The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks

TL;DR

This paper investigates the Plausibility Trap, wherein probabilistic engines like large language models are inappropriately used for deterministic tasks, leading to wasted resources and reduced reliability. It combines literature review with case studies (OCR and fact-checking) and micro-benchmarks to quantify efficiency gaps and trust costs, culminating in the Tool Selection Engineering (TSE) framework and the Deterministic-Probabilistic Decision Matrix (DPDM) to guide tool choice. Key findings show a substantial latency penalty for AI-based deterministic tasks (approximately $6.5$-fold) and $O(N^2)$-level computational overhead in vision-transformation paths, underscoring the risk of hallucination and verification overhead. The work advocates digital sustainability and intentional friction in education, arguing that true AI literacy includes knowing when not to use AI and prioritizing the right tool for the job to preserve precision, efficiency, and trust.

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.

Figures (7)

• Figure 1: The Energy Efficiency Gap. A logarithmic comparison of computational cost.
• Figure 2: The Anatomy of Overkill. A structural comparison showing why Generative AI incurs massive computational overhead for simple extraction tasks compared to traditional methods.
• Figure 3: The Convenience Penalty. Comparing average time-to-task completion between a deterministic OCR workflow (Google Lens) and a Generative AI workflow (Gemini). The probabilistic path introduces a $\sim6.5x$ latency overhead for simple extraction lab2025benchmark.
• Figure 4: An example of how users were asking chatbots to verify if large numbers were odd or even as a part of a viral trend.
• Figure 5: Algorithmic Sycophancy: The model validates a false premise to satisfy the user's prompt structure.