Token Velocity: Why Payment Tokens Lose Value

Here’s the paradox: a project launches a token to pay for services. Users grow, transactions multiply — but the token price flatlines or drops. This isn’t a bug. It’s the velocity problem — a fundamental issue affecting all payment tokens, explained by a single formula from the 18th century.

What Is Token Velocity

Token velocity is how many times a token changes hands in a given period. If 1 token participated in 10 transactions over a year, its velocity = 10.

Velocity reflects holding time: the faster a token moves through the “buy → use → sell” cycle, the higher V. Bitcoin with V ~5 is held for months. A utility token with V ~50 passes through a wallet in hours.

Why This Is a Problem

High velocity means users have no incentive to hold the token. They buy the token at the moment of payment and immediately spend it. The service provider receives the token and immediately sells for stablecoins. Nobody holds — so there’s no buy pressure, no scarcity, no price appreciation.

The Exchange Equation: MV = PQ

At the core lies Fisher’s equation, adapted for tokenomics:

Rearranging for fundamental market cap:

What This Means in Practice

Suppose a protocol processes $50M in transactions per year. With different velocities, we get different justified market caps:

With the same transaction volume, increasing V from 5 to 50 reduces justified market cap by 10x. This is the velocity problem: a token can serve a massive throughput yet be worth pennies.

Historical Context

The exchange equation was formulated by economist Irving Fisher in 1911 (as MV = PT, where T = transaction volume) to analyze money supply. The PQ variant (Q = quantity of goods) is an adaptation used in GDP analysis. Kyle Samani of Multicoin Capital adapted the framework for crypto in 2017 and showed that most utility tokens are destined for low valuations due to high velocity.

Velocity Sinks: How to Slow Down Circulation

A velocity sink is a mechanism that forces or motivates holders to keep the token longer. The more tokens locked, the fewer in free circulation, the lower the effective velocity.

Types of Velocity Sinks

Impact of Sinks on Price

By combining mechanisms, a protocol can substantially reduce effective velocity:

Simulation: How Sinks Change Price Over Time

Consider a model protocol with growing transaction volume. We simulate 24 months and show how different mechanism combinations affect fundamental price.

Simulation parameters:

• Starting monthly volume: $2M, growing +5% per month
• Total supply: 100M tokens
• Velocity: 20
• Three scenarios: no sinks, staking (30% locked), staking + burning (30% + 0.5% of supply burned per month)

Staking uniformly scales price by +43% (multiplier 1/(1-0.3)). But burning compounds: by month 24, supply has decreased ~11%, providing additional price growth.

import pandas as pd

months = 24
initial_monthly_pq = 2_000_000  # $2M/month
growth_rate = 0.05               # +5% per month
total_supply = 100_000_000       # 100M
velocity = 20
staking_pct = 0.30               # 30% locked
burn_rate = 0.005                 # 0.5% of supply burned per month

rows = []
supply_current = total_supply

for m in range(1, months + 1):
    monthly_pq = initial_monthly_pq * (1 + growth_rate) ** (m - 1)
    annual_pq = monthly_pq * 12

    # No sinks
    mcap_raw = annual_pq / velocity
    price_raw = mcap_raw / total_supply

    # Staking only
    mcap_stake = annual_pq / (velocity * (1 - staking_pct))
    price_stake = mcap_stake / total_supply

    # Staking + burning
    supply_current -= supply_current * burn_rate
    mcap_burn = annual_pq / (velocity * (1 - staking_pct))
    price_burn = mcap_burn / supply_current

    rows.append({
        'month': m,
        'annual_pq': annual_pq,
        'price_raw': price_raw,
        'price_stake': price_stake,
        'price_burn': price_burn,
        'supply_after_burn': supply_current
    })

df = pd.DataFrame(rows)
print(df[['month', 'annual_pq', 'price_raw', 'price_stake', 'price_burn']].to_string(index=False))

Common Mistakes

What to avoid when designing velocity sinks

Case Study: Lessons from TON

The TON blockchain illustrates the velocity problem at scale. Data from the network’s 2024–2025 dynamics shows a classic imbalance.

The Numbers

More wallets activated, but transactions and fees collapsed. Meanwhile, emissions grew 26%. Tokens are being generated significantly faster than demand for their use is created.

Analysis Through MV = PQ

For TON in early 2025: daily fees ~5,200 TON. At ~$3 per TON at the time (mid-2025 prices varied significantly — from ~$6 to ~$1.4), annual fee PQ ≈ $5.7M. With velocity ~15 and circulating supply of ~2.5B (total supply ~5.1B, but roughly half is frozen in inactive early wallets):

This doesn’t mean TON is “overvalued” in the traditional sense — the market price incorporates expectations of future transaction volume growth and ecosystem development. But the model shows that current fundamental demand is insufficient to support the price without a speculative component.

Advanced Analysis: Combining Mechanisms

The most resilient projects combine multiple velocity sinks. Here’s how market leaders do it:

Ethereum combines staking (strong lock) and burning (permanent supply reduction) — resulting in one of the lowest effective velocities on the market. Curve achieves even lower velocity through extremely long locks (up to 4 years) in exchange for revenue sharing. Note: the Eff. V estimates above are approximate order-of-magnitude values based on on-chain transfer volume relative to market cap; exact numbers depend on methodology and measurement period.

Combined Effect Formula