Understanding the Reasons Behind Public Disappointment with AI

Reevaluating AI: The Hidden Potential Beneath the Noise

In recent discussions about artificial intelligence, it’s easy to be underwhelmed—largely due to the pervasive noise of superficial promises and overhyped solutions. Much of what we encounter are marketing-driven pitches from tech entrepreneurs offering simplistic fixes for problems that often don’t exist or are overstated. This environment has become a chaotic landscape of flashy AI gimmicks and quick-money schemes, rather than a serious exploration of transformative technology.

However, beneath this cacophony lies a quieter revolution. Skilled programmers and developers are quietly leveraging AI to craft bespoke automation solutions aimed at simplifying their workflows. Unlike one-size-fits-all platforms, these custom automations are tailored to specific needs, often requiring only minor adjustments when implemented differently. Achieving a universal AI solution capable of handling all tasks remains a distant goal—one that may be decades away.

The key insight is that the most significant breakthrough won’t come from automating entire jobs outright. Instead, the future lies in automating the process of automation itself. Typically, a task can be broken down into a series of steps—often five or fewer—and these steps may involve loops, memory systems, and interactions with external APIs.

At first glance, this might sound straightforward. But the real challenge lies in orchestrating these steps. Each phase requires meticulously crafted prompts, accurate sequencing, and seamless integration of memory components into the communication process. Furthermore, connecting to APIs to execute specific functions adds another layer of complexity, necessitating multiple AI agents—each specialized in prompt design, architecture construction, memory management, and API interactions.

Encouragingly, all these components already exist. AI systems have been generating their own prompts for some time, with research such as the 2023 paper here illustrating advanced progress. Recently, innovations like the Multi-Chain Protocol (MCP) have emerged, enabling APIs to provide direct instructions to language models within the communication protocol itself.

Additionally, tools like AgentForge now incorporate YAML-defined architectures, empowering AI to design comprehensive workflows from scratch. These capabilities facilitate prompt sequencing, memory handling, and API integration—all without requiring users to write any code.

The exciting takeaway? The current technological landscape has already laid the groundwork for automating the automation process itself. While progress isn’t instantaneous, it marks the beginning of a new era—one where the last manual task we ever need to automate is automating the rest.