Author: KarenZ, Foresight News
Who made the most money from FOMO? Breaking down the top 20 profit leaders.
If you view the FOMO profit leaderboard as a record sheet, looking only at the final numbers tells you who made more, but not whether those profits came from frequent trading or a few large positions.
When combining the top 20 trades by volume, average position size, and coin-specific profits, three distinct strategies emerge: some achieved hundredfold returns by buying early, others secured absolute gains with large capital stakes in tokens that already had high market caps, and others relied on high-frequency rotation to find opportunities—yet ultimately, account rankings were still determined by one or two core positions.
As of the article's writing on September 4, the combined PnL of the top 20 was $82.3781 million, averaging $4.1189 million per person, with a median of $2.912 million.
Top-ranked Unipcs (Bonk Guy) earned $14.26 million, nearly 100 million RMB, accounting for 17.3% of the combined PnL of the top 20.
Full view of the top 20 FOMO profit leaderboard
A total of approximately 24,500 transactions were completed across 20 accounts, averaging about 1,225 transactions per account, with a median of approximately 746 transactions. The simple average of each account’s “average holding time” is about 4 days and 9 hours, with a median of 4 days. This statistic reflects account-level averages and is not weighted by the transaction volume of each trade.
Commonality one: The rankings are driven by a small number of "super positions."
The most typical example is Unipcs (Bonk Guy).
From July 15 to 17, Unipcs purchased approximately $67,495 worth of PONS in three transactions, with an average market cap of about $6.1 million. As of the time of writing, this PONS holding is worth approximately $7.43 million, representing a paper gain of about 108 times. This single core position alone is enough to explain why he has consistently held the top spot.
Beyond PONS, Unipcs has achieved over 10x paper gains on DELTA and microduck; positions in USELESS, MarsCoin, Basecat, and BOW (Longbow) have also generated approximately 1x to 3x in paper gains. Its strategy is not limited to betting on a single token, but rather continues to seek secondary opportunities that amplify returns beyond a core position.
The average holding period for Unipcs remains at 7 days and 2 hours, indicating that "frequent trading" and "holding core positions long-term" can coexist.
DumbCrayonEater is closer to "a single position changes destiny," with total profits of $8.93 million. The AI position generated approximately 345x returns, or $7.35 million; the account's average holding period reached 11 days and 21 hours—the longest among the top 20. Although the total number of trades reached around 2,200, it was the AI position, not the distribution of profits across thousands of trades, that truly determined the account's scale.
This concentration of profits is not isolated: third-place Salem’s AI generated approximately $6 million in profits; fourth-place Nate (co-founder of LONG)’s AI generated about $4.56 million; Burgz’s AI contributed approximately $3.9 million; Blockworks Research analyst AJC’s PONS position achieved a 128x return, contributing $3.03 million in paper gains; Wood’s AI generated about $2.57 million; RugDalio’s PONS contributed approximately $2.23 million; LP 1’s AI generated about $1.9 million. Roughly comparing to the total PnL of the leaderboard, these core positions typically account for over 80% to 90% of an account’s profits.
Salem's total PnL is $6.2969 million. He invested $9,913 when AI's market cap was approximately $370,000, making multiple trades early on; even as AI's market cap rose to millions, tens of millions, and eventually around $200 million, he continued to increase his position. After subsequent additions, his average entry market cap rose to approximately $16.2 million, yet AI still generated about $6 million in profit for him, accounting for roughly 95% of his total PnL.
Nate's primary profits also come from AI. Nate first bought approximately $773 worth of AI when its market cap was under $100,000; later, he acquired multiple tokens as AI's market cap rose to millions and tens of millions of dollars. FOMO account data shows he accumulated a total investment of about $50,000, with an average purchase market cap of approximately $2.7 million. His AI position has an unrealized profit of about $4.56 million, accounting for roughly 92% of his total PnL.
WLFI advisor ogle ranks seventh. On July 14, he first purchased $4,974 when PONS had a market cap of approximately $470,000, followed by subsequent transfers, receipts, additions, and reductions in position. Due to subsequent trading volumes far exceeding the initial purchase, his aggregated investment amounted to approximately $3.72 million, with an average purchase market cap of about $157 million; as of writing, PONS has generated approximately $3.77 million in profit for him, accounting for over 99% of his total PnL.
Among the top 20, some traders accumulated results using multiple medium-leverage positions. Frogman’s CASHCAT and MarsCoin contributed approximately $1.03 million in realized gains (doubled) and $1.12 million in paper gains (3x), respectively; Avast’s MarsCoin and CASHCAT contributed approximately $2.45 million and $1.17 million (4x), respectively; change’s profits came from VVV (124%), MOLT (155%), STONKBROKER (69%), and leveraged positions combined.
This set of differences shows that a "super position" does not necessarily mean buying only one coin. More accurately, it means that the majority of the account's profits ultimately concentrate in one to three positions that clearly outperform all others, rather than being evenly distributed across all trades.
Second commonality: They bet not just on tokens, but on the window of opportunity for ecosystem launch.
Based on the large position sizes listed in the table, at least 15 of the top 20 have significant profits tied to AI or PONS: AI appears in 9 accounts' large positions, and PONS appears in 7.
PONS is closely tied to narratives such as AI, Robinhood Chain, launchpads, tokenized asset pairing, and fee rebates. Positions like CASHCAT, MarsCoin, and "Niu Lai" represent traders' bets on the热度 of new narratives.
They may not be on the same chain, but they share similar temporal characteristics: both are in the stage of rapidly attracting capital and attention within a new ecosystem.
The leaderboard includes individuals who achieved hundredfold returns by buying extremely early, as well as those who made large investments after the token had already reached medium or even high market capitalization. Unipcs, DumbCrayonEater, AJC, Wood, and Cardinal Saint emphasize the high multiples from early pricing; Frogman, Avast, Cosby, and "230" highlight acquiring absolute returns with large capital after certainty increased.
Therefore, being "early" doesn't necessarily mean buying in the first minute or on the first day of listing. More importantly, it means completing your research and building a position aligned with your judgment before ecosystem liquidity, users, and attention are fully unleashed. Buying extremely early increases potential multipliers, while weighting heavily later increases absolute profits—both can land you on the leaderboard, but their risk profiles are entirely different.
Commonality three: High frequency and long-term holding are not contradictory.
Looking at the top 20 by number of trades and average holding time reveals that "trading frequency" and "holding patience" are not on the same axis.
Frank is the most typical high-frequency account: approximately 4,400 trades with an average holding time of just 1 day and 7 hours; Change has about 2,700 trades with an average holding time of 2 days and 9 hours; Burgz has around 2,400 trades with an average holding time of 1 day and 17 hours. These accounts indeed exhibit rapid rotation characteristics.
But high trading volume doesn’t mean the core position is held for a short time. Unipcs has about 2,600 trades, yet an average holding period of 7 days and 2 hours; DumbCrayonEater has around 2,200 trades with an average holding period of 11 days and 21 hours; Nate has approximately 1,900 trades and an average holding period of 7 days and 10 hours. They likely hold their core positions, in which they have strong conviction, for extended periods alongside numerous peripheral trades.
On the other end are accounts that trade selectively. “230” has only 80 transactions, while Cosby, LP 1, Frogman, RugDalio, and ogle have approximately 220, 203, 235, 236, and 252 transactions respectively. Low frequency does not necessarily mean long-term holding: RugDalio’s average holding time is just 2 days and 9 hours, while ogle’s is 7 days and 5 hours. Transaction volume, average holding time, and position concentration must be considered together.
Therefore, existing data can support descriptions such as "high-frequency rotation," "low-frequency concentration," and "core position long-term holding," but it cannot directly prove that someone can consistently buy low and sell high.
For them, high frequency is a tool for identifying opportunities or managing risk; it’s the large-position trades that determine rankings.
Commonality four: Most large gains remain unrealized.
Based on the currently verifiable position status, most of the large positions on the leaderboard still include unrealized gains and have not been fully cashed out.
Ethermonk is one of the few cases where realized gains can be clearly observed. His CASHCAT position has realized approximately $1.45 million in profits, with a return of about 55%; his "Niu Lai" position has realized approximately $794,000 in profits, with a return of about 122%, and both positions have been fully closed. Meanwhile, he still holds a floating profit position in microduck, with an unrealized gain of approximately $420,000, or about 1.3x.
This is a more comprehensive position management approach: closed positions secure profits, while open positions retain the potential for further gains. Compared to viewing only total PnL, this breakdown better reflects the current price risk still carried by the account.
What did this ranking truly teach us?
First, assess whether the ecosystem can sustainably generate new value, then examine the specific token. When a new ecosystem launches, narratives and attention can attract initial funding, but whether this momentum can be sustained depends on actual revenue, trading volume, liquidity, and user growth. Only when these metrics continue to improve can the token’s value capture logic be further validated.
Second, valuation can be assessed by comparing it to similar projects. It’s hard to determine if a token is expensive or cheap just by looking at its market cap of $10 million or $100 million. A more effective approach is to compare it with similar launchpads, meme leaders, or ecosystem tokens on other chains to identify potential undervaluation.
Third, large outcomes require both low costs and sufficient position size. High multiples typically come from earlier entry points, and substantial profits also depend on the scale of investment.
Fourth, being bullish doesn’t mean holding forever. You can retain a core position that determines your account’s upper limit, or take profits in stages and recover your principal as the price rises. Ethermonk has fully closed positions in CASHCAT and “Ox Coming,” while still holding microduck—these represent two different risk profiles.
Fifth, the concentrated accumulation by top accounts reflects the formation of a fundamental consensus. The top 20 frequently build large positions sequentially on the same two tokens, indicating that narratives and attention are indeed important clues for identifying opportunities; however, when such concentration is already visible on the leaderboard, the entry cost, potential multiple, and exit liquidity for later participants may be entirely different.
Finally, it is essential to recognize the survivorship bias behind the rankings. Early entry, concentrated positions, and long-term holding can yield hundreds of times returns—or lead to losses approaching zero. The profit leaderboard only shows accounts that succeeded and remained at the top; it cannot be used to infer that others using the same strategy generally profit.
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