Forensic Technopolitics
Three forensic domains stress-tested by two analytical methods. A 3 × 2 matrix, applied to every published piece, with a visible audit so the reader can see which moves we ran and which we didn't.
Why “Forensic”?
Most technology analysis works backwards from conclusions. Analysts start with a narrative— “AI will transform everything,” “Europe is falling behind,” “supply chains are resilient”— and find evidence to support it.
Forensic analysis works forwards from evidence. We start with physical facts: where materials come from, how factories actually operate, what regulations actually say, how decisions actually get made. Then we follow the evidence to conclusions, even uncomfortable ones.
This approach takes longer. It produces fewer confident predictions. But it catches the things that matter—the single points of failure, the impossible compliance positions, the scenarios everyone assumes away.
Three Forensic Domains
The three layers of technology power that get analysed: the physical supply chain, the regulatory environment, and the flow of people and capability between them.
The physical layer — silicon from sand to server
Tracing the physical reality of technology production through every step of the value chain. Where the chokepoints are, who controls them, what substitution actually costs.
What This Means in Practice
- Mapping the 300+ steps from raw materials to finished semiconductors
- Identifying single points of failure and geographic concentration risks
- Tracking capacity investments, yields, and timeline slippages
- Tracing how disruptions propagate downstream into dependent industries
Questions We Ask
- “Where does the neon for chip lithography actually come from?”
- “What happens when a single Taiwanese fab goes offline?”
- “Which European investments are on track, and which are vapour?”
The regulatory layer — what breaks when rules collide
Running a single technological advancement through two operating systems — the US Innovation-First model and the EU Rights-Driven model — to find where compliance with one becomes non-compliance with the other.
What This Means in Practice
- Side-by-side comparison of US and EU regulatory approaches
- Identifying compliance conflicts and impossible positions
- Tracking enforcement patterns and regulatory drift
- Anticipating second-order effects of policy changes
Questions We Ask
- “Can you comply with both the AI Act and US export controls?”
- “What does "adequate" data protection mean after Schrems II?”
- “How do CHIPS Act and EU Chips Act subsidies interact?”
The human layer — where the people actually move
Tracking the flow of senior engineers, fab technicians, AI researchers, and regulatory experts — and the second-order effects of where they land versus where they leave. The most underserved domain in existing technology-policy commentary.
What This Means in Practice
- Mapping skill density by region, not by national headline
- Tracking senior-engineer flow between fabs, hyperscalers, and policy units
- Identifying capability accumulation in unexpected geographies
- Connecting macro industrial policy to individual professional reality
Questions We Ask
- “Where are TSMC's senior process engineers being recruited for the German fab?”
- “Which European universities produce AI safety researchers, and where do they go?”
- “What is the half-life of the regulatory expertise concentrated in Brussels?”
Two Analytical Methods
Both methods get applied across all three domains. Scenario modelling gives the reader a structured menu of futures. The Long-Memory Filter compares the present to the last thirty years of industrial cycles so that hype and structural change can be told apart.
Futures that compound, not extrapolate
Three scenarios per event — Low / Medium / High friction — using Value at Stake methodology. The output is not a prediction; it is a stress test, designed to give the reader a structured menu of preparation moves rather than a guess.
What This Means in Practice
- Three scenarios per event: Low, Medium, High friction
- Value at Stake quantification per scenario
- Identifying trigger events and decision points
- Stress-testing strategies against multiple futures
Questions We Ask
- “What if US-China decoupling accelerates beyond semiconductors?”
- “How does European strategic autonomy reshape supplier relationships?”
- “What triggers the shift from friction to bifurcation?”
The 30-year cycle benchmark
Pattern-matching the current event against the last thirty years of industrial cycles. The 1986 US-Japan Semiconductor Agreement, the 1990s offshoring wave, the 2000s globalisation consensus. The lens that separates Silicon Hype from Stone Truth.
What This Means in Practice
- Pattern-matching against the last thirty years of industrial cycles
- Identifying overconfident projections and hidden dependencies
- Separating temporary noise from structural change
- Calibrating urgency: what matters now vs. what can wait
Questions We Ask
- “Is this genuinely new, or a cycle repeating?”
- “Who benefits from this narrative?”
- “What does the 1986 Semiconductor Agreement tell us about Pax Silica?”
The 3 × 2 Matrix
Three domains × two methods = six analytical moves. Every Stone Briefing applies between two and four cells. Every Deep Dive is eligible for all six, but the Methodology Audit only claims the cells the body visibly applies.
| Scenario Modelling | Long-Memory Filter | |
|---|---|---|
| Supply Chain | Three-tier futures for the physical chokepoints | 30-year cycle pattern-match on the physical layer |
| Policy | Low / Medium / High friction across regulatory regimes | 30-year regulatory precedent and enforcement drift |
| Talent | Capability-flow scenarios across the human layer | 30-year skill-density and migration cycles |
Hedging is not weakness. It is the calibration.