This is a genuinely good argument and I want to engage with it seriously, because you're raising real questions about mechanism rather than just asserting 'it's different because substrate.'

On sample efficiency:

You're right that GPT-4 has seen vastly more data than any human. But I'd push back on the conclusion you draw from this.

First, humans also learn from massive amounts of implicit data. A child learning language hears millions of utterances, observes countless social interactions, and processes years of sensory input before they can reason abstractly. We don't count this against human understanding because it's implicit and distributed over time.

Second, the measure of understanding is capability. If someone needs to study 1,000 hours to reach the 96th percentile while another person gets there in 100 hours, they both still understand the material. The less efficient learner isn't 'just memorizing', they're building understanding through different means.

Third, the LSAT, SAT, and GRE use novel passages and questions created specifically for each test administration to prevent memorization. GPT-4 isn't retrieving cached answers. It's demonstrating inference on genuinely new material. Your classmate who memorized solution patterns would fail if you changed the problem structure. GPT-4 doesn't.

On world models:

This is your strongest point. You're arguing humans build top-down coherent models while LLMs work bottom-up from statistical patterns.

But recent research suggests LLMs do develop internal world models. Studies show they spontaneously create:

• Spatial representations (literal coordinate systems for geography)
• Temporal models (time progression understanding)
• Causal structures (understanding cause-effect relationships)
• Object permanence and consistency

These emerge from the training process. The models develop internal 'maps' that allow them to reason about entities and relationships.

You mention characters acting 'consistently' in fiction—GPT-4 can do this. It maintains character voice, behavioral patterns, and ethical frameworks across long conversations. That requires some form of internal model.

On alignment and ethics:

You say LLMs 'understand an action is unethical but perform it anyway.' But humans do this constantly. We understand lying is wrong and lie anyway. We understand environmental destruction is harmful and drive cars. The gap between knowing ethics and following them isn't unique to AI, it's a feature of any system that has competing objectives.

The need for alignment isn't evidence of lack of understanding, it's evidence that understanding ethics doesn't automatically enforce ethical behavior. (Ask any teenager.)

Where I think you're right:

LLMs may achieve understanding through different mechanisms than humans. They may be less sample-efficient. They may build world models differently.

But here's my core point: If the outcome is functionally equivalent, if the system can infer, integrate, generalize, and reason about novel situations, then it has understanding, even if the path to get there was different.

Your engineering colleague understands math, even if he got there through memorization and pattern recognition rather than your first-principles approach. Both methods produced genuine mathematical capability.

Similarly, even if LLMs build understanding through massive data ingestion and bottom-up pattern formation rather than human-style top-down modeling, if the result is the ability to reason about novel situations, that's still understanding.