The article highlights a growing concern in software development: the over-reliance on AI-generated outputs, which can create a false sense of certainty. Developers often mistake the fluency of AI responses for validity, leading them to accept AI suggestions without critical examination. This reliance can erode the necessary balance between belief and evidence, as teams may stop questioning their assumptions and instead outsource their judgment to AI. AI's strength lies in its ability to produce coherent suggestions quickly, but this can also introduce risks. The distinction between proof, evidence, and belief becomes blurred. Developers might take AI outputs at face value, leading to decisions based on borrowed certainty rather than earned understanding. This creates a form of technical debt that manifests not in code but in how teams perceive knowledge and authority. To mitigate these risks, the article proposes several practices. Teams should treat AI-generated outputs as hypotheses, demand traceability for claims, and require human explanations for AI-assisted decisions. It emphasizes the importance of maintaining a critical mindset, ensuring that AI serves to expand options rather than prematurely close off exploration. By questioning the source and validity of AI suggestions, developers can better navigate the complexities of software engineering without losing their grounding in reality.