Abstract
The ubiquity of Large Language Models (LLMs) is driving a paradigm shift where user convenience supersedes computational efficiency. This article defines the "Plausibility Trap": a phenomenon where individuals with access to Artificial Intelligence (AI) models deploy expensive probabilistic engines for simple deterministic tasks-such as Optical Character Recognition (OCR) or basic verification-resulting in significant resource waste. Through micro-benchmarks and case studies on OCR and fact-checking, we quantify the "efficiency tax"-demonstrating a ~6.5x latency penalty-and the risks of algorithmic sycophancy. To counter this, we introduce Tool Selection Engineering and the Deterministic-Probabilistic Decision Matrix, a framework to help developers determine when to use Generative AI and, crucially, when to avoid it. We argue for a curriculum shift, emphasizing that true digital literacy relies not only in knowing how to use Generative AI, but also on knowing when not to use it.
Abstract (translated)
大型语言模型（LLMs）的普及正在推动一种范式转变，其中用户便利性超越了计算效率。本文定义了“可信度陷阱”：拥有访问人工智能模型权限的个人会为简单的确定性任务——如光学字符识别（OCR）或基本验证——部署昂贵的概率引擎，从而导致资源浪费的现象。通过微基准测试和关于OCR及事实核查的案例研究，我们量化了这种“效率税”，即大约6.5倍的延迟惩罚，并探讨算法谄媚的风险。为应对这一问题，我们介绍了工具选择工程和确定性-概率决策矩阵这一框架，旨在帮助开发者决定何时使用生成式AI以及最重要的是何时避免使用它。我们认为应该进行课程改革，强调真正的数字素养不仅在于知道如何使用生成式AI，还在于知道在什么时候不应该使用它。
URL
https://arxiv.org/abs/2601.15130
