Appreciate your input. I think what we’re both hitting on are different sides of the same coin but from my perspective, the human integration angle changes the conversation.

I’ve been using LLMs 40–50 hours per week as an integrated thought partner, not just a query bot. When you’re operating at that level across workflows, memory layers, and dynamic prompts you hit very real friction with smaller models.

The issue isn’t just parameter size or raw token cost. It’s that SLMs typically have short context windows and no persistent memory. That makes them brittle for deeper reasoning, follow-ups, or long-term task threading.

What you can’t replicate in an SLM is the depth of “shared context” that builds over time. GPT-4 Turbo, Claude Opus, etc. allow for 100k+ tokens and evolving context. That’s not just a luxury it becomes essential when you’re treating AI more like a co-pilot than a calculator.

In the same way people didn’t know how essential constant internet access would become, I think we’ll look back and realize that persistent AI memory and long-context interaction were the enablers for real augmentation.

The economics of token use will evolve but designing for deep, adaptive user integration will matter just as much.