Stakeholders in Tokenomics

Most projects start tokenomics design with a pie chart: 30% team, 20% investors, 15% ecosystem. But where did these numbers come from? Why 30% and not 15%? Why these specific groups? Without answering “for whom are we designing and why each group needs the token”, the allocation is random — not designed.

Stakeholders are the starting point of any token economic model. Their composition, motivation, and time horizon determine every subsequent decision: allocation, vesting, supply model, utility mechanisms. This article provides a complete stakeholder typology, an analytical framework for their interests, and a practical approach to balancing them.

What Are Stakeholders

A stakeholder is any participant who influences the ecosystem or depends on it. In traditional loyalty systems (Web2), the project fully controls the economy: issues points, sets exchange rules, and can change terms unilaterally.

In tokenized systems (Web3), the situation is fundamentally different:

Stakeholder Typology

Stakeholders fall into two groups by their interaction horizon: short-term and long-term. This distinction is critical for design: short-term participants create liquidity and attention, long-term ones create sustainability and value.

Short-Term Stakeholders

These participants come for quick gains. They don’t plan to stay in the ecosystem for years, but they serve a vital function — bringing capital and attention.

Long-Term Stakeholders

These participants form the ecosystem’s foundation. Their departure destroys the system, so tokenomics must provide them with sustainable motivation.

Stakeholder Matrix

The typology shows who participates in the system. But for designing tokenomics, you need a practical tool — the stakeholder matrix. For each participant, define three parameters: motivation, token acquisition model, and holding horizon.

Conflicts of Interest

The hardest part of working with stakeholders is managing conflicts. Each group has its own utility function, and these functions contradict each other.

Worked examples of R and C by group:

Team vs Investors

Resolution: investor cliff no shorter than 6 months post-TGE, team vesting at least 12 months longer than investor vesting. A common concrete instantiation of this rule in 2025 is team 4-year vesting with a 1-year cliff against investor 2–3-year vesting with a 6-month cliff — giving the team roughly 12–24 months of additional runway beyond the investor tail.

Investors vs Users

Investors receive tokens at a discount to market price. When vesting ends, they sell — creating price pressure. Users who bought on the open market lose value.

Resolution: smooth vesting (monthly, not quarterly), cliff after price stabilization, utility mechanisms (staking, governance) that create alternatives to selling.

Traders vs Liquidity Providers

Traders profit from volatility. Liquidity providers earn from fees but lose to impermanent loss (IL), which grows with volatility. The better it is for traders, the worse for LPs.

Resolution: concentrated liquidity, dynamic fees, IL compensation through additional reward incentives.

Quantitative Conflict Example

Consider a project with total supply of 100,000,000 tokens. The 20 / 15 / 30 / 20 / 15 split below is illustrative only, not a recommendation. For context, 2025 industry benchmarks cluster as: Community + Ecosystem 35–45% (often > 50% combined), Team 17–20%, Investors 12–18%, Treasury 20–25%. The example here runs Community slightly low to keep the arithmetic clean — see vesting benchmarks for current ranges.

In month 13, investors create 7.1x more sell pressure than the team (liquidity vesting has already finished by then). This is a predictable conflict — it must be offset by utility mechanisms that create demand.

import json

stakeholders = {
    "Team":       {"alloc": 20_000_000, "cliff": 12, "vesting": 48, "tge": 0.00, "p_sell": 0.10},
    "Investors":  {"alloc": 15_000_000, "cliff": 6,  "vesting": 24, "tge": 0.05, "p_sell": 0.50},
    "Liquidity":  {"alloc": 15_000_000, "cliff": 0,  "vesting": 12, "tge": 0.20, "p_sell": 0.30},
}

results = []
for month in range(1, 49):
    pressure = {}
    for name, s in stakeholders.items():
        tge_tokens = s["alloc"] * s["tge"]
        remaining = s["alloc"] - tge_tokens
        vesting_months = s["vesting"] - s["cliff"]
        if vesting_months <= 0:
            monthly_unlock = 0
        elif month <= s["cliff"]:
            monthly_unlock = 0
        elif month <= s["vesting"]:
            monthly_unlock = remaining / vesting_months
        else:
            monthly_unlock = 0
        pressure[name] = round(monthly_unlock * s["p_sell"])
    pressure["month"] = month
    pressure["total"] = sum(v for k, v in pressure.items() if k != "month")
    results.append(pressure)

# Peak pressure months
peak = max(results, key=lambda x: x["total"])
print(f"Peak pressure: month {peak['month']}, {peak['total']:,} tokens")
for name in stakeholders:
    print(f"  {name}: {peak[name]:,}")

Common Stakeholder Analysis Mistakes

Mistake checklist

From Stakeholders to Supply Models

Stakeholder analysis is the second stage of the tokenomics design process. The output is a completed matrix where each stakeholder’s motivation, acquisition model, and horizon are defined. The matrix then drives the choice of supply models:

Each stakeholder maps to a specific supply model. If you can’t find a model for a stakeholder, reconsider whether they need the token at all. Sometimes stablecoin payments or fiat compensation is a more honest solution than tokenization for its own sake.

Stakeholders also define requirements for mechanism design: what incentives and penalties are needed so that honest behavior by each participant leads to system-wide sustainability.