Over the past few months since launching my book “AI-First Nation,” I’ve been reflecting on how rapidly the development landscape is transforming with the rise of AI coding assistants. These reflections have crystallized into some insights I’d like to share with our growing community of AI enthusiasts and professionals.

The Changing Reality for Developers

AI coding agents like GitHub Copilot, Anthropic’s Claude Code, Amazon CodeWhisperer, and various LLM-based assistants have altered how code gets written. As someone who has been in the tech trenches for decades—from building HPC clusters in the 1990s to developing AI Singapore’s apprenticeship programmes—I have never seen a shift happen this quickly.

The reality we face is stark, but important to acknowledge: junior developers with only basic skills will increasingly struggle to find jobs. Why? Senior developers now use AI to handle routine coding tasks previously delegated to juniors. A single experienced engineer with AI tools can now accomplish what might have needed 2-3 junior developers before.

The Blue Ocean of AI-Enhanced Development

At AI Singapore, we faced a similar challenge when building our AI engineering team. We needed to find a way to compete with tech giants like Google, Microsoft, and Facebook who were fishing in the same “red ocean” of talent.

Our solution was to seek a blue ocean strategy—developing AI engineers through our AI Apprenticeship Programme (AIAP) rather than competing for the limited pool of existing talent. Now, with over 400 Singaporean AI engineers trained and a 90% placement rate, we have proven that developing your own timber works.

This same principle applies in today’s AI-transformed landscape. The blue ocean isn’t in competing for entry-level development roles that AI can increasingly fulfill—it is in mastering how to work with and direct these AI tools effectively.

Computer Science Is Not Dead – It’s More Vital Than Ever

Let me be absolutely clear: this does not mean computer science education or learning to code is becoming obsolete. Quite the opposite. What it does mean is that:

1. The entry bar for development roles is rising significantly
2. Deeper CS fundamentals are becoming more valuable, not less
3. Understanding how to guide and evaluate AI-generated code is critical

This is where I see a concerning disconnect. Those “Learn Python in 3 Months and Get a Six-Figure Job” bootcamps that have proliferated across the industry? Those are increasingly selling a fantasy. The rudimentary skills they teach are precisely what AI coding agents excel at generating.

PLUS-Skilling vs. Quick-Fix Promises

As I have written previously about PLUS-skilling versus re-skilling, what professionals need is not a quick coding course—it is adding AI capabilities to their existing domain expertise. A mechanical engineer who learns how to effectively prompt and direct an AI coding assistant to build sophisticated simulations is exponentially more valuable than someone who just completed a 3-month Python course.

In the AI Singapore 100E programme, where we have worked with over 200 organizations to build AI solutions, we have consistently seen that the most successful projects combine deep domain knowledge with AI expertise—not just coding skills alone.

Finding Your Path Forward

So what does this mean for different groups navigating this changing landscape?

For Computer Science Students

Double down on fundamentals. Data structures, algorithms, system design, and software architecture are more important than ever. The ability to understand what makes code good or bad, efficient or inefficient, will remain valuable even as AI generates more of the actual code.

For Working Professionals Looking to Transition

Rather than rushing into a short bootcamp promising a career transformation, consider how to PLUS-skill yourself by combining your existing expertise with AI capabilities. The accountant who can leverage AI tools to build fraud detection systems is far more valuable than a bootcamp graduate with surface-level coding skills.

For Organizations Building Technical Teams

The era of judging candidates primarily on coding tests may be ending. Look for people who demonstrate strong problem-solving, who can effectively direct and evaluate AI-generated solutions, and who understand both the technical and domain aspects of problems.

Building True AI Readiness

At AI Singapore, we have developed the AI Readiness Index (AIRI) to help organizations understand their preparedness for AI adoption. A key component of this framework is assessing skills readiness—not just coding abilities, but the overall capacity to identify where AI can add value and how to implement it effectively.

The future does not belong to those who can simply write code—that is increasingly becoming a commodity skill. It belongs to those who can articulate problems clearly, evaluate solutions critically, and direct AI tools strategically.

Growing Our Community Together

As we continue building our community of AI enthusiasts who want to leverage these technologies to better themselves, their organizations, and humanity, I believe we need to move beyond the simplistic “learn to code” narrative.

Instead, let us focus on developing a more nuanced understanding of how AI fits into the broader technology landscape and how different skills complement each other to create value. That is the path to becoming an AI-First Nation—not through quick fixes, but through deep, continuous learning and adaptation.

The days of “3-month bootcamp to six-figure salary” may be fading, but the opportunities for those willing to invest in deeper learning have never been greater.

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Let us continue this conversation. What skills do you think will remain uniquely human as AI coding capabilities advance? What mix of technical and domain expertise is most valuable in your field? I’d love to hear your thoughts.

Laurence Liew is the author of “AI-First Nation: A Blueprint for Policy Makers and Organisation Leaders” and Director for AI Industry Innovation at AI Singapore.