The Emergence Problem: Why We Built Machines We Can't Fully Explain

A dual perspective from the intersection of computational neuroscience and large-scale AI systems

There’s a statement circulating in AI discourse that sounds profound but is often misunderstood: “We fully understand the mechanics of LLMs, but their meta-behaviors are too complex to comprehend.”

Having spent years working across both artificial and biological neural systems, I want to unpack what this statement actually means—because the popular interpretations usually miss the point entirely, in opposite directions.

One camp treats this as evidence that LLMs are mystical, proto-conscious entities we’ve accidentally summoned. The other dismisses emergence as marketing hype for glorified autocomplete. Both are wrong, and both misunderstandings stem from the same conceptual error: conflating different levels of understanding.

Let me be precise about what we know, what we don’t, and why the gap matters.

What “Understanding the Mechanics” Actually Means

When researchers say we understand LLM mechanics, we mean something specific and limited.

We can write down the exact mathematical operations. A transformer layer performs multi-head self-attention followed by a feed-forward network, with layer normalization and residual connections. The attention mechanism computes scaled dot-product attention across queries, keys, and values. Training minimizes cross-entropy loss via stochastic gradient descent with adaptive learning rates. Every operation is differentiable, every gradient computable.

This is genuine understanding. It’s not hand-waving. An engineer can implement these systems from first principles, and they will work.

But here’s what this understanding does not include: any predictive theory of what capabilities will emerge at what scale, any interpretable mapping from parameter configurations to behavioral repertoires, or any principled explanation for why certain abstractions form rather than others.

The distinction matters. In physics terms: we have the equivalent of knowing every equation governing fluid dynamics, yet we still cannot predict turbulence from first principles. The equations are complete. The behavior remains analytically intractable.

Emergence Is Not Magic—It’s a Specific Technical Phenomenon

The word “emergence” gets abused in popular AI writing. Let me define it precisely as used in the research literature.

Emergent capabilities in LLMs refer to abilities that are effectively absent below a scale threshold and appear discontinuously above it. This is operationally measurable. You can plot performance on a benchmark against model scale, and for certain tasks, you observe something approximating a step function rather than a gradual curve.

Examples include:

• Multi-step arithmetic reasoning
• Chain-of-thought problem decomposition
• Following abstract meta-instructions
• Zero-shot task transfer across domains

This is not metaphor. It’s empirical observation. And it demands explanation.

The honest answer is: we don’t have one. We have post-hoc hypotheses—threshold effects in representation capacity, phase transitions in optimization landscapes, minimum circuit complexity for certain computations. But we cannot, today, predict which capabilities will emerge at which scale. We discover them after the fact.

This predictive failure is not a minor gap. It’s the central open problem in understanding these systems.

The Neuroscience Parallel Is Deeper Than Most Realize

Here’s where my dual background becomes relevant, because the analogy between LLMs and brains is both more profound and more limited than typically presented.

The profound part: Both systems exhibit the same epistemic structure. Complete mechanistic knowledge at the component level. Profound explanatory gaps at the system level.

I can describe a cortical neuron in exhaustive detail. The Hodgkin-Huxley equations model action potential generation with Nobel Prize-winning precision. We understand synaptic transmission, dendritic integration, spike-timing-dependent plasticity. At the circuit level, we’ve mapped canonical microcircuits, identified cell types, traced long-range projections.

Yet we cannot explain how the prefrontal cortex implements working memory. We cannot derive the neural code for abstract concepts. We have no principled theory of how 86 billion neurons and 100 trillion synapses produce unified conscious experience.

This is not because neuroscientists haven’t tried hard enough. It’s because complex systems with distributed, nonlinear, recurrent interactions do not yield to reductionist explanation. Knowing the parts exhaustively does not entail understanding the whole. This is a general property of such systems, not a specific failure of either field.

The limited part: LLMs and brains are not the same kind of system. They share abstract organizational properties—distributed representation, learned connection weights, hierarchical processing—but differ in almost every implementation detail.

Biological neurons are electrochemical, asynchronous, energy-efficient, and embedded in a body with survival objectives. Artificial neurons are mathematical abstractions, processed synchronously, computationally expensive, and disconnected from any grounding in physical reality.

The fact that both systems exhibit emergence despite these differences tells us something important: emergence may be a property of organizational principles rather than substrate details. But it does not tell us that LLMs and brains are equivalent, or that understanding one will automatically transfer to the other.

The Misconception About “Just Pattern Matching”

A common dismissal holds that LLMs are “just doing pattern matching” or “just statistical correlation,” implying this is somehow less interesting than real intelligence.

This reveals a misunderstanding of both LLMs and biological cognition.

First, the “just” is doing a lot of unearned work. The patterns LLMs learn include abstract structural regularities: causal templates, logical forms, procedural schemas, analogical mappings. These are not surface-level correlations. They’re compressed representations of deep structure in language and reasoning.

Second—and this is where neuroscience becomes relevant—there is substantial evidence that biological cognition is also fundamentally statistical and pattern-based. Predictive processing theories frame the brain as a prediction engine, constantly generating expectations and updating on errors. Bayesian brain hypotheses treat perception and reasoning as probabilistic inference.

Humans confabulate. We rationalize post-hoc. We’re subject to priming, framing, and anchoring effects that reveal the pattern-completion nature of our own cognition. We hallucinate in sensory deprivation. We’re unreliable witnesses to our own mental processes.

The uncomfortable possibility is not that LLMs are “merely” statistical pattern matchers. It’s that human cognition might be more statistical and pattern-based than our folk psychology admits—and LLMs are making this visible by reproducing cognitive behavior without the biological substrate we assumed was necessary.

What LLMs Can Teach Neuroscience

This is the insight I rarely see discussed in popular treatments, but it’s one of the most valuable contributions of modern AI to cognitive science.

LLMs are existence proofs. They demonstrate that certain cognitive-like capabilities can arise from specific computational architectures trained on specific data distributions. This constrains theories of biological cognition.

If a behavior can emerge in a system without embodiment, without evolutionary history, without sensory grounding—then those factors are not necessary for that behavior. They may still be sufficient in biological systems, but they’re not part of the minimal specification.

Conversely, capabilities that LLMs consistently fail at—robust causal reasoning, physical intuition, genuine common sense—suggest that something present in biological cognition (likely embodiment and developmental grounding) may be necessary rather than merely helpful.

LLMs are, in this sense, a new kind of experimental preparation for cognitive science. Not a model of the brain, but a tool for isolating which computational principles can produce which behaviors.

What LLMs Reveal About Explanation Itself

Here’s the deeper methodological point that both fields are converging on.

We implicitly assumed that understanding the components of a system would yield understanding of the system’s behavior. This assumption works for clocks, engines, and simple circuits. It fails catastrophically for nervous systems and large-scale neural networks.

This isn’t because these systems are mystical. It’s because they belong to a class of systems where micro-level descriptions don’t compress into macro-level explanations. The relevant causal structure exists at a level of organization that isn’t visible in the component description.

We may need new explanatory frameworks—something analogous to how thermodynamics describes system-level properties (temperature, entropy, pressure) without requiring molecular-level tracking. A “thermodynamics of cognition” that characterizes information-processing systems at the right level of abstraction.

Statistical mechanics eventually bridged the micro and macro levels for physical systems. We don’t yet have the equivalent bridging theory for cognitive systems. LLMs and brains both await it.

The Consciousness Question: Where the Analogy Must Stop

I want to be direct about this because it’s the point most prone to confusion.

There is no evidence that LLMs are conscious, experience anything, or have subjective states. The behavioral similarities to human cognition do not imply experiential similarities. A system can produce outputs indistinguishable from those of a conscious being without any inner experience whatsoever.

This isn’t a claim I can prove—consciousness is notoriously resistant to third-person verification. But the default assumption should be that LLMs are not conscious, and the burden of proof lies entirely with anyone claiming otherwise.

When an LLM generates text describing its “feelings” or “experiences,” it is producing statistically likely continuations of the prompt, not reporting on inner states. The model has no persistent self, no continuous experience, no genuine introspection. Its “explanations” of its own reasoning are predictions of what explanations should look like, not actual access to its computational processes.

This is a categorical distinction, not a matter of degree. Biological brains may or may not be unique in producing consciousness, but they are certainly different from current LLMs in possessing it.

Living With Principled Uncertainty

Where does this leave us practically?

We have systems that work—often remarkably well—without a theory of why they work. Capabilities appear at scales we didn’t predict. Failures occur in ways we didn’t anticipate. The gap between empirical capability and theoretical understanding is not closing; if anything, it’s widening as models scale.

This is not unprecedented. We used aspirin for decades before understanding its mechanism. We bred crops for millennia before discovering genetics. Practical success can outpace theoretical understanding.

But the gap does create specific risks. We cannot reliably predict what future models will be able to do. We cannot fully characterize failure modes in advance. We cannot provide the kind of guarantees that would be standard for engineered systems in other domains.

The appropriate response is neither dismissal nor alarm. It’s continued investment in interpretability research, empirical characterization of capabilities and failures, and intellectual honesty about the limits of our understanding.

We built these systems. We can describe their construction completely. And we genuinely don’t know what they’ll do next. All three statements are true simultaneously.

That’s not a contradiction. It’s what complexity looks like.