The first calculator for peaqOS V1 Liquid Machines. Last verified 2026-05-21. 🆕 peaqOS V1 launched May 2026 — this is an early-preview planning tool.
TL;DR
peaq does not publish the economics of a tokenized machine (revenue split holder↔operator, fees, lock-ups, the exact MCR formula). So we do not derive machine earnings. We model the investor's return from a yield assumption they choose (3 scenarios), discounted by the machine's credit rating, shown as a risk-adjusted range with prominent disclaimers. Nothing is presented as a peaq figure unless it is one.
What peaqOS V1 actually is (verified)
- Activate (live): peaqID (DID) + omnichain wallet + Machine NFT.
- Qualify (live): Machine Credit Rating (MCR) — Moody's-style AAA→NR, queryable cross-chain, based on revenue + uptime + a trust multiplier that climbs from self-reported → on-chain-verified → hardware-signed.
- Liquid Machines / Scale (in V1 testing, "Tokenize" marked coming soon in docs): Machine NFT → vault → fractionalized via ERC-3643 (permissioned RWA standard, KYC-gated transfers). Financed through Initial Machine Offerings (IMOs).
The model
baseApy = scenario (Conservative 0.08 / Base 0.15 / DualMint-ref 0.20)
trustMult = MCR trust-discount (AAA 1.00 → NR 0.10) // DePINly heuristic
adjustedApy = baseApy × trustMult
monthlyMid = capital × adjustedApy / 12
monthlyLow/High= monthlyMid × (1 ∓ band[MCR]) // band 0.15 → 0.60
yieldOverHold = monthlyMid × holdMonths // simple interest
resaleDelta = capital × resale% − capital
totalReturn = yieldOverHold + resaleDelta
recoupCushion = capital / monthlyMid (months, if resale → 0)
riskScore = MCR risk + scenario bump (0–100)
benchmarks = capital × {S&P 0.10, USD 0.05, PEAQ 0.10} × years (yield-only)
Key design decisions
- APY scenario and MCR are different axes. The scenario is the advertised yield; the MCR is a trust discount on whether that yield will be realized — not a second credit adjustment. Scenarios carry no implied rating.
- Simple interest (revenue is taken, not re-staked).
- Comparisons are yield-only and nominal (benchmarks carry no trust discount); resale impact is shown separately so it's an honest comparison.
- "Payback" reframed as months of yield to recoup capital if the token can't be resold — a downside cushion, since you retain the asset.
Assumptions & confidence
| Assumption | Value | Confidence | Basis |
|---|---|---|---|
| peaqOS structure / MCR / ERC-3643 | per above | 🟢 High | Official peaq blog + docs + Purple Paper |
| DualMint robo-farm ~20% APY | reference scenario | 🟡 Medium | Single projected case (co-founder "approximately 20%"), not realized/average |
| Scenario APYs (8/15/20%) | user-chosen | 🟡 Estimate | Conservative/base assumptions; 20% anchored to DualMint |
| MCR trust-discount (1.00→0.10) | DePINly heuristic | 🔴 Low | Not a peaq formula — illustrative |
| Monthly band (0.15→0.60) | DePINly heuristic | 🔴 Low | Illustrative uncertainty by rating |
| Revenue split / fees / lock-ups / liquidity | — | 🔴 Unknown | Not published by peaq (UNVERIFIED) |
NOT modeled (surfaced in the UI)
Machine downtime/failure; revenue below projection; secondary-market liquidity (KYC-gated, possibly ~0 buyers); operator counterparty risk; unpublished revenue-split & fees; regulatory change (VARA Dubai is one jurisdiction); ERC-3643 transfer restrictions; hardware obsolescence over a multi-year hold.
Confidence rating
🟡 (low end) — The peaqOS structure is well-sourced, but the machine economics are opaque and Liquid Machines is V1/preview. The output is an investor-side ROI from a self-chosen yield, never a guarantee. A single deterministic number would be dishonest; we show ranges + heavy disclaimers.
Sources (verified 2026-05-21)
- Meet peaqOS
- Machine RWA Framework
- World's first tokenized robo-farm
- Decrypt — robo-farm
- docs.peaq.xyz · Purple Paper · CoinMarketCap PEAQ
Code
- Formula:
src/lib/peaq-subdepins/liquid-machines-calc.ts - UI:
src/components/calculators/peaq/LiquidMachinesCalculator.tsx - Page:
src/app/calculate/peaq/run/liquid-machines/page.tsx
These figures are point estimates inside a wide band and depend on local demand, hardware, uptime and token price. They may be wrong in either direction. Nothing here is financial advice — always do your own research.
Methodology updated 2026-05-21 · View source on GitHub →