tokenomics litepaper
Introduction
This page provides an overview on how pump.science funds research initiatives exclusively through trading volume of tokens launched on Solana. In addition, it justifies market capitalization (market cap) thresholds set for tokens launched on pump.science that fund specific research experiments. These thresholds are critical for determining the financial feasibility of supporting scientific research projects through trading fees. By tying the thresholds to measurable targets, like market cap, we ensure alignment with trader behavior while maintaining the necessary funding for research experiments.
Tokenomics
The tokenomics for pump.science tokens have been designed to maximize transparency, fairness, and funding potential. The key elements of the tokenomics structure are outlined below:
Custom Bonding Curve: Each token launch begins with a custom bonding curve, identical to the parameters used on pump.fun. The bonding curve ensures that tokens start at an initial market cap of ~$5k USD. As liquidity is added, the price increases along the bonding curve, and at a liquidity threshold of 85 SOL, liquidity is migrated to an automated market maker, Meteora.
Liquidity Migration: Upon reaching 85 SOL in liquidity:
82 SOL is migrated into a Meteora constant product liquidity pool (LP).
3 SOL is allocated to fund the first research experiment, ensuring immediate funding impact.
Anti-Bot Measures: To prevent bots from sniping early token supply without relying on whitelists or secondary token purchases, bonding curve trading fees are set to be exorbitantly high at the start. These fees decrease over time, giving users a fair opportunity to compete with bots and acquire tokens at reasonable prices.
Token Issuance:
A total of 800M tokens are issued along the bonding curve.
Upon migration to the LP, 150M tokens and 82 SOL are transferred to the LP.
50M tokens are airdropped (approximately) pro rata to holders of previously launched pump.science tokens ($URO, $RIF, etc). The airdrop allocation is based on the time-weighted average value of holdings in each wallet over a specified time period.
Airdrop Mechanism: Holders of previously launched pump.science tokens receive airdrops of future token launches. The relative value of each wallet's holdings determines the allocation, incentivizing long-term participation in token launches to secure future airdrop allocations.
Research Funding via LP Fees:
Research is funded through LP fees generated by trading activity.
The migrated liquidity is locked in the Meteora pool; however, LP tokens are not burned.
Instead, claim authority over the LP tokens is granted to pump.science, allowing the platform to use LP fees for research funding.
90% of LP fees go to the research
10% of LP fees go to pump.science as a platform fee
This tokenomics model aligns incentives across traders, researchers, and long-term token holders, creating a sustainable ecosystem for funding scientific discovery.
Market Cap Thresholds for Funding
The goal of setting market cap thresholds for pump.science launches is to find the conditions at which the compounds launched on the platform can be advanced to the next experiment based on sufficient funding. Besides the worm experiments, which are funded directly via the bonding curve liquidity, all subsequent experiments must be funded directly through LP trading fees. The three primary experiments under consideration are fly experiments (~$3k), mice experiments (~$30k), and human studies (~$50k). The question becomes at which market cap threshold are these experiments executed? Setting these market cap thresholds for funding each experiment requires a balance between feasibility and financial sustainability. The thresholds must be high enough to ensure sufficient funding, while being what is realistically achievable. Misjudging these thresholds could either stall promising experiments due to a lack of funding, erode trader confidence in the platform, or lead to financial insolvency. To address these challenges, we employ a multi-faceted approach grounded in real-world market data.
1. High-Performing Pump.Fun Token Launches
This method evaluates tokens with the highest net buy volume and lowest volume-to-market-cap ratio, offering a conservative estimate of achievable market cap and trading volume. The analysis included tokens from the top 30 by market cap on pump.fun, focusing on their volume required to achieve specific market caps.
The results (see Figure 1) demonstrate a strong linear correlation between log(volume) and log(market cap) for top performers like URO, RIF, and EVAN. Among these, RIF exhibited the most conservative volume-to-market-cap relationship, making it a reliable baseline for estimating fee revenue.
Using RIF's data, a linear model was created to predict the market cap thresholds required to generate sufficient fees for funding experiments. The calculated thresholds are as follows:
Fly Studies: $7M
Mouse Studies: $312M
Human Studies: $1.4B
The analysis also explored the impact of varying Automated Market Maker (AMM) fee percentages on market cap thresholds. Several important assumptions are made. First, that the main (locked) Raydium liquidity pool is a constant (1/3) of all volume (based on historical data). Second, an assumption is made that the fee rate has no impact on volume. A fee rate of 0.25%, widely accepted by traders, necessitated a human study market cap of ~$1.4 billion—a level unlikely to be achieved at scale. While increasing fees could lower thresholds, it risks liquidity migration to lower-fee pools, presenting a trade-off between fee revenue and market participation. Importantly, this method assumes that the research must be paid for at the moment that the market cap threshold is reached, an assumption that is removed in other methods.
2. Worst-Performing Memecoins ("Rug Pulls")
To evaluate worst-case scenarios, this method analyzed lifetime trading volume relative to peak market caps of rug-pulled tokens. Tokens were selected based on community input and media reports, and their lifetime volume was plotted against their maximum market cap. The correlation was modeled on a log-log scale (see Figure 3).
This method provided lower threshold estimates compared to top-performing tokens, primarily due to significant sell volume during a token’s decline. However, sell volume does not directly contribute to research funding, as fees are accrued in project tokens rather than SOL.
The calculated thresholds are as follows:
Fly Studies: $900k
Mouse Studies: $7M
Human Studies: $17M
The optimal market cap threshold for each experiment likely lies somewhere between these two estimates. This brings us to the third approach, which may help drive toward a closer number.
3. Weekly Volume Analysis
This method examined weekly trading volume data for Solana memecoins on Raydium, focusing on pools with 0.25% fees and locked liquidity. A two-week period (December 8-21) was analyzed to estimate sustainable fee revenue under typical trading conditions.
The results showed a strong correlation (R = 0.92, p < 2.2e-16) between market cap and weekly Raydium LP fees (see Figure 5). However, older coins with declining volume were included in the dataset, likely inflating threshold estimates. Adjusting for a longer time horizon (e.g., two weeks) produced more realistic thresholds, such as:
Fly Studies: $16M
Mouse Studies: $145M
Human Studies: $354M
Modifying the approach slightly by giving a longer time horizon, 2 weeks for example, estimates can become much more reasonable. Tokens that reach at least a 5M market cap sustained for two weeks can generate fees that are sufficient to pay for the fly experiments. However, these datapoints are still biased toward older coins, potentially skewing the fees to be artificially low relative to each market cap.
Returning to the initial analysis of RIF, but assuming that two weeks are available to capture fee revenue, rather than a single day (dividing the initial result by 14), the result is much more feasible, with thresholds of ~$500k, ~$20M, and ~$100M. For these numbers to be the thresholds, then they must be maintained as the average market cap for at least 2 weeks.
Results and Recommendations
The combined insights from all three methodologies provide a robust framework for setting market cap thresholds. The recommended ranges are:
Fly Studies: $500k
Mouse Studies: $20M
Human Studies: $100M
These thresholds balance risk with feasibility, while incorporating safeguards against worst-case scenarios (from rug pulls).
Additionally, the analysis will be reviewed periodically and updated based on evolving market conditions and expanded datasets. By refining these thresholds over time, pump.science will optimize the balance between financial viability and scientific impact.
Conclusion
By employing multiple data-driven approaches, this analysis establishes a framework for setting market cap thresholds to fund initiatives using trading volume for tokens on Solana. These thresholds optimize the trade-off between funding feasibility and trader accessibility, ensuring meaningful scientific research can be supported through tokenomics. Future efforts should focus on expanding the dataset, refining models, and incorporating additional variables, such as dynamic fees, and base market conditions (volume), to enhance threshold accuracy and scalability.
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