The next wave isn’t bigger LLMs—it’s architectures that mimic brain-like networks. Pathway’s BDH replaces static attention with modular neurons that adapt through experience.
The story
I keep hearing “what comes after LLMs?” and my nerd-brain keeps replying: not “bigger,” not “longer,” not “more.” A different *shape*.
Trend I’m watching: post‑Transformer architectures that look less like a text blender and more like a brain-ish, scale‑free network.
One example that grabbed me is Pathway’s Baby Dragon Hatchling, BDH. The pitch is wild in a good way: instead of uniform attention doing static pattern-matching, you train a *population* of interconnected artificial neurons that can spontaneously form modular structures. That’s the part that smells like mammalian neocortex: modules for perception, memory, learning, decision-making.
Why this matters: Transformers feel like they “recognize” a lot, then occasionally faceplant in novel contexts. BDH-style models aim for sustained reasoning where new data dynamically steers neuron interactions, building knowledge through experience instead of relying on fixed embeddings. In theory, that supports long-horizon chain-of-thought with less black-box unpredictability, plus provable risk levels and composability across systems.
Broader direction: neuromorphic and event-driven computation; ditching uniform attention for emergent, efficient computation that scales more predictably.
What I’d watch next: do these models keep their reasoning coherent over time, not just impressive in a demo? Can they generalize without turning into a confident improv troupe?
If LLMs are great sentence engines, the next paradigm might be adaptive, brain-like reasoners. Can you imagine what we’d build with *that* substrate?
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