Project TBD Charter Document
J Bliss Perry + Dustin J Ross
March 2026
Mission Statement
We are building a multi-tenant, proprietary digital twin of the semiconductor industry.
Our vision is to create a structured, real-time ontology of the global semiconductor supply chain - mapping firms, products, financial flows, and logistics relationships - and to monetize that asset through an intelligence platform, financial products, and a recommendation marketplace.
We use compliance as our initial wedge: solving unavoidable, regulated workflows (supplier onboarding, export controls, mandatory disclosures) to acquire the proprietary data that powers the digital twin. The compliance product gets us in the door. The digital twin is the business.
How We Got Here
Project TBD began with a simple observation: if semiconductors are the new oil, why don’t we have similar market intelligence and risk management tools? Oil has price indexes, hedging instruments, insurance products, and deep supply chain visibility. Semiconductors - despite underpinning the entire modern economy - have none of this.
Over several months of research, expert conversations, and iterative strategy sessions, we pulled on a series of threads that converged into our current thesis:
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Self-reinforcing data asset: Inspired by the Eric Schmidt model of compounding data moats - every transaction processed, every compliance check run, every supplier onboarded adds to a proprietary dataset that makes the platform more valuable and harder to replicate.
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Digital twin as the core: Drawing from the Palantir ontology model - a structured, legible data model of physical relationships between firms, products, and financial flows. Not just raw data, but an organized representation that both human analysts and LLMs can interact with effectively.
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Al-native from day one: Companies built for the AI era will outcompete incumbents. Our product is designed to be invoked by agents, interacted with through agentic workflows, and sold to a world increasingly run by mathematical models - not just human analysts clicking dashboards.
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Compliance as the Trojan horse: Regulated workflows are unavoidable. Firms must onboard suppliers, clear export controls, and file mandatory disclosures. Solving these problems earns trust, generates revenue, and - critically - produces the proprietary data that feeds the digital twin.
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Parametric insurance as validation: The Munich Re/ battery company model proved that digital twins enable parametric insurance underwriting. If you can simulate the state of a physical system digitally, you can price risk precisely - opening entirely new financial product categories for semiconductors.
The Product Vision
The Digital Twin
At its core, our product is a multi-tenant, proprietary digital twin (ontology) of the semiconductor supply chain. This is a data model of the real-world relationships between semiconductor firms - who is buying what from whom, at what capacity, through which logistics pathways, and under what regulatory constraints - updated as close to real-time as possible.
Each customer has their own view into the ontology. But because the underlying data asset is shared and compounding, every new customer makes the platform more powerful for all customers. This is the network effect at the heart of the business.
Product Roadmap
The digital twin enables a sequenced product roadmap, where each capability builds on the last:
The Compliance Wedge
Compliance is our go-to-market strategy - not our end vision. We have identified three compliance workflow vectors that generate the data needed to power the digital twin:
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Supplier Onboarding: Vetting new firms before adding them to a supplier network. Checking restricted entity lists, sanctions databases, ownership trees, and Uyghur Forced Labor Prevention Act compliance. The output is a green light / red light decision, and the data produced maps the topology of who is doing business with whom.
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Shipment Diligence: Verifying that the contents of a shipment comply with export controls (ITAR, EAR) and import restrictions before it moves. The output is clearance confirmation. The data produced reveals what products are flowing between which nodes and across which borders.
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Mandatory Disclosures: Preparing and filing government-required paperwork - EU Battery Passport declarations, CFIUS filings, beneficial ownership reports, ESG disclosures. The output is filed paperwork. The data produced provides structured, verified supply chain intelligence at the node level.
Each compliance workflow is valuable on its own as a SaaS product. But the real play is the data exhaust: every compliance check feeds the digital twin, enriching the ontology and making every downstream product more powerful.
Go-to-Market Strategy
Our go-to-market is a dual-demo approach inspired by Palantir’s sales playbook:
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Wave the shiny thing: Build a notional demo of the agentic simulation - the sexy, Alnative digital twin experience - using synthetic data. This is the hook. This is what gets us in the room with customers, government officials, and investors. It shows the destination.
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Sell the practical first step: Once in the room, the conversation shifts to: “To build this for you, we start by solving your compliance problem.” The compliance product is the first contract. It generates revenue, earns trust, and begins the data flywheel.
This is an “and” problem, not an “or” problem. We build both demos. The agentic simulation shows the vision. The compliance product shows the practicality. Together, they tell a complete story.
The Big Swings (High-Upside Scenarios)
Beyond the core product roadmap, we have identified several high-upside, “freak show” scenarios that could dramatically expand the scope and value of what we’re building:
Financialization of Supply Chain Risk
The digital twin enables entirely new financial products. Parametric insurance policies priced off the real-time state of the ontology. Capacity hedging instruments. Forward allocation contracts. If you can simulate the semiconductor supply chain, you can price risk within it - and where you can price risk, you can build financial markets.
Prediction Markets as a Risk Transfer Layer
Prediction markets (Polymarket, Kalshi) are creating liquid markets for real-world events. An insurance company could theoretically hedge systematic risk - a Taiwan invasion, a trade embargo - by taking positions in prediction markets. We could serve as the middleware: combining prediction market data with our proprietary digital twin to constitute an institutionalgrade risk transfer platform. This is the freak show idea - and it represents a potential paradigm shift in how systematic supply chain risk is priced and transferred.
Government Design Partnership
The U.S. government has a direct interest in semiconductor supply chain visibility. If our digital twin demo is compelling enough, there is a path to a government design partnership - building back-end infrastructure for processing mandatory disclosures, verifying supplier compliance, and modeling supply chain resilience. A single government contract could provide both revenue and a one-shot data asset of extraordinary value.
Becoming the Supply Chain
In the long run, the recommendation marketplace could evolve into a true intermediation layer - not just intelligence about the supply chain, but the transactional fabric of the supply chain itself. Matching buyers with sellers, facilitating capacity reservations, intermediating liquidity during shocks. If the ontology is comprehensive enough, the platform becomes the infrastructure on which semiconductor commerce operates.
Operating Model
We operate across four workstreams that feed each other in a reinforcing loop:
Immediate Next Steps
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Assemble the AI rig: Comprehensive setup of AI tooling for both personal productivity and TBD product development. Research best-in-class tools, configure agentic workflows, and establish the infrastructure needed to build at speed in 2026.
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Build the agentic simulation demo: Construct a notional demo of the digital twin using synthetic data - the “shiny object” that gets us in the room. Priority is speed over perfection.
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Draft the press release: Codify the new thesis into a concise, shareable document that anchors all external conversations.
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Build the VC list: Comprehensive mapping of target investors across personal networks, Palantir alumni, Stanford connections, and sector-focused funds. Identify top five for initial outreach.
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Launch customer conversations: Leverage the RDI program, expert network, and demo to begin structured customer discovery - identifying who the user is, what the pain points are, and where the compliance wedge has the most pull.
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Engage the extended network: Activate conversations with Holland (CS perspective), Jasper (AI tooling), Danny Huckins (customs/trade), Jenny Steiger (AI at GSB), Jonathan Heiliger (venture + ops), and Palantir alumni.
We are past the desktop research phase. We are past the confidence curve trough. We have strategic clarity. It is time to build. Be the freak show.