From Physio to Engineer: AI Prototypes Faster, Redefines Junior Dev Roles
In my journey transitioning from physiotherapy to software engineering, one of the most transformative experiences has been observing how rapidly AI-assisted tools like Cursor AI, Replit, GitHub Copilot, and ChatGPT have reshaped coding workflows.
When I started my software engineering career as an intern, then as a junior developer, these powerful AI tools didn't exist. I've personally experienced a significant transition, akin to seeing the evolution from having no mobile phones to suddenly witnessing the introduction of the iPhone—something very distinct to my Gen Y generation.
With minimal coding experience initially, these new AI tools later became invaluable for quickly building prototypes or Minimum Viable Products (MVPs). They bridged my skill gap, helped me write functional code, taught best practices, and enabled rapid experimentation without the friction that traditionally accompanies early-stage learning.
However, as my startup, CliniScribe AI, scaled and moved towards robust, production-level applications, it became clear that AI-assisted coding has inherent limitations. Key areas such as software architecture, scalability, performance optimisation, maintainability, and security demand depth of human experience and intuition—attributes that AI tools, at least currently, cannot fully replicate.
I now firmly believe that about three years of solid commercial full-stack engineering experience is necessary to fully leverage these AI-powered coding platforms. My initial years gave me the necessary foundation to guide the AI effectively, interpret its outputs accurately, and reliably transition AI-assisted code into production-ready solutions.
Yet, there's a rapidly evolving caveat: this experience gap is closing swiftly. AI advancements are now significantly accelerating productivity for junior and lower-level developers, empowering them to achieve tasks that previously required years of experience.
This leads to a compelling but challenging insight: AI tools can significantly displace or even replace junior developers, especially for early-stage or small-scale projects. Routine coding tasks, basic implementations, bug fixes, and initial debugging can increasingly be handled efficiently by AI.
Sam Altman, in a recent interview, shared advice relevant to this transition. When asked what career advice he'd give to a high school graduate today, he emphasised:
"The obvious tactical thing is just get really good at using AI tools. Like when I was graduating as a senior from high school, the obvious tactical thing was get really good at coding. And this is the new version of that."
Altman further stressed the importance of cultivating resilience, adaptability, and interpersonal skills—the "meta ability" to learn and adapt as the world rapidly changes around us.
For aspiring developers or those early in their coding careers, this shift underlines an urgent need to pivot towards skills less easily automated by AI, such as deeper system-level design, security, performance optimisation, and architectural patterns. Additionally, focusing on being product engineering-oriented—developing skills that directly create and enhance user value—is increasingly critical. AI is reshaping what it means to be a junior developer, moving them away from simple coding tasks and towards roles demanding strategic thinking, creative problem-solving, and collaboration.
In short, AI isn't eliminating the developer role entirely; it’s redefining it. The next generation of junior developers will succeed not just by writing code, but by effectively harnessing AI and applying their human ingenuity to solve complexities that AI can't yet fully manage.
How do you see AI reshaping roles in software engineering, particularly at the junior level? I'd love to hear your thoughts and experiences in the comments below.