It’s a challenge many new graduates face, and the rise of AI is only accelerating the shift. What we’re seeing is not just a change in programming languages or toolsets, it’s a deeper transformation in what companies actually value.
In the near future, technical skills will still matter. But they won’t be enough on their own. Companies are placing increasing importance on adaptability, curiosity, and real-world problem solving. Instead of just memorizing syntax, the focus is shifting to understanding why a certain technology exists, what problems it solves, and how it contributes to the business as a whole.
This is especially important in Europe, where regulatory complexity and legal compliance (like GDPR, AI Act, or sector-specific standards) play a crucial role in shaping the tech landscape. Companies don’t just need developers, they need people who understand how technology interacts with legal frameworks, ethical concerns, and long-term business sustainability.
And here’s the paradox: while many employers are currently focused on hiring senior profiles, especially those who can lead AI initiatives or handle secure, large-scale software projects, there’s no future pipeline of senior talent without juniors. Companies will need to reinvest in junior roles sooner or later. And smart organisations are already doing so, especially in domains where growth is steady but talent supply is drying up.
For new graduates, the key is to understand the bigger picture:
You won’t be able to master everything. Focus on one area and go deep, whether it's backend, DevOps, AI ethics, or cybersecurity.
At the same time, stay aware of broader trends. Follow where the demand is rising. Right now, for instance, there’s growing need for professionals who understand security, infrastructure, and systems thinking—not just those who can build user interfaces or plug together APIs.
Don’t just study frameworks, understand architecture. Many entry-level developers today rely on what some call "vibe coding": using AI tools to generate snippets, clone apps, or bootstrap features without a solid understanding of what’s happening underneath. These tools are powerful, but also risky. Projects built on shaky foundations often fail, become impossible to maintain, or worse, get compromised by security flaws.
AI can accelerate development, but it still depends on what it’s been trained on. It can’t invent secure, scalable systems out of thin air. That still requires human expertise and domain understanding. And the people who can connect AI tooling to real-world value will remain in high demand.
So don’t build your career on "just liking to code". Build it around a problem you care about solving - or even better, a business model or product idea you believe in. Start small. Learn in public. Share your progress. And most importantly: adapt as fast as the industry does. That’s how you stay relevant, and how you build something that lasts.
