Trapped in Sophisticated Immaturity: The Fundamental Limit of LLMs


In my view, there are two fundamental methods of learning in the world. The first is the direct acquisition of information through firsthand experience. The second is having others explain to you what concepts are, what they’re used for, and what the world looks like.

For us humans, we possess both capabilities. We can learn through direct experiential engagement—observing raw phenomena, forming hypotheses, conducting experiments, and building understanding through unmediated interaction with reality. We can drop objects to understand gravity, feel emotions directly rather than just reading about them, and discover patterns through our own exploration and trial-and-error processes.

Simultaneously, we learn through mediated instruction—receiving knowledge that others have already processed, analyzed, and conceptualized for us. We read textbooks, listen to explanations, and absorb frameworks that represent humanity’s accumulated wisdom and interpretations of the world.

However, as AI researchers already known, for current LLMs, only the second pathway exists. LLMs operate entirely within the realm of pre-processed, human-interpreted knowledge. They are trained on humanity’s documented understanding—our explanations, theories, and conceptual frameworks—but they fundamentally lack the capacity for genuine first-person exploration or discovery.

This limitation manifests in a deeper problem: the tendency toward casual speculation about any topic—what we observe as “hallucination”—combined with a complete reliance on externally provided information, prevents them from achieving true intellectual maturity.

Genuine maturity emerges from the hard-earned wisdom of direct consequences, failed hypotheses, and the humbling experience of being wrong about reality. It comes from the discipline of uncertainty—knowing when you don’t know, understanding the weight of your words, and developing the restraint that comes from having been genuinely wrong about important things. True wisdom is forged through the sobering experience of making predictions that fail, offering advice that proves harmful, or holding theories that crumble under scrutiny.

This represents a crucial asymmetry. While LLMs can generate confident-sounding explanations about virtually anything, this very capability may be precisely what prevents growth toward wisdom. Without the corrective force of direct experience, without stakes that make accuracy matter, without the possibility of genuine failure and learning from it, they remain trapped in a kind of sophisticated immaturity—like well-read individuals who have never left the library to test their knowledge against the world.

The grounding that comes from direct world engagement—the ability to validate, contradict, or nuance received knowledge through personal experience—remains beyond their reach. They are, in essence, articulate and informed recipients of human interpretation rather than independent explorers of reality.

Therefore, when I hear claims that new models will be capable of independent scientific research and exploration—and thus independent discovery of new scientific knowledge—within the next year or two, I am left with deep skepticism. After all, this core limitation—the inability to conduct their own experiments, make novel observations, or experience phenomena directly to form independent understanding—is a formidable barrier in their current architecture.

Of course, the exponential development trend of AI also guarantees situations that I, as a limited human, simply can’t predict. But for now, the gap between a well-read “student” and an independent “scientist” who can explore the unknown remains immense.

By the way, for LLMs, programming doesn’t require embodied intelligence, or rather, “programming” itself is inherently “embodied” for LLMs. This is why LLMs show the best training and application results when it comes to coding. The same also holds true for the creation of literature, images, and videos!


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