AI is changing how the rewards from crypto airdrops are distributed.
In the past, studios made money because they had technical skills that ordinary people didn't: running interactions at scale, writing automated scripts, analyzing on-chain data, and managing accounts.
Projects needed these capabilities to generate user numbers, trading data, and market buzz, and paid for them with the promise of future tokens.
But now, AI has lowered the technical barrier.
An ordinary person can also use AI to build scripts, bots, and data tools. Studios no longer have the clear informational and technical edge they once did.
The problem is that projects have also benefited from the cost reductions brought by AI.
Tasks that once had to be outsourced to studios can now be handled in-house; on-chain data that once had to be incentivized through airdrops is no longer so scarce.
Naturally, projects will reconsider how to allocate their token budgets: should they go to addresses farming interactions, or be invested in liquidity, exchanges, and channels that can actually generate revenue?
That's why I believe traditional airdrops are becoming less attractive.
In the future, it may be more worthwhile to study early-stage projects with real profitability, as well as products that distribute tokens through public sales, user benefits, and other means.
Another direction is projects that are part meme, part airdrop farming, where users' trading, promotion, and community activity are themselves part of the product ecosystem.
But with perps and prediction markets, you have to calculate the costs carefully. Fees, slippage, and trading losses all cost real money. Points aren't the same as returns, and trading volume isn't the same as profit.
Now that AI has leveled the technical playing field for airdrop farming, the real competition is no longer about who can farm the most, but who can spot worthwhile projects early.
In the past, studios made money because they had technical skills that ordinary people didn't: running interactions at scale, writing automated scripts, analyzing on-chain data, and managing accounts.
Projects needed these capabilities to generate user numbers, trading data, and market buzz, and paid for them with the promise of future tokens.
But now, AI has lowered the technical barrier.
An ordinary person can also use AI to build scripts, bots, and data tools. Studios no longer have the clear informational and technical edge they once did.
The problem is that projects have also benefited from the cost reductions brought by AI.
Tasks that once had to be outsourced to studios can now be handled in-house; on-chain data that once had to be incentivized through airdrops is no longer so scarce.
Naturally, projects will reconsider how to allocate their token budgets: should they go to addresses farming interactions, or be invested in liquidity, exchanges, and channels that can actually generate revenue?
That's why I believe traditional airdrops are becoming less attractive.
In the future, it may be more worthwhile to study early-stage projects with real profitability, as well as products that distribute tokens through public sales, user benefits, and other means.
Another direction is projects that are part meme, part airdrop farming, where users' trading, promotion, and community activity are themselves part of the product ecosystem.
But with perps and prediction markets, you have to calculate the costs carefully. Fees, slippage, and trading losses all cost real money. Points aren't the same as returns, and trading volume isn't the same as profit.
Now that AI has leveled the technical playing field for airdrop farming, the real competition is no longer about who can farm the most, but who can spot worthwhile projects early.