It’s easy to see why people are disappointed with AI

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

In the current landscape, it’s understandable why many people feel underwhelmed by artificial intelligence advancements. Much of the narrative has been clouded by aggressive marketing from tech entrepreneurs pushing flashy solutions—often overhyped, sometimes unnecessary—that seem more focused on profit than genuine innovation. This environment feels like a chaotic marketplace of “AI slot machines,” where the latest buzzwords are tossed around to attract investment, regardless of practical value.

However, beneath this noisy commercial veneer lies a quieter, more promising reality. Skilled developers and programmers are leveraging AI to craft tailored automation solutions that significantly streamline their workflows. These tools aren’t one-size-fits-all; instead, they are custom-built to meet the specific needs of individual tasks. No single platform currently does everything, nor is one likely to in the near future.

The most impactful breakthrough isn’t simply replacing jobs through automation, but rather automating the process of automating itself. Typically, automating a task involves several steps—often between one and five—that may include loops, utilize memory components, and interact with multiple APIs. While this may sound straightforward, each step requires careful prompt engineering, precise sequencing, and well-structured memory management. Connecting these steps seamlessly demands multiple specialized agents: one to generate prompts, another to architect the process (including memory integration), and yet another to facilitate API calls and data handling.

Fortunately, the tools and frameworks needed to orchestrate such complex automation are already in place. Researchers have been developing solutions for some time. For example, a 2023 publication introduces new methodologies that enable AI systems to generate their own prompts effectively (Read the paper here). More recently, the MCP protocol has emerged as a robust API that embeds instructions directly within its framework, streamlining communication with language models. Additionally, innovations like YAML-defined architectures within platforms such as AgentForge now make it possible for AI to design comprehensive automation workflows—from prompt sequencing to memory management—without requiring traditional programming.

What does this mean for the future? We are approaching a point where automating the process of automation itself becomes feasible. This evolution indicates that many of the repetitive, time-consuming tasks that have traditionally burdened workers can be delegated to AI-driven systems, effectively allowing us to focus on more strategic, creative pursuits.

In essence, while the current hype might seem disconnected from everyday realities, the underlying technology is advancing toward an