Project TBD: Meat + Potatoes (V4)

Why: Semiconductors are the Oil of the 21st Century

Oil determined power in the 20th century. In the 21st century, semiconductors will 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. 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 financial and analytical infrastructure — price indexes, hedging tools, risk/insurance products — for semiconductors and/or other critical industries to US national interest.

How: Building a Data Asset

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

Our proposed roadmap is as follows…

1. Identify initial wedge for data acquisition: incentivize initial acquisition of proprietary data by providing solution for unavoidable and regulated workflow as a narrow wedge
2. Scale data asset: 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. Leverage data asset to build financial and analytical infrastructure products: 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?

What: One Wedge (And One Industry) to Get the Ball Rolling

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.

Once sufficient supply chain data is aggregated, the platform shifts from visibility to risk pricing and market infrastructure.

The proprietary data asset enables four scalable extensions:

1. Hedging & Benchmark Layer: create capacity utilization indices and lead-time volatility benchmarks that underpin parametric shortage hedges and forward allocation contracts; begin with price discovery; expand toward clearing as authority compounds.
2. Insurance & Structured Risk Products: model yield risk, concentration exposure, chokepoint correlation, and export-control shocks to structure chip continuity, output, and political risk insurance; originate and syndicate policies (SageSure-style), capturing underwriting economics without holding balance-sheet risk.
3. Real-Time Intelligence & Marketplace: live node stress indicators, alternative sourcing simulation, and anonymized excess inventory matching; this deepens engagement and embeds our indices into operational decisions.
4. Trading & Capacity Intermediation (long-term): leverage benchmark authority and system-wide visibility to structure capacity reservations and intermediate liquidity during shocks.

The model builds from compliance abstraction → intelligence authority → risk pricing → market infrastructure. The objective is not SaaS revenue, but becoming the industry’s central layer for price discovery and risk transfer.

Next Steps: Seeking Your Guidance

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, nuclear components, robotics, chemicals, defense industrial base, rare earth minerals, batteries, 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.

Our proposed 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.

We are asking your perspective on the following:

• Is semiconductor manufacturing a compelling industry 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?

Our dreams far exceed a compliance SaaS company: we will begin where incentives and urgency are strongest and then build into industry’s central layer for price discovery and risk transfer.