Understanding why AI has failed to impress many people

Unlocking the True Potential of AI: Beyond the Hype

In recent times, it’s understandable why many feel unimpressed with AI advancements. A significant part of the conversation is often overshadowed by oversimplified marketing claims and endless pitches from tech entrepreneurs offering quick-fix solutions—think plastic wrap with PFAS—targeted at problems that don’t really exist. The tech world sometimes feels like a chaotic marketplace of fleeting trends and spammy shortcuts, rather than a space for meaningful innovation.

However, beneath this noise, a quieter revolution is happening. Experienced programmers and developers are leveraging AI to craft custom automation tools tailored to specific workflows. These implementations, while powerful, often vary greatly from one context to another and usually require substantial modifications to adapt. This means we shouldn’t expect a universal AI solution that seamlessly handles all tasks—at least not yet.

The most significant breakthrough isn’t about replacing entire jobs. Instead, it’s about automating the process of automation itself. Typically, automating a task involves several steps—often between one to five—each with its own logic, looping mechanisms, and memory components, and often interacting with external APIs. While this might sound straightforward, orchestrating such processes requires meticulous prompt design, proper sequencing, and well-structured memory management.

To implement these multi-step workflows effectively, we need multiple AI agents: one to generate prompts, another to design the system architecture—including memory integration—and yet another to interact with APIs and pass data efficiently. Fortunately, we already possess many of the necessary tools.

For example, in 2023, researchers published a foundational paper demonstrating how AI models can independently generate their own prompts. Additionally, the MCP protocol now provides a standardized API that embeds instructions directly within the communication protocol, simplifying the interaction with language models. Meanwhile, innovations like YAML-defined architectures in tools such as AgentForge make it possible for AI systems to construct complex workflows from scratch—sequencing prompts, managing memory, and handling API calls—all without writing a single line of code.

All these advancements point toward a future where automating the process of automation becomes a solved problem. While it’s not an easy feat, it’s likely the last hurdle we need to overcome in AI-driven automation. And when that day comes, it will truly transform how we work, innovate, and solve problems.

Stay tuned—what’s coming next is nothing short of revolutionary.