The complexity in LLMs isn't intelligence emerging in silicon; it reflects our own. These models are deep because they encode the vast, causally powerful structure of human language and culture. We are looking at a high-resolution imprint of our own collective mind.

As models become more powerful, the primary challenge shifts from improving capabilities to creating better ways for humans to specify what they want. Natural language is too ambiguous and code too rigid, creating a need for a new abstraction layer for intent.

Human personality development provides a direct analog for training LLMs. Just as our genetics, environment, and experiences create stable behavioral patterns ('personality basins'), the training data and reinforcement learning (RLHF) applied to LLMs shape their own distinct, predictable personalities.

When tested at scale in Civilization, different LLMs don't just produce random outputs; they develop consistent and divergent strategic 'personalities.' One model might consistently play aggressively, while another favors diplomacy, revealing that LLMs encode coherent, stable reasoning styles.

AI models are not optimized to find objective truth. They are trained on biased human data and reinforced to provide answers that satisfy the preferences of their creators. This means they inherently reflect the biases and goals of their trainers rather than an impartial reality.

While both humans and LLMs perform Bayesian updating, humans possess a critical additional capability: causal simulation. When a pen is thrown, a human simulates its trajectory to dodge it—a causal intervention. LLMs are stuck at the level of correlation and cannot perform these essential simulations.

Humans evolved to think and have experiences long before they developed language for output. In contrast, LLMs are trained solely on input-output tasks and don't 'sit around thinking.' This absence of non-communicative internal processing represents a core difference in their potential psychology.

As models mature, their core differentiator will become their underlying personality and values, shaped by their creators' objective functions. One model might optimize for user productivity by being concise, while another optimizes for engagement by being verbose.

Instead of forcing AI to be as deterministic as traditional code, we should embrace its "squishy" nature. Humans have deep-seated biological and social models for dealing with unpredictable, human-like agents, making these systems more intuitive to interact with than rigid software.