It's an Alien Intelligence: Stop Comparing It to a Child

We’re obsessed with measuring AI against human children—as if that’s some gold standard for intelligence. It’s like judging a spaceship by how well it floats. Completely absurd.

LLMs aren’t children. They’re not growing up, going to school, or worrying about acne. They aren’t mini-humans waiting to mature. They’re something altogether stranger, an alien intelligence, shaped by statistical patterns instead of evolution, neurons, playground drama, and awkward teenage sex.

Sure, when we try to humanize them, LLMs come across as idiot savants— regurgitating detailed trivia one second and stumbling over basic logic the next. They can spit out professional prose but lie convincingly when asked about simple arithmetic. But measuring them this way is fundamentally misguided. It’s like mocking a dolphin for its inability to ride a bicycle.

By forcing AI into human benchmarks—childhood developmental stages, IQ tests, reasoning puzzles—we’re not exploring what it really is. We’re blinded by our anthropomorphic arrogance. We want mirrors, but these things aren’t mirrors. They’re telescopes pointing toward something unknown.

These aren’t silicon toddlers. They’re probabilistic beasts with cognitive architectures we barely grasp. Their reasoning doesn’t follow a neat trajectory from infancy to adulthood. Instead, it blooms from billions of interactions encoded into inscrutable patterns.

So let’s drop the infantilizing benchmarks. Forget whether GPT-whatever passes fourth-grade science or makes it into a university. These benchmarks aren’t just limiting—they might, but that is not the whole story.

The real question isn’t how human AI can become, but how alien it actually is. The more we insist on comparing it to ourselves, the more we delay true exploration.

It’s time to stop looking for ourselves in the machines. Let’s dare to see what these things actually are.

Consider these examples, though they’re merely scratching the surface:

Chaos Parsing of Garbled Text

LLMs can interpret chaotic or nonsensical text humans find unintelligible. Presented with seemingly meaningless strings like "(alt+eqn={>; {};The,\stock...", models extract coherent statements about stock movements. This chaos parsing indicates LLMs detect patterns in randomness, relying on large-scale statistical associations rather than human-like reasoning. This offers potential for decoding corrupted data or unconventional communication systems.

Implicit Function Reasoning from Coordinate Pairs

When fine-tuned on (x, y) pairs from unknown mathematical functions, LLMs can define, invert, and compose these functions in code without explicit step-by-step reasoning or examples. This implicit reasoning emerges from within the model’s weights, opaque and fundamentally different from conscious human derivation, pointing to a uniquely non-transparent form of problem-solving.

Emotional Simulation for Non-Human Entities

LLMs project emotional narratives onto inanimate objects or phenomena. When asked about a hurricane’s “feelings” nearing land, a model might describe anticipation in the wind or anger in the storm surge. Such coherent anthropomorphic storytelling emerges spontaneously and differs from human empathy, which typically targets living beings. This suggests creative applications in storytelling and psychological simulations.

Numeracy from Literacy

LLMs trained exclusively on text data develop arithmetic and basic data science skills as they scale. Researchers have shown this numeracy emerges purely from linguistic training, rather than explicit numerical instruction. Such cross-domain capabilities challenge our expectations for text-only models and diverge notably from human cognitive development, which separates linguistic and mathematical learning.

Hyper-Rational Behavior in Economic Games

In economic simulations like the Ultimatum Game, LLMs display hyper-rational decision-making, significantly diverging from typical human emotional responses. Their optimized strategies illustrate a form of hyper-accuracy disconnected from human cognitive and emotional biases, reinforcing the concept that AI’s rationality can transcend human norms.

The bottom line? AI isn’t a child. It isn’t growing up. It’s something fundamentally different. The sooner we accept that, the sooner we can truly begin to understand it.