Dario Amodei suggests that the massive data requirement for AI pre-training is not a flaw but a different paradigm. It is analogous to the long process of human evolution setting up our brain's priors, not just an individual's lifetime of learning, which explains its sample inefficiency.

Even with vast training data, current AI models are far less sample-efficient than humans. This limits their ability to adapt and learn new skills on the fly. They resemble a perpetual new hire who can access information but lacks the deep, instinctual learning that comes from experience and weight updates.

Karpathy claims that despite their ability to pass advanced exams, LLMs cognitively resemble "savant kids." They possess vast, perfect memory and can produce impressive outputs, but they lack the deeper understanding and cognitive maturity to create their own culture or truly grasp what they are doing. They are not yet adult minds.

Language models work by identifying subtle, implicit patterns in human language that even linguists cannot fully articulate. Their success broadens our definition of "knowledge" to include systems that can embody and use information without the explicit, symbolic understanding that humans traditionally require.

Despite AI's impressive capabilities, it lags significantly behind humans in learning efficiency. Today's models are trained on amounts of data that would take a person tens of thousands of years to consume, while a human child achieves language fluency in under ten years, indicating a fundamental algorithmic difference.

A critical weakness of current AI models is their inefficient learning process. They require exponentially more experience—sometimes 100,000 times more data than a human encounters in a lifetime—to acquire their skills. This highlights a key difference from human cognition and a major hurdle for developing more advanced, human-like AI.

The "memory" feature in today's LLMs is a convenience that saves users from re-pasting context. It is far from human memory, which abstracts concepts and builds pattern recognition. The true unlock will be when AI develops intuitive judgment from past "experiences" and data, a much longer-term challenge.

The Fetus GPT experiment reveals that while its model struggles with just 15MB of text, a human child learns language and complex concepts from a similarly small dataset. This highlights the incredible data and energy efficiency of the human brain compared to large language models.

AI models use simple, mathematically clean loss functions. The human brain's superior learning efficiency might stem from evolution hard-coding numerous, complex, and context-specific loss functions that activate at different developmental stages, creating a sophisticated learning curriculum.