Long-Term Governance and Economic Models for LEMM Vault & LEMM Studio¶

0. Scope, framing, and assumptions¶

This document answers “Long-term governance and economic models for LEMM Vault & LEMM Studio” using:

• Existing LEMM internal specs (Vault governance & safety, text-to-symbolic-to-audio system, Architecture A).
• The working organisational idea:
• LEMMAI.org (or similar) as a non-profit “owner” of LEMM Vault + fingerprint / safety APIs.
• LEMMAI.studio (or similar) as the music tools / product layer.
• External reality:
• Global recorded music is dominated economically by three majors (Universal Music Group, Sony Music Group, Warner Music Group), with independents collectively large but fragmented.
• Rights enforcement and royalty flows are structured around collecting societies / PROs (ASCAP, BMI, SESAC in the US; PRS, PPL, MCPS in the UK; SACEM, GEMA etc. in the EU) and trade bodies (IFPI, RIAA, WIN, etc.).
• The EU AI Act, EU Data Governance Act (DGA) and ongoing AI copyright litigation are converging on stricter expectations for training data transparency, licensing, and “trusted data intermediaries”.

We assume:

• LEMM Vault is a “clean, community-owned, training-only dataset + safety system” as already specified in the LEMM Vault design doc (rights records, revocation, non-redistribution, similarity checks, training-only enforcement, lineage).
• LEMM Studio is a family of models and tools (symbolic + audio, text-to-music, continuation, plugins) sitting on top of Vault-trained models and other clean datasets.

This document focuses on governance and economics, not the technical details of the music system.

Deliverables structured as required in the research prompt:

1. Option space overview (2–4 end-to-end candidate architectures).
2. Vault governance model(s).
3. Studio governance charter(s) per model.
4. Economic flows and value-sharing mechanisms.
5. Funding and capital structures.
6. Legal/regulatory envelope.
7. Risk register and mitigations.
8. Migration path (Phase 0 → Phase 2).
9. Recommended target design.
10. Implementation checklist (first 12–24 months).
11. Sources / references table.

1. Option space overview: governance & ownership paradigms¶

1.1 Shortlist of paradigms¶

We consider four long-term “shapes” for LEMM:

1. Model A – Single foundation (Vault + Studio inside one non-profit)
2. One legal entity (global non-profit / foundation) owns Vault, models, trademarks, and operates Studio directly.

3. All staff are employees of the foundation; commercial activities (subscriptions, B2B deals) run through internal units.

4. Model B – Dual structure: foundation (Vault + mission) + steward-owned Studio company

5. Foundation (LEMMAI.org) owns Vault, trademarks, “clean data” and safety IP.
6. For-profit but steward-owned Studio company (LEMMAI.studio) builds tools and interfaces.

7. Foundation gives Studio a training-only data license and sets mission guardrails; Studio shares revenue and reports to the foundation via contracts and board seats.

8. Model C – Data cooperative / multi-stakeholder co-op + Studio

9. Vault is legally a data cooperative with contributors as members.
10. Co-op either owns or co-owns the Studio company (possibly with workers and a foundation).

11. Democratically elected bodies make many key decisions; economic surplus is shared via co-op patronage dividends.

12. Model D – On-chain / DAO-heavy model

13. LEMM Vault membership and governance represented via tokens / smart contracts.
14. Studio is either fully on-chain or a conventional company governed by token-holders.
15. Rewards and votes are mediated by tokens or similar cryptographic primitives.

1.2 Who controls what in each paradigm?¶

Key control axes:

• Vault policies (admission, “clean” definition, training-only rules).
• Model training and deployment (which models can use Vault, safety constraints).
• Studio product/pricing (UX, market positioning, commercial strategy).

Model A – Single foundation¶

• Vault policies
• Controlled by the foundation board (potentially with advisory councils).
• Training & deployment
• Same board and executive team decide data use, model releases, and safety policies.
• Studio product & pricing
• Same board approves product and pricing strategy, within non-profit constraints.

Suitability: high mission alignment, simple; but commercial agility and funding flexibility can be constrained by non-profit law and risk tolerance.

Model B – Foundation + steward-owned Studio¶

• Vault policies
• Decided by Foundation board, with contributor representation and possibly a Vault Council.
• Training & deployment
• Foundation sets “Data Use Charter” and issues licenses to Studio and other licensees.
• Foundation can veto training uses that violate “clean” / training-only commitments.
• Studio product & pricing
• Studio board retains most product/pricing autonomy, inside boundaries defined by:
  • Data license terms (no misuse of Vault data).
  • A mission lock (e.g. steward-ownership principles).
  • Foundation golden-share or veto on sale / drastic mission changes.

Suitability: balances mission lock and commercial agility, matches LEMM-AI.org + LEMM-AI.studio intuition.

Model C – Data co-op + Studio¶

• Vault policies
• Member assembly (contributors) elects a board or council that controls admission, revocation rules, and data use.
• Training & deployment
• Co-op board or data committee approves what models use Vault and under what licensing terms.
• Studio product & pricing
• Studio may be wholly or majority owned by the co-op; board includes contributor-elected directors.
• Product strategy must pass co-op’s governance filters; profits shared via patronage dividends.

Suitability: strongest sense of “community ownership”, but legally and operationally more complex. Harder to move fast on product; co-op law is jurisdiction-specific and not optimised for venture-scale tooling.

Model D – DAO-heavy¶

• Vault policies
• Token-weighted voting or hybrid (governance token for decisions, membership token for identity).
• Training & deployment
• Smart contracts encode some rules; in practice off-chain operators still decide.
• Studio product & pricing
• Either run by the DAO or by a company bound to token governance.

Suitability: high experimentation value, but very risky legally (securities, AML, tax) and misaligned with “legally conservative defaults” and “simplicity where possible”.

1.3 High-level comparison¶

Initial ranking for LEMM:

1. Model B as primary target: Foundation + steward-owned Studio.
2. Model A as simpler fallback or transitional form.
3. Model C as a layer (Vault contributors’ co-op or association) plugged into Model B, not the entire stack.
4. Model D excluded except for very limited, non-core experiments (e.g. signalling badges, not governance or payouts).

2. Vault-specific governance & rights representation¶

We treat LEMM Vault as a community data institution: a structured dataset with rights, policies, and safety guarantees. Governance needs to cover:

• Who counts as a “member” or “contributor”.
• How they are represented.
• What rights they get (governance, economic, transparency).
• How revocation affects both governance and economics.

2.1 Membership model¶

Recommended: multi-class membership under a non-profit foundation, with optional contributor association / co-op overlay.

Member classes (inside the foundation or in a parallel association that elects into the foundation):

1. Individual contributors
2. Any person whose works are accepted into Vault.
3. One-person-one-membership.

4. Rights: vote in contributor elections, access to dashboards, participate in advisory bodies.

5. Institutional contributors

6. Labels, publishers, production libraries, ensembles.
7. Membership is at the organisation level, with internally decided representation.

8. Rights: limited governance representation (e.g. capped board seats), access to usage dashboards.

9. Public-interest & expert members

10. Researchers, digital rights NGOs, artist unions, etc.
11. Provide oversight on safety, fairness, and rights; typically part of advisory councils or independent board seats rather than mass voting.

Representation mechanics (Vault-side):

• Create a Vault Contributors’ Assembly as a body linked to the foundation:
• Individual contributors: one vote per person.
• Institutional contributors: votes scaled by contribution but capped (e.g. max 5× an individual).
• The Assembly elects:
• 2–3 seats on the foundation board (out of, say, 9).
• Members of a Vault Policy Council that:
  • Co-designs data admission rules.
  • Reviews major policy changes (definition of “clean”, revocation rules, model eligibility).

2.2 Vault policy control¶

We need to decide who can change:

• Definition of “clean” / allowed content.
• What rights must contributors grant.
• What kinds of models can be trained on Vault (e.g. text-to-music vs pure audio style transfer, etc.).

Proposed split of powers:

• Foundation board
• Final authority on:
  • Legal compliance (copyright law, AI regulation).
  • Safety and non-exploitation baseline.
  • Approval of data licenses to Studio and external licensees.
• Vault Policy Council (elected by contributors)
• Co-drafts policy proposals.
• Has consultative veto on any change that materially weakens:
  • “Clean” definition.
  • Training-only guarantees.
  • Revocation rights.
• Veto can be overridden only with a supermajority of the foundation board and explicit public statement.
• Studio board
• Cannot unilaterally change Vault rules; can request changes with justification but must go through Board + Policy Council.

2.3 Contributor rights beyond the technical spec¶

Technical rights from the Vault spec (consent, training-only, revocation, lineage) are necessary but not sufficient for a “community-owned” story.

Add formal rights:

1. Governance rights

2. Propose:

   • Right to elect a portion of the foundation board (e.g. 2–3 out of 9).
   • Right to elect or participate in the Vault Policy Council.
   • Right to petition for review of specific policies or uses (e.g. a model category they consider problematic).

3. Economic rights

4. Not equity, but participation in a Vault Reward Programme (detailed in section 4).

5. Rights are prospective (future flows only) and explicitly non-guaranteed to avoid creating a de facto security.

6. Transparency rights

7. Access to:
   • Aggregate statistics on how frequently their tracks appear in training batches and evaluation sets (within privacy limits).
   • High-level information on which models used Vault and in what way.
8. Ability to export their own track-level usage logs (or an aggregated summary) for their records.

2.4 Lifecycle: join, active, inactive, revoke, exit¶

Stages of a contributor’s relationship:

1. Onboarding & acceptance

2. Track accepted into Vault; rights record created; contributor becomes “active member” for governance once a minimal threshold is met (e.g. ≥ 1 accepted track).

3. Active

4. Tracks eligible for training; member can vote and receive rewards (if thresholds met).

5. Inactive

6. Contributor account dormant or no tracks currently accepted; limited or no voting rights.

7. Revocation

8. Contributor revokes future training for some or all tracks.

9. Governance and economic consequences:

   • Those tracks are excluded from future training.
   • Contributor keeps governance rights for a fixed period (e.g. 2–3 years) if they significantly contributed in the past, then gradually decays to zero.
   • Contributor may still receive rewards for past periods in which their tracks were used (no retroactive clawback).

10. Exit

11. Contributor consciously leaves: revokes all tracks and withdraws membership.
12. Past usage and rewards remain settled; no future governance or rewards.

This lifecycle avoids a situation where a single large contributor can credibly threaten: “Give us more control or we revoke and collapse the project.”

3. Studio governance and control¶

3.1 Studio decisions that matter¶

Key decision areas for LEMM Studio:

• Model strategy
• Which architectures to develop and deploy (symbolic, audio, hybrid).
• Release cadence, open/closed model decisions, which APIs to support.
• Safety & moderation
• Handling prompts like “make it sound like [hit song]”.
• Non-reproduction thresholds; jurisdiction-specific filters (e.g. lyric reproduction).
• Commercial strategy
• B2C pricing (subscriptions, credits).
• B2B/API licensing (per-call, per-seat, revenue share).
• Deal terms with labels, DSPs, and industry partners.

3.2 Studio governance under Model B (foundation + steward-owned Studio)¶

Studio company structure

• Legal form: for-profit company in an EU jurisdiction, configured as a steward-owned company.
• Ownership:
• Voting control held by “stewards”: founders + key staff + foundation (through a golden share or special voting share).
• Economic rights:
  • A capped pool of investor shares with limited returns.
  • Profit allocation rules that direct a fixed slice to the foundation and the Vault Reward Pool.

Board composition

• Example 7-seat board structure:
• 3 seats: management / founding team.
• 2 seats: independent / expert directors.
• 1 seat: appointed by the foundation (with veto over mission-critical decisions and sale of the company).
• 1 non-voting observer: elected by the Vault Contributors’ Assembly.

Control splits

• Studio board decides:
• Product roadmap (models, UX, integrations).
• Day-to-day commercial terms within guardrails.
• Foundation controls:
• Data license and its terms (e.g. training-only, safety minima, transparency).
• Which models are allowed to use Vault data at all.
• The right to cease or restrict the license if Studio violates commitments (after due process).

3.3 Escalation path when Vault and Studio disagree¶

When Studio wants to do something Vault governance sees as misaligned (e.g. an aggressive B2B deal with a major label that would pressure training-only rules):

1. Studio proposes the action to the foundation, with impact analysis.
2. Foundation board reviews and, where it touches Vault rules, solicits input from the Vault Policy Council.
3. Possible outcomes:
4. Approve: Foundation signs off; Studio proceeds.
5. Approve with conditions: Additional safety / transparency / economic conditions attached.
6. Reject: Foundation refuses to extend the license to cover that use; Studio must not proceed.
7. Disputes can escalate to:
8. A formal mediation process (e.g. independent third-party).
9. In extreme cases, the foundation revoking or not renewing Studio’s data license and seeking a replacement operator in the long term.

4. Economic flows and value sharing¶

4.1 Value flows overview¶

Core flows:

• Inward:
• B2C: subscriptions and usage fees from creators using LEMM Studio.
• B2B: licensing fees / revenue shares from partners (labels, DSPs, tools).
• Grants / philanthropy: from foundations and public bodies.

• Possibly limited ethical investment.

• Outward:

• Operating costs (infra, staff, safety).
• R&D and model training costs.
• Governance and legal costs.
• Vault Reward Programme distributions.
• Reserves for resilience.

4.2 High-level economic diagram (Model B)¶

Creators & Companies
    │
    │  (subscriptions, API fees, B2B deals)
    ▼
LEMMAI Studio (for-profit, steward-owned)
    │
    │  (license fee + revenue share)
    ▼
LEMMAI Foundation (non-profit, Vault owner)
    ├─ Core ops: governance, safety, infra subsidies
    └─ Vault Reward Pool → Contributors

Contributors
    ▲
    │  (non-exclusive license for training-only use)
    └── LEMM Vault (clean dataset + safety indexes)

4.3 Candidate Vault reward mechanisms¶

We need mechanisms that:

• Respect training-only commitments (no resale of raw works).
• Are simple and explainable (creators understand what they are signing up for).
• Are legally cautious (avoid creating tradeable profit-linked instruments that look like securities).

We define a Vault Reward Programme (VRP) funded via a percentage of Studio’s net revenue (or gross margin) and other sources.

Mechanism 1 – Global revenue pool with simple contribution tiers (v1)¶

• Foundation earmarks, for example, 10–20% of Studio net revenue each year for the VRP (after covering minimal foundation operations).

• Contributors are grouped into tiers based on accepted content volume and quality metrics:

• Tier 1 – Seed: 1–9 accepted tracks.

• Tier 2 – Core: 10–99 accepted tracks or equivalent stems/MIDI.

• Tier 3 – Heavy: ≥100 accepted tracks or significant multi-year contributions.

• Each tier receives a portion of the VRP:

• Tier 1: small but non-zero fixed or per-track payments (symbolic but meaningful).

• Tier 2: proportional share of a larger portion of the pool.
• Tier 3: proportional share with caps to avoid dominance by a few entities (e.g. max 5% of pool per contributor).

Pros:

• Very simple to implement and explain.
• Encourages contribution without needing complex usage analytics.

Cons:

• Only loosely correlated with actual impact on models.
• May over-reward bulk uploaders of low-value content if there are no quality filters.

Mitigations:

• Minimum quality and diversity rules; content that fails internal evaluation does not count.
• Caps per contributor to prevent capture by large catalog owners.

Mechanism 2 – Usage-weighted pool based on training presence (v2)¶

Later, once infrastructure matures:

• For each training run, record approximate contribution weights per track / contributor:
• For a given run, track A provides 0.005 of training samples, track B 0.0001, etc.
• Aggregate across runs and models into a “training presence score” for each contributor over a reward period.
• Distribute VRP proportionally to these scores, with:
• Smoothing (logarithmic or square root) to avoid big corporate dominance.
• Floors for small contributors so they are not starved.

Pros:

• Better alignment between reward and actual model training use.
• Still privacy-preserving, because it relies on aggregate counts rather than per-output tracking.

Cons:

• Requires robust data pipeline and lineage tracking.
• Could still be gamed by uploading “cheap”, repetitive content unless training pipelines are curated.

Mechanism 3 – Hybrid: tiered + usage-weighted¶

• Allocate VRP into two sub-pools:
• Base pool (e.g. 40%): distributed by tier (as in Mechanism 1).
• Usage pool (e.g. 60%): distributed by usage-weighted scores (as in Mechanism 2).

This hybrid design combines simplicity and fairness and mitigates extreme concentration.

4.4 Non-monetary value¶

Alongside cash rewards, contributors receive:

• Governance privileges (section 2).
• Product benefits:
• Priority access to new models and features.
• Increased usage quotas or discounted subscriptions.
• Visibility:
• “Powered by LEMM Vault contributors” credits.
• Optional contributor discovery pages (opt-in) showcasing some artists.

These non-monetary benefits are important in early phases when monetary flows are small or legally constrained.

4.5 Legal and perception checks for reward mechanisms¶

We need to minimise the risk that Vault rewards are treated as securities or employment-like compensation.

Conservative defaults:

• Frame rewards as royalty-like participation in license revenue for training use, not as profit-linked shares or investment returns.
• Do not make rewards tradeable or tokenised by default.
• Avoid promising fixed or guaranteed returns; emphasise that rewards are contingent on Studio success and foundation decisions.
• Use thresholds:
• Below some annual payout (e.g. €100), treat rewards as small honoraria with simplified tax handling.
• Above thresholds, apply KYC/AML and robust tax documentation, possibly restricting higher rewards to contributors in jurisdictions where compliance is realistic.

5. Funding and capital structure¶

5.1 Funding sources over time¶

1. Early phase (0–2 years)
2. Grants and philanthropy: arts, AI safety, open data and digital rights foundations.
3. Friends-and-family / angel funding for Studio (with capped returns consistent with steward-ownership).

4. In-kind support (cloud credits, legal pro bono).

5. Growth phase (2–5 years)

6. Studio revenue (subscriptions, API usage).
7. Ethical venture or revenue-based financing, under strict mission lock.

8. Public funding (EU programmes for data intermediaries, AI, creative industries).

9. Steady state (5+ years)

10. Majority of funding from Studio revenue and B2B deals.
11. Ongoing grants targeted at specific public-interest work (safety research, dataset governance, educational access).

5.2 Capital structure by model¶

Model A – Single foundation¶

• Foundation raises grants and possibly programme-related investments.
• Harder to accept conventional equity; can use:
• Recoverable grants.
• Revenue-sharing agreements.
• Suitable for modest scale, but may limit growth of Studio.

Model B – Foundation + steward-owned Studio¶

Foundation:

• Funded by:
• A share of Studio revenue (license fees, revenue share).
• Grants and donations.
• Owns a controlling stake or golden share in the Studio, to enforce mission lock.

Studio:

• Can raise limited equity or quasi-equity (e.g. non-voting shares with capped returns).
• Investors accept that:
• Company cannot be sold to the highest bidder.
• Dividends and exit options are constrained by steward-ownership rules.

This structure follows the pattern of foundation-owned or steward-owned companies seen in parts of Europe and in open-source ecosystems.

Model C – Data co-op + Studio¶

• Co-op capital comes from:
• Small member contributions.
• Co-op or public financing programmes.
• Studio may be majority owned by the co-op; raising external capital requires careful structuring to avoid control slipping away.

Model D – DAO-heavy¶

• Capital via token sales or similar; legally high-risk; not recommended as a primary funding strategy.

5.3 Protecting against funder capture¶

Mechanisms:

• Mission lock in foundation charter:
• Clear statement that Vault exists for clean, training-only use, with community representation.
• Explicit prohibition on sale of Vault as an asset bundle.
• Separation of economic and voting rights:
• Investors may receive economic participation but no or limited voting power.
• Caps on investor returns and influence:
• For Studio, use steward-ownership structures with capped dividends or buy-backs.
• Transparency:
• Public disclosure of major funders and their rights.

6. Legal and regulatory envelope¶

6.1 Anchors: EU and US¶

Key regulatory themes:

• AI and training data transparency
• EU AI Act: general-purpose AI models must provide detailed summaries of training content, including where copyright works are involved.
• Data intermediaries and altruism
• EU Data Governance Act (DGA): defines data intermediation service providers and recognised data altruism organisations, with trust marks and governance requirements.
• Copyright and AI litigation
• Ongoing cases in the US and EU focus on input-side copying: using copyrighted works without permission to train generative models.
• Collecting societies / PROs
• Longstanding frameworks for collecting and distributing music royalties, with implications for how LEMM explains its reward flows and training-only licenses.

6.2 Vault as a “trusted data intermediary”¶

Position LEMM Vault as:

• A specialised data intermediary focused on music works for AI training and safety, not a rights collecting society or DSP.
• In EU terms, potentially able to register as:
• A data intermediation service provider under the DGA.
• For some data subsets, a data altruism organisation, if works are donated for public-interest model training (e.g. educational or research models).

This requires:

• Structural separation between data provision and data use (which Model B naturally supports).
• Strong transparency and accountability rules.

6.3 Interaction with collecting societies and watchdogs¶

LEMM must coexist with:

• PROs / collecting societies:
• They collect performance and mechanical royalties for public uses (radio, streaming, live).
• LEMM’s training-only use is a separate licensing domain and must be treated as such in contributor terms.
• Trade bodies and watchdogs (IFPI, RIAA, GEMA, etc.):
• They are actively litigating unlicensed AI training.
• LEMM must be clearly on the “licensed, consent-based” side of that line.

Design implications:

• Contributor terms must clearly state that:
• LEMM Vault is a training-only licence, not a performance or mechanical licence.
• Contributors remain free to work with PROs, labels, and others for traditional uses.
• For label / publisher contributions:
• Vault membership is limited to works where those entities demonstrably hold training-time rights.
• Economic flows may be structured as B2B licences sitting alongside the community VRP.

6.4 Red lines on reward design¶

To stay legally conservative:

• Avoid tokenised, tradeable “profit-rights” instruments for contributors.
• Avoid marketing language that frames contributions as an “investment” with expectation of financial return.
• Keep governance rights and monetary rewards logically distinct:
• Governance via membership and elections.
• Rewards as royalty-like or thank-you payments from revenue, adjustable over time.

6.5 Generative AI legal trends and LEMM’s positioning¶

Given the direction of AI copyright cases:

• Courts are focusing heavily on unlicensed training on copyrighted works and verbatim or near-verbatim outputs.
• LEMM should:
• Emphasise that all Vault works are consented and documented.
• Provide clear training data summaries for models that use Vault.
• Maintain a robust non-reproduction safety layer (fingerprints vs Vault and external catalogues).

This gives LEMM a differentiator: “Legally and ethically constrained models trained on consented works, with auditable lineage.”

7. Risk and abuse analysis¶

7.1 Governance risks¶

7.2 Economic risks¶

8. Migration path and phasing¶

We design three phases over 5–10 years:

Phase 0 – Founder-led, low-friction (0–18 months)¶

Legal setup

• Incorporate LEMMAI Studio as a company that can actually hire and ship.
• Treat LEMM Vault initially as a product operated by Studio, but with strong contractual commitments in its own T&Cs:
• Training-only licences.
• Non-redistribution.
• Revocation mechanism.

Governance

• Studio founders and an advisory panel (including external experts) set policies.
• Start collecting a Vault Contributors’ mailing list / forum to prepare for later representation.

Economics

• No formal VRP yet; contributors invited under clear “training-only, no royalties for now” terms, with transparent roadmap to future reward programme.
• Funding from small investment and grants.

Phase 1 – Hybrid governance (18–48 months)¶

Trigger:

• Vault reaches significant size (e.g. tens of thousands of tracks).
• Studio reaches meaningful recurring revenue or active user base.

Legal setup

• Incorporate LEMMAI Foundation (non-profit) and transfer ownership of LEMM Vault, fingerprint indices and core safety IP from Studio to Foundation under a formal agreement.
• Foundation licenses Vault back to Studio under a training-only, safety-bounded licence with revenue share.

Governance

• Establish foundation board with:
• 2 founder / Studio seats.
• 2 independent / expert seats.
• 2 contributor-elected seats.
• 1 seat for public-interest / civil-society representative.
• Create a Vault Policy Council elected by contributors, with consultative veto on core Vault policy changes.

Economics

• Launch Vault Reward Programme v1 (tiered pool).
• Fix a conservative share of Studio net revenue (e.g. 10%) for foundation licence fees, from which:
• Foundation takes an operations budget.
• Remaining portion allocated to VRP.

Phase 2 – Mature governance (4+ years)¶

Trigger:

• Studio revenue is sustainable; Vault contributions are steady.
• Initial legal and regulatory environment stabilised.

Legal setup

• Mature steward-ownership structure for Studio:
• Golden share or perpetual purpose trust controlling mission.
• Clear caps on investor returns.
• Optional: create a Vault Contributors’ Association or Co-op which formally elects a portion of the foundation board.

Governance

• Foundation board composition evolves toward more community: e.g. 3 contributor seats, 2 staff / Studio seats, 2 independents, 1 public-interest.
• Vault Policy Council integrated into formal bylaws.
• Periodic constitutional conventions (e.g. every 4 years) to revisit governance in a controlled way.

Economics

• VRP evolves to hybrid tiered + usage-weighted scheme.
• Foundation explores additional revenue sources, such as:
• Providing white-label fingerprint / safety services to third parties under strict conditions.
• Grants for public-interest data and safety work.

9. Recommended target design¶

9.1 Chosen architecture¶

Target: Model B with co-operative flavour

• LEMMAI Foundation (non-profit)
• Owns LEMM Vault, fingerprints, safety IP, and trademarks.
• Recognised as a trusted data intermediary in EU terms if possible.

• Hosts the Vault Contributors’ Assembly and Vault Policy Council.

• LEMMAI Studio (steward-owned for-profit)

• Operates LEMM Studio products and APIs.
• Licensed by the foundation to use Vault for training under strict training-only and safety conditions.
• Shares revenue with the foundation via licence fees and revenue share.

• Governed by a board with a foundation-appointed director and a contributor observer.

• Vault Reward Programme

• Run by the foundation, funded from Studio revenue.
• Starts simple (tiered) and evolves to more usage-based weighting.

This directly implements the idea: LEMMAI.org owns the Vault + fingerprint API, LEMMAI.studio delivers tools, with strong guardrails against both corporate capture and speculative governance.

9.2 Why not the alternatives?¶

• Pure single foundation (Model A)
• Simpler but constrains Studio’s ability to raise capital and hire at tech-startup speed.

• Risk that everything moves too slowly or collapses under one overloaded board.

• Pure data co-op ownership (Model C)

• Conceptually attractive but likely too heavy for v0/v1; co-op law is complex, and enforcement across global contributors is non-trivial.

• Works better as an overlay (contributors’ association that elects into the foundation) than as the sole controlling entity.

• DAO / tokenised governance (Model D)

• Incompatible with “legally conservative” and “simplicity” constraints; high risk of speculation and regulatory blowback.

10. Implementation checklist (first 12–24 months)¶

What a founding team actually needs to do in the next one to two years:

10.1 Legal and organisational¶

1. Incorporate LEMMAI Studio in an EU jurisdiction.
2. Draft and publish:
3. LEMM Vault contribution terms (training-only, consent, revocation, rights representation).
4. Minimal internal governance rules for Vault policy decisions.
5. Design founder agreements and early investment docs that anticipate later steward-ownership transition (no automatic sale, mission-aligned investors).
6. Prepare the constitution of LEMMAI Foundation, but delay formal incorporation until Phase 1 triggers are met (size, revenue).

10.2 Governance scaffolding¶

1. Stand up a Vault contributors’ mailing list / forum and invite early contributors.
2. Create an informal advisory panel including:
3. An artist representative.
4. A copyright / AI law expert.
5. A data governance / co-op governance expert.
6. Document an internal “Vault Governance Roadmap” that explains:
7. Where governance is centralised now (founders).
8. Milestones that will trigger formal elections and foundation incorporation.

10.3 Economic mechanisms¶

1. Implement detailed training lineage logging and basic usage analytics from day one, even before VRP launches.
2. Decide initial licence fee schedule from Studio to future foundation (e.g. commit to 10–20% of net revenue).
3. Design VRP v1 on paper:
4. Tier thresholds.
5. Exclusions (e.g. tracks contributed by Studio itself or by purely corporate catalogues).
6. Communicate clearly with contributors:
7. “There is no VRP yet; here is when and how we expect to launch it; here is how you will be represented in the decision.”

10.4 Compliance and stakeholder engagement¶

1. Map AI regulatory obligations (EU AI Act; training data transparency) to existing Vault lineage and logging features.
2. Start conversations with:
3. Collecting societies and trade bodies (to avoid surprises and explore alliances).
4. Public-interest organisations (digital rights, artist unions) to build trust.
5. Commission a legal note on:
6. Reward design and securities risk.
7. Data intermediary status and DGA compliance (EU).

11. Sources and references¶

11.1 Internal LEMM documents¶

11.2 External environment: industry structure & watchdogs¶

11.3 Data governance, co-ops, and stewardship¶

11.4 AI, copyright, and regulation¶

This document is intended as a design blueprint, not legal advice. A founding team should use it to brief legal counsel and key stakeholders, then adapt details to chosen jurisdictions and real-world constraints.

11. Sources and references¶

11.1 Internal LEMM documents¶

(Internal LEMM documents are assumed to live in the LEMM-AI internal repos / knowledge base and are not publicly linked.)

11.2 External environment: industry structure & watchdogs¶

11.3 Data governance, co-ops, and stewardship¶

11.4 AI, copyright, and regulation¶