Our Approach

Forensic Technopolitics

Analysis that starts with physical reality, stress-tests against policy friction, models compound futures, and filters through three decades of technology industry experience.

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.

Four Analytical Lenses

01
Supply Chain Forensics

Following the silicon from sand to server

Tracing the physical reality of technology production through every step of the value chain.

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
  • Understanding the human capital bottlenecks often overlooked in analysis

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 vapor?
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02
Comparative Policy Stress-Testing

What breaks when regulations collide

Analyzing how regulatory frameworks interact, conflict, and create unintended consequences for organizations operating across jurisdictions.

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 post-Schrems II?
  • How do CHIPS Act and EU Chips Act subsidies interact?
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03
Scenario-Based Drift Modeling

Futures that compound, not extrapolate

Moving beyond linear forecasting to model how today's tensions could evolve into fundamentally different operating environments.

What This Means in Practice

  • Three core scenarios: Managed Friction, Bifurcation, and Escalation
  • Modeling cascade effects across supply chains and regulations
  • 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?
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04
Experience-Led Signal Filtering

Thirty years of pattern recognition

Applying three decades of technology industry experience to separate genuine signals from noise, hype, and motivated reasoning.

What This Means in Practice

  • Recognizing patterns from previous technology transitions
  • Identifying overconfident projections and hidden dependencies
  • Understanding organizational and political dynamics behind announcements
  • 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 are they not telling us, and why?
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See the Methodology in Action

Our analysis applies these four lenses to the most pressing questions in AI regulation, semiconductor supply chains, and digital sovereignty.