The Algorithmic Wasta: AI Hiring and the Remaking of Social Capital in Gulf Labour Markets

Abstract

Objective: This article develops the concept of algorithmic wasta to explain how artificial intelligence (AI) hiring tools remake social capital in Gulf labour markets, where intercessory networks have long shaped access to jobs, promotion, and information. It asks whether automated screening weakens, absorbs, or disguises network advantage under labour-nationalisation regimes. Methodology: The study is a theoretical and integrative essay. It synthesises peer-reviewed research on wasta and social capital, empirical audits of bias in résumé screening and large language models, economic evidence on Saudi Nitaqat quotas, and selected comparative regulatory texts, including New York City Local Law 144 and the European Union AI Act. No new survey, interview, or administrative dataset is analysed, and claims about specific Gulf employers are avoided. Results: Four ideal-typical pathways are specified: displacement, in which transparent matching reduces brokers' informational advantage; translation, in which network signals such as referrals become algorithmic features; dual tracking, in which algorithms govern high-volume screening while networks allocate elite roles; and compliance wasta, in which networks mobilise to satisfy automated or quota metrics. Conclusion: AI hiring will not abolish social capital in the Gulf; it will remake it. The article recommends local fairness audits, bilingual Arabic–English validity testing, candidate notice and appeal, and nationalisation metrics that track career progression rather than headcount alone.