The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks

TL;DR

This paper highlights the inefficiency and risks of using probabilistic AI models for simple deterministic tasks, proposing a framework to guide optimal tool selection and improve digital literacy.

Contribution

It introduces the 'Plausibility Trap' concept, quantifies its resource costs, and offers a decision matrix to help developers choose appropriate AI tools.

Findings

Probabilistic engines incur ~6.5x latency for deterministic tasks.

Using AI for simple tasks leads to resource waste and algorithmic bias.

Framework aids in optimal tool selection to improve efficiency and literacy.

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.