Executive Summary

Enterprises, open-source maintainers, and software vendors increasingly face the risk that their code will be ingested by large language models (LLMs) without explicit permission, exposing them to IP, compliance, and reputational harms; this is a practical problem for an addressable base of roughly 200,000 organizations that could spend about $50,000 a year on legal/compliance and licensing services (a $10.0B market). The pain is acute for regulated industries and commercial software vendors who must prove provenance and opt-outs, and for maintainers who want technical means to keep shared code out of model training corpora. You could build a combined legal and technical product: a well-drafted “no‑LLM” license plus a compliance stack that includes automated code labeling, provenance attestations, repository policy enforcement plugins for major hosts, model-training-blocking webhooks for cloud providers, and an enterprise console for audit and takedown workflows. Pricing could follow the assumed $50K ACV per large customer while also offering lighter plans for mid-market maintainers; success will require both defensible legal language and robust detection/technical enforcement to be credible. This market is attractive now because scrutiny around model training is rising, alternative license experiments are normalizing “no‑AI” clauses, and major platform players are adding policy controls that make enforcement feasible; market indicators in our assessment are strong (market score 92/100, revenue potential 88/100) and competition is currently low. To stand out you must combine legally vetted, jurisdiction-aware license language with pragmatic engineering: frictionless developer UX, signed metadata that travels with code, and partnerships with code hosts and cloud providers for policy enforcement; be candid that adoption will be slow, that legal enforceability across borders is uncertain, and that community backlash and false positives in detection are realistic challenges to manage.

Market Opportunity

Prevent LLM training/use of shared code — no‑LLM license + enforcement targets a $10.0B = 200,000 orgs x $50K ACV (enterprise legal/compliance + licensing services) total addressable market with low saturation and a year-over-year growth rate of 28%.

Key trends driving demand: Model-training-scrutiny -- creators and enterprises demand explicit permission/opt-outs as models ingest public code.; Responsible-licensing -- new license experiments (ethical/no-AI clauses) increase awareness and adoption for alternative licenses.; Platform-enforcement -- code hosts and cloud providers are adding policy and access controls making license enforcement practical.; AI-provenance tooling -- ML advances enable dataset provenance detection and leakage scanning, enabling monitoring and forensics..

Key competitors include Creative Commons (CC), Hippocratic License / Ethical Source Initiative, RAIL (Responsible AI Licenses) / similar model licenses, GitHub / Microsoft (policy and repo controls).

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Prevent LLM training/use of shared code — no‑LLM license + enforcement

Executive Summary

Market Validation

Market Opportunity

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