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

arXiv: https://arxiv.org/abs/2506.16653 · HTML: https://mlsanity.com/paper/2506.16653/

## 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.

## Builds on (most cited first, 5 of 5)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Sycophancy in Large Language Models: Causes and Mitigations](https://mlsanity.com/paper/2411.15287/) (2024)
- [Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications](https://mlsanity.com/paper/2503.01245/) (2025)
- [Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models](https://mlsanity.com/paper/2505.08803/) (2025)

## 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.