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-world 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 within private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers – so we can gain speed without losing security and accuracy.