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When AI still accepts more and more novel competences, coding of younger, as we knew, quickly becomes the past. Tasks that once were bread and butter for younger programmers – such as repetitive scripts, HTML system or elementary Devops configurations – are now reliably supported by AI assistants such as chatgpt, Github Copilot and Amazon Codewhisperrer.
This is not just an improvement in speed and performance – we look here at a grave structural change. So where does it leave basic programmers? And you say more broadly where it leaves the software industry as a whole?
Disappearing level for beginners
For decades, software engineering as a field had a fairly predictable path: start from scratch, build some land pages, save test cases, solving problems with minor errors. As your skills develop, you can go to architectural thinking and product properties.
But now AI significantly changes the way the lower end of this ladder works, because it can perform most of the tasks at the younger level.
As a result, beginners in the industry are increasingly asked to contribute at a level that once required many years of experience. It is not just about writing a code – it is about understanding systems, constructing problems and working with AI as a team member. This is a high order. To say that, I believe that there is a way forward. It starts with a change in the way we learn.
If you are just starting, avoid relying on artificial intelligence to do the trick. It is tempting, clear, but in the long run it is also harmful. If you skip manual practice, you lack a deeper understanding of how the software really works. This understanding is crucial if you want to grow into a kind of programmer that can lead, an architect and direct artificial intelligence instead of replacing him.
The way I see it in the near future, the most valuable people in technology will not be those who write an excellent code. They will be those who know what to build, why it matters and how to get the AI system to perform most of the work purely and efficiently. In other words, Coder of Tomorrow looks more like a product manager with solid technical knowledge.
The teams are also changing
Based on everything we discussed above, I also feel the need to indicate that not only people have to think about their roles. Whole bands are changing. Where we used to have clearly defined roles-Developer Front-End, signs specialist, Devops engineer, QA tester-soon we will see one programmer managing the entire pipeline with the facilitate of artificial intelligence.
Developers from AI-AUUGMENTINE will replace immense teams that were necessary to postpone the project. In terms of performance, there is a lot to celebrate this change – a shortened communication time, faster results and higher bars for what one person can achieve realistically.
But of course this does not mean that the teams will completely disappear. The structure will simply change. Cooperation will focus more on strategic decisions, equalize the product and make sure that AI tools are used responsibly and effectively. The human contribution will be more or less in implementation and more about the direction.
Ai creates a novel career path
If we look at five to seven years, I suspect that the idea of the “programmer” we know today will change into something completely different. We will probably see more hybrid roles – the creator of the part, designer, partial product thinker. As already mentioned, the basic part of the task will not be writing the code, but shaping ideas for working software using artificial intelligence as the main tool for creation. And maybe even as a co -creator.
Technically liquid will still remain a key requirement – but it’s not enough to just know how to cod. You will have to understand thinking about the product, user needs and how to manage AI results. It will be more about the design of the system and the strategic vision.
For some, this may sound intimidation, but for others it will also open a lot of doors. People with creativity and talent for solving problems will have great opportunities for them.
The landscape changes, yes – there is no fact. But for those who want to adapt, it can be argued that they change in their favor. The end of the younger coding is not the end of learning. This is a sign that we must again consider what talents we grow, how we create teams and what makes someone a great programmer.
In my opinion, instead of mourning the loss of basic tasks, the industry as a whole should focus on building skills that cannot be automated. At least not yet. This means implementing a hybrid approach and learning how to work with AI as a partner, not a competitor.
Roman Eloshvili is the founder CoplyControl.