11 Random Learnings about AI in 2026 as an Experienced Developer

It has become increasingly difficult to discuss artificial intelligence in software development without being overwhelmed by noise. Between bold predictions, marketing-driven narratives, and outright skepticism, the discourse is often more confusing than enlightening.

Part of this comes from clear conflicts of interest. Some industry leaders promote the idea that developers will disappear or that manual coding is becoming obsolete—claims that conveniently align with the products they are building. At the same time, less experienced developers may not fully grasp the limitations of these tools, especially when applied to large-scale, complex systems. This creates a distorted perception of what AI can realistically achieve today.

I listed below some of my thoughts on the current state of AI in software development, based on my experience and observations. These are not predictions or definitive statements, but rather reflections on the ongoing transformation.

01 - The Danger of Denial

On the other end of the spectrum, some voices dismiss the entire shift as mere hype driven by influencers. This is equally misleading. The reality is that the profession is already evolving.

Even in relatively standard development environments, AI tools are becoming part of everyday workflows. Many companies—far from being cutting-edge AI startups—are actively ensuring that their developers have access to these tools. Ignoring this change is not a viable strategy.

02 - A Rapidly Evolving Tooling Landscape

One of the most striking aspects of this transformation is the speed at which tools are improving. Workflows that felt unimaginable just weeks earlier can quickly become standard practice.

The rise of AI agents, in particular, has significantly changed how some developers approach their work. These shifts are not theoretical—they happen abruptly, and adapting to them requires continuous learning.

03 - Learn by Doing, Not by Listening

In such a fast-moving environment, relying solely on opinions—whether optimistic or pessimistic—is insufficient. The only reliable way to understand these tools is to use them.

Testing them in real scenarios, against real constraints, is what reveals both their strengths and their limitations.

04 - Why AI Won't Replace Developers Anytime Soon

Despite the noise, there are strong reasons to believe that AI will not replace developers in the near future.

Even with highly optimized context handling, current models struggle to maintain a comprehensive and accurate understanding of large systems. This limitation appears to be tied to fundamental technical constraints, such as context windows. Even if those limits were significantly expanded, it is unclear whether models could match the level of holistic reasoning that experienced developers bring.

Concrete examples highlight these shortcomings. Tasks like managing multiple pull requests merged in parallel, requiring precise rebasing and awareness of evolving code states, remain difficult for AI agents to handle reliably.

05 - Persistent Weaknesses: UI and Beyond

Some areas remain particularly challenging. UI generation, for instance, is still far from consistently reliable in many real-world cases.

While AI can assist, it often lacks the nuance and precision required for polished, production-ready interfaces.

06 - A Shift in the Developer's Role

What is truly changing is not the existence of the role, but its nature.

Developers are spending less time writing code and more time reviewing, validating, and guiding generated output. The role increasingly resembles that of a supervisor, ensuring coherence and quality across the codebase.

This also requires adapting the structure of projects themselves. Clear, consistent patterns within a codebase become essential, as they provide direction to AI systems and improve the quality of their outputs.

07 - Expertise Matters More Than Ever

Far from reducing the need for expertise, AI raises the bar.

Effectively guiding a model requires precise, technical instructions. Evaluating its output demands a strong understanding of what correct, maintainable code looks like. Without that expertise, it becomes difficult to detect subtle errors or architectural issues.

08 - Hiring Is Evolving Too

These changes are likely to impact hiring practices as well.

Traditional technical interviews focused on algorithmic exercises may gradually give way to evaluations of how candidates use AI tools, how they think creatively, and how effectively they can guide and critique machine-generated code.

09 - Tools Are Still Catching Up

Interestingly, the tools themselves are not yet fully adapted to these new workflows.

Not long ago, developers had to write detailed markdown documentation in each folder to help AI understand a codebase. Today, some tools can navigate and interpret projects efficiently without such guidance.

The ecosystem is evolving quickly. Tools that were considered essential a year ago are already being replaced by simpler, more effective approaches—sometimes even lightweight CLI-based solutions. Code quality has improved to the point where constant validation of every change is no longer always necessary.

It is likely that we will soon see a new generation of development environments emerge, including IDEs and even ADEs (Agent Development Environments) as I call them designed specifically for this paradigm.

10 - Rewiring Developer Habits

Adapting to this shift is not just technical—it is cognitive.

For developers with years of experience writing code manually, delegating tasks to an agent does not come naturally. It requires actively changing ingrained habits.

It is still common to start implementing a feature manually, only to realize midway that an AI agent could have handled it.

11 - The Productivity Myth

Finally, it is important to address the widely promoted idea of massive productivity gains.

The notion of a 10x improvement, often illustrated through idealized scenarios, does not hold up in real-world team environments. Software development involves much more than writing code: reviews, feedback cycles, waiting times, and context switching all play a significant role.

In fact, some of these aspects can become more time-consuming. Code reviews, for example, may take longer when dealing with AI-generated code, as it often requires deeper scrutiny.

Overall, while development may indeed be faster than before, the gains are far more modest than commonly claimed.

Conclusion

At its core, code is not an art form—it is a tool to implement functionality.

From this perspective, AI is neither a threat nor a miracle solution. It is a powerful, imperfect tool that is reshaping the profession without eliminating the need for developers.