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arXiv · 2601.15130

The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks

Ivan Carrera·Daniel Maldonado-Ruiz
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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.

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Ivan Carrera, Daniel Maldonado-Ruiz. 2026-01-21. The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks. https://arxiv.org/abs/2601.15130

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