Explain Like I'm a Specific Expert

1. For the Experienced Software Engineer

You’re familiar with distributed systems, APIs, and the complexity of building scalable software, so let’s frame LLMs in terms you’ll recognize. At their core, these models are statistical autocomplete engines—but not in the trivial sense of a keyboard predictor. Instead, they’re trained on vast amounts of text to learn the probabilistic relationships between words, phrases, and even abstract concepts. Think of it like a highly optimized key-value store where the "keys" are sequences of tokens (words or subwords) and the "values" are predicted next tokens, weighted by likelihood. The magic isn’t in any single prediction but in the composition of predictions: each output becomes part of the next input, allowing the model to generate coherent, multi-step reasoning or creative text.

The skepticism about "just predicting the next word" is understandable, but the key insight is that language is a compression mechanism for knowledge. By learning to predict text, the model implicitly learns to model relationships, analogies, and even causal structures embedded in human communication. It’s not "intelligence" in the human sense, but it’s a surprisingly effective way to approximate useful behavior. The distributed nature of training (across thousands of GPUs) and inference (often via optimized transformer architectures) means these models are as much a feat of systems engineering as they are of machine learning. The real challenge—and opportunity—is in building reliable, production-grade systems around them, much like you’d architect a fault-tolerant microservice.

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2. For the PhD Physicist

From a mathematical perspective, large language models are high-dimensional, non-linear function approximators trained to minimize a loss function over a sequence prediction task. The architecture is a transformer, which uses self-attention—a mechanism that computes weighted interactions between all pairs of tokens in a sequence—to capture long-range dependencies. The novelty isn’t in the linear algebra itself (which is indeed well-understood) but in the scale and emergent properties that arise from training on massive datasets. The attention mechanism can be viewed as a dynamic, data-dependent kernel that learns to weigh the relevance of different parts of the input, somewhat analogous to how a Green’s function encodes propagation in a physical system.

What’s genuinely novel is the observation that, beyond a certain scale, these models exhibit behaviors not explicitly programmed into them—such as few-shot learning, rudimentary reasoning, and even the ability to perform simple arithmetic or code generation. This isn’t "intelligence" in the sense of a first-principles model of cognition, but rather a demonstration that sufficiently large neural networks can approximate a wide range of functions when trained on diverse enough data. The hype often obscures the fact that these models are still fundamentally interpolative; they don’t "understand" in the human sense, but they’ve become remarkably good at pattern matching in high-dimensional spaces. The open questions—such as whether these models can generalize beyond their training distribution or exhibit true compositionality—are where the interesting physics-like problems lie.

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3. For the Venture Capitalist

When evaluating an AI startup, the key questions are: What’s the moat? How defensible is the technology? And are the founders’ claims credible? Large language models are expensive to train (often tens of millions of dollars per run) and require significant infrastructure, which creates a natural barrier to entry. However, the real defensibility comes from three areas: (1) proprietary data or fine-tuning techniques, (2) domain-specific adaptations that generalize poorly to competitors, and (3) network effects from user-generated feedback loops (e.g., reinforcement learning from human preferences).

The technology itself is not a moat—transformers are open-source, and the big players (OpenAI, Google, Meta) have already commoditized the base models. The differentiation lies in how the model is deployed: Is the startup building vertical-specific applications (e.g., legal, medical, or financial LLMs)? Do they have a unique data flywheel? Are they solving a real pain point with measurable ROI? Founders who claim their model is "better" purely because of architecture tweaks are likely overpromising; the real value is in the system around the model—integration, reliability, and user experience. The most credible teams will have a clear go-to-market strategy, not just a slightly improved benchmark score. The winners in this space won’t just be the best model builders; they’ll be the best product builders.