> For the complete documentation index, see [llms.txt](https://osintelligence-llc.gitbook.io/osintelligence/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://osintelligence-llc.gitbook.io/osintelligence/part-v-the-frontier/22-sovereign-sustainability/references-and-provenance.md). # References & provenance ### References **Power, grid, carbon:** IEA (2024–2025). *Energy and AI: Energy Demand from AI.* · Nature (2025). "Data centres will use twice as much energy by 2030 – driven by AI." [d41586-025-01113-z](https://www.nature.com/articles/d41586-025-01113-z) · *Electricity Demand and Grid Impacts of AI Data Centers* (2025). [arXiv:2509.07218](https://arxiv.org/abs/2509.07218) · IEEE Spectrum (2024). "Will Energy-Hungry AI Create a Baseload Power Demand Boom?" [DOI 10.1109/MSPEC.2024.10630483](https://doi.org/10.1109/MSPEC.2024.10630483) · de Vries, A. (2023). "The growing energy footprint of artificial intelligence." *Joule* 7(10):2191–2194 · Patterson, D., et al. (2021). "Carbon Emissions and Large Neural Network Training." [arXiv:2104.10350](https://arxiv.org/abs/2104.10350) · Gupta, U., et al. (2022). "Chasing carbon: The elusive environmental footprint of computing." *IEEE Micro* 42(4):37–44. **Water:** Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). "Making AI Less 'Thirsty': Uncovering and Addressing the Secret Water Footprint of AI Models." [arXiv:2304.03271](https://arxiv.org/abs/2304.03271); republished in *Communications of the ACM* (2024). **Jevons + rebound:** Jevons, W. S. (1865). *The Coal Question.* Macmillan · Sorrell, S. (2009). "Jevons' Paradox revisited: The evidence for backfire from improved energy efficiency." *Energy Policy* 37(4):1456–1469 · Greening, L. A., Greene, D. L., & Difiglio, C. (2000). "Energy efficiency and consumption – the rebound effect – a survey." *Energy Policy* 28(6-7):389–401. **Edge + SLM + scaling:** Chen, J., & Ran, X. (2019). "Deep Learning With Edge Computing: A Review." *Proc. IEEE* 107(8):1655–1674 · Murshed, M. G. S., et al. (2021). "Machine Learning at the Network Edge: A Survey." *ACM Computing Surveys* 54(8) · Abdin, M., et al. (2024). "Phi-3 Technical Report." [arXiv:2404.14219](https://arxiv.org/abs/2404.14219) · Jiang, A. Q., et al. (2023). "Mistral 7B." [arXiv:2310.06825](https://arxiv.org/abs/2310.06825) · Gemma Team (2024). "Gemma: Open Models Based on Gemini Research and Technology." [arXiv:2403.08295](https://arxiv.org/abs/2403.08295) · Dennard, R. H., et al. (1974). "Design of Ion-Implanted MOSFETs with Very Small Physical Dimensions." *IEEE JSSC* 9(5):256–268 · Esmaeilzadeh, H., et al. (2011). "Dark Silicon and the End of Multicore Scaling." *ISCA 2011* · Patterson, D. A., & Hennessy, J. L. (2017). *Computer Architecture: A Quantitative Approach* (6th ed.) §1.9 · Kaplan, J., et al. (2020). "Scaling Laws for Neural Language Models." [arXiv:2001.08361](https://arxiv.org/abs/2001.08361) · Hoffmann, J., et al. (2022). "Training Compute-Optimal Large Language Models." [arXiv:2203.15556](https://arxiv.org/abs/2203.15556). **Systems + method:** Senge, P. M. (1990). *The Fifth Discipline.* Doubleday · Sterman, J. D. (2000). *Business Dynamics.* McGraw-Hill · Forrester, J. W. (1961). *Industrial Dynamics.* MIT Press · Argyris, C., & Schön, D. A. (1978). *Organizational Learning.* Addison-Wesley · Popper, K. (1959). *The Logic of Scientific Discovery.* · Wicherts, J. M., et al. (2016). "Degrees of Freedom in Planning, Running, Analysing, and Reporting Psychological Studies." [*Front. Psychol.* 7:1832](https://doi.org/10.3389/fpsyg.2016.01832) · Wang, X., et al. (2023). "Self-Consistency Improves Chain of Thought Reasoning in Language Models." *ICLR 2023* · Andreessen Horowitz / Casado & Lauten (2019). "The Empty Promise of Data Moats." · NVIDIA (2025). *Data Flywheel* corporate documentation. **In-series companions:** [*Sovereign Optimization Flywheel*](/osintelligence/part-ii-the-discipline/8-sovereign-optimization-flywheel.md) (the five-axis parent) · [*Sovereign Domain Pruning*](/osintelligence/part-iv-the-evidence-what-worked/16-sovereign-domain-pruning.md) (Chapter 16; the 219-hour Phase-B envelope + L₃) · [*Sovereign Imatrix Calibration*](/osintelligence/part-iv-the-evidence-what-worked/15-sovereign-imatrix-calibration.md) (Chapter 15; L₁) · [*Sovereign CTI-NER*](/osintelligence/part-iv-the-evidence-what-worked/14-sovereign-cti-ner.md) + [*Corpus-Sovereign Self-Distillation*](/osintelligence/part-iv-the-evidence-what-worked/18-corpus-sovereign-self-distillation.md) (Chapter 18; the L₄ chain) · [*Sixteen Practices*](/osintelligence/part-ii-the-discipline/5-sixteen-practices.md) (Chapter 5; §1.5 Pair + §5.6 recursive moat) · [*The Sovereign Triad*](/osintelligence/part-i-the-architecture/1-the-sovereign-triad.md) · [*The External Sentinel*](/osintelligence/part-i-the-architecture/2-the-external-sentinel.md) · [*Sovereign Safety Architecture*](/osintelligence/part-i-the-architecture/3-sovereign-safety-architecture.md) (the four-axis envelope this paper extends at industrial scale) · *Multi-Token Prediction on Consumer Blackwell* (the L₅ empirical-anchor companion, in the source repository) · [*Sovereign Big-Model Compression*](/osintelligence/part-iv-the-evidence-what-worked/17-sovereign-big-model-compression.md) (the strongest single datum for the democratization claim: a 122B mixture-of-experts served for one operator on one 12 GB card, at higher fidelity and equal-or-better speed than the quantization-only alternative it replaced). ### AI-assistance disclosure Large language models were used as research tools in the preparation of this chapter: Claude Opus 4.7 (Anthropic). The model versions and roles named above keep the provenance of this chapter auditable. No AI system is listed as an author or credited as a contributor, in line with COPE and ICMJE guidance: an AI system cannot take responsibility for the work, cannot assert competing interests, and cannot enter a licence agreement. The author verified every claim in this chapter against the sealed artifacts and is solely accountable for it. **Citation (preferred):** Kistner, J. (2026). *Sovereign Optimization Compounds at Industrial Scale: Power, Water, and the Democratization of Local Inference*, version 1.0.0. OSINTelligence LLC research whitepaper. Cited in-series by title. **License:** CC BY 4.0 (text). Code and data artifacts MIT per repository license. **Corresponding author:** Jamey Kistner, , OSINTelligence LLC (Columbus, OH). *** *The Sovereign Stack · Sovereign Sustainability · Chapter 22 · Part V · v1.0.0 · License CC BY 4.0 · © Jamey Kistner, OSINTelligence LLC*