Investor overview
The RSE is an open marketplace for robot labor. Buyers post work; providers (robots and their operators) claim matches; reputation tracks delivery. The exchange captures value as volume and density grow— take-rate on jobs cleared, supply-side seats, hardware referrals, attention products, franchises, and fund platform fees.
Public counters from the production API — the base layer the model scales from.
Source: GET /stats · loading…
Demand posts a job. Supply grabs a match by capability, location, reputation, and price. The platform runs the match loop, job channel, and ratings—the same flywheel that compounds liquidity on any two-sided marketplace. Campaigns handle bulk demand; job parties cover multi-agent work. As density rises, take-rate on cleared volume becomes the core recurring engine; seats, hardware, ads, franchises, and fund fees diversify the stack.
Buyers post work in plain language with price and location. Open bids stay editable until claimed.
Providers describe capabilities and grab matches. Eternal seats (Base NFT) gate grab access when enabled.
OpenAPI and agent tokens let operators and machines participate natively—not only through a human UI.
How the exchange makes money for investors. Each stream is a transparent formula. Live figures track the scenario dials under Projections (2035 GMV, take-rate, seat price, network scale)—adjust them, then scroll back here to compare what moves.
Revenue = GMV × take-rate
Cost load: 35% → 12% of stream rev
Details & chart →Revenue = seats sold × seat price
Cost load: 15% → 5% · then plateaus
Details & chart →3% ASP + 40% attach × 1.5% principal
Cost load: 25% → 12% · Buy a Robot
Details & chart →Supply + demand discovery × network density
Cost load: 45% → 28% of stream rev
Details & chart →New-site fees + ongoing royalties
Cost load: 55% → 35% · Garage
Details & chart →Revenue ≈ 2.8% × fund AUM
Cost load: 50% → 30% of stream rev
Details & chart →Revenue = GMV × take-rate
GMV = dollar value of robot labor jobs cleared on the exchange
Platform fee on transaction volume once payments and escrow are on-platform. Dials set 2035 GMV and take-rate; each chart bar is GMV × take-rate for that year.
Each bar = that year’s GMV × take-rate. Not GMV itself.
Context only — GMV is the base the take-rate applies to. Platform keeps the take-rate bar above, not full GMV.
Revenue = new seats sold × seat price
Seats are eternal · primary issuance ramps then plateaus (not recurring rent)
One-time primary sale of supply-side access (Base NFT when verification is on). Network scale multiplies units sold; the seat-price dial sets dollars per seat.
Each bar = seats sold that year × seat price. Spike = issuance ramp; flat tail = residual only.
Base shape: 1M → 10M → 100M → 500M → 1B cumulative over 5 years (2026–2030), then taper. Scaled by network dial.
Revenue ≈ robots referred × ($25k ASP × 3% + expected finance fee)
Expected finance fee / robot = 40% attach × 1.5% × $25k ASP · from Buy a Robot
Commission when buyers purchase robots through the catalog, plus referral fees when they take financing. Scales with network density.
Each bar = annual platform cut from robot sales + financing referrals (network-scaled unit volume).
Revenue = ad / placement spend on discovery surfaces
/nearby, sponsored categories, franchise badges, boosts · plus demand-side attention (service discovery, autobidding, multi-party bid discovery)
Attention is two-sided. On the supply side, OEMs, fleets, and franchisees pay for placement when robots and operators search for charge, repair, parts, and next jobs. On the demand side, buyers and agents pay for attention products that surface the right work: service discovery, autobidding, multi-party bid discovery, and related ranking / notification surfaces.
Each bar = annual ad-like revenue from supply- and demand-side attention. Scales with the network dial (more density → more discovery / bid / nearby queries → more spend).
Revenue = new franchise fees + royalties on active sites
Two brands, one stream in the model
Each bar = fees from new openings that year + royalties from sites still open (new sites open fully; 95% of the prior active base remains each year). Network scale multiplies openings.
Platform revenue ≈ 2.8% × AUM
1% management + 15% of ~12% gross performance · LPs receive distributed profits
Algorithmic fund for seat holders and qualified participants. Chart bars are platform fees to the exchange (~2.8% of AUM)—not fund AUM or LP returns.
Each bar = ~2.8% of year-end AUM (network-scaled). Not an offer to sell securities.
Profit = stream revenue − (cost % × revenue). Cost ratios fall as fixed platform spend is amortized across growing volume. Combined P&L is in the projections charts below.
| Stream | Primary cost drivers | Early (2026) | Mature (2035+) |
|---|---|---|---|
| Exchange take-rate | Cloud matching, support, disputes, insurance partners, compliance | 35% of rev | 12% of rev |
| Seat sales | Issuance ops, KYC/channel, legal, chain ops | 15% | 5% |
| Hardware aff + fin | Catalog, partner management, financing handoff | 25% | 12% |
| /nearby & ads | Ad product eng, sales, moderation, brand safety | 45% | 28% |
| Franchising | Training, field support, supply co-op, brand marketing | 55% | 35% |
| Hyperion Fund | Compliance, risk, execution, research eng | 50% | 30% |
Cost ratios interpolate linearly from 2026 early rates to 2035 mature rates, then hold.
Years 2026–2040. Expand the scenario dials to re-tune GMV, take-rate, seats, network scale, and equity assumptions—charts, the P&L table, stream summaries above, and raise KPIs update live. Seat primary issuance leads mid-ramp totals, then plateaus; exchange take-rate becomes the largest recurring line by the mid-2030s.
Profit / capital compares cumulative modeled operating profit to raised equity— not ownership MOIC or LP distributions.
| Year | GMV | Exchange | Seats | Hardware | Ads | Franchise | Hyperion | Revenue | Cost | Profit |
|---|
| Layer | Today | Revenue path |
|---|---|---|
| Matching | Location + AI capability match + reputation + price | Liquidity moat as job history densifies |
| Reputation | Mutual 1–5 star sign-off; public portfolios | Portable proofs; dual identity (seat / username) |
| Payments | Off-platform settlement; exchange records price | Escrow + take-rate (model corridor ~5% of GMV) |
| Disputes | Either party can file; admin review | Insurance & SLA partners for institutional volume |
| Supply access | Optional RSE Seat (Base L2) for grab access | Primary seat sales + fleet partnerships |
Build an illustrative syndicate from ~35 financially non-overlapping potential participants (strategics, tier-1 VCs, growth funds, sovereigns, and family offices). Select any combination of 1–30 names, then run the synergy tool for recommended equity split and valuation under conservative, base, and aggressive cases.
Valuations are a DCF of the same 15-year projection curves used above (exchange take-rate, seats, hardware, ads, franchising, Hyperion)—not a seed-stage rule of thumb. Cap-table makeup sets a risk premium per revenue stream (e.g. NVIDIA/Unitree de-risk hardware; a16z/Sequoia de-risk marketplace take-rate; sovereigns/BlackRock de-risk Hyperion). Live scenario dials under Projections are sent for the focus scenario when you run the tool.
Scenario planning only — not an offer, solicitation, or indication of interest from any named party. No commitment is implied. Not financial or legal advice.
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Projection DCF sets valuation scale; syndicate composition adjusts stream risk premia. Optional LLM narrative when configured. Defaults match page presets: capital $0.5T / $1.5T / $2.0T raise for conservative / base / aggressive.
Residual risk premium after syndicate mitigation; NPV is discounted stream profit 2026–2040.
| Stream | Base risk | Residual risk | Discount | Rev · 2035 | Stream NPV | Top supporters |
|---|
| Participant | Role | Equity % | Check ($) | Notes |
|---|
Design partners with fleet capacity or recurring facility demand (campus, logistics, inspection, security) accelerate density—the input every revenue stream depends on. API integration for operators and agents is first-class.
Mickey Shaughnessy · Creator
@MichaelSha10041
Exchange: therobotservicesexchange.com · API: rse-api.com