Coders are understandably complaining about AI coding problems, with the technology often delivering what’s become known as “AI slop,” but their concerns are signaling a more strategic issue about how enterprises calculate coding ROI.
The issues, according to IT analysts and consultants, go far beyond vastly faster production of code accompanied by the kinds of errors generated by AI agents that don’t truly understand the human implications of their code.
Even if the resulting code functions properly, which it often doesn’t, it is introducing a wide range of corporate risks, ranging from legal (copyright, trademark, or patent infringements), cybersecurity (backdoors and inadvertently introduced malware) and accuracy (hallucinations, as well as models trained/fine-tuned on inaccurate data). Some of those issues are generated by poorly-worded prompts, and others occur because the model improperly interpreted proper prompts.
