Core Assumptions
Revenue Streams Per User/Year
| Stream | Conservative | Optimistic | Notes |
|--------|-------------|------------|-------|
| Platform subscription | $60 | $120 | $5-10/mo |
| Data licensing (health) | $20 | $200 | Research market rates |
| Data licensing (behavioral) | $10 | $100 | Ad/research markets |
| AI training data premium | $5 | $50 | Longitudinal data is scarce |
| Ripple licensing | $0 | $20 | Posthumous interaction fees |
| Token economy (net) | $0 | $50 | Speculative until scale |
| **Total per user/year** | **$95** | **$540** |
Cost Per User/Year
| Cost | Amount | Notes |
|------|--------|-------|
| Hosting/storage | $10 | Scales with data volume |
| AI inference | $20 | Heavy users cost more |
| Support/ops | $5 | At scale, mostly automated |
| R&D allocation | $10 | |
| **Total per user/year** | **$45** |
Net Per User/Year
- Conservative: $95 - $45 = $50 profit/user
- Optimistic: $540 - $45 = $495 profit/user
Scale Projections
1,000 Users
| Metric | Conservative | Optimistic |
|--------|-------------|------------|
| Annual revenue | $95K | $540K |
| Annual costs | $45K | $45K |
| **Net profit** | **$50K** | **$495K** |
| Company valuation* | $500K | $5M |
*At 10x revenue multiple (SaaS standard)
Reality check: This is friends, family, early adopters. Proving the concept. Probably losing money on ops/salaries. But the unit economics are positive.
100,000 Users
| Metric | Conservative | Optimistic |
|--------|-------------|------------|
| Annual revenue | $9.5M | $54M |
| Annual costs | $4.5M | $4.5M |
| **Net profit** | **$5M** | **$49.5M** |
| Company valuation* | $95M | $540M |
Reality check: Real company. Small team, mostly automated. Data pool starts to become genuinely attractive to researchers and health orgs. This is where the data licensing revenue starts to materialize. Series A territory.
1,000,000 Users
| Metric | Conservative | Optimistic |
|--------|-------------|------------|
| Annual revenue | $95M | $540M |
| Annual costs | $45M | $45M |
| **Net profit** | **$50M** | **$495M** |
| Company valuation* | $950M | $5.4B |
Reality check: Unicorn territory. Data pool is now a significant research asset. 1M longitudinal personal histories is extraordinary — nothing like it exists. Health orgs, governments, AI companies will pay serious money. Token economy becomes real. This is where users start getting paid back.
At 1M users with optimistic data licensing: each user earns ~$370/year from their data. Platform fee is $120. Net to user: +$250/year. The model flips.
100,000,000 Users
| Metric | Conservative | Optimistic |
|--------|-------------|------------|
| Annual revenue | $9.5B | $54B |
| Annual costs | $4.5B | $4.5B |
| **Net profit** | **$5B** | **$49.5B** |
| Company valuation* | $95B | $540B |
Reality check: This is Facebook/Google scale. At this point Headstone isn't a company — it's infrastructure. A constituency. Governments negotiate with it. The data pool is a global public good. The token economy is real money for real people. Average user earning $370-4,950/year from their own data.
For context: Meta's revenue per user is ~$45/year (mostly ads). Headstone's conservative projection is $95/user with no ads and the user getting a cut.
The Flip Point
The model flips — where users earn more than they pay — somewhere between 500K and 2M users depending on data licensing success.
That's the milestone that changes everything. Not just economically. Psychologically. Politically. When Headstone can credibly say "we pay our users" — the growth becomes self-reinforcing.
What These Numbers Depend On
1. Data licensing actually materializing — the health/research data market is real but requires regulatory navigation (HIPAA, GDPR etc). This is the biggest uncertainty.
2. Decentralization not killing monetization — if data is truly user-held and encrypted, aggregating it for licensing requires user opt-in at scale. The token incentive is the mechanism, but it has to work.
This page summarizes the full specification. See the full document for complete details.