Curious if others have arrived where I am now re AI/LLMs: I’m kind of bored of them.

Exploring the Diminishing Excitement of AI and Large Language Models: A Personal Reflection

In recent years, AI and Large Language Models (LLMs) have revolutionized the way we access information, automate tasks, and augment our daily lives. As a dedicated user experimenting with various platforms—from Gemini and Perplexity to Claude, ChatGPT, and NotebookLM—I’ve gained valuable insights into their capabilities and limitations. However, I find myself reaching a plateau, feeling somewhat disconnected from the initial excitement these technologies once sparked.

The Reality of Working with AI: More Effort than Expected

One recurring challenge I’ve encountered is how much effort it often takes to coax a useful response from these systems. Typically, I find myself investing considerable time—sometimes multiple rounds of refining prompts—to achieve a satisfactory outcome. In many cases, it feels more labor-intensive than simply doing the task manually or using a straightforward tool. This paradox diminishes the appeal of relying heavily on AI for routine tasks that could be handled just as efficiently without intricate prompt engineering.

Limitations in Practical Use Cases

Despite experimenting with various AI platforms over nearly two years, I’ve observed that their usefulness doesn’t always align with my lifestyle or specific needs. While I value their ability to synthesize information and generate content, I often find that mainstream search engines like Google still serve my needs effectively, providing factual data quickly and reliably. The core difference lies in the nature of the output: AI excels at synthesis and creative augmentation, but not necessarily in delivering quick, factual answers in a way that seamlessly integrates into my workflow.

Reflections on Engagement and Future Use

Out of all the tools I’ve tried, NotebookLM has stood out as the most practical and useful for my purposes. The others, despite their innovative features, often feel underwhelming—leading me to question whether further engagement is worthwhile. After investing time in learning prompt engineering and best practices, I’ve reached a point where my enthusiasm has waned. At this stage, I find myself more interested in reorganizing my pantry than experimenting with the latest AI models.

Are Others Experiencing the Same?

I recognize that my perspective might be in the minority. However, I’m curious: have others found themselves in this “done that, got the T-shirt” phase with AI and LLMs? Is this sense of boredom or fatigue common among long-term users who have explored the full potential—and limitations—of these technologies?

Conclusion