Explaining Why AI Fails to Impress Most People

Reframing AI: From Capitalist Hype to Practical Automation

In today’s landscape, it’s no surprise that many people remain unimpressed with the current state of artificial intelligence. A significant factor contributing to this skepticism is the proliferation of overly optimistic or commercialized narratives propagated by tech entrepreneurs—often more interested in selling shiny solutions than providing meaningful innovations. The market is flooded with approaches that amount to quick fixes or sensational “AI slot machines,” rather than substantive advancements. Unfortunately, this environment can overshadow the genuine progress quietly taking place behind the scenes.

Despite the noise, developers and programmers are leveraging AI in practical ways to streamline workflows and automate repetitive tasks. These implementations are highly specific and typically do not translate easily across different contexts or projects, often requiring substantial reconfiguration. It’s important to recognize that a universal, all-encompassing AI solution capable of handling every task remains a distant goal—probably for quite some time.

Rather than aiming to replace entire jobs outright, the more realistic and impactful approach involves automating the process of automation itself. This means creating systems that can automatically coordinate tasks—usually involving just a handful of steps, sometimes looping back, utilizing memory components, and connecting to various APIs. At first glance, this might seem straightforward, but in practice, each step demands precise prompt engineering, proper sequencing, and seamless memory integration. Additionally, multiple AI agents often need to work in concert: one to generate prompts, another to structure the overall architecture, and a third to handle API interactions.

Fortunately, we’re already equipped with many of these capabilities. AI models have been generating their own prompts for some time now. Notably, research like the 2023 paper “Title of Paper” explores these developments. More recently, innovations such as the Multi-Chain Protocol (MCP) have emerged, allowing AI to directly receive detailed instructions through APIs. Furthermore, tools like AgentForge now incorporate YAML-defined architectures, enabling large language models (LLMs) to autonomously design complete automation systems—sequencing prompts, managing memory, and orchestrating tasks—all without writing a line of code.

All that remains now is patience. While this is a complex challenge, it marks the final frontier for automation—making AI not just a hype-driven tool but a practical, integrated component of efficient workflows. The future, although intricate, is closer than ever to delivering on its promise.