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#代币经济机制 Seeing the discussion about Jupiter's buyback this time, I suddenly recalled the lessons from the 2017 ICO wave. Back then, all projects were touting token burns and buybacks, making beautiful promises. But what was the result? Most projects' token prices were halved again and again. The fundamental issue has never been whether buybacks make sense logically, but whether the entire tokenomics design is sound.
This time, Jupiter's $70 million buyback of JUP, with little change in price, is not surprising. Looking at the trajectory of projects over the years, for buybacks to be effective, the prerequisites are quite strict — there must be no structural selling pressure, the team must have genuine long-term commitments, and the token must have real utility. Although the Solana ecosystem has been rapid, the team’s unlocking and frequent selling have never stopped over the years. Buybacks are like plugging leaks while continuously drilling holes.
Toly's suggested staking incentives are worth considering. This hits another dimension of the problem: instead of repeatedly pumping the price with money, it’s better to give holders real expected returns. A 25% APY staking reward, compared to the illusory expectations of buybacks, is at least tangible. This reminds me of some projects from early years that truly outperformed the cycle; in the end, they retained long-term holders through solid revenue mechanisms.
But I also see new risks — if staking incentives ultimately become just a guise for token dilution, then it’s back to the old path. The key is whether these projects have genuine revenue support for these promises. Jupiter’s trading volume data is indeed good, but how much real profit can it translate into? This number determines whether the entire incentive system is sustainable.
Waiting to see how they finally choose. This decision will send a signal — whether they continue with the traditional financial buyback logic or truly innovate tokenomics design. After so many cycles, good projects are already beginning to differentiate.