Meat + Potatoes (V3)

Why: Semiconductors are the Oil of the 21st Century

In the 20th century, oil determined power. In the 21st century, semiconductors now determine economic and geopolitical leverage. Yet these supply chains remain:

• Opaque beyond Tier 1
• Globally fragmented
• Politically entangled
• Structurally fragile

In contrast, oil markets have evolved sophisticated financial and data infrastructure, primarily:

• Benchmark pricing (Brent, WTI)
• Financial hedging infrastructure
• Insurance and risk transfer markets
• Real-time shipment visibility
• Inventory transparency

The semiconductor industry lacks such financial, insurance, and intelligence infrastructure that would enable companies to adequately manage price and availability risk. This poses the question: if semiconductors are the “new oil,” why can’t we have similar market intelligence and risk management tools?

Our hypothesis is to complete the analogy with oil by building exchange/hedging mechanisms, insurance/risk policy solutions, and real time supply chain intelligence platforms for semiconductors and/or other critical industries in the national interest of the US and its allies.

How: Building a Data Asset

Core to building any of this infrastructure is the compilation of a compounding supply chain intelligence data asset that transforms fragmented industrial data into system-level insight.

To build this data asset from scratch, we must:

1. Provide a solution for a unavoidable and regulated workflow (e.g. compliance) as a narrow wedge to incentivize the initial acquisition of proprietary data in the asset.
2. Leverage network effects to scale the data asset and enable more powerful product features such as simulation and shock response, attracting more customers and growing the data asset in a self-reinforcing cycle.
3. Apply this aggregated data asset towards downstream financial and insurance products. ****

Much of the data asset consists of public trade, regulatory, and geopolitical signals, yet its primary differentiator is an aggregation of proprietary data that can power some of the market infrastructure that has failed to develop around semis. Acquiring such data is a problem of incentives: how can we create a positive cycle, where acquiring more data unlocks additional supply chain intelligence features, attracting more customers and therefore even more data?

Due to the geopolitics, dual-use implications, and regulation of semiconductor manufacturing, firms in the industry must find solutions to mandatory compliance problems such as:

• sanctions screening
• export controls
• forced labor traceability
• supply chain disclosure.

By providing such needs through initial workflows built on top of provided customer proprietary data, we gain visibility into supplier networks and facility metadata. Furthermore, additional mapping of supply chain topography can be generated within the platform itself through individual product features such as supplier onboarding and counterparty discovery.

Yet compliance is only our foot in the door. Increased data scale will enable more advanced features which generate not just box-checking compliance but actual competitive advantage:

• Simulation
• Shock response planning
• Inventory optimization

Fundamental to the data asset is an architecture which balances privacy and network effects:

• Proprietary customer data remains siloed
• Publicly available data is exposed globally to all customers.
• Shared entity-resolution layer enables integration of both.

Anonymized aggregation across these layers enables system-wide risk scores, chokepoint detection, substitution modeling, and scenario simulation. As more firms onboard, the graph deepens, onboarding friction falls, and the intelligence layer compounds.

@Dustin J Ross TODO on downstream finance/insurance application

What: One Wedge (And One Industry) to Rule Them All

The long-term vision of the data asset as a “digital twin” is clear yet immediate attention must be paid to its initial curation. Picking one initial category of mandatory workflows (compliance) in one highly regulated and clustered critical industry (semiconductors, defense industrial base, etc.) will allow the creation of network effects in small subsets of the economy and a base to expand to other industries, workflows, and supplier tiers.

This initial wedge focuses on complex compliance and disclosure requirements — export controls, sanctions, CFIUS exposure, supply chain reporting — for enterprise operators in highly regulated, strategically significant verticals such as semiconductor-adjacent manufacturing. Solving these problems will generate near-term revenue and establish the structured data backbone required for higher-order capabilities. Companies will face the strongest incentives to provide initial swaths of proprietary data because these workflows are mandated by top-down regulation and law.

Our dreams far exceed a compliance SaaS company: we will build the intelligence infrastructure that critical industries lack, simply beginning where incentives and urgency are strongest.

We are asking your perspective on the following:

• Is semiconductor manufacturing a representative and compelling example for initial focus?
• Is compliance a logical initial wedge towards acquiring a proprietary data asset?
• What do you view as the most fruitful next step?
• With whom should we connect to probe these hypotheses?

Semis Nuclear Quantum computing Defense industrial base Robotics Rare earth minerals Batteries Chemicals (super vague)