HackerRank, a platform that helps companies assess technical talent, has made its AI interview system Chakra generally available to customers. The tool, which entered beta about six months ago, has already conducted over 500,000 interviews, according to the company. Early adopters include Snowflake, Snorkel, and Capgemini, while HackerRank also tested the system internally.

Chakra is designed to simulate real work rather than administer a standard coding test. Candidates receive a task within a live code repository and work in a shared canvas that includes an AI assistant. As they write code, Chakra observes their process and can ask follow-up questions about their approach, such as how they would adjust their solution under new constraints.

The system generates a detailed report for hiring managers after each interview. HackerRank says Chakra consolidates what was previously a three-stage process — recruiter screening, take-home assessment, and engineer follow-up — into a single interview session.

CEO Vivek Ravisankar told TechCrunch that the shift reflects a broader change in how technical ability should be measured. He noted that AI can now produce correct answers with minimal effort, making the traditional focus on output less meaningful. Instead, he said, employers need to evaluate the thinking behind a solution, which he described as "AI fluency" — the ability to frame problems for AI, assess its responses, and guide it toward a desired outcome.

Ravisankar argued that giving candidates access to AI during interviews reduces the temptation to use hidden tools, citing internal data showing fewer suspicious activity flags in Chakra interviews compared to conventional HackerRank assessments. The rate of flagged behavior varied by region and role level, but overall declined by 70 to 80 percent.

The company, founded in 2012 and backed by Y Combinator, has grown to serve over 3,000 enterprise clients, including Amazon, Nvidia, and Replit, and supports a global community of more than 30 million developers. Chakra represents a strategic pivot from HackerRank’s original model, which emphasized solving coding challenges correctly. Ravisankar compared the transition to Apple’s shift from iPod to iPhone, noting that while the legacy product still has value, the future lies in AI-integrated evaluation.

He emphasized that Chakra is intended to score candidates, not replace human decision-making. Final hiring decisions, he said, remain with people, while AI handles structured evaluation using consistent criteria set by employers. Ravisankar maintained that properly tuned AI can apply rubrics uniformly, reducing the influence of subjective factors such as educational background.

However, experts have raised concerns that even standardized AI evaluation can perpetuate or amplify bias if the training data or design reflects existing inequities. Automated hiring tools have already attracted regulatory attention; New York City, for example, mandates bias audits and candidate notification for certain AI-driven recruitment systems. HackerRank stated that compliance with such regulations is integrated into Chakra’s design and rollout.

The broader use of AI in hiring continues to provoke debate over transparency, accountability, and fairness. While some argue that algorithmic assessment can reduce human inconsistency, others warn that without careful oversight, it may entrench systemic disparities under a veneer of objectivity.