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Part of the challenge we face with LLM deployment is that knowledge and reasoning ability are not fundamentally text-based. The text-generating capabilities of the language model are taken to wrap a domain of intelligence, but the space beyond the threshold of the chat interface is dark and ill-mapped, bounded in ways not yet fully understood. The current frontier bet — the "world model" as predictor of visual or physical continuations — risks recreating similar integration issues at a different layer. Despite the probable value as they mature in limited domains, reality itself is fundamentally not visual or physical. It's an information space of interrelated concepts.

Author here. This is Part II of "Chat Is Not Where It's At." Part I diagnosed why 95% of enterprise AI pilots fail: the belief that structured representation is over, that model weights can replace schemas and databases. This piece asks: okay, so where is it actually headed? My argument is that the next paradigm looks less like a new kind of AI and more like a new kind of computer — natural language at the interface, structured meaning underneath, the system maintaining a persistent model of its domain that humans and machines navigate together. The six assertions in the middle are intentionally stated without much hedging. Happy to defend any of them.

The computer was supposed to be a cognitive partner. Engelbart called it augmentation. Kay called it a bicycle for the mind. We got productivity apps, app stores, and now chat interfaces. This essay examines the parallel evolution of UI and AI over 40 years - and why the convergence of semantic infrastructure and learning-based systems finally makes cognitive partnership achievable.

AI Investors want returns in 2026, meanwhile product CEOs are quietly saying “not so fast.” Mattel just pulled back on their OpenAI partnership. Investors are demanding returns that CEOs say they can't deliver in 2026. The gap between AI hype and AI utility is becoming impossible to ignore. Here's why that gap exists—and why scaling the model won't close it.

The commercial labs aren't building what they say they are.

AGI is invoked like a destination but kept conveniently vague. Meanwhile, the cognitive partner people actually imagine when they say "AI" requires architecture the labs aren't really pursuing.

Certainly. The emphasis is on alone in the title. Those new designs will come from an increase in the number of focused interaction loops on the subject of rocketry that the llm enhances, will not simply eminate from the model itself.

LLMs are fundamentally inadequate to the tasks we desire to project them into. Not because they're dumb or unimpressive, but question-answering isn't collaboration. A model can remix everything humanity has ever written. It cannot act, observe, and adapt. That's where the utility we're chasing actually lives. No amount of scaling closes the gap.

In "From Words to Worlds: Spatial Intelligence is AI’s Next Frontier" Li states directly "I’m not a philosopher", proceeds to make a philosophical argument that elevates visual perception as basis for evolution of intelligence.