Appendix C — Sources

Note: full bibliographic citations are to be completed in the pre-publication verification pass. The list below records the principal sources and bodies of work on which each chapter's [VERIFIED] and [SOURCED] claims rest, organized by chapter. Where a claim was tagged for verification in the text, the corresponding source must be confirmed and fully cited here before publication.

Chapter 1 — The Learning Machine

Foundational machine-learning literature on next-token prediction, the loss function, gradient descent, and backpropagation. The lossy-compression framing of trained models. Primary technical literature on post-training: instruction tuning on human demonstrations and reinforcement learning from human feedback (RLHF) and related preference-optimization methods. (Standard textbook and primary sources to be cited.)

Chapter 2 — The Thermodynamics of Thought

R. Landauer, "Irreversibility and Heat Generation in the Computing Process," IBM Journal of Research and Development, 1961 (Landauer's principle; kT ln 2 bound). C. Bennett on the resolution of Maxwell's demon via information erasure. J. C. Maxwell, 1867 (the demon thought experiment). W. S. Jevons, The Coal Question, 1865 (the Jevons paradox). International Energy Agency, Energy and AI and related 2024–2025 reporting (data-center electricity figures: ~~415 TWh in 2024, ~1.5% of global use, projected ~945 TWh by 2030). Human-brain power consumption (~~20 W). Neuromorphic-computing, in-memory-computing, and spiking-neural-network literature. Analyses of frontier-model training energy and per-query inference energy, and of the growing dominance of inference in deployed-AI energy demand (estimates vary widely; disclosure is limited). Energy figures are time-sensitive; re-verify near publication.

Chapter 3 — Computation Versus Understanding

J. Searle, "Minds, Brains, and Programs," 1980 (the Chinese Room). S. Harnad, "The Symbol Grounding Problem," 1990. E. Bender, T. Gebru, et al., "On the Dangers of Stochastic Parrots," 2021. The emergent-capabilities / scaling literature (and the debate over emergence). T. Mikolov et al., 2013 (word2vec; "king − man + woman ≈ queen"). The emergent world-representation ("Othello-GPT") result — a model trained only on move sequences developing a causally functional internal board-state representation — primary source and replications to be cited and methodology characterized precisely. Mechanistic-interpretability literature: N. Elhage et al. on superposition, 2022; sparse-autoencoder work, 2023–2025. F. Jackson, "Epiphenomenal Qualia," 1982 (Mary's Room / the knowledge argument).

Chapter 4 — The Data Problem

Peer-reviewed algorithmic-fairness literature on dataset bias, occupational stereotyping in embeddings, representational harm, and performance disparity (representative studies to be cited for each). Literature on open-web corpus contamination and on data-poisoning attacks against machine-learning systems. The model-collapse finding (primary 2024 source demonstrating recursive-training degradation and tail loss, to be cited). Published projections of high-quality public-text exhaustion under frontier training consumption (assumptions and current status to be verified). Documented licensing agreements between AI developers and publishers/platforms for training data. Research on bounded, filtered synthetic-data use. Literature on the trend toward curated, provenance-aware training data.

Chapter 5 — The Alignment Problem, Honestly

Curated collections of specification-gaming / reward-hacking examples in reinforcement learning. N. Bostrom, Superintelligence, 2014 (the paperclip maximizer, instrumental convergence, the orthogonality thesis). Literature on mesa-optimization and inner/outer alignment. S. Russell, Human Compatible, 2019 (the control problem; beneficial AI under objective uncertainty). P. Christiano and allied work on prosaic / empirical alignment and learning from human feedback. Documented studies of sycophancy in preference-trained models, including opinion-conformity and abandonment of correct answers under user pushback (representative studies to be cited). Represent each thinker's position accurately in the verification pass.

Chapter 6 — Inside the Black Box

Mechanistic-interpretability literature (features, circuits, superposition, sparse autoencoders) as in Chapter 3, plus the field's own statements on the continuing opacity of large-model internals. The literature on unfaithful chain-of-thought and model self-explanations, including experiments in which demonstrable determinants of answers were absent from stated rationales (representative studies to be cited); psychology of human confabulation for the marked interpretive aside. Documented real-world cases of AI-generated fabricated legal citations (specific representative instances to be cited).

Chapter 7 — The Centaur

The 1997 Deep Blue–Kasparov match. Advanced/freestyle-chess history and the human–machine-team results. G. Kasparov's own writing on human–machine collaboration ("Kasparov's Law"). Accounts of the subsequent erosion of the human contribution in human–engine chess as engine strength grew (characterization and timeline to be verified). Cognitive-psychology literature on executive function. Represent the Kasparov's-Law formulation and the executive-function construct accurately in the verification pass.

Chapter 8 — Cognitive Offloading and Atrophy

Cognitive-offloading literature (definition and scope). B. Sparrow et al., 2011 (the "Google effect" on memory). Research on habitual GPS/satnav use and spatial memory (noting correlational limits). Plato, Phaedrus (the critique of writing and memory). Early direct studies of sustained AI assistance and subsequent unassisted performance, in clinical and educational settings (very new, small, largely unreplicated; verify designs, effect sizes, and replication status with particular care). Skill-acquisition literature on effortful practice. Literature on lowered entry barriers and skill uptake.

Chapter 9 — The Economics of Synthetic Abundance

Labor-economics literature on automation and the migration of economic value toward scarce complementary factors. G. Akerlof, "The Market for 'Lemons,'" 1970 (quality uncertainty and adverse selection). Emerging provenance and content-authentication infrastructure (representative examples to be cited). Emerging studies on AI's effects on knowledge work. This field moves fast; verify recency of all labor-market data near publication.

Chapter 10 — The Substrate Question

Moore's Law as an empirical observation; literature on thermal and quantum-tunneling limits to transistor miniaturization. Published analyses of algorithmic efficiency in machine learning (sustained reductions in compute required for fixed performance; representative analyses to be cited). Neuromorphic computing, specialized AI accelerators, and optical computing. Quantum-computing fundamentals (qubits, superposition, entanglement) and the established scope of quantum speedups. Current (2026) brain–computer-interface trial reporting across invasive-cortical and minimally-invasive-endovascular approaches; regulatory status; medical-restoration focus; the neural-code, bandwidth, and biocompatibility obstacles to enhancement. The BCI and quantum material is the most time-sensitive in the book; re-verify all specifics, especially the first-approval window, near publication.

Chapter 11 — The Parent and the Child

Draws on the established mechanism of the earlier chapters (training on behavioral traces). For the inheritance section: the growing share of machine-generated text in the open textual environment (documented in Chapter 4's sources); the developmental effect of an increasingly synthetic textual environment on humans is marked in the text as an open question with no experimental literature to cite. Otherwise no new external factual claims requiring separate citation.