c4t

In practice, this means organizations need to pay closer attention to how AI is presented and disclosed. Among other requirements:

people must be informed when they are directly interacting with certain AI systems, unless this is already obvious
providers of generative AI systems must enable AI-generated or manipulated content to be identified through machine-readable marking
deepfakes must be clearly disclosed and
AI-generated or manipulated text published to inform the public on matters of public interest must be labelled in certain circumstances.

When organizations deploy vector-based “Traditional” Retrieval-Augmented Generation (RAG) systems, performance issues are often attributed to model choice or hyper-parameter tuning.

AI-assisted coding is no longer experimental. It is reshaping how software is written, reviewed, and deployed at a pace few anticipated. For some, this is a productivity revolution or change of paradigm. For others, it is a fundamental architectural risk. For most, it is both.

AI-assisted coding is no longer a future-facing concept for software development. It is already embedded in daily engineering workflows. But despite widespread experimentation and growing adoption, the reality of how developers use AI today is far more grounded than popular narratives suggest.