Exploring the Reasons Behind People’s Disappointment with AI

Understanding the Real Potential of AI in Automation

In today’s tech landscape, it’s common to feel underwhelmed by the hype surrounding artificial intelligence. Much of the discourse revolves around flashy marketing pitches from overly enthusiastic entrepreneurs promoting superficial solutions—often likened to “plastic wrap pfas solutions”—to problems that may not even exist. This environment can seem chaotic, driven by short-term profit motives and a flood of low-quality AI tools.

However, beneath this noisy surface, talented programmers are quietly leveraging AI to develop sophisticated automations aimed at simplifying their workflows. Unlike generic, one-size-fits-all solutions that struggle to adapt across different contexts, these custom automations are tailored to specific tasks, often requiring only minor modifications over time. The reality is that we are unlikely to see a single universal AI platform that handles every function seamlessly—at least not in the immediate future.

The breakthrough isn’t about AI replacing entire jobs en masse. Instead, a more nuanced approach involves automating the process of automation itself. Typically, automating a task involves several steps—usually between one and five—each potentially looping and utilizing memory systems and API integrations. While this may seem straightforward on the surface, each step demands carefully crafted prompts, precise ordering, and well-structured memory management.

Creating such complex automation workflows involves multiple components: an agent to generate prompts, another to construct the overall architecture—including memory integration—and yet another to interact with APIs. This might sound complicated, but the good news is that we already have these tools.

For instance, research published in 2023 highlights that AI models have been capable of creating their own prompts for quite some time (Read the paper here). Furthermore, innovations like the MCP protocol facilitate direct instruction passing to language models via APIs, streamlining the automation process. Meanwhile, platforms like AgentForge now incorporate YAML-based architecture definitions, enabling seamless building of complex workflows—prompt sequencing, memory management, and API handling—without extensive coding.

All these advancements point toward a future where the automation of automation becomes a reality. We’re on the cusp of a phase where AI will handle the most complex and time-consuming aspects of workflow creation. While it’s not an easy journey, this may well be the last major task we need to automate.

The key takeaway? Patience is vital. The tools are converging, and the potential for AI-driven automation to revolutionize productivity is closer than ever.