Death of the junior developer: How AI is forcing colleges and students to pivot

“Earlier, if you wanted to build anything in tech, you needed to know programming languages,” he says. “Now I can just describe what I want in simple English and get something working.”

Coding was for long the gatekeeper that determined who could build software, tech tools and apps. That barrier to entry has now been lowered by AI. The ability to generate working code, which was once the domain of trained engineers, is now more widely accessible.

Simpler tasks can be done faster and by people without any technical skills. And software companies, which often billed clients by the number of hours an engineer spent on a task, are now forced to redefine what is worth a developer’s time.

This shift is being felt by young Indians across the workforce, from final-year students appearing for placements to entry-level software engineers and mid-level engineers at information technology (IT) companies.

Masand, who also works at a Bangalore-based AI startup, says he began freelancing in his second year despite not having the skills to build full-fledged software applications at the time.

“I didn’t really know how to make production-ready software,” he says. “But I could ask questions, use AI and deliver something that worked.”

Masand says what stood out to his clients was not how the code was written, but how quickly it was delivered.

“I was building things in a day and solving problems that existed in their current workflow” he says. “That’s what people kept noticing.”

In that sense, AI is not just changing how engineers work but it is also allowing them to start working much earlier than before.

Tanish Taneja, a research intern at Microsoft Research, says AI tools are not just optional but are built into the workflow, where interns are given access to GitHub Copilot and encouraged to use it for most coding tasks, particularly when working with internal data.

Instead of writing code line by line, Taneja, who is also a final-year student at IIIT Hyderabad says the process now starts with defining the problem, which includes answering questions like what needs to be built, how it should work, and what constraints to consider.

“The code itself is often generated by the system, with engineers stepping in to review, guide and refine it,” says Taneja.

“If you go back to the early years of software development, the essential tools were the right machine, an operating system, compilers, and later IDEs or even just a terminal. Without those, you simply couldn’t build software at scale,” says Karan M.V., director of international developer relations at GitHub. “AI coding assistants are now entering that same category,” says Karan, adding that tools like GitHub Copilot are no longer just optional add ons or productivity experiments.

AI tools can now assist engineers, in some cases completely taking over human intervention. GitHub, which hosts software projects for over 100 million developers globally, sees these tools becoming foundational to software development. AI tools are now being used not just to generate code, but also to assist in processes like code review, where systems can identify bugs and suggest fixes before a human steps in to evaluate them, says Karan.

Companies such as Infosys, Microsoft, IBM and Amazon are embedding coding assistants into core workflows and pushing engineers to adopt them as part of their day-to-day work.

“AI is transforming how software is built, but more importantly it is changing how developers spend their time,” says Rajiv Kumar, president and managing director, Microsoft India Development Centre.

With companies now integrating AI directly into how software is written, tested and deployed, educational institutions and their students are scrambling to adjust to a world where the very fundamentals of coding are being automated. In a sense, however, they are playing catchup with a technology where each day sees new enhancements.

Taneja says that even colleges that are relatively ahead in responding to the shift have been slow in doing so compared to the speed at which AI is changing workflows across enterprises. His own institution, IIIT-Hyderabad, has introduced courses on large language models and AI agents and starts core computer science training early.

Yet much of this remains theoretical. “There’s no formal push yet on learning how to use these tools for building,” says Taneja, adding that this is likely true across most colleges.

While AI tools can reduce the need to write code line by line, foundational knowledge cannot be replaced, argues Balaraman Ravindran, head of the department of data science and artificial intelligence at IIT Madras. “The math fundamentals remain the same,” he says. “Engineers still need a deep understanding of algorithms, program design, requirement specification and testing.”

With AI making it easier for students to generate code, some professors now require students to disclose their AI usage, submit the prompts they used and explain how the code was generated. In some cases, written assignments are supplemented or replaced by viva-based evaluations, where students are questioned on their code to test whether they actually understand what the system has produced.

“If the user has knowledge of good code design and of the exact requirements they can use these tools to develop good code,” says Ravindran. “But testing the AI developed code is still a challenge.”

That places a different kind of burden on engineers and by extension on how they are being trained. “At the end of the day, a human is still responsible for the code that is delivered.”

Adithya Kolavi, a 21-year-old graduate who runs CognitiveLabs, an AI startup, is also a research fellow at Microsoft. Kolavi, who operates within some of India’s most cutting edge developer circles, says AI coding tools are “basically default,” in the environments he is part of.

“Almost everyone I interact with is using some combination of Copilot, Cursor, Claude,” he says. “People rarely start from a blank file anymore. They start from a prompt or partial scaffold and iterate. The gap now is not whether you use these tools, but how well you use them.”

But that reality is hardly universal. Paras Chopra, co-founder of software company Wingify and founder of Lossfunk, an AI community lab, points to a very different picture just outside these circles. “The disconnect between what’s being taught and how things actually are is just growing,” he says.

In one instance, Chopra recalls, neither students nor the faculty of a premier design school had been exposed to tools such as Claude Code, something that, in parts of the industry, has already become a default way of writing software.

Between Kolavi’s world, where AI tools are the default and Chopra’s example, where they are barely known, sit students either anxious or unaware of how the next generation of engineers are being trained and how unprepared many of them may be for what comes next.

Priyanka Jain, a final-year engineering student from Pune University, says the pace of change has made it difficult to sense which skills will remain relevant. “Whatever complaints you have about AI models today, they will solve them within months in the next update,” says Jain. “If coding systems are not better today, they will be tomorrow. If peer review isn’t great today, it will improve very quickly.”

For students on the verge of graduating, that speed is creating an anxiety about learning the wrong things. “There’s a lot of uncertainty about whether the skills we are building today will actually make us employable tomorrow,” Jain says.

While colleges are not exactly anti-AI, they still remain uncomfortable with the usage of AI instead of training graduates how to make the best use of it, Jain adds. “It feels like institutions are still figuring out how to deal with AI, while the rest of the world is already learning how to work with it and get better alongside it,” she says.

Across conversations Mint had with students, what stood out was that the ones who are ahead are experimenting on their own.

Gupta says that the nature of learning is also changing. “Learners are moving beyond foundational concepts toward applied use cases like prompt engineering, AI-assisted coding, agentic workflows—skills they can directly use in real world development,” he says.

There seems to be a shift from execution to direction, which is also beginning to show up in how companies think about skills.

“We are seeing the roles of individual contributors evolve from being doers to being directors,” said Mohak Shroff, vice president of engineering at LinkedIn. “The most important skill of this new AI era is agency.” By agency, Shroff means the ability to identify problems, take ownership and push through constraints, rather than wait for clearly defined tasks.

According to LinkedIn’s latest Skills on the Rise 2026 report, demand is rising not just for AI and automation skills, but also for collaboration, stakeholder management and project leadership across roles.

“These aren’t new skills, but they have often been treated as secondary,” said Shroff, referring to communication, judgment and prioritisation. “As AI handles more execution, being human—setting direction, making decisions, and providing context—becomes central to how work gets done.”

Junior and entry-level engineering positions are among those with high exposure to AI, with enterprises now beginning to assess the near-term feasibility of deploying AI in these roles, says Ethanur.

Smaller tasks that once defined entry-level roles, like writing smaller modules or fixing bugs or executing well-defined instructions are among the first to be automated. These are predictable and rule-based problems and therefore easiest for AI systems to handle.

A good coder, until recently, was someone who wrote clean and readable code. Someone who was good at logic, reasoning and could build systems that fit into existing codebases seamlessly for years together. Today, being a good software engineer is increasingly about speed, adaptability and the ability to work effectively with AI. But most of all, it also involves knowing when to rely on AI generated code and when to intervene.