance between design fidelity, application integration, and maintainability.
Today, I pass the mock-up and specifications to the AI and receive a working draft in moments. That is where my actual work begins: I review the output, integrate it into the codebase, and intervene wherever needed. This frees up time to focus on architectural quality and getting closer to the client’s design requirements, even in the most intricate edge cases
The real value of my work does not lie in writing every single line of code. It lies in understanding what the client truly needs and describing it with absolute precision.
When requirements change
The most critical moment I recall happened on a project that was already well under way. The client fundamentally changed the requirements, meaning part of the application had to be rewritten without shifting the delivery date. This happens regularly in software projects that adapt as they take shape.
A scenario like that demands technical judgement: how does this alter the architecture? What happens to existing features? Which tests must be revised? How will the timeline be impacted? AI can accelerate the boilerplate and speed up specific modifications, but it cannot answer those strategic questions, nor can it guarantee that changes won’t cause regressions. Those assessments remain firmly with the people designing the system.
Where the time goes
The time I reclaim from repetitive coding tasks goes directly into system architecture and deeper collaboration with the client, where I can be far more proactive. I’ve learnt that value comes from precision: if the brief given to the AI is vague, the output will be just as vague. That is why I now devote more attention to defining requirements and writing thorough documentation—both provide the AI with necessary context and make verifying its output significantly easier.
There is also a personal upside. Working with less friction means less overtime racing against deadlines and more room for personal life. That is an immediate benefit I am experiencing, even if it cannot be taken for granted on every project.
What matters to software stakeholders
For an IT manager or a CTO, the question is no longer whether developers use AI. It is what remains in the developer’s hands. Generating raw code is becoming trivial; steering development still demands deep expertise: grasping business requirements, architecting coherent solutions, critiquing generated code, and safeguarding long-term maintainability. The developer is, more than ever, becoming a software architect.
In my work, AI has shifted the focus from manual coding to design and oversight. At TC Consulting, we use AI to accelerate development without ever delegating the decisions that dictate software quality. Architecture, verification, and technical accountability remain central—that is how speed translates into genuine value for the client.
