LLMs in Coding and their Impact on the Commercial Software Engineering Landscape

Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers -- so they can gain speed without losing security and accuracy.

@article{belozerov2025_2506.16653,
  title={ LLMs in Coding and their Impact on the Commercial Software Engineering Landscape },
  author={ Vladislav Belozerov and Peter J Barclay and Askhan Sami },
  journal={arXiv preprint arXiv:2506.16653},
  year={ 2025 }
}