The impact of AI on the business world, especially in the tech sector, is increasingly fueling the question: "Will we lose our jobs?" However, recently, despite this general cloud of fear, I've observed many companies, particularly corporate ones, quietly opening junior positions and bringing young talent into their teams. This paradoxical situation holds important clues about AI's current capabilities and the areas where the human factor remains indispensable.
This observation feels similar to the "technology hype" cycles I've encountered countless times in my twenty years of experience; every new technology is initially touted as a game-changer, but in reality, adaptation is much more nuanced and gradual. Companies, while evaluating the efficiency gains brought by AI, are not abandoning long-term human capital investment.
Why Does This Paradox Emerge?
There are several underlying reasons for this situation. Firstly, AI's current capabilities, while able to automate some repetitive and rule-based tasks, are not yet at the desired level in areas requiring complex problem-solving, creativity, critical thinking, and human interaction. Secondly, companies' long-term strategies not only meet today's needs but also aim to create new talent pools for future growth and innovation.
In my experience, I've seen that bringing a software project to life, especially in a manufacturing ERP, requires much more than simple data entry. Human skills like understanding processes, empathizing with users, and anticipating unexpected edge cases are areas that AI cannot yet fully replicate. For example, understanding instant feedback from an operator on a production line and quickly adapting the code requires not just technical knowledge, but also the ability to grasp context.
ℹ️ Current Limitations of AI
AI, especially generative AI models, learns from a specific dataset, making it difficult to understand situations outside or insufficiently represented in that dataset. This makes human intervention indispensable for managing exceptional cases and problem-solving, particularly in corporate processes.
Another important point is cost. Yes, AI tools promise efficiency, but the integration, maintenance, and especially the training of custom models can incur significant costs. For a small or medium-sized company, the annual cost of a junior developer might be much lower than the initial setup and maintenance costs of a complex AI solution. While working on an AI-powered production planning module for my own side product, I initially had very ambitious goals; however, even providers offering fast inference like Groq or Cerebras, the time I spent on prompt engineering for every small optimization and the API costs were much higher than I expected. Especially when setting up a RAG architecture, finding the right chunking and embedding strategies required significant trial and error and human effort.
What Are AI's Current Role and Limitations?
Today, AI is positioned more as a tool or an assistant. It is highly successful in areas such as automating repetitive tasks, analyzing large datasets, making predictions, and directing people to more strategic work. As a system administrator, I use AI-powered tools to analyze journald logs or optimize fail2ban patterns. This can reduce a task that would take me hours manually to minutes.
However, AI's limitations are also quite clear. The lack of "common sense" is one of AI's biggest handicaps. The ability to evaluate a scenario with all its context, make ethical decisions, or generate creative solutions for an entirely new problem has not yet reached human levels. In a process automation I developed for an internal banking platform, while expecting AI to perform fraud detection, we saw that the system could only catch known patterns, but was insufficient against a new type of attack. Detecting new attack vectors and defining new rules against them was still the job of human analysts.
# Let's show the limitation of AI in rule-based fraud detection with pseudo-code
def detect_fraud_ai(transaction_data, known_patterns):
is_fraud = False
for pattern in known_patterns:
if transaction_data.matches(pattern):
is_fraud = True
break
# What happens if AI encounters an unknown pattern here?
# It cannot "creatively" define a new anomaly.
return is_fraud
# A human analyst, however, updates these patterns over time, defines new patterns
def human_analyst_update_patterns(new_anomalies):
new_known_patterns = analyze_anomalies(new_anomalies)
return new_known_patterns
This example clearly shows how well AI works within defined rules, but the ability to go beyond rules or set rules belongs to human intelligence. When writing a kernel module blacklist rule (e.g., prohibiting the algif_aead module against CVE-2026-31431), AI can list existing CVEs, but evaluating which module poses a risk in which scenario and the potential impact of the blacklist on system performance is still the job of an experienced expert.
What Value Do Junior Talents Bring to Companies?
Companies hire junior talent not only for cost advantages but also for long-term strategic goals. Juniors are open to learning, flexible, and more eager to adapt to new technologies. This is an invaluable trait, especially in the rapidly changing world of technology. In my own team, I've repeatedly seen how quickly young colleagues adopt a new framework or an AI library and integrate it into a project.
💡 The Value of Juniors
Junior talent not only offers a fresh perspective but also represents a long-term investment in the company's technology culture and knowledge base. Their energy and eagerness to learn add dynamism to the team.
While developing an ERP for a manufacturing company, my team and I worked with a young designer and a junior frontend developer to improve the UX of operator screens. Their fresh perspectives made us rethink interfaces we had been using for years, which had become "normal" to us but were actually not very efficient. While we were dealing with the big picture, this young colleague applied the latest patterns in Vue or React, solving N+1 query issues in old code that I hadn't even noticed, using eager-loading. This was not just a cost advantage, but also an innovation and continuous learning advantage.
Furthermore, juniors generally carry less "technical debt." That is, they are not blindly tied to a specific technology or approach. This provides significant flexibility for the company when transitioning to new technologies or trying different approaches. In one project, when moving from an old C# .NET monolith to a FastAPI + Vue microservice architecture, junior colleagues' more objective and adaptive approaches accelerated the transition process during moments when the senior team struggled to break old habits. Their tendency to write clean code and their openness to learning helped us lay a solid foundation for the new architecture.
Why Are Knowledge Transfer and Mentorship Critical?
Hiring junior talent is not just about what they will bring to us, but also about what we will bring to them. Knowledge transfer and mentorship are among the most important ways for a company to sustain its knowledge base. As a senior, it's my responsibility to teach junior colleagues not only how to write code but also problem-solving methodologies, trade-off analyses, and system architecture principles.
For me, mentorship is not just about code review. Explaining why I increased the wal_buffers setting of a Postgres database or why I chose allkeys-lru for a Redis OOM eviction policy gives them a deeper understanding of how the system works, beyond just teaching them a setting. One day, a junior colleague was struggling to understand why a CI/CD pipeline kept giving "disk full" errors. When I directed him to the Docker disk space issue and explained the docker system prune command and why such maintenance should be done regularly, we not only solved a problem but also gave him a valuable lesson in observability and system maintenance.
This process is like building a "knowledge graph" within the company. Each new junior adds new nodes to this graph and strengthens the connections between existing nodes. In this way, institutional memory does not just reside in documents but also lives and evolves in living human minds. Previously, in an ERP project, the maintenance of a critical module was disrupted when an experienced team member left. This painfully showed me how important knowledge transfer is. Since then, I have made sure to train every new junior with the understanding that knowledge transfer is not just a task, but also a long-term strategy.
How Should This Situation Affect Our Career Planning?
The rise of AI makes our career planning even more critical. For those at the junior level, this means they need to focus more than ever on fundamental software engineering principles, problem-solving abilities, and adaptability. Learning to use AI tools efficiently means not just being an "AI operator," but gaining the ability to integrate AI into our workflows by understanding its limitations.
⚠️ Knowing Only AI Is Not Enough
Merely knowing how to use AI tools may not be sufficient for long-term career success. Fundamental engineering principles, critical thinking, system design, and human interaction skills are areas that AI cannot yet, and likely will not, replace in the future.
My advice, especially to young colleagues:
- Build a Strong Foundation: Master topics like algorithms, data structures, network fundamentals (VLAN, routing, MTU), and operating system knowledge (systemd, cgroup, journald). These are fundamental building blocks that will not change, no matter what technology emerges. For example, being able to correctly read an
EXPLAIN ANALYZEoutput in Postgres is critical to understanding whether an optimization suggestion offered by AI will actually work. - Develop Your Problem-Solving Ability: AI can offer you answers, but asking the right questions and breaking down a complex problem into smaller parts is still your job. When debugging a routing flap issue I experienced in a network, AI could list potential causes, but I had to dive deep into the system to understand BGP routing decisions and figure out which router chose which route and why.
- Invest in Human-Centric Skills: Communication, teamwork, mentorship, and empathy are skills that AI cannot replicate and will always be valuable in the business world. Listening to user complaints in a manufacturing ERP, understanding their real problems, and developing a solution is not something AI can do alone.
- Strengthen Your Adaptability: Be open to learning new technologies, but think critically before jumping on every new hype. When integrating native packages with Flutter on the mobile side, sometimes understanding the performance advantages of native bridging or the Play Store's metadata rejection rules is much more valuable than just jumping into a tool because it's "new."
Looking Ahead: Human and AI Collaboration
The rise of AI will change the nature of jobs rather than completely eliminate the workforce. While repetitive, low-value-added tasks will be automated, humans will focus on more creative, strategic, and socially skilled tasks. This creates an opportunity window for junior talent, as they have the potential to adapt more quickly to these new business models.
My belief is that successful companies in the future will be those that view AI not as a competitor but as a partner, combining human talent with AI power. In this collaboration, juniors will form the foundation of a powerful new generation workforce, equipped with the efficiency provided by AI, but also possessing the indispensable qualities of human intelligence. Therefore, instead of fearing AI, we should learn how to work better with it and shape our career paths according to this new reality.
My clear position is this: while AI will automate some tasks, the need for young, dynamic, and eager-to-learn minds to build complex systems, understand human needs, and drive innovation will continue. Companies see this, and therefore, quietly, they continue to invest in junior talent. This is a great opportunity for us.
