by ninetyninenine | 2025-10-19 15:52:12+00:00
>For the vast majority of evolutionary history, very similar forces have shaped us and squirrels. The mutations are random, but the selections are not.

Selection only filters for what survives. It doesn’t care how the system gets there. Evolution is blind to mechanism. A squirrel’s brain might work in a way that produces adaptive behavior, but that doesn’t mean its “understanding” of the world is like ours. We don’t even know what understanding is at a mechanistic level. Octopuses, birds, and humans all evolved under the same selective pressures for survival, yet ended up with completely different cognitive architectures. So to say a squirrel is “closer to us” than an LLM is an assumption built on vibes, not on data. We simply don’t know enough about either brains or models to make that kind of structural claim.

>The agent has no intrinsic goals nor ability to develop them.

That’s not accurate. Context itself is a form of learning. Every time an LLM runs, it integrates information, updates its internal state, and adjusts its behavior based on what it’s seen so far. That’s learning, just at a faster timescale and without weight updates. The line between “context” and “training” is blurrier than people realize. If you add memory, reinforcement, or continual fine tuning, it starts building continuity across sessions. Biologically speaking, that’s the same idea as working memory feeding into long term storage. The principle is identical even if the substrate differs. The fact that an LLM can change its behavior based on context already puts it in the domain of adaptive systems.

>There is no evidence to support that a transformer model’s inefficiency is hardware based.

That’s just not true. The energy gap is almost entirely about hardware architecture. A synapse stores and processes information in the same place. A GPU separates those two functions into memory, cache, and compute units, and then burns enormous energy moving data back and forth. The transformer math itself isn’t inherently inefficient; it’s the silicon implementation that’s clumsy. If you built an equivalent network on neuromorphic or memristive hardware, the efficiency difference would shrink by several orders of magnitude. Biology is proof that computation can be compact, low energy, and massively parallel. That’s a materials problem, not a theory problem.

>In most every other aspect of intelligence, notably continuous learning driven by intrinsic goals, LLMs fail.

They don’t “fail.” They’re simply different. LLMs are already rewriting how work gets done across entire industries. Doctors use them to summarize and interpret medical data. Programmers rely on them to generate and review code. Writers, lawyers, and analysts use them daily. If this were a dead end, it wouldn’t be replacing human labor at this scale. Are they perfect? No. But the direction of progress is unmistakable. Each new model closes the reliability gap while expanding capability. If you’re a software engineer and not using AI, you’re already behind, because the productivity multiplier is real.

What we’re seeing isn’t a dead end in intelligence. It’s the first time we’ve built a system that learns, generalizes, and communicates at human scale. That’s not failure; that’s the beginning of something we still don’t fully understand.