Don’t DIY Your Tokenomics. These 3 Teams Did, and Burned $135K
At Simplicity Group, we've seen firsthand how hiring a full-time tokenomics lead internally can backfire. While building in-house can be effective for many roles, tokenomics is a different beast. Done poorly, it breaks it.
Here are three real examples where teams burned a combined $135,000 by trying to handle token design themselves.
1. The ChatGPT Tokenomics Plan
One team shared what looked like a solid tokenomics document: structured tables, projections, and logic. On the surface, it was presentable. But when we reviewed it, we found it had been written entirely by ChatGPT.
The internal team couldn’t explain any of the logic. Worse, there was a basic math error. The model was multiplied by 0.3 instead of 0.03, which inflated projected revenues and valuations 10x.
The result? Three to four months of modeling, planning, and preparing for investor conversations - all built on false assumptions.
What we did:
Built a proper token model
Introduced meaningful token sinks
Corrected revenue projections and valuation logic
Rebuilt vesting schedules to align with utility
2. Excel Isn't a Simulation Engine
Another client believed they had fully modelled their token economy in Excel. The spreadsheet was complex, with detailed formulas and assumptions, but fundamentally flawed.
Excel models are deterministic. They lack time progression, recursion, and behavioral logic. Circular dependencies caused errors, and the assumptions had no grounding in real user behavior.
Their projections showed a 13-14x token price in two years based largely on staking mechanics. What they missed: users don’t magically get more money. Demand didn’t justify the growth.
Five months of work, and salaries for two employees, were effectively wasted.
What we did:
Rebuilt the model in Machinations
Created a dual-token economy structure
Redesigned staking and claiming systems
Provided dynamic simulations and usable metrics
3. The Nine-Figure Fantasy
This team valued their token in the mid-nine figures. When we asked how many active users they had, the answer was under 10,000.
They had allocated 55% of the supply to staking rewards, without creating any real value in return. They assumed that as TVL increased, token price would follow. But without understanding behavioral economics or actual market mechanics, the design was unsustainable.
We raised a simple question: if the token went to zero, would the protocol still work? They said yes, which meant the token held no true value.
After two months of executive discussions and modeling, the core assumptions still didn’t hold up.
What we did:
Rebuilt the reward structure based on real incentives
Designed emissions using behavioral principles
Recalibrated allocations, vesting schedules, and valuations
Introduced three new utilities that tied token price to protocol success
The Bottom Line
Some internal hires have deep tokenomics expertise, but most don’t. In many cases, well-meaning teams spend months building flawed systems that look convincing but don’t function in real markets.
Token design demands economic modeling, simulation, game theory, and behavioral insight. Without that, you're risking your entire project.
The teams above didn’t realize that until after they had already lost time, trust, and capital.
Don’t make the same mistake.
Don’t DIY your tokenomics.