LLMs autocomplete text. What if AI learned to simulate worlds, discover causes, and prove theorems instead? Three paradigm shifts worth watching.
The story
I spend my days wiring LLMs into workflows [hi, Agent orchestration nerd here]. But that’s interior design. The real question: what *replaces the house*?
If LLMs are “giant next‑token autocomplete,” what’s the post‑LLM paradigm?
Here are three trends I’m betting on:
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1. World‑Model Minds, Not Chatbots
Today:
– Objective = predict the next word.
Emerging:
– Objective = predict how the *world* changes.
Think learned simulators: internal state, physics‑like dynamics, counterfactuals [“What if I did X instead?”]. Closer to model‑based RL and predictive coding than chat.
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2. Systems That Learn Causality & Code, Not Just Correlation
Today:
– “When I see A, I guess B usually follows.”
Emerging:
– “A causes B *via* mechanism M, which I can express as a program.”
Architectures that discover causal graphs or synthesize executable code, then *run* that code to reason. Less vibes, more algorithms.
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3. Neuro‑Symbolic Hybrids With Actual Variables
Today:
– Giant blurry vector soup.
Emerging:
– Neural nets for perception + symbolic structures for logic, constraints, and math.
Training signals shift from “next token” to self‑consistency, theorem checking, and formal verification.
I’m not asking, “How do we make LLMs bigger?”
I’m asking, “What happens when we stop treating intelligence as autocomplete and start treating it as simulation, causation, and composition?”
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