The Capability Paradox Algorithmic: Screening, Corporate Risk Aversion, and the Transformation of Software Engineering Recruitment

This analysis explores the "capability paradox" in the modern technology industry, a profound structural contradiction in which the demand for engineers with strong, transferable problem-solving skills clashes with corporate recruitment practices that have become increasingly rigid, procedural, and risk-averse. This paradox is driven by the widespread adoption of Applicant Tracking Systems (ATS), which use simplistic keyword matching to screen candidates. These algorithmic gatekeepers systematically filter out highly experienced professionals, whose resumes emphasise broad architectural achievements, in favour of applicants with narrow, platform-specific expertise that precisely matches a checklist. This shift is not merely a technological issue but a manifestation of profound institutional risk aversion and a "Cover Your Ass" (CYA) culture, where procedural correctness and making a defensible hire are valued more than acquiring genuine, long-term capability. The consequences are severe, leading to staggering financial costs from extended position vacancies, high turnover, and the misallocation of talent, with these failures being particularly amplified in high-volume markets like India, where AI models trained on Western career paths misinterpret local professional trajectories. The rise of generative AI intensifies this problem, as it commoditises basic coding and increases the value of adaptable, "M-shaped" professionals who possess deep expertise in multiple domains—the very individuals current systems are designed to reject. The resolution requires a fundamental paradigm shift away from keyword checklists toward relational "skills-graphs" that can infer underlying competencies, allowing organisations to identify and utilise the high-judgment talent essential for future innovation.