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Chronicles

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Nansen: 86% of traders lost $251M in total on the Solana memecoin Libra that Argentine President Milei promoted, while the remaining traders profited $180M

- Citing onchain data, Nansen Research said that through Tuesday, 86% of traders lost a total of $251 million on the LIBRA token …

The Block RT Watson

Context & Ripple Effects

Milei’s now-deleted promotion was followed by LIBRA’s rapid rise and collapse; this analysis puts a wallet-level loss distribution behind that earlier boom-and-crash episode.

The result also fits subsequent reporting that retail traders often absorbed losses in Solana memecoin markets, rather than sharing evenly in the gains created during a token’s brief surge.

First-order effects

  • Nansen’s data indicates that most LIBRA traders were left with aggregate losses of $251 million, while a smaller set of traders captured $180 million in profits.
  • The figures sharpen the economic consequences of Milei’s promotion for affected holders and add scrutiny to LIBRA’s trading dynamics.

Second-order effects

  • Onchain analytics becomes more central to evaluating memecoin promotions: headline market-cap moves alone obscure which participants realized gains and which remained exposed to the collapse.
  • The uneven outcome reinforces reputational risk for prominent endorsers and for Solana-linked memecoin venues when politically associated tokens draw retail participation.

Third-order effects

  • If similar distributions recur, political-brand memecoins may deepen the crypto legitimacy gap by making public endorsements look less like market access and more like highly asymmetric speculation.
  • Repeated wallet-level evidence of concentrated gains could increase pressure for clearer disclosure and accountability around token promotion, though the corpus does not establish what policy response would follow.

The trend: LIBRA is part of a broader pattern in which celebrity- and politician-linked memecoins generate concentrated gains for a minority while many later buyers bear losses.