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.

The long-standing trend of centralizing all data into a single warehouse is incompatible with the speed of AI. Large-scale data migrations are too slow. The future architecture will involve AI models operating closer to data sources for faster, decentralized operation.

Future AI expressivity won't come from adding more identical layers, but from 'nesting' levels with different update frequencies. This allows some parts of the system to adapt rapidly (like working memory) while others preserve core knowledge (long-term memory), mimicking human cognition.

Solving key AI weaknesses like continual learning or robust reasoning isn't just a matter of bigger models or more data. Shane Legg argues it requires fundamental algorithmic and architectural changes, such as building new processes for integrating information over time, akin to an episodic memory.

Instead of relying on opaque model weights, continual learning is more reliably achieved by having AI build explicit, external 'world models' like knowledge graphs. This approach makes the model's understanding inspectable and correctable by humans, enabling more robust causal analysis.

The idea of separating "fact learning" from "skill learning" is a false dichotomy. Models need a base of internalized facts to reason effectively. The key is developing intelligence to compress what's important and discard what isn't, much like lossy human memory.

Dell's CTO identifies a new architectural component: the "knowledge layer" (vector DBs, knowledge graphs). Unlike traditional data architectures, this layer should be placed near the dynamic AI compute (e.g., on an edge device) rather than the static primary data, as it's perpetually hot and used in real-time.

Research shows it's possible to distinguish and remove model weights used for memorizing facts versus those for general reasoning. Surprisingly, pruning these memorization weights can improve a model's performance on some reasoning tasks, suggesting a path toward creating more efficient, focused AI reasoners.

New AI models are moving away from brute-force computation. By selectively focusing on relevant data, much like the human brain indexes memories, they can achieve massive performance gains and cost reductions, overcoming a major bottleneck in current architectures.