Executive Summary

Many open-source authors and organizations that publish code now face unauthorized harvesting of their repositories for large model training, exposing them to reputational harm, potential legal disputes, and loss of control over derivative commercial uses; this is not hypothetical — the addressable market is roughly 3.0M potential customers and a conservative $9.0B TAM at a $3K ACV. The problem is acute for mid-sized software vendors and foundation-backed projects that lack in-house legal or technical tooling to assert and enforce training-time constraints, and for enterprises that must prove provenance to regulators and insurers. The product to build is a combined legal and technical stack: standardized, machine-readable licenses that explicitly opt code in or out of LLM training; a global registry and signed metadata for repositories; continuous monitoring and model-provenance audits that flag likely uses of registered code; and a service layer offering takedown support, contractual enforcement, and enterprise APIs/SDKs to vet training data. Pricing and go-to-market can target $3K ACV per organization for basic registry and monitoring, with premium for forensics and legal retention, but the technical challenge of reliably detecting training usage and mapping it to specific models will require investment in signal engineering and industry partnerships. This is an attractive moment — market sentiment, regulatory interest in AI provenance, and a string of high-profile lawsuits create real buying triggers, which is reflected in a market score of 95/100 and a revenue-potential assessment of 90/100. The space remains lightly contested, so differentiation should come from legally-vetted license text, cryptographic provenance (signed commits/artifacts), tight integrations with major code hosts, and trusted forensic capabilities; realistic risks are adversarial scraping, cross-jurisdictional enforcement complexity, and the need to build standards and ecosystem trust before scale is possible. Pursuing this idea makes sense if you can finance initial legal partnerships and technical R&D to solve attribution and create a defensible network effect.

Market Opportunity

Prevent LLM training/usage on shared code — license + enforcement service targets a $9.0B = 3.0M potential customers (open-source authors + orgs publishing code) x $3K ACV total addressable market with low saturation and a year-over-year growth rate of 30% (enterprise compliance spend + AI governance).

Key trends driving demand: AI-training-provenance -- enterprises and regulators demand traceability of training data and contractual constraints on model data sources, creating demand for data-level licenses and provenance tooling.; Legal-litigation-risk -- high-profile lawsuits against model trainers raise liability and insurance costs, motivating preventative licensing and monitoring.; Developer-reputational-concerns -- creators are increasingly unwilling to have work harvested for LLMs without consent, increasing opt-in/opt-out market.; Platform responsibility pressure -- code hosts and cloud providers face pressure to respect content license preferences and may integrate enforcement tooling..

Key competitors include Hippocratic License (ethical source movement), SSPL / Commons Clause (license restrictions precedent), FOSSA, GitHub (Microsoft).

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Prevent LLM training/usage on shared code — license + enforcement service

Executive Summary

Market Validation

Market Opportunity

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