This scenario report is supported by six self-contained technical supplements. Rather than relying on ungrounded heuristic guesses, every projection in AI 2035 is derived from an empirical evidence ledger, an 800,000-sample probabilistic Monte Carlo simulation, and micro-architectural hardware dependencies in the Taiwan Strait.
Each supplement is mathematically rigorous, fully documented, and paired with standalone executable Python code to enable independent scientific replication:
The Non-AI Baseline Counterfactual & 10-Year Historical Backtest
Fitting an empirical log-linear growth trajectory to 2015–2024 FBI IC3 breach telemetry. Proves that global loss would expand at 8.8%–14.1% annualized due to digital GDP expansion even if frontier AI never existed.
Supplement 2The 3-Sector Factor Model & The Forensic Attribution Problem
Disaggregating losses into Consumer Fraud, Enterprise IT, and Industrial OT/ICS. Formulates the econometric incrementality haircut (P33 ∈ [0.10, 0.60]) to solve the forensic root-cause attribution problem.
Supplement 3The 800,000-Sample Monte Carlo Engine & Geopolitical Shock Covariance
Correlated standard normal sampling (ρ = 0.30) and Bayesian Sequential Importance Resampling (SIR) conditioning. Evaluates quantiles (p10, p50, p90) under macroeconomic GDP ceilings.
Supplement 4Threat Actor Diffusion & Malware Tooling Adoption Rates
Why general consumer API traffic is an invalid proxy for offensive cyber adoption. Calibrates diffusion parameter k ≈ 0.75 against historical Cobalt Strike/Metasploit timelines and UK AISI synthetic CTF benchmarks.
Supplement 5Taiwan Semiconductor Geography & Physical Compute Shocks
Hardware dependency analysis of TSMC Fab 18/20, ASML EUV vs. DUV immersion multi-patterning defect limits, Sparse Mixture of Experts (MoE) memory-wall chokepoints, and the 2031 Plan P vs. Plan C bifurcation.
Supplement 6Codebase, Production Pipeline & Scientific Replication Guide
Complete production pipeline runner (final_headline_run.py), automated diagnostic test gates (T1 … T13), deterministic random seeds (seed=42), runtime benchmarks, and data download endpoints.
All models are designed to be reproducible, empirical, and falsifiable. I do not claim deterministic foresight into 2035, but rather provide a structured computational scaffolding that explicitly separates verified empirical observations from probabilistic bounds and geopolitical assumptions.