原文标题：《Follow the (fake) Data：Understand the「airdrop farming stack」and the industry around it》
Original author: Kerman Kohli
Original translation: Kaori, BlockBeats
One thing that has been bothering me more and more over the past few years is the industry’s increasing reliance on “data”. I put that in quotes because most of it is fake/not true. To show what is going on and how it works, I thought I’d write a longer post around the whole issue.
When I started researching this article, I realized how industrialized this whole thing is and how many investors have been fooled by it. The whole thing is a big joke and shows how far the industry as a whole still has to go.
Layer 1 Valuation
Our problem starts with these overpriced, overhyped tokens that investors are willing to pay billions for. All you need is a fancy whitepaper and you can distort the unit economics of it all. My research started with Dune and I found this dashboard that calculates CAC for multiple airdrops.
This is a good start, although I do want to point out that these CACs are all underestimated numbers (from the project's perspective) because they are simply: USD value ($) / claimed addresses. This calculation does not take into account the percentage that actually retained the airdrop. Given that typically only 10%-20% of addresses hold airdrops, it's safe to assume that these CAC numbers are 5x-10x higher than what you see above.
The second thing is that we have an implied hierarchy of airdrop value:
Layer 1 / layer 2 / hyped protocol = thousands of dollars spent
Small to medium apps = hundreds of dollars
Luckily, the two are not mutually exclusive! If you use the right application on the right chain, you will get both airdrops.
So ideally you want to focus your airdrop on-chain first and then interact as much as possible. Okay, but the question is what happens next?
Finding the right airdrop
Luckily for you, the airdrop hunter, there is an entire industry built just for you to find airdrops. Typically, these airdrop discovery sites require you to perform some very specific "actions" and need to have on-chain proof that you performed those actions. It doesn't matter if it was your grandma or your bot that performed the actions, just make sure the transaction is seen on-chain.
All of these “missions” platforms are really just airdrop discovery sites in disguise. This would generally not be a problem if these sites attracted high-quality users, but the users attracted using these sites tend to be highly hired and represent the short-term speculation that the industry as a whole suffers from.
Let’s reach out to our trusted friends at dApp Radar and see what airdrops might be happening at this time.
Based on this, my game plan would most likely be:
Use zkSync as my base chain
Layer Zero bridges my funds
Metamask as my chain wallet
This is all probably just my natural workflow and doesn’t require any extra work. But the question is, what do you need to do to understand where you rank in these potential airdrops? To my surprise, there is an entire community of “airdrop simulators” that have emerged. These people exist to help you understand where you stand relative to other airdrop farmers. Simply search for “airdrops” and you can find dashboards that use the standards of past airdrops to simulate how projects will distribute their airdrops.
What is fascinating is the level of detail that is plotted. Look at all the columns mapped out in this table. Any Score, Last Transaction Time, Transaction Count, Unique Contracts, Total Transaction USD Amount, Unique Active Days/Weeks/Months, Wallet Age, and Block Time.
Why bother with airdrop calculations when your “community” has already done it for you?
Gaming the system
If you're surprised at how tedious the whole thing is to plan, wait until you see the next part: If you know all the permutations and combinations of things that work for standards, then you can start automating and building efficient systems around it. I spent some time researching and found these two amazing tools. Might try them out, just to report how corrupt the whole airdrop game is.
The first is our friends at nftcopilot.com , who have built a flexible dashboard for you to automate and setup your farm.
What is amazing is the depth and detail they go into with it. In the product, you can create “Groups” where you can customize the following parameters:
1. Number of transactions routed through the bridge
2. Bridge Networks (Ethereum, Polygon, Binance Smart Chain, Arbitrum, Avalanche, Optimism, Metis, Aptos)
3. The configured random operation includes the configured chance probability, sleep interval, and maximum number of random transactions per transaction.
Let’s be clear, this is far from accretive and value-destructive for the entire ecosystem.
Counterfeit products to justify false valuations.
If you zoom out, what's really happening is that the cost to justify these airdrops is less than the potential return from the effort. Another site I found helps make the deal clearer by detailing the price and possible ROI.
Now you can use your own simplified math to figure out how much money you want to put in and how much you expect to see. Phew, that solved a real problem for everyone. In a way, it can be more profitable for VCs to rip off airdrops than to invest in actual projects. Liquidity is faster and there is less psychological burden.
As long as the cost of an airdrop is lower than the potential reward, airdrops will prevail.
Final Results
So what do you think is the result of a coin farming sub-industry built on top of these over-hyped projects? It’s just a battle to see who builds the bigger botnet industry on top of it. If the on-chain numbers are inflated, then unsuspecting actors who don’t know how to dissect the data will report what they see and ultimately deceive the end retail investor into believing that the project they are investing in has real market traction.
Take a look at the tweets below. If you were on Twitter, you might be mesmerized by this and think “Wow, this thing is really starting to work, I should buy in”. The more people believe in the data, the further this cycle perpetuates. Here are some examples of data misuse I’ve found on Twitter.
Based on what I’ve shown you in this article, do you think any of these traction metrics numbers are real? Of course not, they’re all fake. The data is fake.
Answer
Permissionless identity.
Until we actually review the metrics based on who is generating this activity, we are all fooling ourselves. Just counting the base numbers as is means you are setting the bar very low for the identities that are included (considering the cost of creating a permissionless identity is zero).
What all of the above problems have in common is that they don’t take into account past behavior or behavior in a broader context. So how do you solve the above problems if you have a stronger identity layer in crypto?
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