As a student or early-career professional, you’ve probably heard this advice repeatedly: focus on your core subjects. Mathematics, science, programming fundamentals—these are often presented as the only legitimate path to success in technology.
To be clear, this advice is not wrong. But it is incomplete.
In today’s rapidly evolving tech landscape, mastering artificial intelligence (AI) is no longer optional. It has become a form of leverage—one that often matters more than being exceptionally strong at manual execution alone. The future belongs not just to those who can code, but to those who can command, direct, and validate intelligent systems.
This belief isn’t theoretical for me. It’s something I arrived at through constraint, experimentation, and real-world experience.
Core Subjects Still Matter—But the Way We Use Them Is Changing
Let’s get one thing straight: core subjects and degrees still matter. They provide the mental frameworks that allow us to think logically, reason critically, and understand how systems work beneath the surface.
A solid foundation in mathematics helps you grasp algorithms and data structures. Computer science teaches you how software is designed, structured, and scaled. Degrees also provide credibility and open doors—especially early in your career.
However, the role these foundations play is evolving.
We are moving away from a world where value is created primarily through manual execution—writing every line of code by hand—toward a world where value comes from decision-making, direction, and validation, often with AI doing the heavy lifting.
The future developer or engineer is no longer just a coder. They are becoming an AI commander, reviewer, and decision-maker.
The Evolution of Roles in Technology
Imagine a world where AI can generate production-ready code in seconds. That world already exists.
But speed alone isn’t intelligence.
AI can generate output quickly, but it lacks:
- Context
- Intent
- Architectural understanding
- Ethical judgment
- Business awareness
These elements still come from humans.
Consider a startup building an MVP. AI tools can generate backend APIs, frontend components, even database schemas. But humans must still decide:
- What problem is being solved?
- How should the system scale?
- What trade-offs are acceptable?
- What risks are worth taking?
AI accelerates execution—but humans define direction.
The same applies beyond coding. In business operations, AI can automate workflows and analyze data, but humans are responsible for interpreting results, assessing risk, and aligning decisions with long-term values.
Learning Through Constraint: My Personal Experience During COVID
My perspective on AI wasn’t formed in an ideal environment. It emerged from limitation.
During the COVID period, I was pursuing my BTech and, like many students, faced real financial constraints. For a significant period, I didn’t have a laptop. Manual coding on a mobile phone was nearly impossible, especially for traditional programming workflows.
Instead of falling behind, I adapted.
I began using ChatGPT primarily for my classes and exams—to understand concepts, revise topics, and prepare for assessments. Surprisingly, there were no major issues. I could follow along, clear exams, and stay academically on track without writing long blocks of code myself.
That was the first moment I realized something important:
Execution was becoming less important than understanding and direction.
From RP Player to Lua Developer
Later, my journey took an unexpected turn.
I entered the FiveM RP ecosystem—not as a developer, but as a player. Over time, curiosity turned into experimentation, and experimentation turned into development. I transitioned into Lua scripting without any prior exposure to the language.
Lua was completely new to me.
When I needed to tweak a script on my local server for the first time, I decided to learn Lua fundamentals properly. In about a week, I reached a point where I could understand:
- Basic syntax
- Logic flow
- Script structure
Once I had that foundation, I made a conscious decision: I stopped deep manual learning and started using AI as a development assistant.
And it worked—exceptionally well.
The Power of AI Assistance
With AI support, I was able to:
- Modify and extend Lua scripts confidently
- Debug unfamiliar codebases across languages
- Work with frontend logic and parts of backend systems
At some point, I realized something that reshaped how I view skill and productivity:
I had effectively become a one-person development unit.
Not because I was an expert in every language—but because I knew how to:
- Ask the right questions
- Review AI-generated output
- Identify errors
- Make informed corrections
AI didn’t replace my learning. It multiplied my ability to execute.
Breaking the “Genius Coder” Myth
There’s a long-standing belief in tech that to be valuable, you must be a “genius-level coder”—someone who memorizes syntax, writes flawless code from scratch, and lives inside documentation.
That belief is outdated.
Manual debugging and conceptual clarity still matter. But extreme execution skill is no longer the primary differentiator.
Today, leverage comes from:
- Understanding systems
- Giving precise instructions
- Evaluating output critically
- Making sound decisions
AI handles repetition. Humans handle judgment.
Mastering AI is not a shortcut—it’s a force multiplier.
Will AI Replace Developers?
This is the most common fear.
The honest answer: AI will change the nature of work—but it won’t eliminate the need for humans.
Just as calculators didn’t replace mathematicians, and frameworks didn’t replace engineers, AI won’t replace developers. Instead, it will raise the baseline and shift focus toward higher-level thinking.
Those who adapt will move faster. Those who resist will struggle.
Practical Advice for Students and Early Professionals
If you’re starting out, here’s the balance I recommend:
- Study core subjects for foundation, not obsession
- Master AI tools early—experiment, understand limitations, and use them responsibly
- Develop high-level thinking—learn to evaluate, validate, and decide
Your goal isn’t to compete with AI. Your goal is to direct it better than others.
Final Reflection
Looking ahead, I’m excited—but also realistic. The landscape is changing rapidly, and adaptability is no longer optional.
AI creates opportunity, especially for those with limited resources. It reduces dependency on privilege and amplifies clarity of thought.
The future of work isn’t about replacing humans with AI. It’s about augmenting human capability with intelligent systems.
When you combine strong fundamentals with AI mastery, you don’t just survive change—you gain leverage.
And leverage, more than raw effort, is what defines success in the modern world.