The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks
It addresses inefficiency and waste in AI deployment for developers and users, though it is incremental as it builds on existing concerns about AI misuse.
The paper identifies the 'Plausibility Trap', where AI models like LLMs are overused for simple deterministic tasks like OCR, leading to a ~6.5x latency penalty and resource waste, and proposes a framework to guide appropriate tool selection.
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.