Indian Developers Will Review Code For a Living

For decades, writing code was the defining skill of a software developer. But lately, several leaders like NVIDIA’s Jensen Huang have openly suggested that the future engineer may not write much code at all. They will, instead, manage fleets of AI agents that do.

And increasingly, that future seems especially visible in India. Over the past year, tools like Claude Code have changed what ‘coding’ even means. A prompt can become a function. A paragraph can become an API. A weekend can become a working application.

The first reaction was fear. If AI can generate in minutes what once took days, what happens to entry-level engineers, or, for that matter, senior developers? But the people building these tools see a completely different picture. 

At Rocket.new, an Indian vibe coding startup that helps founders and teams spin up full-stack products from intent, co-founder and CEO Vishal Virani has watched the shift from the front row. He does not buy the idea that developers are scared.

“I don’t think developers are scared of AI tools—they have been the earliest adopters. Engineering teams today are arguably the single biggest power-users of AI across any function. What we’re seeing is a skill shift, not a fear wave,” he tells AIM.

In his view, engineering has gone through similar transformations before. Cloud computing reduced the need to manage servers. DevOps reduced the friction of releases. Frameworks removed boilerplate. Each wave removed manual effort and pushed engineers higher up the stack.

AI just quickens this process. “AI compresses weeks of work into days, so the premium shifts from writing code manually to orchestrating, validating, and shipping outcomes fast,” he says.

That word, ‘validating’, marks the point where the story changes.

When software was slow to write, writing was the core skill. When software becomes instant, validation becomes the bottleneck. Rocket’s own pitch reflects this reality. The company is trying to deliver production-ready systems, with databases, auth, and deployment already in place.

“Rocket isn’t just vibe coding. It’s vibe solutioning—from intent to a real working product.” But even Virani admits that cheap creation produces a predictable side effect: noise.

“We’re already seeing early signs. Whenever creation becomes cheap, the internet gets flooded with low-effort content and low-quality software.”

This is where the next layer of companies appears.

At CodeRabbit, Director of Developer Marketing Aravind Putrevu sees what happens after teams fully embrace generation. Code is no longer handcrafted. It is produced by agents, often in bulk. Pull requests pile up faster than humans can review them.

This creates a strange problem: nobody really knows what is going into production.

“I think people are not writing code anymore. They’re generating code,” he says. “Now, reading code is more important than writing code. Understanding what is being generated is more important.”

That sentence quietly flips the entire profession.

For years, interviews tested candidates’ ability to implement algorithms from scratch. Now the critical skill is diagnosis: can they scan a thousand lines of code that developers did not write and spot what will break, see the edge case the model missed and predict how it behaves under load?

Putrevu describes modern review tools as guardrails for speed. If AI is the accelerator, review is the brake. “You can’t stop moving faster because technology allows you to do that. You just need to have safer guardrails around you so that you can execute well.”

CodeRabbit acts like an automated peer reviewer. It flags risky logic, suggests fixes, and serves as a last line of defence before deployment.

AI Watching AI

Further down the pipeline sits CodeAnt, which focuses on debugging and quality. Founder and CEO Amartya Jha sees what happens when fundamentals are weak. Students rely on AI to finish assignments. Junior engineers copy-paste entire suggestions without understanding them. Bugs multiply quietly.

“Not using AI tools today is like trying to become a software engineer without Git 10 years ago. You can do it, but you’ll be at a serious disadvantage,” he tells AIM.

But he is more worried about the opposite. “The real risk isn’t students using AI. The real risk is students using AI without understanding what it’s doing.”

Then came a line that deserves to be on every engineering classroom wall.

“If you don’t learn fundamentals like data structures, debugging, system design, or security principles, AI will just help you produce bad code faster.”

Faster does not automatically mean better. Though it often just means faster mistakes.

This exposes an uncomfortable reality for much of the Indian IT industry. For decades, the system rewarded repetition: write the same type of features 50 times, follow the checklist, stick to the process. Deep understanding was helpful, but not always necessary.

Now repetition is automated. Syntax is free. Boilerplate is instant. The only scare skill left is judgement.

For freshers, the bar rises. Knowing a language is not enough. One must understand how systems behave. We are entering the era of vibe engineering.

For startups, a new hierarchy emerges: teams that combine AI speed with disciplined review will ship faster and break less. Teams that blindly trust generation will drown in their own bugs.