Model Your Token Economy Before You Launch
A pie chart doesn’t prove your token works. Here’s a three-step way to simulate the real buy-and-sell pressure your business creates — before you ever reach mainnet.
Most projects can explain their token to themselves and almost no one else. The allocation pie chart says who gets what, but nothing about whether demand will ever meet supply. Our tokenomics simulation turns that static picture into a moving one — modelling realistic market pressure from your actual business metrics, usually in under fifteen minutes of setup.
Step 1 — Map your vesting
Start with the supply side: allocations, vesting timelines, maximum supply and who holds what. Crucially, specify how much each cohort is likely to sell versus hold when tokens unlock. Sell behaviour at unlock is where most launches quietly break, so it belongs in the model from the first minute.
Step 2 — Tie tokens to business KPIs
Next, connect demand to something measurable. Pick a primary KPI — transactions, monthly active users, total value locked — and define its relationship to the token. How many tokens does an active user consume per month? What share of fees flows to the treasury? You can layer in secondary KPIs and model negative-pressure scenarios too.
Step 3 — Run the model
Add the project details, run the simulation, and you get projections that translate qualitative growth hypotheses into quantifiable token pressure — dilution curves, price paths and treasury runway you can actually defend to investors.
Three worked examples
The same framework fits very different designs. A fee-per-transaction network (like an EVM execution layer) charges a small fee per transaction and routes half to treasury — model transaction growth and you get direct, growth-linked buying pressure. A mobile game keyed on monthly active users can be disaggregated into gear purchases, trading fees and tournament spend; with two million MAUs at ~$3 monthly spend, treasury inflows translate to a concrete per-user buying pressure. And a data-indexing network that pays all query revenue to participants shows more subtle secondary pressure — staking-yield optimisation drives fresh token demand as query volume grows, while penalties for misbehaviour burn supply.
Why it matters
Grounding token demand in measurable performance — rather than a hopeful projection divorced from operations — is what separates a defensible model from a nice-looking chart. It lets founders stress-test assumptions before launch and lets investors evaluate the economics on evidence.
Speculation gets you a listing. Mechanics grounded in real business metrics get you a token that still makes sense a year later.