Why Non-Tech Users See AI Patterns Differently

After 2.6 years using AI tools since late 2022, I've identified patterns that technical teams often miss.

I'm not an AI doomer; quite the opposite. I embrace the AI world and find it fascinating. What you're seeing in my essays is curiosity, not catastrophizing.

I come at this from a non-tech background as an autodidact. I've been using AI since late 2022, watching patterns emerge, connecting dots that others might miss because I'm neurodiverse and see discrepancies before the workable structure. My approach is observational. I notice things about user experience, economic signals, and behavioral patterns that the technical crowd sometimes overlooks.

These essays aren't warnings to avoid AI. They're insights about understanding the landscape we're experiencing in real time. I think it's important to see the full picture, both the incredible capabilities and the economic realities shaping future access.

I'm sharing what I've discovered through direct experience and pattern recognition. Sometimes that means pointing out uncomfortable truths about training data, energy costs, or market dynamics. But understanding these forces helps us make better decisions about how we engage with AI tools.

My goal is awareness, not alarm. The more we understand how these systems actually work technically, economically, socially, the better positioned we are to use them effectively.

I'm excited about AI's potential especially as we enter the next phase of reasoning models. I just think we should go in with our attention in all directions.

=Mr. A =Attention Maps - Autodidact AI analyst specializing in behavioral pattern recognition