I've been in this industry for twenty years, and often questioned what the "senior" title truly meant. But with AI entering our lives so rapidly, I see this definition becoming more fragile, even misunderstood, than ever before. This role, once defined by deep technical knowledge, the ability to solve complex problems, and end-to-end system mastery, has now, for some, transformed into merely being able to write the right prompts?
In my opinion, the automation and speed brought by AI further clarify the essence of the 'senior' role: experience, workflow knowledge, and the ability to manage trade-offs. Pure technical knowledge is still important, but it's no longer sufficient on its own.
What Did 'Senior' Mean Before AI?
Years ago, even before AI became so widespread, being 'senior' for me meant being able to find the deepest bug in a system, to catch correlations no one else saw. I remember spending hours debugging a network loop that crashed the entire company network, or finding the correct vacuum settings when performance plummeted due to a PostgreSQL WAL bloat issue.
This wasn't just about knowing commands or using a library; it was about understanding all layers of the system, knowing how every piece interacted, from hardware to software, network to database. Being able to read journald logs to figure out why a systemd unit was OOM-killed and correctly setting cgroup memory.high limits was a true expertise.
AI Arrived, So What Changed?
With the advent of AI, many routine and repetitive tasks have been automated. Now, instead of writing a complex regex pattern, I can describe the log pattern I want to AI and get the output in seconds. In one of my side projects, log analysis and debugging processes that used to take me hours are now summarized in minutes with a single prompt.
This situation expects us to focus more on the "why" and "what" questions, rather than just answering "how." Critically evaluating the solution offered by AI, providing the correct context, and comparing the results with business realities has become the cornerstone of the new senior role. In software development, writing boilerplate code or creating simple test scenarios are now tasks taken over by AI.
ℹ️ The Changing Role
AI takes on many repetitive and rule-based technical tasks, offering us the opportunity to think more strategically, define problems, and understand complex inter-system relationships.
Is True Seniority Just 'Prompt Engineering'?
With the recent rise of prompt engineering, some have started to see this skill as the new "seniority" criterion. Yes, writing the right prompt is critical for getting efficient results from AI. But this is just the tip of the iceberg.
When using AI for production planning in a manufacturing ERP, understanding the factory's real constraints, instant fluctuations in the supply chain, and operator feedback was much more critical than simply designing the prompt correctly, rather than blindly applying the model's output. AI can offer you the most optimized route suggestions; however, it cannot know if that route is truly feasible, if a machine on the production floor is currently broken, or if an operator is on leave during that shift.
Data, Workflow, and People: Where Does a Senior's Real Power Lie in the Age of AI?
In my opinion, true seniority in the age of AI still lies in knowing the cost of saying "yes" or "no," in contextualizing the solution produced by AI within the business, and in foreseeing potential side effects. Knowing why we implement a transaction outbox pattern, what kind of risks eventual consistency poses in a workflow, or in which scenarios optimistic lock works better than pessimistic lock is not information AI will automatically give you.
One of the most important things I've learned in my 20 years of experience is that software architecture is often not just about code; the real architecture lies in organizational workflows. AI can speed up these workflows, even optimize them, but the correct design of these workflows, meeting the real needs of the business, and being adoptable by people, is still the responsibility of experienced professionals.
⚠️ Not Just Technology
True seniority is not just knowing how to use the latest AI model, but also being able to foresee
idempotencyissues,data integrityrisks, andobservabilityneeds that may arise when integrating that model's output into an enterprise ERP system.
What Could Be a Senior's Most Expensive Mistake?
My most expensive mistake in my career was never a segmentation fault or an SQL injection. It was usually a "yes" or "no" given without proper trade-off analysis or without listening to all stakeholders. When I inadequately calculated the number of VLANs in a network segmentation project, the subsequent management complexity and security risks were the result of a simple "yes, this will be enough" answer I gave initially.
In the age of AI, this situation becomes even more critical. Blindly trusting the "perfect" solution suggestions offered by AI, ignoring real-world constraints, can be one of the biggest mistakes a senior can make. When setting up predictive monitoring systems, interpreting the anomalies offered by AI, it is essential to consider the human factor, field experience, and business dynamics.
💡 The Value of Experience
AI can analyze data and extract patterns, but interpreting the human behaviors, corporate policies, and historical "whys" behind these patterns is still the job of an experienced senior.
In conclusion, being 'senior' in the age of AI means not only mastering technology but also being able to correctly blend that technology with business realities, the human factor, and corporate workflows. In my opinion, the true 'senior' title will belong to those who can intelligently use the opportunities offered by AI to produce context-driven, flexible, and sustainable solutions even for problems we haven't encountered before.
So, in your opinion, what competencies have become indispensable to deserve the 'senior' title in the age of AI?
