Large language models (LLMs) like ChatGPT have taken the world by storm, seemingly capable of writing stories, translating languages, and even coding software. But can they *really* do anything we ask of them? New research suggests there are fundamental limits to what LLMs can achieve, even with their impressive abilities. A recent paper, "Defining Boundaries: A Spectrum of Task Feasibility for Large Language Models," explores what tasks are simply out of reach for these powerful AI systems. The researchers categorize these "infeasible tasks" into four main groups: physical interactions (like dusting a bookshelf), virtual interactions (like booking a flight), non-text input/output (like processing images), and self-awareness (like reflecting on their own emotions). These limitations stem from the very nature of LLMs as text-based models trained on massive datasets. They lack the physical embodiment to interact with the real world, the real-time connection to external systems to handle virtual tasks, the sensory inputs to process images or audio, and the conscious awareness to understand their own existence. While some LLMs can *sound* self-aware, this is just a clever mimicry of human language based on their training data, not true understanding. The study also tested whether LLMs can identify when a task is beyond their capabilities. It turns out they do have some capacity to discern feasible versus infeasible tasks, especially when explicitly prompted to do so. However, in real-world interactions, users don’t often provide explicit cues, and LLMs are less adept at self-regulating their responses. This highlights the need to develop more robust methods to teach LLMs when to “say no” and admit their limitations. The researchers explored fine-tuning strategies to improve this “refusal awareness” but found there's often a trade-off. LLMs become better at refusing infeasible tasks, but sometimes at the cost of being less helpful overall. While the research emphasizes the boundaries of current LLMs, it also points to exciting possibilities for the future. More sophisticated models may overcome these limits by integrating with external tools and databases, incorporating non-text data, and developing more refined mechanisms for expressing uncertainty. As LLMs continue to evolve, understanding their limitations is crucial for developing truly helpful, responsible, and capable AI assistants.