Generative AI’s Coming Dominance in Algorithm Design
Rahul Karne, Akhil Dudhipala, Pavan Kumar Pativada · International Journal of Computer Applications · 2025
Generative AI (GenAI) is rapidly overtaking classical methods in algorithm design, driven by breakthroughs such as GANs, Transformers, and self-play reinforcement learning.We present a concise, controversial survey arguing that GenAI will become the primary driver of algorithmic innovation.To substantiate this claim, we include a quantitative meta-analysis of publication trends (2013-2022) demonstrating a 20× surge in GenAI research relative to traditional algorithm work.We compare GenAI-designed algorithms against human-crafted counterparts across interpretability, guarantees, adaptability, scalability, and development cycle (Table 1).We critically examine trade-offs-opacity, overfitting, ethical bias, and resource intensiveness-drawing on several highly cited ethics studies to highlight accountability and safety concerns.Finally, we outline future directions, advocating hybrid human-AI workflows, efficiency improvements, and robust governance to ensure GenAI's advances remain aligned with societal values.This review's bold stance and rich, high-impact references aim to catalyze debate and position the paper for widespread citation.