Explain Like I'm a Specific Expert

1. The Experienced Software Engineer

Think of training an LLM not as "teaching" it, but as extreme lossy compression. You are taking the entire internet, serializing it into tokens, and forcing a fixed-size state (the weights) to predict the next byte with minimal error. To minimize the loss function across billions of parameters, the model cannot simply memorize; it is forced to build internal abstractions to generalize patterns. The "intelligence" you're skeptical about is an emergent property similar to what you see in distributed systems: complex global behavior arising from simple, localized optimization rules. The model builds a compressed world model because that is the most efficient way to solve the compression problem.

Generation is essentially a stateless function call where the input context is the request payload and the weights are the binary. It predicts the next token probabilistically, but because the weights encode semantic relationships, the probability distribution collapses around coherent concepts. Your skepticism about "next word prediction" is valid if you view it as a simple lookup, but at this scale, accurately predicting the next word requires modeling syntax, logic, and facts. It isn't reasoning in the human sense; it's that the shortest path to accurate compression is to simulate reasoning.

2. The PhD Physicist

Fundamentally, this is high-dimensional statistical mechanics applied to symbolic sequences. The model maps tokens to vectors in a latent space (embeddings), where semantic relationships are encoded as geometric relationships within a manifold. Training is the minimization of a cross-entropy loss function via stochastic gradient descent, navigating a non-convex loss landscape to find a basin of attraction that generalizes. The "learning" is simply the adjustment of weight matrices to align the model's probability distribution with the empirical distribution of the training data. You are correct that the underlying operations are standard linear algebra; there is no new mathematics here.

The novelty lies in the architecture (Transformer) and the scale, not the algebra. The attention mechanism allows for $O(N^2)$ connectivity, enabling long-range dependencies without the vanishing gradient problems of RNNs. While you're right to be wary of hype, the emergent properties arise from phase transitions observed in scaling laws: as parameters and data increase, the model undergoes sharp transitions in capability. It is not magic, but rather the observation of critical phenomena in a high-dimensional parameter space where quantity effectively transitions into quality.

3. The Venture Capitalist

View training as a massive CAPEX event that converts compute and data into static assets (weights). The technology relies on predictable scaling laws: performance is a function of compute, data, and model size. However, the base model is rapidly becoming a commodity with shrinking margins. The founders' claims about "proprietary models" are only credible if they have exclusive access to high-quality data or specialized compute clusters, as the architecture itself is open source. The real cost driver is inference; unit economics depend on optimizing token generation speed versus accuracy, and margins will be squeezed by hyperscalers.

Defensibility does not come from the model architecture, which leaks quickly, but from the data flywheel and integration. A defensible moat requires a feedback loop where user interactions generate proprietary data to fine-tune the model (RLHF), creating a compounding advantage. When evaluating the startup, look for workflow embedding: are they wrapping an API, or are they owning the data layer where the model operates? The technology is real, but the business value lies in vertical integration and data ownership, not the underlying "intelligence."