AI doesn't store data like a traditional database; it learns patterns and relationships, effectively compressing vast amounts of repetitive information. This is why a model trained on the entire internet can fit on a USB stick—it captures the essence and variations of concepts, not every single instance.

Karpathy identifies a key missing piece for continual learning in AI: an equivalent to sleep. Humans seem to use sleep to distill the day's experiences (their "context window") into the compressed weights of the brain. LLMs lack this distillation phase, forcing them to restart from a fixed state in every new session.

Modern LLMs use a simple form of reinforcement learning that directly rewards successful outcomes. This contrasts with more sophisticated methods, like those in AlphaGo or the brain, which use "value functions" to estimate long-term consequences. It's a mystery why the simpler approach is so effective.

The "Omniscience" accuracy benchmark, which measures pure factual knowledge, tracks more closely with a model's total parameters than any other metric. This suggests embedded knowledge is a direct function of model size, distinct from reasoning abilities developed via training techniques.

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.

Google's Titans architecture for LLMs mimics human memory by applying Claude Shannon's information theory. It scans vast data streams and identifies "surprise"—statistically unexpected or rare information relative to its training data. This novel data is then prioritized for long-term memory, preventing clutter from irrelevant information.

Artificial Analysis found that a model's ability to recall facts is a strong function of its total size, even for sparse Mixture-of-Experts (MoE) models. This suggests that the vast number of "inactive" parameters in MoE architectures contribute significantly to the model's overall knowledge base, not just the active ones per token.

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.

Unlike humans, whose poor memory forces them to generalize and find patterns, LLMs are incredibly good at memorization. Karpathy argues this is a flaw. It distracts them with recalling specific training documents instead of focusing on the underlying, generalizable algorithms of thought, hindering true understanding.