Crypto Value Lab v 0.1.0
X Substack

Valuation frameworks for crypto assets

Oracles, Data & Infrastructure

Infrastructure networks evaluated through secured value, usage requests, integrations, revenue, and security collateral.

Static methodology preview. Live data pipeline planned. Not investment advice.

Valuation Framework Categories

The model set is selected for the economic behavior of this asset class.

Secured valueUsage demandIntegration network

Class Bubble Map

Static class view of valuation gap and evidence quality.

Data Infrastructure Bubble Map

Static methodology preview. Live data pipeline not yet connected.

Premium to model-implied value Discount to model-implied value Model-implied parity Asset Evidence AKT Data Infrastructure AKT: valuation gap 19%, evidence 67/100 19% 67 LINK Data Infrastructure LINK: valuation gap -6%, evidence 66/100 -6% 66 RENDER Data Infrastructure RENDER: valuation gap 14%, evidence 64/100 14% 64 GRT Data Infrastructure GRT: valuation gap 8%, evidence 61/100 8% 61 PYTH Data Infrastructure PYTH: valuation gap 3%, evidence 58/100 3% 58 QNT Data Infrastructure QNT: valuation gap -3%, evidence 55/100 -3% 55 CVL Valuation Gap
X-axis: Composite Model-Implied FDV / Actual FDV - 1 Row labels keep every asset visible; evidence score is shown at right Size: FDV or market cap Border: method confidence Opacity: data completeness

Models & Implied Value

Each model states what it measures and where its confidence breaks down.

Security demand

Secured Value Multiple

Relates token value to economic value secured or serviced by the network.

Model-implied value method Applies a secured-value multiple to verified secured value.

Core inputs

secured valueservice tierintegration quality

Secured value should not be treated as revenue.

Usage demand

Request / Usage Multiple

Capitalizes requests, jobs, or usage units where they are economically meaningful.

Model-implied value method Applies a usage multiple to monetizable demand.

Core inputs

requestsusage unitsmonetization rate

Usage must be filtered for incentives and non-economic calls.

Revenue

Revenue Multiple

Applies a revenue multiple to network or protocol revenue.

Model-implied value method Capitalizes annualized revenue with scenario multiples.

Core inputs

annualized revenuemultiple rangemargin quality

Revenue comparability varies materially by asset class.

Network breadth

Integration Network Score

Scores integrations, distribution, and service criticality.

Model-implied value method Uses an integration score as a confidence modifier.

Core inputs

integrationscriticalityretentionswitching cost

Integration count alone can overstate network value.

Security collateral

Staking / Security Collateral

Evaluates staking collateral and its role in service assurance.

Model-implied value method Adjusts model ranges for collateral depth and security requirements.

Core inputs

staked valueservice riskslashing design

Collateral depth is only useful if tied to real service demand.

Method Ratings

Method confidence reflects evidence quality, source availability, and token-level capture clarity.

Medium confidence

59/100

Usage and integration evidence

Integrations matter most when they show recurring economic demand.

Low confidence

52/100

Security collateral evidence

Collateral and staking are useful only when linked to service assurance.

Scenario Simulator

A local assumption shell for testing scenario sensitivity without live data.

Scenario sensitivity

Infrastructure scenario

Model-implied FDV $16.0B

Static methodology preview. Changes here recompute a local scenario number only.

Source Trail

Planned source families for turning this framework into a data-backed dashboard.

CoinGecko

price, market cap, FDV, supply, and volume

Source

DefiLlama

TVL, fees, revenue, and DeFi volumes

Source

Coin Metrics

BTC-style realized cap, MVRV, and network metrics

Source

Dune or manual source trails

custom on-chain datasets and methodology notes

Source

Asset List

Assets currently mapped to this valuation class.

Asset Class Model coverage Data completeness Tokenholder capture Confidence Link
LINK Chainlink Data Infrastructure Medium 69/100 Partial 61/100 Open
PYTH Pyth Network Data Infrastructure Medium 57/100 Partial 55/100 Open
GRT The Graph Data Infrastructure Medium 61/100 Partial 60/100 Open
RENDER Render Data Infrastructure Medium 65/100 Partial 50/100 Open
AKT Akash Network Data Infrastructure Medium 53/100 Partial 55/100 Open
QNT Quant Data Infrastructure Medium 57/100 Partial 60/100 Open