Coffee: Dan/Bliss/Dustin

Attendees: Dustin J Ross, Bliss Perry, Dan Iancu Date: January 21, 2026 Type: Advisor Meeting

Summary

Professor at Stanford GSB, Romanian (Ploiești - former oil refinery region)
Quantum computing background: undergrad thesis (2004), advisor Michel Devoret (Nobel winner, now at Google AI)
Operations research focus: linear programming, integer programming, decision modeling
Supply chain research: diamond-shaped supply chains, network shocks, risk management

Four key opportunity areas identified:
1. Predictive models for extractive industries (oil, rare earths, minerals)
2. Universal supplier database - “who supplies what, where, at what grade”
3. Data aggregation from dispersed sources (websites, government records, trade associations)
4. Deep supply chain mapping (identifying diamond-shaped dependencies)
Current tools surprisingly primitive: mostly Excel sheets and supplier interviews
Companies struggle to map beyond tier-1 suppliers, even Toyota had issues post-2011 Tohoku earthquake

No comprehensive database of global suppliers exists
Traceability becoming critical due to EU regulations (CSDT compliance) and US provenance requirements
Companies need contingency planning for geopolitical shocks (coups, tariffs, export controls)
Scoring systems needed for risk assessment (geopolitical, geographic, economic factors)
Both defensive (resilience) and offensive (expansion) applications valuable

AI agents could automate data collection via web crawling, translation, voice calls
Graph theory principles apply across different supply chain types despite industry variations
Data fusion challenge: combining open source intel + proprietary supplier mapping + company-specific data
Incentive structures needed for companies to share anonymized supply chain data

Main bottlenecks: cooling systems (−273°F), decoherence, error correction
Limited to specific problem classes (cryptography, parallelizable combinatorial problems)
Won’t replace classical computing for most applications
Hardware challenges vary by technology (semiconducting qubits vs adiabatic)

Start with data aggregation as foundation for multiple products
Focus on one industry initially (batteries, semiconductors suggested as high-impact)
Timing critical - need to hit when problems are “hot” (tariffs, supply shocks)
Build proprietary data asset first, then layer analytics and marketplace features

Dan offered introductions to quantum computing experts:
- Chad Rigetti (Rigetti Computing)
- Michel Devoret (Google AI, Nobel winner)
Need to identify supply chain practitioners at major companies (Toyota, TSMC mentioned)
Research existing players and competitive landscape
Explore Stanford connections for industry contacts
Consider focusing on battery supply chains given EV trend momentum