Project Tokenomics Consulting
Most tokenomics problems surface not on TGE day but in 6-18 months. Team releases token with aggressive emission, a few months everything grows—then investor unlock cliff, +300% circulating supply in one week, price collapses. Users see: "token fell" and leave. Product could be excellent—tokenomics killed it.
Tokenomics consulting is primarily work with numbers and incentives, not narrative.
What Actually Gets Analyzed
Supply Schedule and Inflation Modeling
First thing we build—complete emission model over time. All token issuance sources: investor unlocks, team/advisor vesting, ecosystem fund, staking rewards, liquidity mining. All on single timeline.
Example problem model:
Month 0: Circulating supply 5% (public sale)
Month 6: +10% (investors unlock 50% in 1 month)
Month 12: +15% (team cliff + first year vesting)
Month 12: Peak selling pressure coincides with bull cycle end
Right model:
Month 0: Circulating supply 8%
Month 6: +2% (investors: 6-month cliff, 24-month linear)
Month 12: +2.5% (team: 12-month cliff, 36-month linear)
Even dilution, no shock unlocks
We calculate:
- Fully diluted valuation (FDV) vs market cap — big gap = future sell pressure
- Token velocity — how fast tokens circulate between holders (high velocity = low price pressure from accumulation)
- Real yield for stakers vs inflation rate — if giving 20% APY staking rewards but 30% inflation—staker loses real value
Value Capture Mechanisms
Critical question: why should the token have value? Answer must be concrete.
Weak answer: "token needed for governance". Governance token has value only if controlled parameters truly matter and are profitable.
Strong value capture mechanics:
- Fee sharing. Protocol takes X% from transactions, distributes to stakers. Real money → real yield.
- Burn-and-mint. Service paid in stablecoin, contract buys and burns token. Demand created by usage.
- Work token. To participate in network need locked tokens. Staking = skin in game, not just reward.
- Governance over treasury. If treasury truly valuable (protocol revenue)—governance token has real option value.
Game Theory and Staking
Staking reward solves: why hold token instead of sell. But mechanism must be designed with game theory.
Staking incentives analysis:
Problem: if staking reward = 100% of inflation,
then stakers don't get diluted, non-stakers lose.
This incentivizes staking but not buying (purchase = inflation negated by stake).
Better model: part of staking reward from protocol revenue (real yield),
part from inflation. Inflation part decreases over time.
APY = protocol_revenue / staked_supply + inflation_rate * (1 - staking_ratio)
As staking_ratio grows → inflation dilution decreases for stakers
→ APY decreases → some unstake → balance
Distribution Analysis
We analyze on-chain holders (Etherscan, Nansen, Dune): do top-10 hold X%—is this concentration threatening decentralization? If one organization controls >33% tokens, it can block governance. >50%—make decisions alone.
For governance tokens: voter participation analysis, whale dominance, delegation patterns. If all votes pass with 95% support—either high trust or low participation with whale dominance.
Numerical Modeling
We build agent-based simulation or at minimum deterministic model:
# Simplified supply/demand dynamics model
import pandas as pd
import numpy as np
def simulate_tokenomics(
initial_supply: float,
monthly_emissions: list, # by months
burn_rate: float, # % of trading volume
trading_volume_growth: float, # CAGR
initial_price: float,
months: int = 48
):
results = []
supply = initial_supply
price = initial_price
volume = 1_000_000 # initial volume $
for month in range(months):
# Emission
emission = monthly_emissions[month] if month < len(monthly_emissions) else 0
supply += emission
# Burn
burn = (volume * burn_rate) / price # token quantity
supply = max(0, supply - burn)
# Volume grows by CAGR
volume *= (1 + trading_volume_growth) ** (1/12)
# Simplified price: P = demand_constant / supply
# In real model: demand_constant changes too
price = (volume * 0.1) / supply # crude approximation
results.append({
'month': month,
'supply': supply,
'price': price,
'fdv': price * max_supply,
'burn_per_month': burn,
'emission_per_month': emission,
'net_change': emission - burn
})
return pd.DataFrame(results)
Even simplified model reveals obvious problems: if month 12 net_change strongly positive and you have no counterbalancing demand—red flag.
Common Errors We Fix
Linear vesting without lockup. Investors bought at preseed $0.01, TGE price $1—they have x100 profit from day one of vesting. Need cliff (minimum 6 months for investors, 12 for team) before vesting starts.
Staking reward from inflation. Project promises 300% APY—sounds attractive. Reality: 300% annual inflation. If token has no real demand growth—price falls faster than rewards accumulate.
Governance without stakes. Governance token cheap to buy for malicious governance decision. Counter-design: timelock on decision execution, quorum requirements, veto mechanics.
Circular staking. Stake token → get more of same token. No external yield source—pure inflation scheme. Works with new user growth (Ponzi mechanics), collapses when growth stops.
Overcomplicated tokenomics. Three tokens (governance + utility + ve-locked), while team can't clearly explain why each exists. Complexity without purpose reduces adoption. Start with minimal design.
Consulting Format
Audit of existing model (3-5 days). Analyze: current allocation, vesting schedules, emission mechanics, token use cases, on-chain data if token already launched. Output: document with key risks and recommendations.
Design from scratch (2-4 weeks). Joint work on: utility definition, allocation + vesting, emission schedule, capture mechanisms, governance design. Output: complete tokenomics document + spreadsheet model + implementation recommendations.
Ongoing advisorship. Monthly review of metrics: token velocity, staking ratio, governance participation, sell pressure indicators (exchange inflows, whale movements).
Consulting useful before smart contracts written—changing tokenomics post-launch incomparably harder and costlier.