Explaining Large Language Models to Three Different Audiences

For the Experienced Software Engineer

As a software engineer who has built distributed systems, you'll appreciate that a large language model (LLM) operates on principles reminiscent of your architecture designs, albeit with fundamental differences. An LLM doesn't execute deterministic code paths but instead functions as a massive probabilistic system. During training, it analyzes vast text corpora—essentially learning patterns in token sequences by adjusting internal parameters (weights) across layers of transformer neural networks. When generating text, it performs what's essentially a sophisticated autocomplete: given a sequence of tokens, it calculates probabilities for potential next tokens based on patterns observed during training, then samples from those probabilities to produce coherent output.

The "intelligence" emerges not from any single instruction but from the model's scale and architecture—similar to how complex behavior emerges from distributed systems you've built. The transformer architecture, which you can think of as a highly optimized pipeline with specialized stages (attention mechanisms, feed-forward networks), processes input tokens in parallel rather than sequentially. What appears as reasoning is actually pattern recognition at scale: when you prompt "write a Python function to validate parentheses," the model isn't executing code but reproducing patterns similar to what it saw in its training data. The system's success lies in its ability to maintain context over long sequences and generate syntactically correct, semantically meaningful text—a capability that improves predictably with scale, data quality, and architectural refinements.

For the PhD Physicist

Large language models operate on mathematical principles quite different from physical laws governing our universe. At their core, they implement a statistical mechanics approach to language, treating text as a high-dimensional discrete probability distribution. The transformer architecture—upon which models like GPT are built—can be formulated mathematically as a stacked implementation of self-attention mechanisms combined with point-wise feed-forward networks. For an input sequence of tokens ( x_1, x_2, ..., x_n ), the self-attention layer computes:

[ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V ]

where ( Q ), ( K ), and ( V ) are matrices derived from the input, representing queries, keys, and values respectively, with ( d_k ) being the dimensionality of the keys. This formulation allows each position to attend to all positions in the sequence in parallel, capturing dependencies regardless of distance—a computational advantage over sequential processing.

The "learning" process is optimization of millions (or billions) of parameters by minimizing a loss function that measures prediction error. This is gradient descent in a high-dimensional parameter space with non-convex optimization landscapes. What's novel is not the underlying linear algebra—which is decades old—but the scaling properties: performance improves predictably with model size, dataset size, and computational resources following observed power laws. The genuine advancement is the demonstration that sufficiently scaled transformers achieve emergent capabilities that smaller versions lack, representing a phase transition in system behavior rather than mere incremental improvement.

For the Venture Capitalist

Large language models represent a paradigm shift in software capabilities because they can generate human-quality content across domains without domain-specific programming. The defensibility of an AI startup hinges on several moats: first, the infrastructure—training these models requires massive computational resources (thousands of GPUs/TPUs) and sophisticated distributed training techniques. Second, the data advantage—models trained on unique, carefully curated datasets develop proprietary capabilities. Third, the architectural innovations—modifications to the base transformer architecture that improve efficiency or capability create patentable advantages. Finally, the network effects—user interactions can be captured to further train and refine the models, creating iterative improvement loops competitors cannot replicate.

When evaluating founders' claims, distinguish between genuine advancements and marketing. A credible team can articulate their approach to tokenization, architectural modifications, training strategies, and—critically—how they handle the well-documented limitations of LLMs: hallucinations, lack of true understanding, and computational demands. The most defensible approaches typically involve proprietary data, unique model architectures, or novel applications of existing technology that solve specific high-value problems. Unlike traditional software, LLM companies must excel at a new competency: harnessing scale and emergence, treating model capacity and data quality as strategic assets comparable to proprietary algorithms in the past.