We’re continuing our Interview Series at StableLab—candid, technical conversations with the people shaping the future of decentralized systems.
This time, we zoomed in on incentives. How do airdrops, liquid rewards, and grants actually work under the hood? What design decisions matter most? And where are the tradeoffs hiding?
To unpack it all, we sat down with two of StableLab’s own: Christian and Johannes. What followed was a refreshingly honest, deeply technical discussion about how incentives shape behavior—and what DAOs can do better.
Meet the Guests: Christian Ziegler & Johannes Loewe
Christian and Johannes are two of the sharpest technical minds at StableLab and they’ve spent years thinking about how to make incentives actually work in practice.
Christian Ziegler leads engineering as our CTO. He’s got a PhD on DAO systems, but more importantly, he knows how to turn research into architecture. Whether it's designing mechanisms for scalable governance or helping non-technical users interact with Web3, he's focused on making complex systems usable.
Johannes Loewe heads up our AI and data work. He’s worked across everything from Bitcoin Cash to zero-knowledge systems, and brings a systems-level view to whatever he’s building. If there’s a messy problem around coordination or incentives, odds are he’s already modeling it in the background.
Three Core Incentive Models
To kick things off, Christian laid out the three main types of incentives most DAOs rely on:
Liquid incentives – Ongoing, automated rewards for actions like staking, LPing, or governance participation. Think: yield farming and liquidity mining programs.
Airdrops – One-time or scheduled token distributions, usually designed to reward past behavior or broaden ownership.
Grants – Custom funding packages, often tied to milestones, timelines, or specific deliverables.
Each model works differently under the hood—and each comes with its own assumptions, tradeoffs, and implementation challenges. Knowing which one to use (and how to use it well) is half the battle in designing effective incentive systems.
A Conversation on Incentives, Sybil Resistance, and Retro Funding
After the main walkthrough, we shifted into a deeper conversation with Christian and Johannes digging into the mechanics, tradeoffs, and design tensions behind real-world incentive systems.
From how sybil resistance actually works to the quirks of retroactive funding, we covered what happens when theory meets the messy edge of implementation.
Raphael: How do liquid incentives actually work under the hood?
Christian: They’re typically built on mechanisms like SushiSwap’s MasterChef contracts. Rewards are calculated per block and distributed based on how much a user has staked. The system only updates when triggered by a transaction—usually when someone claims rewards. Most rewards are calculated at the time of the claim. Blockchain systems require explicit transactions to change state—there’s no background processing.
Mel: So there's no automatic payout happening in the background?
Christian: Exactly. Nothing happens until someone interacts with the contract. That’s why claims are the trigger—not just for receiving rewards, but for updating everyone else’s share too.
Kene: Can DAOs tweak how those incentives behave?
Christian: Yes, governance can fine-tune parameters like the reward rate or how long the incentives run. But the architecture is relatively rigid once deployed. You have to design carefully upfront.
Raphael: How do we know if these incentives are working?
Christian: A common metric is retention. If users stay in the pool after the rewards end, that’s a sign they’re aligned with the protocol beyond just yield farming. It’s a soft but useful signal of long-term commitment.
Mel: Shifting to airdrops—what’s the usual technical approach?
Christian: Airdrops typically rely on Merkle trees. Instead of uploading thousands of individual addresses, you just upload a Merkle root. Then users prove eligibility off-chain and claim their tokens on-chain, which dramatically reduces gas costs. Merkle trees can include both address and amount data. The proof is verified on-chain, but all calculations happen off-chain, which is key for scalability.
Kene: That sounds efficient. But what’s the catch?
Johannes: Wallet visibility. If the airdrop doesn’t emit a standard transfer event, the tokens may not show up in the user’s interface. That breaks discoverability. People don’t interact with assets they don’t know they’ve received.
Kaf: Are Layer 2s changing the game here?
Christian: Absolutely. L2s are making large-scale airdrops far more feasible. With lower fees and faster settlement, we can do things that were previously cost-prohibitive on mainnet. But that also introduces new complexity in terms of coordination and infrastructure compatibility.
Raphael: Let’s talk about grants. They seem more flexible—what’s the trade-off?
Christian: Grants are less standardized. They can be distributed through vesting contracts, time locks, or multisigs. That makes them more adaptable to different types of work—but also harder to measure. If you're funding a liquidity program, your KPIs might mirror those of liquid incentives. But if you’re funding infrastructure or research, you need a completely different evaluation framework. Grants require tailored evaluation.
Nneoma: Retroactive funding is getting a lot of attention lately. What’s the biggest issue with it?
Mel: Predictability. If contributors can guess what kind of work will be rewarded retroactively, they might start optimizing for the payout instead of for real impact.
Christian: That’s a critical point. If information about evaluation criteria leaks before the cutoff date, you’ve undermined the mechanism. Timing and opaqueness matter. Early cutoffs and non-transparent processes can reduce gaming behavior.
Johannes: Also, RPGF is inherently backward-looking. It rewards things that already happened. That makes it hard to use for strategic alignment. Large or well-connected actors often have a better shot at standing out, which can entrench inequality instead of leveling the playing field.
Raphael: Sybil attacks are always a concern. How do attackers game incentive systems?
Christian: They can use exchange-funded wallets, vary their transaction patterns, or even pay humans to solve CAPTCHAs. There’s no perfect defense. The goal is to make it expensive enough to discourage abuse and subtle enough to preserve legitimacy.
Kene: What are some of the actual techniques used for detection?
Christian: Graph analysis of wallet interactions is one. You can also look at behavioral heuristics—timing, activity bursts, cross-wallet overlaps. But there’s always a trade-off. You might flag legitimate users. Off-chain signals like Discord activity help too, but they’re easily faked. Combining on-chain and off-chain data remains the most pragmatic approach.
Mel: I liked that airdrop that sent LP tokens directly to Uniswap delegates. It felt like a way to activate governance, not just distribute tokens.
Christian: That was a clever idea. Framing delegates as treasury participants can shift the mindset. But there’s a risk in dropping unfamiliar token types. Standards like ERC-20 dominate not because they’re ideal, but because they’re supported across the ecosystem. Other standards—ERC-721, ERC-1155, tokenized assets—exist, but most wallets can’t interpret them. Visibility is power. If users don’t see it, they won’t engage with it.
Incentive Design Is Governance
Designing incentives isn’t just a technical challenge, it’s a governance choice. Every decision like how you run airdrops, structure grants, defend against sybil attacks, or operate across L2s bakes in assumptions about who your DAO values, how it distributes power, and what outcomes it wants to drive.
What we learned in this session is simple: the details matter more than they seem.
The difference between claim-based and send-based airdrops affects who even notices the reward.
L2s lower costs but add friction—especially when DAOs aren’t ready to coordinate across chains.
Retroactive funding is promising, but only if it stays unpredictable. Once contributors start optimizing for payouts, you lose the signal.
Sybil resistance will never be perfect. The goal isn’t to stop abuse entirely—it’s to make it expensive enough to be rare.
Token standards impact discoverability. If users can’t see or understand what they’ve received, they won’t act on it.
At StableLab, we see incentive design as a core layer of DAO strategy—not just a line item in the treasury. Good incentives don't just reward past behavior—they shape future coordination.
And the takeaway is clear: how a DAO pays people is how it expresses its values.
For Those Interested in Diving Deeper
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Coming Up Next
In our next session, we sit down with Nick from Hats Protocol to explore how roles, responsibilities, and reputation are being reimagined through smart contract-enforced credentials. We talk about modular governance, onchain org charts, and what it means to build secure, scalable coordination layers.
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