Companies are testing unconventional 'Eyes-Closed Question' (ECQ) tactics during virtual interviews to prevent candidates from using AI tools. This trend highlights the growing administrative burden on tech firms as they struggle to manage a flood of AI-generated job applications and verify authentic technical talent.
A new and unconventional interview practice has emerged in the tech sector, where candidates are asked to switch off screen-sharing and answer questions with their eyes closed. This method, often referred to as an 'Eyes-Closed Question' (ECQ), is designed to ensure that candidates are providing their own answers rather than reading from a script or utilizing AI tools like ChatGPT or Claude in real-time.
The adoption of this tactic stems from a significant challenge currently facing many tech companies: the explosion of AI-generated job applications. Recruitment teams have reported being overwhelmed by the sheer volume of candidates. In some instances, companies have received as many as 4,800 applications for a single opening, many of which appear to be generated by AI. This high volume has made the screening process extremely time-consuming, forcing companies to implement stricter, albeit unusual, measures to filter out automated or low-effort submissions.
From a business perspective, this highlights the rising costs and inefficiencies associated with hiring in the age of AI. When a large percentage of applications are AI-generated, HR departments spend excessive time reviewing unqualified candidates. This administrative pressure is pushing companies to experiment with new assessment models, including practical coding tests on GitHub or sourcing candidates directly from industry events, rather than relying on traditional application pools.
However, the ECQ method has faced criticism from both job seekers and some industry professionals. Critics argue that forcing candidates to answer complex technical questions without visual aids or screen access may not accurately measure their skills. There is also a concern that such methods could alienate top-tier talent who might find the process intrusive or unprofessional, potentially leading them to withdraw from the recruitment process entirely.
For investors and companies, this trend reflects a broader issue regarding human capital management. As AI continues to change how work is done, the challenge for companies is to build robust systems that can identify genuine talent without creating barriers that deter qualified individuals. The shift toward more verifiable, hands-on assessment methods is likely to continue as firms try to balance the need for efficient hiring with the requirement to maintain high standards of candidate evaluation.