Understanding Why Many Feel Disappointed with AI

Understanding the Real Potential of AI: Beyond the Hype and Capitalist Hype Machines

In recent years, public perception of artificial intelligence has been largely shaped by sensationalized headlines and exaggerated claims. It’s no surprise that many feel underwhelmed by AI’s actual capabilities. Too often, what’s showcased are opportunistic entrepreneurs trying to sell simplified “solutions”—often little more than flashy interface layers or “plastic wrap”—for complex problems that might not even exist. This environment can feel like a chaotic, capital-driven arena, filled with promotional noise and little substantive progress.

However, beneath this clutter lies a quieter, more innovative movement among developers and programmers. Many are leveraging AI to engineer tailored automation systems designed to streamline their workflows and solve specific challenges. These implementations are highly individualized, rarely transferable without significant adjustments. We’re not yet at the stage of an all-in-one solution capable of tackling every task seamlessly—but that’s likely to change in the near future.

The most transformative breakthrough isn’t about automating entire jobs instantaneously. Instead, it involves automating the process of automation itself. Typically, achieving an automated task involves several steps—often between one and five—utilizing memory components and APIs to interact with other systems. The process may include loops, multiple prompts, and context retention, making it a complex puzzle to assemble.

At face value, this may seem straightforward. But achieving such automation requires constructing custom prompts, ordering steps correctly, managing structured memory, and integrating APIs—all orchestrated across multiple specialized AI agents. One agent might generate prompts, another might build the architecture—including memory management—and yet another handles API communications, passing the right data along the way.

The exciting news is that much of this infrastructure already exists. AI models have been capable of generating their own prompts for some time. A recent 2023 publication (https://arxiv.org/abs/2310.08101) illustrates this progress. Additionally, the introduction of the Multi-Component Protocol (MCP) API allows for direct instruction delivery within the protocol itself, simplifying how models interpret and execute tasks.

Furthermore, innovations like the YAML-based architecture support in tools such as AgentForge make it easy to enable an AI to construct comprehensive automation frameworks from scratch—coordinating prompts, managing memory, and connecting APIs—all without extensive coding from the user.

What’s the takeaway? We’re in a transition phase. The tools, protocols, and architectures are becoming more integrated and accessible. The fundamental challenge of automating