A post on Reddit by a senior engineer at a MAANG-class company sent shockwaves through the tech industry. The engineer claimed that some interns and recent graduates with excellent academic records seem to have no problem discussing AI tools, prompts, and industry buzzwords, but stumble when asked about operating systems, memory management, and algorithms.
“All they know are AI prompts and system design buzzwords,” according to the post.
That complaint may sound harsh. But this article struck a nerve because it touched on an issue that is increasingly worrying universities, recruiters, and technology leaders.
If AI can complete assignments, what exactly are students learning?
8.5 CGPA Paradox
For many years, a strong CGPA has been considered proof that a student has mastered a subject. Today, that assumption is no longer so simple.
Many engineering graduates leave college with strong grades, multiple certifications, polished LinkedIn profiles, and AI-powered projects. But recruiters across the industry continue to talk about the skills gap.
The concern is not that students lack intelligence.
The concern is that modern tools make it easier to produce results without fully understanding the processes behind them.
Students can now ask AI assistants to generate sorting algorithms, build web applications, explain database queries, and even debug errors.
The completed assignment could be amazing.
But the bigger question is whether students were able to do these tasks independently.
Google to CHATGPT
Every generation has had shortcuts. Calculators have reduced manual calculations. Google has reduced memory power. Stack Overflow now gives you instant access to coding solutions. However, these tools still required users to interpret the information and connect the dots themselves.
But generative AI changes the equation.
Often students create answers rather than helping them find them. The massive changes over the past few years explain why educators around the world are treating AI differently than previous technologies.
Even if students copy code from Stack Overflow, they must adapt it. When AI tools generate the entire solution, much of the pain associated with traditional learning can disappear.
And in education, struggle has often been part of the process.
What exactly are these “basics”?
The basic skills that graduates in the AI era seem to lack are surprisingly practical.
Graduates may be able to use AI to generate complete applications. But can they explain why one database query runs faster than another? Do they understand why the system crashes when there is a sudden spike in traffic?
Can you identify why a program consumes excessive memory? Do they know why some algorithms take seconds while others take hours?
These are the basics of computer science. can generate syntax. It’s impossible in theory.
As a result, many hiring managers are increasingly testing how candidates think rather than just what code they write.
What the data suggests
The rise of AI coding tools is dramatic.
GitHub’s developer survey shows near-universal adoption of AI-assisted coding among developers. These tools help programmers write code faster, reduce repetitive tasks, and increase productivity.
At the same time, there are growing concerns about over-reliance on AI.
A 2026 study involving thousands of developers found that organizations are increasingly focused on expertise, ownership, and problem-solving ability, rather than just short-term productivity gains.
Meanwhile, 37% of entry-level tasks in India are already performed by AI, which is higher than the global average, according to a report by Cognizant and Pearson. This creates an unusual situation.
As AI capabilities improve, employers won’t necessarily lower their expectations.
Many people are growing it.
What AI can’t do
AI can write code, explain concepts, generate projects, and solve programming questions.
But you can’t sit in on a job interview and explain why the system failed. Architectural decisions made during design review cannot be defended. We are not responsible if critical applications crash.
These tasks still rely on human understanding.
In fact, some technology leaders argue that fundamentals are even more important in the AI era because engineers need to verify whether the answers generated by AI are correct. After all, bot sitting is increasingly becoming a real job.
Therefore, in this scenario, the lower the comprehension level, the harder it will be to spot mistakes.
Can CHATGPT pass my university course?
This may be the most uncomfortable question for higher education. If AI systems can complete assignments, write reports, solve coding exercises, and explain concepts, what exactly are universities measuring?
knowledge? Do you understand? Or is it simply the ability to submit completed work?
Universities around the world are already redesigning their assessments to meet the challenges of generative AI. Oral exams, project demonstrations, and practical problem-solving exercises are gaining traction because they test understanding rather than output.
Assignments do not prevent students from using AI. That battle is over.
The challenge is to ensure that students learn while using it.
Because if the software breaks, the system fails, or interviews become difficult, employers care little about how good a person’s prompts are.
They want to know if the person operating the keyboard understands what the machine outputs.
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