Explain Like I'm a Specific Expert

1. Senior backend engineer
   Think of the model as a 200-layer, purely functional, eventually-consistent key/value store whose keys are every token prefix it has ever seen and whose values are probability vectors over the next token.
   At “compile time” (pre-training) it sees ~10²¹ bytes of text. Each layer is a residual stream: a 4096-dimensional float vector that acts like a message bus. Attention heads are tiny MapReduce jobs that run over the bus, move information between positions, and write back deltas. Feed-forward blocks are wide, sparse MLPs that memorize/idempotently cache local patterns (think Bloom-filter on steroids). The whole thing is trained with nothing more exotic than autoregressive cross-entropy—minimize log P(next_token | prefix) with AdamW and weight decay. After a few × 10²³ FLOPs the cache ends up containing enough world knowledge, source-code idioms and human dialogue priors that a single forward pass (≈ 200 parallel matmuls on GPU tensor-cores) can autocomplete a function, explain a bug, or role-play a support agent. No symbolic reasoning module, no hand-coded grammar: just a giant, heavily compressed, queryable frequency table that generalizes because the same vector operations that predict “;” after “printf(” also predict “)” after “f(x”. The surprise is that the statistical regularities of text alone force the network to internalize a fair amount of semantics, causality and even debugging strategy in order to keep the loss going down.

Generation is a vanilla client-server loop: you POST a prompt, the stack converts it to a list of token IDs, runs the 200-layer DAG once per new token, samples from the softmax (top-p or beam), appends the token, and streams the delta back. Temperature is literally a scaling knob on the logits—no magic, just controlled randomness. Caching (KV-cache) makes autoregressive inference O(1) per token after the first forward pass, so 100-token answers are ~100 serial matrix multiplies. The only distributed-systems headaches are the usual ones: weight sharding across A100s, pipeline bubbles, NCCL timeouts, and making sure your CUDA kernels stay compute-bound at 80 GB/s memory bandwidth. If you can keep a 1 TB model resident on 8 GPUs and pipeline requests, you get ~50 ms per token—good enough for a production REST endpoint. Everything else (RLHF, safety filters, tool use) is post-processing on top of this substrate.

2. Theoretical physicist
   The learnable object is a sequence-to-sequence probability measure Pθ(xt+1|x≤t) parameterized by a deep transformer. Parameters θ ∈ ℝ^D with D ≈ 10¹¹ are optimized by minimizing the KL divergence between the empirical text distribution and the model, i.e. the cross-entropy loss L(θ)=−⟨log Pθ⟩. Gradient descent on L(θ) is equivalent to a high-dimensional, damped Hamiltonian flow with noise (AdamW ≈ momentum + friction). The surprising empirical fact is that the loss landscape possesses a large, nearly convex basin whose bottom reaches ⟨L⟩ ≈ 1.2 bits per byte, well below the Shannon entropy of English (~0.7–0.8 bpp). In that basin the network implements an implicit variational approximation to the true conditional distribution over natural language strings.

What is “novel” is not the linear algebra—matrix multiplication has been around since the 19th century—but the scaling law L(N) ∝ N^{−α} with α ≈ 0.76 for transformers. It implies that generalization error falls predictably with model size N, dataset size D, and compute C, so a 10× larger model requires only ~5× more data and ~10× more FLOPs to cut the error in half. This power-law is reproducible across nine orders of magnitude and has no analogue in earlier kernel or graphical-model approaches. The associated emergent quantities—analogous to critical exponents—are not put in by hand; they are measured. They imply that language, viewed as a stochastic process, possesses long-range correlations that can be captured by a hierarchical operator product expansion remarkably similar to those found in 2-D conformal field theories. Thus the hype is confined to marketing; the scaling law itself is an experimental fact that any serious statistical-mechanics treatment must explain.

3. Venture capitalist
   The core asset is a pre-trained transformer—an 800 GB lump of floating-point weights that cost $20–100 M to produce and cannot be legally copied if the startup keeps the final checkpoint private. That spend buys you a commodity API wrapper around an irreproducible model: the dataset mix, cleaning recipe, and RLHF annotations are not in the open literature, so a rival would need to replicate the entire compute pipeline plus human-feedback collection to match quality. Marginal inference cost is ~$0.002 per 1 k tokens on A100s; gross margin therefore scales with volume and GPU contract leverage, not with head-count. Watch the burn rate: if they’re fine-tuning on <10 B tokens they’re probably just calling OpenAI; if they’re renting 4 k H100s for six months they may really be training a 70 B param model.

Defensibility questions to ask founders: (1) Do you own the golden dataset? Proprietary docs, support logs, or regulatory filings are hard to crawl and give vertical-specific accuracy. (2) Is post-training aligned to your vertical? Generic base models commoditize fast; RLHF plus retrieval that cuts hallucination from 20 % to <3 % in legal or medical Q&A is a moat. (3) Can you productize the feedback loop? Every customer interaction should automatically become labeled data, tightening the model the way ad clicks tighten ad-targeting. If they can’t articulate a data fly-wheel, the “AI” is just a wrapper and Google or OpenAI will launch the same feature next quarter.