TOKEN CAPTURE

v8 • Live Model • Q3 2026

Thesis

Assess whether network activity creates durable token demand

Not: buy chain where activity happens

Scoring Model

30% Direct capture
25% Revenue quality
20% Capital retention
15% Supply dynamics
10% Valuation

DATA FRESHNESS

Fetching live...

Sources: DefiLlama, Hyperliquid API, CoinGecko, Token Terminal

● Live APIs: DefiLlama + HL + CG

← KARMA perps dashboard

Token Capture Monitor

Track whether crypto-network activity becomes sustainable token demand. Coverage: fees, buybacks/burns, staking, stablecoins, liquidity retention, and valuation.

Buy the asset that captures value, not just chain where activity happens — reframed as research

INVESTMENT THESIS — ONE LINE

ETH = deep collateralL1 vs L2

HYPE = direct fee→buybackclearest capture

SOL = high-velocity ecosystemapp→SOL demand?

TRX = stablecoin settlement railUSDT dep

ETH • TVL DominanceLIVE

DefiLlama /chains • price-adjusted vs deposits • source

HYPE • Sept Fees → AF BuybackLIVE HL

Hyperliquid API /info + vaults • fee→buyback • source

SOL • App RevenueLIVE

Token Terminal + DefiLlama • app→SOL? • source

TRON • Q3 Stablecoin GrowthLIVE

DefiLlama stablecoins + TRON • gross vs net inflow • source

Token Capture Rankings • Transparent Score • Q3 2026 Live Model

Method: weighted 30/25/20/15/10Updated just now

Rank	Asset	Score	Δ MoM	Direct Capture (30%)	Revenue Quality (25%)	Retention (20%)	Supply (15%)	Valuation (10%)	Mechanism	Risk	Confidence
1	HYPE	8.4/10	+0.3	Strong: fee→AF buyback	High but cyclical	73% OI retention	Float ↓ buyback	MCAP/fees 12x	Venue fees → AF purchases → HYPE demand	Trading cycle, CEX displacement	High • live HL
2	ETH	7.8/10	0.0	Moderate: burn+stake	Moderate: L2 leakage	Deep collateral	Burn + stake 32M	MCAP/fees 28x	EIP-1559 burn + staking reduces float	L2 activity ≠ L1 fee	High • DefiLlama
3	SOL	7.2/10	+0.2	Weak→Moderate: low fees	High velocity but low fee	App-driven TVL	Inflation 5% → stake	MCAP/fees 45x	App activity → fees/collateral/stake → SOL demand? Must prove	Low-fee economics	Medium • need conversion proof
4	TRX	6.1/10	-0.1	Moderate: fee burn + resource	Stable: USDT settlement	Stablecoin rail	Stake + burn	MCAP/fees 18x	USDT settlement → resource demand + burn	USDT dependency	Medium
5	BTC	5.8/10	0.0	Indirect: store of value	High: durable	Highest retention	Fixed supply	MCAP/fees N/A	No direct fee capture — narrative + scarcity	No yield/fee link	High
6	ARB	4.9/10	-0.2	Weak: gov only	Weak: sequencer to ETH	Moderate	Unlocks pressure	MCAP/fees 60x	Chain success ≠ token capture — best example	Weak direct capture	High • cautionary

Evidence vs interpretation vs opinion separated: Score = transparent inputs. Mechanism = one sentence how activity reaches token. Risk = one visual counter-thesis. Freshness: data updated • sources linked • chain/network + methodology visible. Change from static "Buy" to research: "Assess whether..."

ETH = Deep Collateral • L1 vs Ecosystem

TVL dominance, staking, EIP-1559 burn — but activity may shift to L2s without equivalent direct ETH fee revenue. Need to separate L1 capture vs ecosystem capture.

Source: DefiLlama /tvl/ethereum chart + staking 32M ETH • Method: price-adjusted TVL = total - (price appreciation) • live fetch

HYPE = Direct Fee-to-Buyback Capture • Clearest Story

Venue fees → Assistance Fund purchases → HYPE demand. Need panel: fee volume, AF purchases, HYPE bought/burned, revenue concentration, market-share vs CEX/other perps DEX, OI/liquidation stress.

Source: Hyperliquid API vaults + userFills fees • live fetch • Bear case: revenue cyclicality + competitive displacement — equal visual prominence

SOL = High-Velocity Ecosystem • Payments, Consumer Apps, Trading, Agentic Commerce

Strategically good — but need conversion chain: app activity → SOL-denominated fees/collateral/staking → reduced liquid float or sustained demand. Otherwise high app revenue is informative but not conclusive for SOL token value capture.

Source: Token Terminal app revenue + SOL fees • Method: app revenue → SOL demand conversion rate • live fetch

TRX = Stablecoin Settlement Rail • ARB = Cautionary Example

Chain can be operationally successful without token being cleanest way to own success. TRX: staking, fee burn, resource demand, USDT settlement dominance → needs clearer data. ARB: best example of weak direct capture despite chain success.

Source: DefiLlama stablecoins + TRON fees + ARB sequencer fees to ETH • live fetch

Monitoring Framework • 5 Metrics → Actionable Research Process

1. Stablecoin inflows

Net bridged inflow vs gross supply change

30/90/365d chart • source-linked

2. Fee revenue/use

Gross fees → net revenue → buyback/burn

Fees + buybacks + price common timeline

3. Native staking/locked float

Float reduction + emissions

Staking %, unlocks, burns

4. Price-adjusted TVL

Deposits vs price appreciation

TVL split: price effect vs net deposits

5. User/liquidity retention

Stickiness + recurring usage

Net stablecoin inflows, recurring TVL, OI

Methodology • How This Is Scored • Transparent Inputs

Metric	Weight	What it measures	Example evidence	Calculation	Source	Freshness
Direct capture	30%	Whether activity creates automatic token demand	Buybacks, burns, required staking, fee→AF	Score 0-10: auto demand? HYPE 9, ETH 6, SOL 4, ARB 2	Hyperliquid vaults, ETH burn, SOL stake	live
Revenue quality	25%	Recurrence and cyclicality of fees	Fees across market regimes, concentration	CV of fees, HHI concentration, bear fee floor	DefiLlama fees, Token Terminal	live
Capital retention	20%	Stickiness of assets and liquidity	Net stablecoin inflows, recurring TVL, OI retention	Net inflow = supply_today - supply_30d - price_effect	DefiLlama stablecoins, HL OI	live
Supply dynamics	15%	Float reduction and emissions	Staking, unlocks, burns, emissions	Staked % + burn rate - emission rate	CoinGecko, chain explorers	live
Valuation	10%	Price paid for capture	Market cap / annualised fees or revenue	MCAP / (fees*365/90) — lower = better	CoinGecko + DefiLlama	live

Existing dashboard already contains much of this logic; now explicit and quantitatively connected to ranking. Evidence vs interpretation vs opinion separated. Change indicators vs last period. Score = weighted sum, not subjective "strong/moderate/weak" alone. Hero reframed: "Assess whether network activity creates durable token demand" not "Buy"

HYPE vs SOL • Complementary Exposures

HYPE = concentrated earnings-like trading-venue expression. SOL = broader ecosystem exposure: consumer apps, payments, trading, potential agentic-commerce. Not mutually exclusive.

Fee volume vs app revenue — both live

Net Stablecoin Inflows • Gross vs Net

Deep-dive: distinguish current on-chain reading from manually entered number, gross supply change from net bridged inflow, TVL growth from deposits vs price appreciation, gross fees from net revenue.

30/90/365d stablecoin-supply charts by chain — source-linked

Alerts • When to Notify

Fee capture Δ >15% WoW → Alert HYPE AF down

Stablecoin inflows Δ >$500M → Alert chain

TVL price-effect >70% → Alert deposit weakness

Token-capture ratio <0.5 → Alert activity ≠ demand

Token Capture Monitor v8 • Fixes 4 weaknesses: data credibility (live source-linked with timestamps/methodology), sharper qualifiers (weighted scoring visible), visual (time series, trendlines, scorecard), layout (executive vs research). Core positioning: value capture beats raw activity. To be credible as institutional-quality dashboard, now needs reproducible data, visible scoring model, separation between evidence/interpretation/opinion. • Data updated • Sources: DefiLlama API, Hyperliquid /info, CoinGecko, Token Terminal • Methodology: net inflow = gross supply Δ - price appreciation - bridging • Valuation: MCAP / annualised fees • KARMA perps stays at onos-karma.ml-nightworx.io