The five physical books worth owning in a field that doesn't have its own book yet — one per layer of the stack.
1. Cloud FinOps, 2nd ed. — Storment & Fuller (O'Reilly, 2023)
The canon of the discipline this field extends. Read it to learn it — and to study the seams, because it barely mentions tokens.
2. AI Engineering — Chip Huyen (O'Reilly, 2025)
The best modern application-layer textbook: model selection, evals, inference optimization. An engineer's book, in an engineer's language.
3. Chip War — Chris Miller (Scribner, 2022)
The silicon layer as narrative: TSMC, fab economics, why compute is geopolitically scarce. The history under every GPU price.
4. Large Language Model-Based Solutions — Shreyas Subramanian (Wiley, 2024)
The only book in print explicitly about LLM cost-effectiveness. It predates the agent era — its gaps are the field's open questions.
5. Hands-On LLM Serving and Optimization — Wang & Hu (O'Reilly, 2026)
Serving systems in book form: batching, quantization, deployment economics. Pair with the weekend-H100 lab.
The sixth book you assemble yourself
Print the canonical papers — Kipply, Chinchilla, the Epoch economics papers, Mooncake, DistServe, FrugalGPT, the FinOps AI working-group papers — into a binder and annotate by hand. In a field this young, the primary literature is the textbook.
For a scored, layered reading list of the wider shelf, see the reading stack in the Engineer's Handbook.