In my previous article on Arbitrum's Incentives Detox, I explored how one of crypto's prominent L2s navigated various funding approaches and deliberately paused incentives to reassess its strategy. That story illustrates a broader shift across crypto ecosystems, changing how resources are allocated and how behaviors are incentivized.
Today, I want to step back and examine how the space has evolved from grant funding to sophisticated incentive mechanisms. The shift from committee-driven selection to market-based mechanisms and from upfront funding to results-based rewards is redefining how resources flow.
Let’s dive in!
Defining the Distinction: Grants vs. Incentives
First, let me clarify what separates these two funding approaches:
Grants fund potential and possibility. They show faith in teams, projects, or public goods that might help an ecosystem flourish. In practice, grants have meant handing over tokens before seeing results, with varying accountability mechanisms, milestone tracking, and committees making subjective judgments about who gets what. Grants bet on what could be, not what already is.
Incentives align actions with desired outcomes. They reward verifiable achievements rather than future promises. The key difference lies in when and why the money flows—incentives are paid when specific results occur, with accountability built into the design rather than relying on trust.
This isn't just theoretical - looking at incentive programs broadly, Blockworks Research's meta-analysis found that optimized programs could generate as much as $38.64 of TVL for every $1 spent.
However, what's interesting is that when targeted correctly, approximately 70% of that activity persisted after the incentives ended, demonstrating that it's possible to create sustainable growth rather than merely renting mercenary capital.
We're witnessing this shift across many ecosystems today, similar to Arbitrum's evolution from STIP to more refined approaches after their Detox period. The conversation moves from "Who deserves funding?" to "How can we automatically reward behaviors that benefit our ecosystem?"
tl;dr: Grants fund promises; incentives reward proof.
The Problem with Using Grants to Drive Incentives
We've observed teams defaulting to grant-style setups across crypto, even when attempting to achieve incentive-style results. Instead of building systems that reward specific behaviors, they create committees that manually review applications promising future actions. Both approaches have legitimate roles:
Grant programs offer invaluable human judgment for identifying promising but unproven innovations with relatively lightweight infrastructure.
Incentive programs trade this nuanced human evaluation for algorithmic precision by designing systems that automatically track on-chain progress and distribute rewards accordingly.
These differences reflect contrasting philosophies about how funding achieves results. Traditional grants view capital as the enabler—the intervention that makes innovation possible (teams receive funding, which leads to X). Incentives flip this model, treating capital as the reward mechanism—the carrot that drives specific behaviors (teams do X to get $).
We've seen this play out across several ecosystems. Remember Avalanche Rush? That was an early example of a focused incentives program that poured $180 million in AVAX tokens into the ecosystem, resulting in TVL skyrocketing from approximately $250 million to over $12 billion at its peak. These are impressive numbers, but the activity declined significantly when the incentives were reduced. The program succeeded in getting protocols to deploy but struggled with creating lasting habits.
However, when misapplied, grant programs face inherent structural challenges. They rely heavily on trust and manual oversight without automated enforcement mechanisms. This creates accountability gaps between funding and delivery, especially when there is no built-in mechanism to recover funds if milestones aren't met.
Another consideration is that grant application processes might inadvertently favor strong communicators over strong builders. With results materializing well after funding is distributed, making real-time adjustments based on performance is difficult. These factors can complicate the measurement of return on investment and the optimization of future funding decisions.
Grant farming, anyone? 🧑🌾
Don't get me wrong; I'm not saying grants are inadequate, but they aren't designed to drive behaviors at scale with the same precision as incentive-based approaches can.
This pattern mirrors what we see in traditional finance, too. VC funding operates like grants—betting on potential and promise before seeing results—while marketing initiatives, such as pay-per-X campaigns, function like incentives, paying only for measurable outcomes. The difference is that crypto has the unique opportunity to build programmable, transparent systems that can outperform both traditional approaches by combining the best elements of each.
Incentive programs can also face challenges. Capital inefficiency remains a core problem, with Blockworks Research finding an average of approximately $5.99 in TVL added per $1 spent on incentives across programs. Plus, the tokenized nature of most incentives creates sell pressure that can undermine the very growth they're trying to catalyze, as Iron Key Capital pointed out when noting that "mercenary investors are highly incentivized to dump these token rewards on the open market, further exacerbating downward price pressure."
The good news?
Protocols are learning from these mistakes. We're seeing new designs with smooth tapers instead of hard stops, safety valves for market volatility, and small-scale testing alongside other, more traditional programs.
The opportunity lies in utilizing crypto's open nature and programmability to build something better.
Beyond TVL: Finding Better Yardsticks
Most crypto incentive programs have used TVL (Total Value Locked) as their success metric. While helpful, constant debates around its validity rage across Crypto Twitter.
The work from the Gauntlet looking into this has been eye-opening. Their analysis of various liquidity incentive programs reveals that when protocols target the right metrics, they can create structural changes that persist beyond the incentives themselves.
This debate shows a broader shift in how we measure incentive program success. Like Blockworks Research's Volume-Adjusted TVL Growth, smarter metrics are emerging, which "penalizes TVL growth when the Volume-to-TVL ratio decreases and amplifies growth when the ratio increases." This better captures whether locked capital is being put to work.
We're also seeing a shift toward measuring the cost per unit of impact, calculating the dollars of TVL, transaction volume, or revenue added per dollar of incentives to gain a clearer picture of ROI. Retention metrics help us understand how much activity sticks around after incentives end, revealing whether programs build lasting habits or attract mercenary capital. Looking at network effects shows how growth in one area affects adjacent ecosystem components, helping determine whether programs create virtuous cycles.
The best metrics strike a balance between simplicity and depth, providing actionable insights that inform future program design.
Next-Gen Incentive Mechanisms
This is where crypto creates possibilities.
By leveraging on-chain data, smart contracts, and transparent ledgers, teams are building programmable incentives that reduce bias, automate rewards, and create tighter feedback loops compared to traditional funding approaches. Several projects are pushing these boundaries:
Royco: Incentivized Action Markets
Royco Protocol represents one of the most innovative approaches to incentives I've seen. They've created what they call "Incentivized Action Markets" (IAMs) - a system where anyone can create markets around onchain actions. Think of it as a marketplace where incentive providers and action providers negotiate directly with each other.
In this model, incentive providers offer rewards for any transaction or series of transactions, while users can express how much incentive they'd need to perform those actions. This creates a price discovery mechanism for onchain behavior - something we've never had before.
Royco's work with Berachain demonstrates the possibilities here. They're building incentive curves that adjust based on user history, network conditions, and real-time metrics—no more one-size-fits-all approaches.
The protocol saw initial traction in the Berachain ecosystem and has since grown to Sonic, with more networks on the horizon. This demonstrates the market's appetite for more sophisticated incentive mechanisms.
Boost: Targeted Incentives for Specific Wallets
Boost takes a different approach, focusing on targeted incentives that match specific wallet behaviors. The platform enables anyone to deploy incentive offers to targeted wallets, allowing them to perform on-chain actions, thereby creating a personalized incentive experience.
What began as RabbitHole has evolved into a more comprehensive incentive protocol. Boost Inbox is the primary interface for users to discover and receive incentive offers based on their onchain reputation and activity. This creates a system where incentives can be tailored to individual user profiles rather than broadcast indiscriminately.
This personalization represents the next frontier in incentive design - matching rewards to specific user segments based on their behavior patterns and potential value to the ecosystem.
Arbitrum's Evolving Approach
As detailed in my previous article, Arbitrum's journey through various incentive models offers insights on where we’re headed. Their initial Short-Term Incentive Program (STIP) and Long-Term Incentives Pilot Program (LTIPP) yielded mixed results, prompting their "Incentives Detox" period—a deliberate pause to analyze what worked and what didn't.
During this detox period, several promising frameworks emerged through the community calls and working groups:
IOSG Ventures' Results-First Model proposed eliminating upfront grants entirely, suggesting protocols receive rewards only after meeting specific, measurable objectives that benefit the ecosystem.
Patterns' "ARB Incentives: User Acquisition for dApps & Protocols" proposal emphasized funding specific user acquisition strategies rather than general TVL, focusing on measurable user growth.
Entropy Advisors' DeFi Renaissance Incentive Program (DRIP) offered a mixed approach, with smaller initial payments for setup expenses and larger rewards held back only when performance targets were met. The team at Entropy made a new announcement on this initiative as this article went to print.
"Make Arbitrum Great Again" by Merkl/Jumper proposed using Merkl's automated infrastructure to distribute incentives based on customizable on-chain KPIs, making liquidity mining rewards more objective and targeted.
Service providers like Vending Machine and Gauntlet contributed analytical expertise throughout the detox process, helping the community understand past performance and design more effective frameworks.
What Grants Can Learn from Incentives
What grants can learn from incentives is clearest in the transition from committee-based selection to market-based allocation. Instead of small groups deciding which projects deserve funding, prediction markets provide a mechanism to leverage collective intelligence and directly bridge the gap between promise and proof. Prediction markets determine where resources flow based on forecasted outcomes.
A recent explainer on the landscape came from my fren Bill at Butter. TL;DR is as follows:
Prediction Markets Let people bet on future outcomes:
Polymarket - World's largest prediction market on Polygon
Kalshi - CFTC-regulated platform for event contracts
Seer - Ethereum-focused prediction market
Truemarkets - Decentralized prediction market for news events
Limitless - Specializes in short-timeframe and user-generated markets
Decision Markets utilize predictions to inform decision-making: Traders bet on the impact of each option on specific objectives, with forecasts determining which option is chosen. Since the future is unknown, these markets surface options most likely to achieve goals.
Futarchy applies decision markets to governance:
MetaDAO - Pioneering futarchy on Solana, where proposals are approved based on predicted token value impact
Conditional Funding Markets apply futarchy to treasury allocation:
Butter - Building markets that forecast proposal impact to guide funding
This shift toward prediction-driven allocation reimagines how we coordinate resources. By replacing subjective judgments with market-based mechanisms, we're creating systems that reflect collective intelligence while maintaining composability.
The evolution from simple grants to sophisticated incentive mechanisms and now to market-based allocation represents a maturation of how crypto ecosystems distribute resources. It’s an exciting space to watch (coming from a guy who spends too much time doing so).
Conclusion
As these mechanisms mature, we'll see a convergence between traditional grants and automated incentives, promising innovations receiving early support through conditional markets while demonstrating impact via verifiable on-chain metrics.
What excites me is moving beyond the binary' grants versus incentives' debate toward nuanced systems where different mechanisms serve distinct purposes: traditional grants for public goods and experimentation, results-based incentives for driving specific behaviors, prediction markets for collective intelligence, and sustainable models for ongoing support.
For builders, this means multiple funding paths based on their project and stage. For ecosystem participants, it promises greater accountability and more efficient treasury management. For service providers, a new category emerges: incentive design specialists who understand behavioral economics, tokenomics, and mechanism design.
Peace,
Sov