LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 19 Jun 2025.
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References · 5
- 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
- 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 356
- 2024Sycophancy in Large Language Models: Causes and Mitigationscited by 101
- 2025Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applicationscited by 26
- 2025Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Modelscited by 5
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