Scaling Llms Won't Get Us To Agi. Here's Why.

Been thinking about whether more training/compute will get us to AGI, or if we need a fundamentally different architecture. I'm convinced it's the latter.

Current transformer architecture is a glorified pattern matcher. It was literally created to translate languages. We've scaled it up, added RLHF, made it chat — but at its core, it's still doing statistical pattern matching over sequences.

When Ramanujan came up with his formulas, when Gödel proved incompleteness, when Cantor invented set theory — these weren't in any training distribution. There was no historical precedent to pattern-match against. These required *seeing structure that didn't exist yet*.

LLMs can interpolate brilliantly within their training data. They cannot extrapolate to genuinely novel structures. That's the difference between pattern matching and understanding.

If I ask an LLM for business ideas, it'll suggest things that match my statistical profile — I'm a tech professional, so it'll say SaaS, consulting, AI tools. Plumbing? Probably not on the list.

But I'm a general-purpose agent. I can decide tomorrow to learn plumbing and start a plumbing business. The LLM sees the shadow of who I've been. I have access to the space of who I could become.

LLMs reason over P(outcome | observable profile). Humans reason over possibility space, not probability space. Completely different.

We need architectures that can:

- Build causal models of the world (not just statistical associations)

- Learn from minimal examples (a kid learns "dog" from 3 examples, not millions)

- Reason about novel structures that don't exist in training data

- Model agency — the ability of entities to change themselves

Scaling transformers won't get us there. It's like building a really good horse and hoping it becomes a car.

Curious what others think. Am I missing something, or is the current hype around scaling fundamentally misguided?