Understanding Why People’s Hopes for AI Are Not Being Met

Understanding the Real Potential of AI Beyond the Hype

In today’s landscape, it’s no surprise that many people feel underwhelmed by artificial intelligence. A significant part of the frustration stems from the pervasive presence of self-promoting tech entrepreneurs touting quick-fix solutions—often marketed as revolutionary, yet primarily serving commercial interests. This environment can resemble a chaotic, profit-driven marketplace flooded with superficial AI “slot machines” promising endless gains, but delivering little substance.

However, beneath this noise lies a quieter, more meaningful development. Skilled programmers and AI researchers are quietly leveraging these tools to craft bespoke automation workflows that streamline complex tasks and improve efficiency. Importantly, these tailored solutions are rarely one-size-fits-all; they tend to be highly specific, often requiring major adjustments to adapt from one implementation to another.

The key to true AI transformation isn’t about replacing entire jobs overnight. Instead, the future lies in automating the process of automation itself. This means developing systems that can autonomously handle a single task—often composed of multiple steps, which may involve repetitive loops and interactions with various APIs. By orchestrating these steps effectively, we can create systems capable of self-assembly and self-improvement.

While this might sound straightforward, the reality is intricate. Each automation step demands carefully crafted prompts, precise task sequencing, and a well-structured memory system to maintain context. Connecting these steps to different APIs adds another layer of complexity, necessitating multiple AI agents: one to generate prompts, another to build and manage architecture—including memory integration—and yet another to handle API interactions.

Fortunately, the technology to achieve this is already within our grasp. AI models have been capable of generating their own prompts for some time, as evidenced by recent research such as this 2023 paper: https://arxiv.org/abs/2310.08101. Additionally, innovations like the MCP protocol now enable direct guidance of large language models (LLMs) through a standardized API, streamlining complex workflows.

Moreover, tools like AgentForge now incorporate YAML-based architecture definitions, allowing LLMs to autonomously design and implement comprehensive automation systems—sequencing prompts, integrating memory, and managing API calls—all without requiring extensive coding.

What does this mean for the future? We are on the cusp of an era where automating the automation process will be the final, most impactful frontier. While this challenge isn’t trivial, it promises to empower us with systems capable