One of the biggest turning points in my career was returning to fundamental engineering principles instead of blindly trying to adapt to every new technology. Amidst the current noise of "AI will change everything, developers will be jobless," I think the opposite: Developers with technical depth, who are true problem solvers, will become more valuable than ever. AI will take over the labor-intensive part of coding, but the questions of "what to code, why to code, and how to integrate" will remain on our desks.
Working on supply chain integrations for a manufacturing ERP, I saw that code written without understanding a business workflow from end to end, no matter how AI-assisted, will only produce errors faster. The developer's true value lies in their ability to decipher these complex flows and transform them into a logically and technically sound structure. This ability requires human intuition and experience that AI cannot automate.
How is AI Changing Our Coding Process?
AI tools can now generate simple functions, boilerplate code, or standard API integrations in seconds. CRUD operations or basic data transformation scenarios that once took me days are now presented to me within minutes with a prompt. This is something I actively use and significantly boosts my productivity.
However, these tools cannot, for example, automatically detect a WAL bloat issue in a PostgreSQL database and dynamically optimize max_wal_size and checkpoint_timeout settings, or adjust an OOM eviction policy in a Redis instance based on the actual need. Nor can they foresee edge cases that might arise in an iSCSI supply chain integration for a manufacturing company and ensure transaction integrity accordingly. This deep knowledge and experience are still the sole domain of human intelligence.
💡 Think of AI as an Assistant
View AI as an assistant that researches for you, generates draft code, and fixes simple errors. Your role is to critically review these drafts, make architectural decisions, design system integrations, and understand the "why" behind the work.
Why is Real Problem-Solving Ability Vital?
AI works by analyzing existing patterns and data. But real-world problems often go beyond predefined patterns. While working on a complex network segmentation for a client project, I spent hours dealing with VLAN tagging complexities. AI could have suggested the best VLAN configurations, but it couldn't provide the insight to detect and fix the actual physical cabling error, the misconfiguration on a switch port, or a minor mistake in BGP routing decisions at that moment.
My 20 years of system and network administration experience have taught me the answers to "where to look first in this type of problem, which logs to check, which commands to run." This is intuition, a problem-solving model, and a form of "experience-based pattern recognition" that AI cannot yet replicate. It took me three days to find out why the late shipment report for a manufacturing ERP was always incomplete. The problem wasn't on the reporting side, but a small logic error in the production planning algorithm. AI cannot easily find these deep business logic errors on its own.
Building Technical Depth and a Horizontal Perspective
The automation brought by AI gives us more time. We should use this time to gain more technical depth and draw connections between different technology areas. For instance, knowing just software architecture isn't enough; you also need to be knowledgeable in areas like network security (switch hardening, ZTNA), system administration (systemd units, cgroup limits), and database optimization (PostgreSQL indexing strategies, WAL bloat).
While developing the backend for my own side project, I frequently encountered issues like container disk fires or build OOM errors. These problems are solved not just with code, but with a wide range of system knowledge, from Linux kernel parameters to memory.high soft limit adjustments. AI can provide me with information on these topics, but making the right decision and optimizing the system end-to-end is still my job.
ℹ️ I Made a Mistake, I Learned
Last month, I caused a container to be OOM-killed in a CI/CD pipeline by writing
sleep 360. I learned the hard way once again that I should use event-driven or a smarter waiting mechanism instead of polling-wait. Making mistakes is part of the learning process, and these experiences make us better problem solvers.
The Future of Developers: Becoming Architects and Solution Partners Who Utilize AI
AI is not a threat to developers; it's an opportunity. It allows us to focus on more complex, more strategic problems. In my experience, successful developers have always been not just coders, but also those who understand the business, make architectural decisions, and know how systems work holistically.
To survive in the age of AI:
- Return to Fundamental Engineering Principles: Master core subjects like algorithms, data structures, network protocols, and operating system principles.
- Develop Domain Knowledge: Deeply understand the business workflows, challenges, and goals of the industry you work in.
- Architectural and Integration Skills: Develop your ability not just to write code, but to design systems, integrate different technologies, and see the big picture.
- Be Security-Conscious: Learn software and network security principles. Topics like JWT/OAuth2, rate limiting, and SQL injection mitigation are now essential for every developer.
- Embrace AI Tools: Use AI not as a competitor, but as a powerful tool to enhance your productivity. Master AI application architecture approaches like prompt engineering and RAG.
AI will be the brain of future systems, but building, managing, and troubleshooting their backbone and nervous system will still fall to us. The role of the developer will evolve from writing code to becoming the architect of systems and a partner in solving complex problems.
What are your thoughts on this? How has AI impacted your career, or how do you foresee it doing so? Would you like to share in the comments?
