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| Natura: | Recurso digital |
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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.19455543 |
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Sommario:
- <p>Automated fact-verification of the claim: "<em>Training and running today's frontier AI models consumes more electricity than entire small countries.</em>"</p> <p><strong>Verdict: PROVED</strong></p> <h3>Key Findings</h3> <ul> <li>A typical AI-focused data centre (which the International Energy Agency (IEA) defines as a facility performing both AI training and inference) consumes <strong>1,050 GWh of electricity per year</strong> — equivalent to 100,000 US households.</li> <li>Nauru, the smallest UN member state with documented electricity data (~11,000 people), consumes <strong>37.89 GWh per year</strong> — the entire nation.</li> <li>A single typical AI-focused data centre therefore consumes <strong>27.7 times more electricity annually than all of Nauru</strong> (1,050 GWh vs. 37.89 GWh).</li> <li>Independent cross-check: a peer-reviewed 2025 study (Harding & Moreno-Cruz, <em>Environmental Research Letters</em>) found US AI electricity alone is comparable to Iceland's total electricity (~19,580 GWh) — <strong>517 times more than Nauru</strong>.</li> </ul> <h3>Files</h3> <ul> <li><strong>proof.py</strong> — Re-runnable Python verification script</li> <li><strong>proof.md</strong> — Structured proof report</li> <li><strong>proof_audit.md</strong> — Full verification audit trail</li> <li><strong>proof_narrative.md</strong> — Plain-language summary</li> <li><strong>proof.json</strong> — Machine-readable structured data</li> </ul> <p>Generated by <a href="https://github.com/yaniv-golan/proof-engine">Proof Engine</a> v0.10.0.</p>