by Taha Iqbal
Independent research on AI alignment, institutional risk, and long-horizon model behavior. Tracking capability trajectories, governance bottlenecks, and the empirical frontier of safety research.
Research focus
AI Alignment
Forecast horizon
2025–2030
Primary domains
Governance · Interpretability
Platform status
Active · Ongoing
AI Forecasting
Timeline & capability research
Governance & Policy
Institutional & regulatory frameworks
Mechanistic Analysis
Technical foundations for oversight
Research Notes
Deep engagement with core literature
Systems Architecture
Quantitative & AI system design
Strategic Threat Analysis
Why compute thresholds alone will fail — and what a complete three-layer governance architecture requires before the efficiency frontier makes them obsolete
Why intelligence community methodology transforms benchmark data into strategic warning
Identifying Intervention Windows Before Threshold Capabilities Arrive
Featured Analysis
Built TQC — Three-Component Quantitative Trading System
C++20 engine with lock-free MPSC ring buffer; HMM 3-state regime classifier (Bear/Sideways/Bull); GARCH(1,1) vol-scaled sizing; kdb+/q analytics layer with walk-forward validation.
AI Safety Research — Forecasting & Governance
Deep engagement with the AI forecasting and governance literature across three tracks: timeline forecasting, institutional frameworks, and mechanistic interpretability as a foundation for oversight.
