Generative AI as a Cognitive Abstraction in Software Engineering: Evidence from Academic and Industrial Practice

Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), has emerged as a transformative cognitive layer in software engineering. This paper presents an evidence-based analysis of GenAI as a cognitive abstraction, synthesising findings from peer-reviewed literature, empirical studies and industry reports. We examine how LLMs function as probabilistic abstraction mechanisms, enabling developers to express intent in natural language while delegating implementation details. Through comparative analysis of academic and industrial adoption patterns, we reveal fundamental differences in incentives, risk tolerance and governance approaches. While academia prioritises methodological rigour and epistemic scrutiny, industry focuses on productivity gains and scalable integration. Both domains converge on the necessity of human oversight. We propose a structured human-in-the-loop framework emphasising validation, accountability and traceability. Empirical evidence indicates GenAI can accelerate routine tasks by 30-55% but introduces risks including security vulnerabilities, bias propagation and technical debt accumulation. The paper argues that GenAI represents not merely a tool but a fundamental abstraction layer that reshapes cognitive workflows while preserving the centrality of human expertise. We conclude with research directions addressing longitudinal effects, verification challenges and organisational implications.