The future of programming is AI — and young developers aren’t ready for it
The role of developers is changing with AI. We must adapt education to prepare students for the future by focusing on Generative AI usage, core/adjacent engineering skills, and soft skills. Santex is already upskilling 100% of our developers.
The world of work is changing. Yet we’re still teaching and training developers like we used to back in 2010. We need to shake up the coursework, rethink the curriculum, and prepare young students for tomorrow’s job market.
AI is disrupting every industry. But for professionals working with code, it also poses an existential question.
According to the Bureau of Labor Statistics, there are now fewer computer programmers in the United States than there were back in the early 1980s. This drop in employment started just a few years ago, right about when ChatGPT came out.
AI assistants are now taking on more of the programming load. And tech CEOs can’t even agree on exactly how much code will soon be written by AI. IBM’s Arvind Krishna predicts up to 30%, while Anthropic’s Dario Amodei goes even further: 90%.
To say the next few decades are uncertain is a massive understatement. And I think we’re not preparing younger generations for the upcoming job market.
At universities and tech schools, we’re still teaching coding like we were 15 years ago. But the role of developers has to evolve. The old magic isn’t so magical anymore. With AI, people with no programming know-how can build workable programs. They can just talk to a chatbot and something will come out. It’s called “vibe coding.”
To stand out, developers need to broaden their skillset. This is especially true for students and juniors, because AI will define their whole professional lives. As teachers, instructors, and leaders, we need to help them on their learning journey.
But what does this upskilling path look like? The other day, I read a Google-led study that offers a way forward. Based on their research, AI-enhanced developers need to excel at:
Generative AI usage: This means understanding Large Language Models and machine learning. It means knowing how to ask the right questions, choose the best tools, and debug the results. AI is helpful, but it needs guidance.
Core software engineering: Even if they’re not manually coding as much, developers still need to know their trade. They should recognize high-quality code and system design, and be able to optimize outputs and assess risks.
Adjacent software engineering: With AI taking care of routine tasks, developers have more bandwidth to broaden their expertise: from cybersecurity to industry regulations and emerging tech trends.
Adjacent non-software engineering: In other words, soft skills. Developers have to communicate with other teams and stakeholders while managing user, product, and business needs. And they have to understand the business model they’re participating in. It’s not just about coding anymore, it’s about explaining why it matters.
So, what does this mean for universities and learning institutions?
On the one hand, the fundamentals are still relevant. According to a McKinsey study, AI can help developers complete coding tasks twice as fast, but only if they know what they’re doing. Junior developers took 7 to 10 percent more time doing the same tasks with AI tools than without them.
On the other hand, young developers need to embrace AI — fast. They need to recognize its benefits and shortcomings, and learn how to work alongside it. Because this isn’t the future, it’s the present. According to a recent Jetbrains survey, almost 70% of developers are already using ChatGPT or Copilot.
At Santex, we’re taking this seriously: 78% of our employees — and 100% of our developers — are currently involved in AI-related upskilling and reskilling processes.
Leveraging AI in the workplace is about more than prompts. It’s way more holistic. Imagine using AI to address user feedback, update documentation, convert designs to code, research new technology, ensure regulatory compliance, carry out test scenarios, identify failures, and more.
In short, AI is transforming the development workflow — and it’s also transforming development education.
Remember that McKinsey study? Turns out, AI doesn’t only increase speed, but also completion rates: developers were up to 30% more likely to finish a complex task using AI tools. Why? Because AI can offer guidance and how-to guides. It can explain new concepts and summarize information. AI can be like a mentor or coach that’s always available, any time.
In rethinking the coding curriculum, we can’t just add new topics and courses. We need to reevaluate the very way we teach.
For young developers, the AI transformation begins in the classroom.
