llm/122b8d72-a8a3-4fcf-8eca-6a52786d1a8b/topic-2-6dc31e87-b439-4d14-9307-59b9e24e5858-output.json
While AI coding tools offer an impressive initial boost for scaffolding and repetitive tasks, professional developers find they quickly buckle under the architectural demands and "big picture" logic of large, complex codebases. Many users report a frustrating transition from efficiency to "babysitting," where the effort required to steer the model and correct its hallucinations often outweighs the time saved by automating the code in the first place. These tools frequently struggle with consistency—often breaking existing features to fix new ones—and lack the nuanced reasoning needed to spot "invisible" bugs that aren't explicitly present in their training data. Ultimately, there is a strong consensus that while AI is a valuable assistant for overcoming "activation energy" or writing simple scripts, the current hype regarding autonomous software development largely ignores the reality of managing the "vibe-coded slop" that emerges at scale.