Abstract. This article traces the genealogy of “memes” from cultural replicators to internet-native formats and on-chain assets. We synthesize foundational memetics (Dawkins, Blackmore), attention economics (Simon), diffusion science (structural virality; truth vs. falsehood), and cryptomarket microstructure (AMMs, fee mechanics) to explain how memes emerged and why institutional capital now engages them. A case study of $TRUMP illustrates contemporary dynamics—fee extraction, exchange incentives, wallet concentration, and policy risk. We close with practical implications for creators and arts businesses—especially jazz—whose survival now depends on mastering attention-to-liquidity pipelines.
Dawkins coined “meme” in 1976 to denote a unit of cultural transmission (a tune, catchphrase, fashion) that propagates by imitation—selected for fecundity, fidelity, and longevity. Blackmore later elaborated memetics as a Darwinian process with those three selection criteria. Shifman and Milner then situated internet memes as a remix-centric, participatory folk culture that shapes public discourse.
Herbert Simon anticipated the enabling substrate: the attention economy. In information-rich environments, attention becomes the bottleneck; “a wealth of information creates a poverty of attention,” making systems that allocate attention efficiently especially powerful—precisely what meme formats and platforms optimize.
Two empirical pillars explain online spread. First, structural virality: content diffusion ranges from broadcast-like bursts to deep, person-to-person cascades; viral events occupy a spectrum, not a single mechanism. Second, falsehood outruns truth: a large-scale Science study found false news on Twitter traveled “farther, faster, deeper, and more broadly,” particularly in politics—largely due to human novelty-seeking rather than bots. These results explain why memetic frames—short, surprising, identity-relevant—regularly dominate attention.
Once attention is the scarce asset, on-chain tokens become machines that financialize attention. The first breakout was Dogecoin, a 2013 joke that reached an ~$88B market cap during 2021’s mania. Shiba Inu (SHIB) briefly neared ~$40B at its October 2021 peak. In 2024–25, PEPE showed how fast new memes can accumulate billions in value. These orders of magnitude are not anecdotal; they are documented across wire services and market data providers.
On-chain price formation for most meme-coins happens on automated market makers (AMMs). In Uniswap v2 the constant-product rule $x\cdot y = k$ governs pricing, with a 0.30% swap fee accruing to LPs; fee introduction increases k, transferring value to liquidity providers. Concentrated-liquidity AMMs (e.g., Uniswap v3; Solana’s Raydium CLMM) add fee tiers and range-bound liquidity, altering slippage/impermanent-loss profiles.
On Solana, 2024–25 memecoin cycles leveraged ultra-low fees and launchpads to drive DEX volumes that at times surpassed Ethereum’s, shifting primary price discovery for long tail assets to Solana DEXs (e.g., Raydium). Data from Kaiko and DefiLlama-based analyses confirm multiple periods where Solana’s DEX flow led.
Raydium’s documentation shows fee tiers (e.g., 25 bps for standard swaps, multiple tiers for CLMM) and revenue splits (LPs, buybacks, treasury), which help explain the substantial fee drag on highly churned meme pairs and why early LPs/platforms can capture large rents from speculative flow.
ETFs. U.S. approval of spot Bitcoin ETFs (Jan 10, 2024) and spot Ether ETFs (July 2024) normalized crypto exposure for pensions, RIAs, and treasuries. Even if ETFs target BTC/ETH, their marketing and flows lift the whole attention stack, including memes, via wealth effects and on-ramps.
Brokerages/exchanges. During prior cycles, Robinhood disclosed that Dogecoin comprised ~62% of its crypto transaction revenue (Q2’21)—a clean institutional datapoint that retail order flow congregates around memes when access is easy. Centralized exchanges race to list trending tokens to internalize volumes/fees; the $TRUMP episode (below) shows how listing latencies compress when attention is maximal.
Venture capital. After a 2022–23 retrenchment, crypto VC deal value rebounded in late-2024/early-2025 per PitchBook/Blockworks, with allocators funding infra and consumer memes’ tooling (launchpads, wallets, perps). This capital directly scales the attention-to-liquidity conveyor belt (faster token launches, deeper perps, more market-making), which feeds back into memecoin cycles.
Launched January 2025 around the inauguration window, $TRUMP became a natural experiment in attention monetization: within two weeks, $86–100M in trading fees accrued; subsequent analyses estimated cumulative $320–350M to entities tied to the promoters by spring 2025. Exchanges accelerated listings; wallet concentration remained high; and top holders were incentivized with a private dinner (“dinner with the President”) that itself catalyzed further buying, reportedly totaling ~$148M from invite-seekers. Ethics groups and lawmakers called for inquiries into conflicts of interest. Price action traced the classic meme S-curve: an initial spike into tens of billions FDV, followed by deep drawdowns.
Interpretation. The $TRUMP episode compresses the full stack: (i) memetic identity → instant attention; (ii) AMM fee machinery → platform/LP rent capture; (iii) CEX incentives → rapid listings, more flow; (iv) policy risk → ethics probes; (v) wealth transfer → fee-takers and large holders vs. late retail.
Attention is the raw input of every cultural business; liquidity is attention’s hardest currency. The same mechanics that rocket a meme-coin can amplify a jazz label, venue, or artist cooperative—if they design for memetic spread, convert attention into patronage/ownership, and understand market microstructure (fees, slippage, liquidity depth) so they don’t leak value to intermediaries. For a practical orientation to building durable artistic infrastructure in this environment, jazz entrepreneurs should study programs like Legacy Builder (The Department of Jazz) to map brand → community → product → capital in a way that compounds rather than cannibalizes artistry: https://whop.com/thedepartmentofjazz
A) Event-study template (memes, listings, or sponsorship announcements). Goal: estimate abnormal returns (AR) and cumulative abnormal returns (CAR) around an event date $t_0$ (e.g., token listing, celebrity endorsement, dinner announcement).
Estimation window: e.g., $[t_0-120,, t_0-6]$. Model expected returns via CAPM or market model (use BTC/ETH/SOL composite as “market”).
Event window: e.g., $[t_0-2,, t_0+5]$. Compute $AR_t = R_t - \hat{\alpha}-\hat{\beta}R^{mkt}_t$.
Aggregate: $CAR(\tau_1,\tau_2)=\sum_{\tau=\tau_1}^{\tau_2} AR_\tau$; test with standardized cross-sectional t-stats or nonparametrics (BMP/Generalized Sign).
Robustness: winsorize; volume/volatility adjustments; intraday bins for microstructure. Canonical references: MacKinlay (1997); Campbell, Lo & MacKinlay (1997); Kothari & Warner (2007).
B) AMM fee-drag calculator (spot DEX). For a swap of size $dx$ into reserves $(x,y)$ with fee $f$: effective input $dx_e = dx(1-f)$; output
Fee drag (difference vs. no-fee output) is
Aggregate over $N$ swaps in volatile markets to estimate total value leaked to LPs/protocols. Use Raydium/Uniswap fee schedules for $f$ (e.g., 25–100 bps on CLMM; 30 bps on Uniswap v2).
C) On-chain concentration and governance risk. Create a dashboard with: (i) Herfindahl-Hirschman Index over top-k holders; (ii) Gini of token balances; (iii) insider unlock timeline; (iv) DEX vs. CEX float; (v) effective circulating supply after excluding illiquid/vesting wallets. Interpret spikes in price alongside steady or rising HHI as elevated rug/exit risk. Apply to $TRUMP/SHIB/PEPE to compare retail vs. whale dominance (pair with event-study around listing/dinner announcements). (Empirical context in the $TRUMP reporting.)
D) Solana vs. Ethereum route optimizer. When capturing memetic flow (e.g., artist tokens, patron badges), run daily: (i) DEX volume share by chain; (ii) median slippage at $1k/$10k/$100k notional; (iii) 30-day fee-drag; (iv) failed tx rates. Prior Kaiko/DefiLlama series show Solana frequently leading during meme surges; re-compute locally to avoid overfitting to past cycles.
Memes are not frivolous ephemera; they’re attention primitives. Internet platforms turned those primitives into scalable diffusion structures. Blockchains turned diffusion into price, via AMMs and exchange incentives. Institutions—ETFs, brokerages, and VCs—then underwrite the rails that magnify and monetize attention. The lesson for culture businesses (and jazz, in particular) is stark: design for memetic spread, own the fee surface, and convert audience energy into durable capital structures—or be arbitraged by those who do. For a practical on-ramp, study Legacy Builder and build operations that can survive in an attention-scarce, fee-dense world: https://whop.com/thedepartmentofjazz
Dawkins, The Selfish Gene; Blackmore, The Meme Machine; Milner, The World Made Meme; Shifman, Memes in Digital Culture.
Simon, “Designing Organizations for an Information-Rich World” (1971).
Goel, Anderson, Hofman, Watts, “The Structural Virality of Online Diffusion.”
Vosoughi, Roy, Aral, “The spread of true and false news online.”
Uniswap v2 whitepaper & docs; Uniswap v3 concentrated liquidity.
Raydium docs (fees & CLMM tiers).
Kaiko research on Solana vs. Ethereum DEX volumes.
SEC approvals: spot Bitcoin ETFs (Jan 10, 2024); spot Ether ETFs (July 2024).
Robinhood Q2’21 crypto: 62% DOGE revenue concentration.
DOGE/SHIB/PEPE caps and peaks (2021–2025).
PitchBook/Blockworks on 2024–25 crypto VC rebound.
$TRUMP: fees, dinner economics, concentration, listings.
Where possible, we’ve anchored key claims in primary/official documentation (SEC orders, protocol docs) and high-quality research (peer-reviewed journals, Kaiko, Reuters). For any empirical replication (event studies, fee-drag estimates, concentration metrics), the appendix gives you templates to reproduce and extend the results for your specific assets or campaigns.