We’re training autocomplete engines and calling it intelligence. The next breakthrough probably won’t come from bigger LLMs – it’ll abandon pattern matching entirely.
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I’ve been thinking about what comes after LLMs, and I keep landing on something uncomfortable: we might need to abandon the idea that intelligence emerges from pattern matching at scale.
Here’s what bothers me about current approaches – we’re essentially training very sophisticated autocomplete engines. They predict what comes next based on massive amounts of text they’ve seen. And sure, that creates something that *looks* like reasoning. But it’s not reasoning. It’s statistical correlation dressed up in a tuxedo.
The next paradigm probably won’t be about bigger models or better data. It’ll be about fundamentally different computational principles.
I’m watching a few threads that feel promising:
Neuromorphic computing – chips that actually mimic how biological neurons spike and connect, not just simulate them in software. This isn’t about copying the brain. It’s about energy-efficient, continuous learning systems that adapt in real-time without retraining.
Quantum-inspired architectures – not necessarily quantum computers, but systems that use superposition-like states to explore multiple solution paths simultaneously. Think less “predicting the next token” and more “holding contradictions until context collapses them.”
Symbolic-subsymbolic hybrids – combining the pattern recognition of neural nets with actual symbolic reasoning engines. Not bolting tools onto LLMs. I mean architectures where symbolic logic and neural processing are fundamentally intertwined from the ground up.
The breakthrough might not even come from AI labs. It could emerge from neuroscience, phys
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