3/ While the airdrop meta has been a highlight of this cycle, the underlying concept of rewarding user adoption is far from novel
It's actually a common strategy in Web2 bootstrapping
4/ Take Paypal:
In its early days, PayPal paid new users $10 to use its product and cover switching costs.
The idea was that paying enough people to join would eventually raise the network value, leading new users to join for free and creating a self-sustaining network
5/ Designing an effective airdrop mechanism is challenging and almost an art form. Key considerations include:
• Who to reward
• The value attributed to their efforts
• When and how to distribute rewards
6/ Most frameworks rely on guesswork, sentiment, or precedents, but using a quantitative framework ensures fairness and strategic alignment with long-term objectives.
7/ Enter the quasi-hyperbolic discounting model:
U(x)= U(t)*β*δ^t
A framework exploring how individuals make choices involving trade-offs between immediate and future rewards, especially where impulsivity and inconsistency affect decision-making.
8/ It uses two population-specific terms:
9/ Present Bias (𝛽): Measures preference for immediate rewards over future ones
1: balanced
0: favors immediate rewards
Discount Factor (𝛿): Reflects how future rewards’ value diminishes over time
1: future rewards valued more
0: steeply discounted
10/ Last year, Pantera Research Lab studied a sample of crypto users to see how they compared on these parameters to benchmark studies on normal people...
11/ Crypto users show high present bias (~0.4) and notably low discount factor indicating a tendency toward impatience and immediate gratification
Not surprising, given the natural periodicity of crypto markets and the generally speculative nature of crypto apps at the moment.
12/ This doesn't mean crypto users won’t wait
It means there’s a shorter timeframe in which they will wait for a higher reward.
In the context of airdrops, vesting schedules or gradual releases can be timed based on recipient tendencies.
13/ Just a few weeks ago, @DriftProtocol implemented a gradual release mechanism where users' rewards doubled if they waited 6 hours to claim
~85% of recipients waited to claim to receive their full allocation
1/ Solana's ecosystem has seen exceptional growth lately:
+ SOL up over 6x from Dec '22 lows
+ active addresses up ~2x
+ TVL up ~2x
But, do these figures reflect fundamental growth, or is there a disconnect between price action and network development? 🧵
2/ While Solana was initially poised as a new Defi hub when it launched, it has diversified over time.
New infra and tooling (like compressed NFTs) have yielded new consumer apps on the platform.
For most of 2023, consumer apps served as the primary gateway for first-time users
3/ However, since Breakpoint in early Nov, Solana's DeFi sector has been reignited...
especially after a series of airdrop announcements for some of the largest projects in ecosystem, leading Jupiter to be the top gateway app for the past month
In the last year, L2s exploded in growth, now representing +150% of Ethereum's daily active addresses
But, not all L2s are growing equally, and many see inflated numbers due to airdrop farmers (& sybils)
So, how do we identify which ecosystems are genuinely gaining traction? 👇
Sybil accounts create multiple fake accounts to exploit token distributions on new platforms.
They artificially inflate user numbers, which complicates the assessment of genuine user engagement and network health, hindering accurate revenue projections and sustainable growth.
My methodology for detecting airdrop sybil attacks uses directional network graphs to visualize transactional relationships between users.
I used a combination of weighted and unweighted degrees to flag suspicious accounts.