Writing Code by Hand Is a Thing of the Past

The software development landscape is changing rapidly, right now.

I have spent more than 15 years working in IT engineering.
I use AI tools actively every day in my work: for building internal tools, automation, analytics, and infrastructure management.

Over the past year, the nature and intensity of the part of my work related to writing code has changed significantly.

Very soon, we will look back on writing code by hand as a fond memory from the past — and we will miss it.

Changes in the nature and cognitive complexity of work

Engineers increasingly have to become operators: they no longer simply write code, but manage entire virtual teams of engineers capable of working in parallel with a level of multitasking that is impossible for humans. This is fundamentally changing the nature of the work and increasing its cognitive complexity.

Last week, I caught myself running several active AI agent sessions at the same time — each in different projects, with different tasks, workflows, and tools. In general, this is a normal mode of operation for a technical or project manager: they often have to manage several teams and projects at once. But for many engineers, this may be something entirely new, and adapting to it will require time and resources.

The feedback loop

I think many people who have seriously explored solving problems with AI have encountered a situation like this: just a little more, just a couple more fixes, one more question, one more answer — and it will finally work exactly as it should. Even though it already seems to be almost working. Just a little more… and suddenly it is already two in the morning.

Why does this happen? For roughly the same reason that the endless feed in a well-known app with photos and short videos is so addictive. In a short period of time, we manage to feel disappointed, excited, roll back changes, start over, and go through several such iterations in a row.

Many of us are not used to working in this kind of feedback rhythm. Usually, the results of solving complex problems come much more slowly — over days, weeks, or sometimes even months. This can disrupt the familiar reward cycle: complete a task, get dopamine, feel accomplished. We are not used to getting such fast results on tasks that AI is now capable of solving. Over time, of course, we will adapt, but this is the reality right now.

Team and individual efficiency

One of the key advantages of using AI agent systems in development is that an entire project — or a significant part of its architecture — can potentially be built by a single engineer. The most expensive and difficult part of the process is removed: communication.

The benefits of solo development with AI are much harder to transfer to team-based development.

Evaluating results

Evaluation in IT is a topic worthy of a whole library of its own. But how are we supposed to understand now what is truly time-consuming, what is difficult, and why?

We now have to build a new frame of reference and rethink complexity from the ground up.

Conclusion

We are already living in this future world of software development.

These changes are already here.

I am deeply inspired by modern tools and the capabilities of the latest generative models — this is genuinely something new. I believe that in the near future, the process of building IT projects, even in the most conservative domains, will undergo major change.