At DePIN Day Denver, Connor Lovely of Proof of Coverage moderated a high-level discussion on DePIN tokenomics with Robert Kochling (1kx), Dylan Bane (Messari), and Tom Trowbridge (Fluence). The panel explored the evolution of token models for decentralized infrastructure networks, focusing on real-world costs, revenue generation, price volatility, and governance.
This conversation offers a blueprint for founders navigating token design in a sector where real-world infrastructure meets crypto-native incentives
Why DePIN Tokenomics Are Different
Unlike DeFi or purely digital protocols, DePIN networks rely on physical infrastructure with real-world costs and revenue. This changes the game:
- Costs are tangible: hardware, maintenance, energy.
- Revenue is predictable: data buyers, storage, compute.
- Token value can be linked directly to network usage.
Tom Trowbridge emphasized that this allows for clearer attribution of revenue to token value through mechanisms like buy-and-burn. In contrast, traditional protocols like Ethereum or Solana lack this direct link, making token modeling more speculative.
Tools for Modeling and Forecasting
Robert Kochling described using classic economics combined with modern simulation tools like agent-based Python models. Core inputs include:
- Cost structures for suppliers
- Revenue projections
- Blockchain and payment infrastructure costs
These simulations help founders test different incentive structures and issuance schedules before deployment.
Managing Speculative Premium and Volatility
DePIN tokens often experience wild valuation swings. The panelists advised:
- Tie incentives to dollar-based values (e.g. fixed USD-equivalent payouts in tokens)
- Design token flows that buffer volatility for providers
- Recognize risks from both upward and downward price movements
Protocols like Helium and Filecoin illustrate what happens when speculation distorts incentive alignment. Robert advocated for treating early contributors as “venture miners” who knowingly take on risk for future upside, but stressed the importance of predictable, fair compensation.
B2B vs Retail: Different Incentive Models
The panel drew a distinction between:
- Enterprise-focused DePINs: need SLAs, high reliability, and stable incentives. Buy-and-burn is effective here.
- Retail-focused DePINs: favor lightweight incentives, abstracted crypto UX, and may benefit from viral growth tactics like multi-level marketing (example: Grass).
Each segment demands its own economic architecture
How Fluence Structures Token Incentives
Tom detailed Fluence’s dual-track system:
- Staking for trust: Nodes must stake Fluence tokens to provide compute.
- USD-denominated rewards: Providers receive stable USD-value rewards in tokens, with 6-month vesting.
This system aims to attract institutional providers by offering downside protection while maintaining long-term upside.
Stablecoins vs Native Tokens
Is a native token even necessary? Not always. Some projects (like Spexi or Glow) operate using USDC. But the panel agreed that native tokens:
- Enable upside for early contributors
- Bootstrap initial network growth
- Introduce flexibility for governance and ecosystem development
Still, stablecoins may work for early-stage networks focused on reliability and fiat-denominated revenue.
Governance: When and How to Decentralize
Governance should come later in the project lifecycle. Robert stressed:
- Avoid complexity early on
- Plan for how emissions impact voting power
- Use layered models (e.g. councils or committees) to mitigate centralization risks
Fluence took the opposite path, launching with on-chain governance from day one. Their system includes a DAO-elected council with veto power but limited terms.
Multi-Chain DePINs: Access vs Complexity
Launching on multiple chains can:
- Expand access to different user bases
- Improve token liquidity
- Introduce infrastructure and UX challenges
Panelists recommended clarity on the rationale: liquidity? user experience? or investor access? Without clear goals, multi-chain deployments risk unnecessary complexity.
