Token Allocation in Tokenomics Models

Token allocation isn’t just a pie chart. It’s a map of power, incentives, and timing.

Founders often approach it like a cap table (20% to team, 15% to investors, 30% to ecosystem) as if the numbers are the point. But token allocation isn’t just about who gets what. It’s about when, why, and under what conditions.

At Tokenomics.com, we audit token allocations not as fixed percentages, but as economic structures. We evaluate how ownership, unlock velocity, and insider concentration affect a token’s long-term viability.

We’ve audited over 750 models and benchmarked more than 2,500 token launches across L1s, DePIN, AI, RWA, and more. Every allocation we audit is measured against real data, from models that held value through volatility, and models that collapsed under misaligned structures. We don’t score based on theory. We score based on price performance and real outcomes.

In this document, we break down:

1. How legacy equity structures impact token launches

2. The three main frameworks for converting equity into token supply

3. What makes a token distribution structurally sound — or fragile

4. How we score allocation fairness based on data from 2,500+ launches

5. What top-performing projects consistently get right

6. Common failure patterns

How We Score Token Allocation

We don’t evaluate token allocation based on how it looks. We evaluate how it behaves — backed by data from over 2,500 launches and 750+ tokenomics audits.

In this vertical — Distribution Fairness — we measure who controls supply over time, how that control shifts, and whether the design aligns with the top-performing models in the same niche / category.

Token allocation norms vary across verticals. A launch that looks conservative for a gaming project might be aggressive for an L1 blockchain solution. That’s why we maintain niche-specific benchmarks across categories like infrastructure, DeFi, gaming, RWA, and AI.

Each project receives a score from 1 to 100, based on allocation structure, unlock dynamics, and concentration. That score is benchmarked against our tokenomics database and translated into a percentile rank.

Here’s what we analyze:

Standardized Pool Classification

We reclassify all allocation entries into five common buckets:

• Investors

• Insiders (team, advisors)

• Foundation

• Community

• Public Sale

This allows us to normalize allocation data across all projects and compare them consistently.

Fairness Benchmarking

Each model is compared against top-performing projects in the same niche. We don’t rely on arbitrary thresholds — we use actual outcomes from token launches across DePIN, L1s, AI, RWA, and more.

Gini Coefficient + Lorenz Curve

We calculate a custom Gini coefficient for each allocation to quantify how concentrated ownership is across pools. The Lorenz curve visualizes this distribution and is included directly in the audit dashboard.

• Lower Gini = more balanced ownership

• Higher Gini = increased risk of insider control or extraction

Circulating Governance Power Over Time

We model who controls circulating supply at critical milestones: TGE, Year 1, Year 2, Year 3, Year 4, and Year 5. This isn’t just about liquidity — it’s about governance influence and control.

Example : If insiders control the majority of circulating supply at launch, the protocol’s early governance is highly centralized → flagged.

We don’t just model decentralization at TGE. We track how it actually unfolds over time.

The score reflects not just initial optics, but long-term alignment between governance access and token release.

We go deeper on these in our DePIN Tokenomics Audit article.

Data Insights: What the Best Performers Actually Do

When we analyzed every token model in our database, we found clear differences between top-performing projects and the rest of the market.

Criteria:
We define “Best Performers” as projects that have been live for over two years and maintain a positive ROI — measured by Current FDV divided by Launch FDV, with a minimum threshold of 2.

The best-performing projects in our database consistently show three things:

• More to the public. Public sales average 9.49%, versus just 6.11% across the rest.

• Less to private investors. Top performers allocate just 10.44%, compared to 17.33% for the broader set. (Excludes projects with no investor rounds.)

• Higher float at launch. Initial circulating supply averages 15.18% — more than 3x the float of typical launches (4.98%). That reduces early price distortion and cushions against dilution shocks.

These aren’t surface-level choices. They shape trust, liquidity, and alignment. And the data backs it.

Shareholders Allocation: Designing Token Models After Equity Rounds

Not every project starts as a token network. Many begin as Web2 companies, raise equity across multiple years, and only later introduce a token. By that point, a large portion of the company—sometimes over 80%—is already owned by investors and early shareholders.

Once a token enters the conversation, that equity ownership becomes a constraint.

When a company with an aging equity cap table decides to launch a token, it’s not just building a new asset — it’s redistributing power. And that creates tension.

Equity holders expect upside. They backed the company early, often over multiple rounds. Now, with a token on the table, they want their share — even if that share dilutes public access or undermines long-term incentives.

The core team may be sidelined. Years later, many of the original contributors are gone. The remaining team now has to justify why they should receive a new allocation — while balancing pressure from legacy shareholders who may no longer be involved, but still hold leverage.

Token design becomes hostage to old agreements. Public float, governance access, ecosystem growth — all of it can be constrained by obligations that pre-date the token. You’re not designing from scratch. You’re negotiating around a structure that was never built for this.

And no one’s incentives are cleanly aligned.

• Founders want control.

• Shareholders want liquidity.

• Users want decentralization.

• The token needs to function.

This isn’t just a technical problem, it’s a structural one. But it’s solvable.

Three Design Models for Converting Equity to Token

Companies in this situation typically adopt one of three approaches to define how tokens are allocated to shareholders:

1. Insider Pool Allocation

Equity converts only from the team/advisors share, not the full token supply.

In this model, equity holders receive tokens pro-rata from the internal insiders/company allocation.

In this approach, the project defines a fixed percentage of the token supply for insiders (e.g. 20%, which is the market standard), and legacy equity holders take their allocation from within that slice. They don’t get additional tokens from community, public, or ecosystem allocations.

Example:

• Total token supply = 1B

• Insider pool = 30% = 300M tokens

• Equity investor owns 80% of the company

• They receive 80% of 300M = 240M tokens

Why it works:

• Public sale, community, and ecosystem allocations remain untouched

• Retains decentralization optics

• Easy to explain, especially if the token launch is years after initial fundraising

Trade-offs:

• Dilutes the core team and current contributors

• Creates internal politics around who gets what within the insider pool

• If poorly communicated, legacy shareholders may feel shortchanged

2. Fully Diluted Pro-Rata Allocation (The worst)

Equity converts into a direct slice of the full token supply.

This approach matches token allocations 1:1 with equity stakes. If someone owns 15% of the equity cap table, they get 15% of the total token supply — regardless of where it comes from.

Why it’s easy (easy ≠ works):

• Straightforward and clean math

• Requires no negotiation with existing token stakeholders

• Often expected by traditional VC investors

Why it’s risky:

• Reduces available supply for community, ecosystem, and float

• Often forces community and public allocations down to single digits

• Governance is dominated by equity holders, not contributors or users

Consequences:

• Projects that follow this model often fail to decentralize

• Price discovery suffers due to constant selling pressure

• Projects with high allocation to previous shareholders plus new allocation for token sale investors often repel Tier 1 exchanges due to non-compliance with listing conditions (e.g. investor allocation > 13%)

• Any governance system is likely to be non-functional or captured at launch

3. Fixed Conversion Ratio

Equity converts into tokens at a pre-agreed multiple (For projects who gave away more than 50% ownership we recommend a 8:1 ratio)

This model creates a buffer — equity holders get exposure, but the token retains structure and flexibility. It’s often used when a company is converting old equity rounds into a token model post-facto.

Example:

• Conversion ratio = 4:1

• Equity investor owns 20% of the company

• They receive 5% of the token supply

Why it works:

• Protects the token model from being overrun by past equity deals

• Provides a compromise that offers upside without full control

• Can be paired with lockups or additional vesting to smooth market entry

Trade-offs:

• May require renegotiation with investors

• Risk of disputes if conversion logic wasn’t clearly defined up front

• Needs strong legal alignment (especially in multi-round cap tables)

Benefit:

• Allows token governance to remain focused on contributors and community

• Helps preserve room for incentive design, float, and ecosystem growth

Choosing the Right Model

There’s no universal answer — the right structure depends on:

• How much equity is already committed

• What promises were made in side letters or SAFEs

• How much control the team wants to preserve

• Whether governance and float are design priorities or afterthoughts

But here’s the one rule that holds:

If the token is meant to represent a new system, it can’t just inherit an old ownership structure.

Conversion needs to be modeled, capped, and designed — not assumed. Or the token will fail before it starts.

Need help ? Request an Audit.

→ Submit your model for review

We’ll map the options, highlight the risks, and tell you what most teams miss.