I hear this question often, not just from students, but even from experienced professionals trying to pivot or remain relevant.
The truth is: yes, the bar is higher. But that’s not necessarily bad news. What's changing is not the need for engineers, it’s what kind of engineer companies are looking for.
The rise of AI is a huge opportunity, not a threat, for students who are curious and willing to adapt. Many fear it because they don’t understand it. But AI isn’t magic, it’s advanced math and code solving patterns and making predictions. And you don’t need to build the next GPT to benefit from it. What students should learn is how to apply AI: how it integrates into workflows, how it solves real problems, and how it can support business value.
When someone applies for a job, companies ask: Will this person help us move faster, cheaper, or smarter? Juniors should frame themselves as contributors to that value chain. That means:
Picking a niche (e.g. AI testing, data labeling, automation assistants)
Learning how a business works, not just how code works
Building projects that simulate real-world use cases (not just toy apps)
Creating public portfolios (GitHub, Devpost, Kaggle, etc.) that show curiosity and context
And yes stay aware of market trends. Companies are shifting to AI-augmented roles. We’re moving toward Industry 5.0 where AI plays a major role in production, logistics, HR, and beyond. That means that every sector is up for disruption, not just IT.
In your workshops, I’d encourage students to:
Follow real-world problems, not just tutorials
Contribute to open-source, even with documentation or test cases
Understand business models and product cycles
Learn how to validate and deploy AI or software in practical environments
And finally prepare them for continuous learning. What they learn today may be outdated in three years. But the mindset of 'learn, test, adapt' will never go out of style.
