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+:PROPERTIES:
+:ID:       6d2e3f4a-5b6c-7d8e-9f0a-1b2c3d4e5f6a
+:ID:       d28adac8-08a1-40c4-ae43-b5d8d7b1743f
+:ID:       528a0f6c-6fd6-41ed-9d59-237958bdaef2
+:ID:       57f9538a-6270-4302-8d07-d742168419eb
+:CREATED:  [2026-05-25 Mon]
+:END:
+#+title: Adoption
+#+filetags: :passepartout:strategy:adoption:growth:
+
+This is the canonical adoption and growth document. It replaces the
+earlier separate notes on growth-strategy, effects-flywheel, adoption
+game theory, and the social-first alternative — those are now absorbed
+here.
+
+Social and institutional adoption are two separate processes with
+independent dynamics. Each has its own phases, defined by different
+metrics. They run concurrently and reinforce each other, but neither is a
+precondition for the other. Each section below defines its own phases.
+
+Phases are distinct from the architecture's development Stages (Stage 0
+= now/Linux-hosted, Stage 1 = social protocol, Stage 2 = verification
+TUI, Stage 3 = Lisp machine, Stage 4 = inference ASIC). The clock for
+both adoptions starts when Stage 2 ships (TUI complete). See
+[[id:dc2e4f22-1c4c-5d4a-a151-f96e5d3b0d70][Development timeline]] for the Stage breakdown.
+
+* Social adoption
+
+Social adoption tracks the growth of the social protocol network — users
+on the social graph, instances participating in the protocol. The phases
+are defined by orders of magnitude of users.
+
+**Phases and dynamics
+
+| Phase | Users | What marks the boundary | Flywheel effect | Growth driver | Revenue | Failure mode |
+|-------+-------+------------------------+-----------------+---------------+---------+--------------|
+| 0 | 0→10K | First organized communities onboarded as full-bundle groups | The bundle is proven for real coordination | Neighboring communities see it working and onboard — organic spread | $20-100K fees | No community finds PMF — the unified bundle is too complex |
+| 1 | 10K→100K | Community refugees and creators arrive; organic spread between neighboring communities | Reputation graph from Phase 0 communities makes migrations stick — users build identity they won't abandon | Platform fees bypassed — economic value accrues on-protocol; creator audiences follow | $1-5M fees + subs | Migrations don't stick — communities leave when crisis passes |
+| 2 | 100K→10M | Contract marketplace reaches critical mass; cross-jurisdiction transactions emerge | Freelancers and cross-border users trust the reputation graph for escrow and arbitration | Marketplace attracts more users; reputation deepens further | $20-100M fees | Contract marketplace stalls — no dispute resolution trust |
+| 3 | 10M→1B | Institution crossover — universities, newsrooms, regulators join the existing network | Institutions join not because they chose to, but because their users are already there | Protocol becomes default identity — traditional barriers to entry become irrelevant | $210-750M | Social graph stalls at niche — never reaches mainstream critical mass |
+
+Each phase spans roughly one order of magnitude of users. The phase
+boundary is crossed when the accumulated effects from the previous phase
+generate enough growth driver to reach the next scale — it is a
+consequence of the flywheel, not a timeline that can be accelerated by
+spending more money.
+
+**Phase details**
+
+**Phase 0 — Organized communities (0 → 10K users):** Onboard HOAs,
+clubs, cooperatives, PTAs — any group that uses 3+ separate tools and
+has a leader who can migrate everyone at once. The group already exists;
+the cold start is solved by group density. Ship all five layers
+(identity, content, payments, contracts, governance) from day one
+because organized communities need all of them.
+
+**Phase 1 — Refugees and creators (10K → 100K users):** Monitor
+deplatforming events. When a subreddit of 10K+ users gets banned, offer
+a ready-made community space within 24 hours. Meanwhile ship creator
+tools (LSAT, Lightning subscriptions) for OnlyFans/Patreon refugees.
+This is the highest-ARPU segment — creators earning $50K-500K/yr with
+strong incentive to bypass intermediaries.
+
+**Phase 2 — Freelancers and cross-border (100K → 10M users):** The
+reputation graph from Phase 0-1 communities now carries real weight.
+Freelancers and small businesses in weak-rule-of-law jurisdictions use
+the protocol for verifiable contracts and escrow. This is the hardest
+technical build (full SCAL stack, arbitration guilds) but the strongest
+moat — no one else offers verifiable contract enforcement without a
+state.
+
+**Phase 3 — Institution crossover (10M → 1B+):** At this scale
+institutions can no longer ignore the network because their users are on
+it. Universities issue verified credentials. Newsrooms publish with
+provenance. Regulators adopt social protocol attestation because it is
+the standard.
+
+* Institutional adoption
+
+Institutional adoption tracks the penetration of verified computing into
+regulated markets. Its phases are defined by market structure transitions
+— each phase marks a shift in how verification is bought, mandated, or
+priced. User counts are not the metric; the metric is whether
+verification has crossed a structural threshold in a domain.
+
+**Phases and dynamics
+
+| Phase | What marks the boundary | Flywheel effect | Growth driver | Key metric | Revenue driver | Failure mode |
+|-------+------------------------+-----------------+---------------+------------+----------------+--------------|
+| 0 | First compliance engagement — gate replaces annual audit ($200K-$1M) with subscription ($50K/yr) | Compliance cost drops 10x — the buyer saves | Competitors must match — institutional sales accelerate | First case study | Domain gate packages, verification appliance | First engagement fails to deliver — poisons reference |
+| 1 | Verification API gateway — value decoupled from instance adoption; any LLM user is a customer | AI safety shifts from probabilistic to structural | Any company using LLMs is a customer decoupled from instance adoption | Instances deployed; API gateway users | API gateway subscriptions; gate rule subscriptions | No high-profile AI harm event to drive conversion |
+| 2 | First regulator encodes a rule as a gate — adoption in that domain becomes mandatory | Enforcement becomes automatic, not annual paper<br><br>Accumulated edge cases from all instances | Every regulated entity in that domain must adopt — step function<br><br>Competitive advantage for adopters — those off-network fall behind | Regulated entities onboarded per domain | Mandatory adoption; insurance products | First regulator encode captured by incumbent (backdoored standard) |
+| 3 | Insurance differentiates on verification — unverified code costs more to operate than verified | Unverified code costs 10x to insure<br><br>Network effect: each new parameter makes the store more valuable for everyone | Economic necessity — not preference, not regulation, but cost of doing business<br><br>No institution can be pressured or acquired to remove accumulated knowledge | Certified instances | Certification fees; insurance premiums | ASIC path stalls — verification remains a performance tax |
+| 4 | Installed base is default infrastructure — a decade of attestations cannot be replicated | Installed base moat — cannot replicate a decade of attestations | New entrants cannot compete regardless of funding | Industry standard | Infrastructure rent; marketplace fees | Technology paradigm shift disrupts category |
+
+**Phase details**
+
+**Phase 0 — Compliance engagements:** The buyer is a compliance officer
+who just failed an audit or sees the cost of the current process
+spiraling. The gate replaces an annual audit ($200K-$1M) with a
+subscription ($50K/yr). First sale funds the team. Each engagement adds
+to the gate rule library.
+
+**Phase 1 — API gateway:** The verification API gateway decouples value
+from instance adoption. Any company using LLMs can route calls through
+the gateway and get a proof log — no instance required. This seeds the
+concept of verifiable computation in the market. Institutional sales
+compound as referenceable case studies accumulate.
+
+**Phase 2 — Regulator encode:** First regulator encode is the single
+most leveraged event. When a regulator encodes a rule as a gate
+specification, every regulated entity in that domain must adopt. Likely
+candidates: EU AI Act, NIS2, or a forward-leaning national regulator.
+After this, growth in that domain becomes mandatory.
+
+**Phase 3+ — Insurance loop:** Actuaries price verified instances lower
+than unverified. The cost of non-verification exceeds the cost of
+adoption. Growth becomes economic necessity.
+
+* How the two adoptions interact
+
+The social and institutional adoptions run on independent clocks, but
+they reinforce each other at every crossover:
+
+- **Revenue funds build:** Institutional compliance sales generate
+  revenue from day one, funding the social protocol development before
+  the social network has any users.
+- **Network provides distribution:** At scale, institutional verification
+  products sell as fulfillment orders to a network that already has
+  users — no cold start for each new product.
+- **Users bridge the gap:** Enterprise employees get DIDs from their
+  company's PDS and can join social protocol communities with zero
+  friction. Social protocol communities naturally need verification for
+  contracts and votes.
+- **Edge cases compound:** The institutional regression suite feeds every
+  deployed instance. The social protocol's contract volume generates
+  real-world edge cases that make the suite more valuable for
+  institutional certification.
+- **Identity converges:** Institutional gate attestations and social
+  protocol reputation both anchor to the same DID. Over time the
+  distinction between "corporate verified identity" and "community
+  reputation" blurs — they are the same cryptographic graph.
+
+**Correlation not causation:** Institutional Phase 2 (regulator encode)
+tends to occur somewhere in the Social Phase 2-3 range, because a
+regulator needs visible evidence that verifiable computing works in
+practice before encoding a rule. But this is correlation, not causation
+— a forward-leaning regulator could encode early (social Phase 1) or a
+conservative one could hold out until social Phase 4. The social network
+does not cause the regulator to act; it provides the proof of concept
+that lowers the regulator's political risk.
+
+**Comparison at a glance:**
+
+| Dimension | Institutional | Social |
+|-----------+----------------+-------------|
+| First customer | CISO, compliance buyer | HOA president, club leader, creator |
+| First revenue | $2-12M (year one) | $20-100K (year one) |
+| Time to $10M ARR | 6-18 months | 2-4 years |
+| Startup cost | Low (revenue-funded) | Low (fees fund growth) |
+| Marketing cost | Sales team + compliance | Community + product-led |
+| Key skill | Enterprise sales | Consumer product + platform design |
+| Moat type | Regulatory + insurance | Installed base + attestation history |
+| Moat durability | Good (legal) | Strong (practical) |
+| Entry vectors | One (compliance pain) | Four (publishing, payments, contracts, identity) |
+| Failure mode | Wrong pricing, too early | Any vector stalls — but all four must |
+
+See also: [[id:92ccd074-04a0-4e45-a44f-9da24ea20a9b][Impact]] — social, cultural, political, scientific, geopolitical, and technological consequences of broad adoption,
+and [[id:72db9428-d6e4-472d-9693-49335f888e48][Game theory]] — why the dynamics are structural, not just plausible.
+
+* References
+
+- [[id:8c7b9812-f8d6-4347-8915-ce8e520b7914][Social protocol entry strategy]] — tactical go-to-market for community
+  onboarding
+- [[id:ed05cab4-88e9-4e25-b7c9-346fa39c69a0][Revenue]] — detailed revenue streams by phase and stream
+- [[id:dc2e4f22-1c4c-5d4a-a151-f96e5d3b0d70][Development timeline]] — Phase Zero vs End State lines-of-code estimates
+- [[id:aa6d062e-a520-5d14-8773-00687ed9c689][Moats]] — competitive barriers
+- [[id:827bc546-e887-5b7c-9b65-6392beaf0920][Verification monopoly]] — evaluation harness and certification