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Gensyn Financial Model

Crypto/Web3 Startup Financials (Free Excel Download)

Decentralised L1 protocol enabling trustless, verifiable machine learning computation by aggregating idle global compute (data centres, desktops, mobile).

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About this model

Gensyn is a decentralised Layer-1 protocol for trustless, verifiable machine-learning computation. It aggregates unused capacity from data centres, desktops, and mobile devices, aiming to let machine-learning workloads be executed across a broad supply network rather than a centrally operated cloud.

The company was pre-seed, with a seed raise implied by its 2022 deck and no operating revenue disclosed. This creates a two-sided bootstrapping problem: compute providers must be rewarded for reliable capacity, while developers need confidence that jobs can be verified and completed at useful cost and scale.

Start the model with compute suppliers, available capacity, utilisation, jobs processed, and price per unit of computation. Protocol fees arise from completed jobs, while validator rewards and token incentives support network security and supply. Separate token flows from cash operating costs, and flex adoption, compute pricing, utilisation, rewards, and infrastructure expense across scenarios.

A turnkey financial model

Live formulas, no hardcoded values

Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.

All assumptions in one tab

Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.

Statements always balancing

For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.

Distinct schedules for clarity

Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.

No hidden macros or external links

There are no unexplained external workbook links or macros to undermine auditability or portability.

Changes flow through the model

Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.

About Gensyn

gensyn.com
Read the pitch deck
Gensyn pitch deck cover
View on makeslides.com
Total raised
$6.5M
Funding round
Seed
Founded
2022
Category
Crypto/Web3
Customer
B2B
Geography
Not explicitly stated

How to build a detailed financial model for Gensyn

A complete walkthrough of the business, drivers, and assumptions behind the downloadable Gensyn model - distilled from its pitch deck and publicly available information.

Product & value proposition

  • Gensyn is an L1 trustless protocol for machine learning computation.
  • Core innovation: work verification via (a) probabilistic approximate verifiability using gradient metadata, and (b) deterministic graph-based pinpoint challenge protocol - enabling untrusted compute nodes to be used reliably.
  • Game-theoretic incentive layer (Truebit-style): slashable deposits + reward bounties ensure honest participation.
  • Key features: tokenised compute market with up-front cost estimation; foundation model warm-starts; ex-ante work estimation (halting-problem mitigation via neural net graph unravelling); functional encryption for private datasets; parallel distributed optimisation.
  • Performance claim: verification is 1,350% faster vs best competition; MNIST classification model verified in 4.58 min (Gensyn) vs 61.67 min (replication) and 0.003 days vs 80.19 days (Ethereum).
  • Positioning: high AI compute scale + high cost efficiency (quadrant chart, competitor positions redacted).

Market

  • TAM: $181bn opportunity by 2031 for decentralised AI compute market.
  • Market driver: computational complexity of SOTA AI systems doubling every 3 months.
  • Supply inefficiency: >60% of general compute is wasted - data centres 61% underutilised, desktop/laptop/tablet 75% underutilised, mobile 25% underutilised.
  • Demand driver: SOTA model compute (PFLOP-days × memory GB) scaling rapidly from BERT Base (2018) to Switch Transformer 1.6T (2021).

Revenue model

  • Token-based: compute buyers pay in Gensyn's native token; suppliers earn tokens for contributing verifiable compute.
  • Protocol takes a fee on compute transactions (implied by token model; explicit fee rate not stated in deck).
  • Reference mentions "Token Valuation Model" as a separate document linked from the deck.
  • Price reference: approximate cost of maxing out deep learning training on Nvidia V100 (or equivalent) per hour cited as market benchmark; exact dollar figure redacted.
  • Revenue streams implied: (1) protocol transaction fee on compute marketplace; (2) potentially native token appreciation as protocol utility grows.

Traction & metrics

  • >150 ML researchers and engineers interviewed (academia + Seed-to-Series-B companies).
  • 50 early adopters in curated Discord community, keen to train immediately.

Competition / moat

  • Centralised compute: AWS and equivalents described as expensive oligopolists with limited supply incentives.
  • Chip performance asymptotic - transistor density gains plateauing (chart: Zeppelin SoC, Ryzen, Tegra Xavier, Qualcomm 855, TI TDA4VM, Apple A15).
  • Competitor quadrant positions (AI compute scale vs cost efficiency): redacted.
  • Moat sources claimed: first-of-kind scalable verification mechanism (1,350% speed advantage); game-theoretic honesty enforcement; ETH1 miner migration as supply source; functional encryption for privacy; censorship-resistant governance.

Team & funding ask / use of funds

  • Founders:
  • Ben: PhD in ML optimisation; ex-founder of an anonymous digital identity startup.
  • Harry: ex-Head of Data Research at ML risk-pricing startup (Cytora); background in applied econometrics.
  • Pre-seed investors: Counterview Capital, id4 ventures, Fair Custodian (listed); angels from DeepMind, Draper Esprit, University of Cambridge.

Recommended financial model

  • Archetype + why: Token protocol / usage-based compute marketplace model. Gensyn is a crypto-native protocol where revenue accrues via transaction fees on a token-denominated compute marketplace. The right model blends (a) a usage-based SaaS P&L for the protocol operator layer and (b) a token valuation / network-value model (Metcalfe-style or transaction-fee NPV). A standard 3-statement is insufficient alone because token float, staking, and slashing mechanics affect "revenue" recognition. A SaaS ARR frame is the closest analogue for the fee-income layer; token economics require a separate tab.
  • Forecast horizon & granularity: 5 years (Year 1–2 monthly, Year 3–5 annual); model should phase-gate on roadmap: Phase 1 (early adopters), Phase 2 (foundation models), Phase 3 (ecosystem DApps).
  • Key drivers & assumptions:
  • Total addressable compute market ($181bn by 2031) → implied CAGR ~30% from current base
  • Gensyn protocol market share by phase
  • Compute supply volume (GPU-hours on network)
  • Protocol take rate (fee % on compute transactions)
  • Token price (if modelling token treasury revenue)
  • Verifier / trainer node count ramp
  • ETH1 miner migration as supply catalyst (Phase 1 tailwind)
  • Average compute job size / PFLOP-days per customer
  • Customer count ramp: 50 Discord early adopters as Year 1 seed; Phase 2 growth
  • Infrastructure / protocol opex (team, cloud nodes, security audits)
  • Scenarios (Base / Bull / Bear - which variables flex):
  • Base: moderate AI compute market growth, 0.1% protocol share by Year 3, 7% take rate
  • Bull: AI compute spend accelerates (doubling every 3 months sustained), protocol becomes dominant standard, 0.5%+ share
  • Bear: adoption lags (slow ETH miner migration, competition from centralised incumbents), take rate compressed, token illiquidity
  • Required sheets / outputs:
  1. Assumptions & drivers (toggle sheet)
  2. Compute marketplace volume (GPU-hours, jobs, $ value transacted)
  3. Protocol revenue (take-rate fees) - P&L by phase
  4. Token economics tab (supply schedule, staking, slashing reserves, treasury)
  5. Headcount & opex
  6. Cash / runway (pre-revenue burn through token raise)
  7. Market sizing build ($181bn TAM bridge)
  8. Scenario toggle (Base / Bull / Bear)
  9. Dashboard / outputs summary

Frequently asked

Is the Gensyn financial model free?+

Yes. The Gensyn model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.

What's included in the model?+

A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.

How was this model built?+

It was built from Gensyn's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.

Can I change the assumptions?+

Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.

Have more financial modelling questions? Contact us

Alex Tapio, ex-Deloitte financial modelling expert

Created by ex-finance professionals

Hey, I’m Alex and I created Finamodel.

Over my years in the finance industry I kept building the same models over and over again. Same structure, same assumptions, different logo. So I started building frameworks to turn them into clean, reusable templates.

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I’m not an expert in every industry, but I’ve built enough models to know what belongs in one. And when something is completely foreign to me, I reach out to my network for experts to work on our models with us.

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