🤖 AI Summary
This study addresses the prevalent misuse of generative AI in tasks that could be efficiently solved by deterministic methods, leading to substantial resource waste and the risk of “algorithmic flattery.” Introducing the novel concept of the “reasonableness trap,” the work quantifies this misapplication through microbenchmarks and case studies in domains such as OCR and fact-checking, revealing approximately 6.5× higher latency overhead and reduced reliability. To mitigate this issue, the authors propose a tool selection engineering framework alongside a deterministic–probabilistic decision matrix, offering developers systematic guidance for appropriate technology choice. The paper further advocates integrating digital literacy education with the critical competency of discerning when not to deploy AI, emphasizing judicious tool adoption over default reliance on generative models.
📝 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.