Code is Commodity. So, How Do You Get Hired?

With AI-powered, assisted, or AI-generated code becoming the norm, how do you assess a developer’s skills? That’s the question many companies are grappling with as the ability to write code is no longer the differentiator. 

Industry experts say that what matters now is whether a developer can work with AI—spotting flaws in generated output, refining architecture, and solving problems that machines cannot yet grasp.

In an interaction with AIM, Vikas Aditya, CEO of HackerEarth, an AI-native talent intelligence platform used by over 7 million developers for technical assessments, said the interview process itself is being rewritten. 

“Previously, people could just talk about past experiences,” he says. Not anymore.  

The real test, he argues, is whether a candidate can look at AI-generated code and recognise when the design is fundamentally broken. 

“If the design itself is not correct, someone with experience or a strong fundamental background will be able to spot it,” he adds.

Aditya is betting that the future belongs to developers who can critique and improve machine output, not just produce it. This has led to the emergence of various companies like HackerEarth that are building skill assessment and evaluation platforms for engineers. 

The company recently launched Vibe Code Arena, a competitive platform where developers go head-to-head with AI models such as ChatGPT, Claude, and Gemini on real-world programming challenges. Participants receive the same problem statements as the machines, but are judged on whether they can outperform, secure, or optimise the AI's solution across metrics including efficiency, maintainability, and architectural soundness. 

The platform features a live leaderboard, making the comparison explicit: what the AI produced, where you stand, and what you missed. The goal is to train developers in the skill that Aditya argues is now non-negotiable—the ability to analyse, debug, and elevate code that they did not write themselves.

The Other End of the Problem

However, the assessment problem does not end with developers. Companies are drowning in volume, starving for quality talent, and bleeding from bad matches—and AI has made all three worse.

Ankit Aggarwal, Founder and CEO of Unstop, a platform that runs the full hiring chain for employers, says companies like his are now rising up to this challenge. The platform verifies identity and monitors assessments via facial recognition, and uses AI to match candidates to roles based on skill data rather than pedigree. 

The service hosts 28 million users, with 6 million active monthly, and handles everything from talent discovery to offer release. Companies like Walmart, Flipkart, Adobe, and Tata use it to broadcast opportunities, run AI-proctored assessments, conduct video interviews, and screen candidates—often without looking at a resume until the final interview stage. 

“If a thousand people apply in a day, AI automatically screens them to get to only a hundred people for an employer,” Aggarwal explains. 

Beyond screening large applicant pools, the platform helps employers structure hiring around measurable signals, such as challenge performance, technical assessments, and skill evaluations, rather than relying on resumes alone. AI systems analyse this data and generate match scores, allowing recruiters to shortlist candidates for interviews.

In addition, performance data from competitions and assessments builds a broader skills record for candidates over time. Employers can use this history to evaluate fit beyond college pedigree. While AI compresses the volume of applications into a manageable pipeline, the final hiring decision remains with recruiters.

Yet even with better screening infrastructure, another structural issue persists: aspiration mismatch.

Aggarwal says firms often hire the wrong candidates because they misread candidate intent. Companies chasing “premier” talent from IITs and NITs frequently miss that these candidates view them as stepping stones. 

“Their attrition levels in a premier talent for many companies is actually high,” he observes. “For most of the companies, they are not aspirational to the premier talent, but they want the premier talent.” 

The result is predictable churn—candidates exit within 15 months for better offers, leaving vacancies and wasting training costs.

Unstop’s fix is aspiration matching. The platform segments candidates by realistic fit: a ₹6 LPA IT services role goes to candidates for whom that package is genuinely aspirational, not to IIT graduates who will leave. 

The system also enforces transparency—if a candidate is not in the top percentile, it tells them explicitly: “You may not even get a response from us.” This, Aggarwal argues, reduces false expectations and ends corporate “ghosting”.

What’s Next for Students? 

Aggarwal advises students to stop waiting for the curriculum to catch up. “All colleges lag at least five to 10 years behind the industry,” he says. “Only by building and doing something different, you will be at par with the industry, not with your college curriculum.” 

He wants students to continuously ship projects, compete in hackathons, and develop three attributes he considers non-negotiable: understanding a problem, finding a solution, and communicating that solution to stakeholders.

Many experts also echo the sentiment. In an earlier interaction with AIM, Abhimanyu Saxena, Co-founder of upskilling company Scaler, noted, “Most colleges [in India] offer a solid grounding in theory, but that doesn’t automatically translate into workplace capability.”

“For example, students can often explain algorithms in detail, yet struggle when asked to design scalable systems, debug in production, or solve complex engineering problems under constraints. This disconnect exists because curricula rarely keep pace with the speed of technological change.” 

He stated that emerging areas such as cloud-native development, DevOps, AI integration, or platform-first thinking receive little attention. “Meanwhile, the workplace demands engineers who can work in agile environments, collaborate across functions, and deliver under ambiguity.” 

There is also a value that organisations place on intuition. “The mindset I value most is curiosity paired with discipline,” said Sudipta Chandra, VP of AI at SaaS company Calsoft, in an earlier interaction with AIM. 

“Engineers must be willing to work with AI tools, understand their failure modes, and question outputs when something feels ‘off,’” he added. 

Chandra further noted that engineers today must go beyond basic coding proficiency. They need working familiarity with AI tools and adjacent frameworks, as well as a clear understanding of evaluation methods. 

In addition, he stated that comfort with versioning systems and observability practices is becoming essential because GenAI systems do not behave like traditional, deterministic software. They require continuous monitoring, structured evaluation, and careful iteration rather than one-time deployment.

Besides, there is also a demand for fluency with the tools reshaping software work. Knowing the failure modes of an AI tool, recognising when it hallucinates dependencies, over-engineers a solution, or introduces subtle security risks, is becoming part of baseline competence.

Tools such as Claude Code, for instance, are emerging as default coding companions in many teams. That makes it necessary for engineers to stay current with evolving best practices, documentation updates, and guidance from the companies that build them. This includes developer outreach materials, changelogs, community discussions, and official evaluation recommendations, which often contain critical information about limitations, prompt strategies, and integration patterns.

In addition, the ecosystem around AI-assisted development is expanding rapidly. New plug-and-play open-source frameworks, observability tools, orchestration layers, and evaluation libraries are released frequently. Engineers who track and experiment with these systems are better positioned to integrate them efficiently rather than reinventing infrastructure.