Understanding Why People Feel Disappointed with Artificial Intelligence

Understanding AI: Beyond the Noise and Hype

In recent times, widespread disappointment with artificial intelligence is understandable. Much of the conversation is dominated by entrepreneurs and tech enthusiasts promoting superficial solutions — often marketed as groundbreaking but frequently little more than shiny plastic wraps over existing problems. The current landscape can seem like a chaotic marketplace of fleeting AI gimmicks, all vying for attention and investment, frequently missing the mark in delivering meaningful value.

However, beneath this buzz-filled surface, a quieter revolution is taking place among developers and programmers. Many are leveraging AI to craft tailored automation solutions that streamline their workflows and simplify complex tasks. Unlike one-size-fits-all tools, these custom automations tend to be highly specific and adaptable, often requiring only modest adjustments when implemented in different environments. This suggests that we won’t see a universal AI solution capable of solving all problems anytime soon — at least not in the near future.

The most significant breakthrough, however, isn’t about replacing entire jobs through automation. Instead, it’s about enhancing the process itself: automating the automation. A typical task automation involves several steps—each potentially iterative—that utilize memory components and safeguard data interaction through APIs. Conceptually straightforward, but practically complex, this process demands design precision. Each step needs carefully crafted prompts, proper sequencing, and well-structured memory management to function coherently. Managing interactions with multiple APIs adds further layers of complexity.

Fortunately, the tools and frameworks to handle this complexity already exist. For example, AI systems have been generating their own prompts for some time. A noteworthy advancement is the 2023 paper introducing the MCP protocol, an API that embeds instructions for language models directly within its structure (read it here). Additionally, innovations like YAML-defined architectures in platforms such as AgentForge now enable AI to autonomously build comprehensive workflows from scratch, seamlessly coordinating prompts, managing memory, and orchestrating API calls — all without human coding.

What does this mean for the future? Essentially, we’re approaching the final frontier in automation—a task so complex that once perfected, it could eliminate the need for further automation efforts. For now, the focus should be on harnessing these tools, understanding their capabilities, and allowing AI to take on the last significant tasks of automation. Patience and strategic implementation are key, as this revolution promises to reshape how we work and innovate in the years ahead.