The AI shortcut problem: Why some 8.5+ CGPA engineering grads struggle with basics

A viral Reddit post from a senior engineer at a MAANG-tier company has struck a nerve across the tech world. The engineer claimed that some interns and newly hired graduates with strong academic records appeared comfortable discussing AI tools, prompts and industry buzzwords, but stumbled when asked about operating systems, memory management or algorithms.

According to the post, "all they know is AI prompts and system design buzzwords."

The complaint may sound harsh. Yet the reason it struck a nerve is because it touched on a question that is increasingly worrying universities, recruiters and technology leaders alike.

If AI can complete the assignment, what exactly are students learning?

THE 8.5 CGPA PARADOX

For years, a strong CGPA was seen as proof that a student had mastered their subject. Today, that assumption is becoming less straightforward.

Many engineering graduates leave college with impressive grades, multiple certifications, polished LinkedIn profiles and AI-powered projects. Yet recruiters across the industry continue to talk about skill gaps.

The concern is not that students lack intelligence.

The concern is that modern tools may be making it easier to produce results without fully understanding the process behind them.

A student can now ask an AI assistant to generate a sorting algorithm, build a web application, explain database queries and even debug errors.

The finished assignment may look excellent.

But the bigger question is whether the student could have done any of those tasks independently.

FROM GOOGLE TO CHATGPT

Every generation has had a shortcut. Calculators reduced manual calculations. Google reduced memorisation. Stack Overflow made coding solutions instantly accessible. Yet those tools still required users to interpret information and connect the dots themselves.

But Generative AI changes the equation.

Instead of helping students find answers, it often creates the answers. This massive shift in the past few years explains why educators worldwide are treating AI differently from previous technologies.

When a student copies code from Stack Overflow, they still need to adapt it. When an AI tool generates an entire solution, much of the struggle that traditionally produced learning can disappear.

And in education, struggle has often been part of the process.

WHAT ARE THESE 'FUNDAMENTALS' ANYWAY?

The fundamental skills that appear to be missing in the AI-age graduates are surprisingly practical.

A graduate may be able to generate a complete application using AI. But can they explain why one database query runs faster than another? Do they understand why a system crashes when traffic suddenly spikes?

Can they identify why a program consumes excessive memory? Do they know why one algorithm takes seconds while another takes hours?

These are the foundations of computer science. The syntax can be generated. The reasoning cannot.

That is why many recruiters are increasingly testing how candidates think rather than simply what code they produce.

WHAT THE DATA SUGGESTS

The rise of AI coding tools has been dramatic.

GitHub's developer surveys have shown near-universal adoption of AI-assisted coding among developers. These tools help programmers write code faster, reduce repetitive work and improve productivity.

At the same time, concerns about over-reliance on AI are growing.

A 2026 study involving thousands of developers found organisations increasingly focusing on expertise, ownership and problem-solving ability rather than short-term productivity gains alone.

Meanwhile, a Cognizant-Pearson report found that AI already performs 37 percent of entry-level work in India, above the global average. This creates an unusual situation.

As AI becomes more capable, employers are not necessarily lowering expectations.

Many are raising them.

WHAT AI CANNOT DO FOR YOU

AI can write code, explain concepts, generate projects and solve programming questions.

But it cannot sit in a job interview and explain why a system failed. It cannot defend architectural decisions during a design review. It cannot take responsibility when a critical application crashes.

Those tasks still depend on human understanding.

In fact, some technology leaders argue that fundamentals matter more in the AI era because engineers must verify whether AI-generated answers are correct. After all, botsitting is increasingly becoming a real job.

So, in this scenario, the less a person understands, the harder it becomes to spot mistakes.

COULD CHATGPT PASS YOUR COLLEGE COURSE?

This may be the most uncomfortable question for higher education. If an AI system can complete assignments, write reports, solve coding exercises and explain concepts, what exactly are colleges measuring?

Knowledge? Understanding? Or simply the ability to submit completed work?

Universities around the world are already redesigning assessments to address the Generative AI challenge. Oral examinations, project demonstrations and practical problem-solving exercises are gaining attention because they test understanding rather than output.

The challenge is not preventing students from using AI. That battle is already over.

The challenge is ensuring students still learn while using it.

Because when software breaks, systems fail or interviews get difficult, employers are rarely interested in how good someone's prompts are.

They want to know whether the person behind the keyboard understands what the machine just produced.