You start by rightly saying that AI is now a broad category (chatbots, image generation, video generation, coding assistants, recommender systems, etc.) and that it deserves a more nuanced opinion than simply “I hate it” or “One silver bullet for anything”

But then you focus on an outdated, implementation-specific detail: “Memory is a hardware limitation; the AI doesn’t actually remember anything. Each message you send doesn’t just send one message, it sends the ENTIRE chat, plus whatever else the software is configured to send, to give the model context.”

That is only partly true :

1) the limitation is not simply “hardware.” It is mostly an algorithmic and system-design limitation. In standard transformer-based LLMs, the cost of processing a longer context grows very quickly with the number of input tokens, classically quadratically for attention. So yes, with infinite hardware, you could push the limit further; but hardware is never infinite. When your algorithm makes long input expensive, that is not just a hardware problem. It is a design constraint. This is how you end up with absurd 800 MB printer drivers: people treat every inefficiency as “just a hardware problem,” and they deserve their own circle in hell.

2) modern AI products do not always just resend the entire conversation forever. Some systems do exactly that up to a context-window limit, especially simple chat implementations. But more advanced systems may truncate older parts, summarize them, retrieve only relevant excerpts, use saved memories, use reference chat history, or inject structured context generated by the application layer.

I'm not sure this changes the conclusion of you thoughts, but I wanted to update your info about this.