A scenario model for financial return, impact return, and cross-stage capital allocation. Every output remains separate and explicitly labeled.
Every section recalculates from your assumptions. Adjust sliders in Section 4 to see the full cascade.
This is a live financial model. Every number you see is computed from the assumptions below, not hard-coded. Changing any slider instantly recalculates the experiment funnel, ownership structure, returns, social impact, and sensitivity analysis.
Defaults represent the base case derived from ShareOS operational data (130+ agents, 62 active ventures, 508 KPIs tracked in real time). Each default has a cited rationale. The base case is aggressive by design: this is a high-conviction thesis, not a conservative projection.
A note on methodology: All financial projections are forward-looking scenarios and subject to risk. Impact outputs are illustrative scenarios until a venture supplies a baseline, counterfactual, measured outcome, duration, subgroup distribution, harms, and an attributable contribution estimate. Financial return and impact return are presented separately and are never added together.
Innovation Intelligence is the engine. Share Holdings uses it to turn proprietary signals into venture formation, ownership, and long-duration compounding. Share Continuum extends that intelligence across venture creation, frontier private markets, and public markets, subject to separate legal, liquidity, risk, and governance constraints.
The structural advantage: Traditional VC owns 15-20% of companies it invests millions to find. Share Holdings creates companies from near-zero cost and owns 40-65% from day one. At the same portfolio value, the return per invested dollar is 3-8x higher. This is not a quantitative improvement on VC. It is a different asset class entirely.
The single most important variable in venture returns is not the conversion rate, not the multiple, not the fund size. It is the ownership percentage at the point of value creation. Share Holdings inverts the traditional ownership equation.
| Era | Period | Cost to Launch | Time to Signal | Typical Ownership |
|---|---|---|---|---|
| Physical Infrastructure | Pre-2005 | $1M-$10M | 18-36 months | 20-30% |
| Cloud + Open Source | 2005-2020 | $100K-$500K | 6-12 months | 15-25% |
| AI-Native | 2020-2026 | $5K-$50K | Days to weeks | 20-40% |
| Autonomous Creation | 2026+ | $50-$100 / exp. | Hours to days | 40-65% |
| Function | Human Cost (Annual) | AI Agent Cost (Annual) | Ratio |
|---|---|---|---|
| Market Research | $80K-$120K | $2K-$5K | 20-50x |
| Product Design | $100K-$150K | $5K-$15K | 10-20x |
| Financial Modeling | $90K-$130K | $1K-$3K | 40-90x |
| Content / Marketing | $70K-$100K | $3K-$8K | 10-25x |
| Operations | $85K-$120K | $4K-$10K | 10-20x |
| Legal / Compliance | $60K-$100K | $2K-$5K | 15-30x |
| Full Stack (7-person team) | $535K-$790K | $19K-$50K | 10-40x |
Capital per attempt: $500K-$5M
Ownership at entry: 15-20%
Cost of failure: $500K-$5M
Signal time: 12-18 months
Annual experiments: 25-40
Cost per experiment: $50-$100
Ownership at creation: 40-65%
Cost of failure: $50-$100
Signal time: 24-72 hours
Annual experiments: 5,000-50,000
The difference is not incremental. It is a 10,000x reduction in the cost of testing a venture hypothesis. This changes the return calculus entirely: failure is nearly free, success is owned at majority, and the portfolio is an order of magnitude larger than any traditional vehicle.
A portfolio of 70 companies at 45% average ownership with $24M average enterprise value generates $756M of LP-attributable value from a $200M vehicle. The same 70 companies at 18% (VC average) generates $302M. Same companies, same outcomes, same multiple. The ownership percentage alone is worth 2.5x more LP value. This is why ownership retention is the most important variable in the model.
Every slider below recalculates the entire model in real time. Defaults represent the base case from ShareOS operational data. Hover each rationale for source citations.
The funnel converts compute spend into a portfolio of owned companies. Every stage recalculates from your assumptions above.
| Stage | Count / Yr | Cumulative Rate | Avg Invest / Co. | Stage Capital |
|---|
| Metric | Traditional VC | Private Equity | Share Holdings |
|---|---|---|---|
| Capital per attempt | $500K-$5M | $50M-$500M | $75 |
| Attempts per $100M | 20-200 | 1-5 | 1,333,333 |
| Cost of failure | $500K-$5M | $50M-$500M | $75 |
| Signal time | 12-18 months | 6-12 months | 24-72 hours |
| Decision data | Qualitative | Historical financials | 508 KPIs, real-time |
A key structural advantage of the Share Holdings model is that creation cost approaches zero, so there is no justification for giving away equity to cover creation cost. Two company paths are modeled: fully autonomous (no human founders) and human-led (scaling leadership hired post-creation).
| Stage | Share Holdings | Traditional VC | Ownership Premium |
|---|
The ownership compounding effect: At Year 10, a portfolio with 45% average ownership generates 2.5x more LP-attributable value than the same portfolio at 18% (VC average). This compounding effect grows as portfolio companies appreciate. Ownership percentage is the single highest-leverage variable in the entire model.
| Type | % of Portfolio | Initial Ownership | Net Ownership (Post-Rounds) | Human Mgmt Cost |
|---|---|---|---|---|
| Fully Autonomous | 50% | 60% | 60% | $0 |
| Human-Led (post-creation) | 50% | 60% | 43% | $50K/yr |
| Blended Average | 100% | 60% | 51% | $25K/yr |
| Year | Experiments (Cum.) | Breakouts (Cum.) | Portfolio Value | Annual Cash Flow | Cash Yield |
|---|
| Metric | Traditional VC | Private Equity | Share Holdings |
|---|---|---|---|
| Ownership at Entry | 15-20% | 51-100% | 51% |
| Target IRR | 20-30% | 15-25% | 38% |
| Target MOIC | 2.5-3.5x | 2-3x | 8.5x |
| Cash Yield (Yr 5) | Minimal | 5-10% | 6.2% |
| Exit Pressure | 10-yr fund | 5-7 yr fund | None (permanent) |
| Operational Support | Board seats | Operating team | 130+ AI agents, 24/7 |
Share Continuum is the proposed cross-stage architecture for deploying one Innovation Intelligence engine through venture creation, frontier private markets, and public markets.
| Capital expression | Role | Primary constraint | Learning contribution |
|---|---|---|---|
| Venture creation and early ownership | Create options and concentrated ownership | Long duration, reserves, execution capacity | Problem, product, customer, and operating evidence |
| Frontier private markets | Scale verified opportunities and capture access | Illiquidity, stale valuations, deal concentration | Growth, unit economics, governance, and exit evidence |
| Public markets and liquidity | Return-seeking exposure, liquidity, and risk control | Volatility, factors, information barriers | Live pricing, competitive, macro, and risk signals |
Governance: this model does not select a legal wrapper or authorize capital movement. Weights must remain inside human-approved ranges and comply with vehicle terms, custody, valuation, liquidity, conflicts, and material nonpublic information controls. A shadow portfolio and attributable-lift benchmark are required before production use.
Returns do not simply scale linearly with vehicle size. The data flywheel creates accelerating returns at smaller vehicle sizes, moderating as vehicle size increases beyond $1B.
| Vehicle Size | Experiments / Yr | Breakouts (10yr) | Portfolio Value (10yr) | IRR | MOIC |
|---|
| Year | Automation % | Cost / Experiment | Experiments / $1M | Portfolio Value / $1M (5yr) |
|---|
Early capital advantage: Capital deployed into this infrastructure today earns exponentially higher returns as automation improves. A dollar deployed in 2026 benefits from every automation improvement through 2035. Late entrants face not just a data deficit but a cost disadvantage that cannot be closed without rerunning the entire experiment history.
Which levers move the needle most? The bars below show the impact on 10-year portfolio value from moving each assumption one standard deviation from its default, holding all others fixed.
| Scenario | Ownership | Conversion | Multiple | Portfolio (10yr) | MOIC | IRR |
|---|
All default assumptions are grounded in cited sources. This section provides the full citation index.
| Assumption | Default | Primary Source | Notes |
|---|---|---|---|
| Cost per experiment | $75 | ShareOS operational data, 2025-2026 | $50-$100 actual range; $225/mo per company infrastructure |
| Signal conversion rate | 22% | ShareOS data + McKinsey (2025) | 220/1,000 experiments, consistent with 18-25% pharma AI discovery rate |
| Revenue conversion rate | 6% | CB Insights (2024), ShareOS portfolio | 27% of signals reach revenue; 21-28% industry benchmark |
| Scale conversion rate | 2% | PitchBook (2025) | 33% of revenue cos reach scale; 30-35% seed-to-Series-A rate |
| Breakout conversion rate | 0.7% | Sequoia/Benchmark portfolio analysis | 1-3% fund-returners in top-quartile VC; 0.7% conservative |
| Initial ownership | 60% | Share Foundry actual terms | 60-80% for fully autonomous; 40-55% with human team post-creation |
| Dilution per round | 15% | PitchBook (2025) | 15-25% standard Series A dilution; 15% is conservative midpoint |
| Revenue multiple | 8x | PitchBook 2025 B2B SaaS comps | 7-10x median; 10-15x for AI-native; 8x blended |
| Compute cost decline | 15%/yr | NVIDIA pricing + OpenAI API history | Actual 2020-2025: ~28%/yr; 15% is conservative |
| Automation today | 55% | McKinsey GI (2024), Goldman Sachs (2023) | 60-70% technically automatable; 46% currently exposed |
| Automation Yr 10 | 85% | OpenAI scaling extrapolation, Accenture (2025) | 86% of executives expect AI transformation by 2030 |
| QALY value | $150K | HHS federal guideline, 2024 | U.S. government standard for regulatory cost-benefit analysis |
| Social/financial ratio | ~2.5x | Acumen Fund, Omidyar Network impact models | Social value typically 2-4x enterprise value in health/productivity sectors |
| Active AI agents | 130+ | ShareOS MongoDB live data | clawos_cronjobs collection, April 2026 |
| Active ventures | 62 | ShareOS MongoDB live data | deals_internal collection, April 2026 |
| KPIs tracked | 508 | Agent org chart v2 | 36 agent groups, 83 sub-agents across 7 workstreams |
© 2026 Share Ventures. Confidential. All rights reserved.
Impact Return Outcomes
Impact return measures the incremental outcomes a venture creates for people and communities. Natural outcome units remain primary. Monetized Social Valuation Impact is an optional secondary lens and must never be added to enterprise value or presented as investor return.
Financial Track (Enterprise Value)
Impact Track (Illustrative Scenario)
Impact Return Scorecard
Evidence gate: the live dollar output above is an illustrative portfolio scenario based on generic proxies. It is not an actual impact result and is not decision-grade for any venture until venture-specific outcome evidence passes the scorecard. Unknown inputs remain null, never zero.