67% Of Ethereum Stablecoin Transfers Are P2P, Yet Institutions Dominate Volume

bitcoinistPublished on 2025-12-24Last updated on 2025-12-24

Abstract

A new analysis of Ethereum stablecoin transactions reveals that while peer-to-peer (P2P) transfers dominate in frequency, institutional activity accounts for the vast majority of transaction volume. According to data shared by an Ethereum Foundation member, 67% of USDT and USDC transfers on the Ethereum network from August 2024 to 2025 were P2P, typically associated with retail users. However, these transfers represented only 24% of the total volume. In contrast, business-related transactions—including business-to-business (B2B), internal business moves, and person-to-business (P2B) transfers—made up just 33% of transaction count but a dominant 76% of the volume. The data, sourced from Artemis, focused exclusively on USDT and USDC transfer transactions, excluding mints, burns, or cross-chain transfers. Ethereum remains the leading network for stablecoins, hosting over 50% of the global supply. Meanwhile, ETH’s price briefly recovered above $3,000 before pulling back to around $2,950.

Data shows 67% of Ethereum transactions involving the stablecoins USDT and USDC are P2P in nature, but the majority of volume lies elsewhere.

Business-Related Ethereum Stablecoin Transactions Dominate Volume

In a new post on X, Ethereum Foundation head of ecosystem James has shared some numbers related to stablecoin transactions on the ETH blockchain. Stablecoins refer to cryptocurrencies that have their value pegged to a fiat currency.

As these assets are relatively “stable” by nature, they have quickly established themselves as the preferred mode of payments, with their volume surpassing combined that of the top five non-stablecoin cryptocurrencies.

But what does the nature of these transactions look like? Below is the data posted by James, showcasing how the transfers related to the Ethereum versions of USDT and USDC break down between retail and business payments.

Businesses seem to be dominating in terms of the volume | Source: @Snapcrackle on X

As is visible in the chart, 67% of USDT and USDC transactions on the Ethereum network that occurred between August 2024 and 2025 were of the peer-to-peer (P2P) type. Such transactions are usually a sign of activity from retail users.

The small size of the users being involved could be why the transaction volume share of P2P transfers was just 24%. In contrast, business-involved payments made up for 76% of the volume, despite occupying a transactions share of just 33%.

The Ethereum Foundation member sourced the data from Artemis’ report on Ethereum stablecoin payment usage. While stablecoins pegged to various currencies exist, Artemis focused on the USD-tied USDC and USDT as they are by far the most popular options, occupying 88% of the sector’s market cap.

These coins circulate on several blockchains, but Ethereum is currently the most dominant network, hosting more than 50% of the global stablecoin supply. “We also only focus on transfer transactions and exclude any mint, burn, or bridge transactions from our analysis,” noted the report.

Artemis has broken down how it classifies transactions. Transfers are considered P2P if they occur between the externally owned accounts (EOAs) of two separate users.

Determining whether a transaction is P2P can be tricky, however, given that it’s not always possible to determine whether two accounts are owned by different entities. Problems also arise for wallets owned by exchanges and other centralized entities. “In our dataset we are able to label many institutional and firm EOA wallets; however, the labeling is not perfect and some EOA wallets that are owned by firms and are not documented in our dataset can be mislabeled as individual wallets,” explained the report.

The second category is business-to-business (B2B), naturally consisting of the moves taking place between two institutional EOAs. Transactions between the same institutional entity fall inside the “Internal B” label.

Finally, there is the person-to-business (P2B) category, accounting for the transfers happening between individuals and businesses. James’ chart clubs all the business categories into one.

The numbers related to the stablecoin transactions on the Ethereum network | Source: Artemis

ETH Price

Ethereum made recovery above $3,000 earlier, but it seems the coin has once again faced a pullback as its price is now back at $2,950.

The trend in the ETH price over the last five days | Source: ETHUSDT on TradingView

Trending Cryptos

Related Questions

QWhat percentage of Ethereum stablecoin transfers are P2P, and what is their share of the total volume?

A67% of Ethereum stablecoin transfers are P2P, but they account for only 24% of the total volume.

QWhich stablecoins were the focus of the data analysis, and why were they chosen?

AThe analysis focused on USDT and USDC because they are the most popular stablecoins, occupying 88% of the sector's market cap.

QWhat is the main reason P2P transactions have a low volume share despite their high transaction count?

AThe small size of the users involved in P2P transactions is why their volume share is low, as these are typically retail users making smaller transfers.

QHow does the Ethereum network classify a transaction as P2P?

AA transfer is classified as P2P if it occurs between the externally owned accounts (EOAs) of two separate individual users.

QWhat was the price of Ethereum mentioned in the article after it faced a pullback?

AAfter facing a pullback, Ethereum's price was back at $2,950.

Related Reads

Former CFTC Chairman, Circle President Tarbert: Preaching Long-Termism While Cashing Out $30 Million Himself

Former CFTC Chairman and Circle President Heath Tarbert has consistently advocated for a long-term vision in public, urging patience from investors as Circle’s stock price has fallen significantly from its peak. However, it has been revealed that since Circle’s IPO, Tarbert has continuously sold his CRCL shares through pre-arranged trading plans, cashing out approximately $30 million, without making any public market purchases. This contrast between his public messaging and personal actions has drawn criticism. Tarbert joined Circle in July 2023 as Chief Legal Officer, leveraging his regulatory experience to help guide the company through its IPO and expansion. Despite promoting stablecoins as long-term infrastructure, he established a 10b5-1 trading plan just before Circle went public, leading to substantial stock sales over the following year. In March 2026, he initiated another plan to sell more shares. His career trajectory highlights a pattern of moving between high-level regulatory roles and influential positions in the financial sector. After resigning as CFTC Chairman in early 2021, he joined Citadel Securities as Chief Legal Officer just 27 days later, during a period of intense regulatory scrutiny for the firm. He later joined Circle, aiding its efforts to navigate regulatory challenges for its public listing. While Tarbert's expertise in policy and compliance is valuable to companies like Circle, his actions—advocating long-term confidence while personally divesting—raise questions about the alignment between his public statements and his private financial decisions, leaving investors who followed his advice to bear the market risks.

marsbit5m ago

Former CFTC Chairman, Circle President Tarbert: Preaching Long-Termism While Cashing Out $30 Million Himself

marsbit5m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

The article titled "Gate Research Institute: Are Crypto Financial Products Sparking a 'Wall Street' Wave—Competition or Convergence?" explores the evolving relationship between the crypto ecosystem and traditional finance (TradFi). The piece begins by reflecting on Bitcoin's original 2009 vision of decentralization, disintermediation, and moving away from banks. It then contrasts this with the 2024 landscape, where key crypto assets like Bitcoin are increasingly held through Wall Street products like ETFs issued by giants like BlackRock. The article questions whether this signifies that TradFi is systematically taking over the rights to issue, price, custody, and distribute crypto financial assets. The core argument is that this is not a zero-sum takeover but rather a bidirectional convergence where each side addresses the other's weaknesses. Crypto offers 24/7 global markets, programmable settlement, and open access but lacks compliant channels, institutional-grade custody, deep fiat liquidity, and mainstream distribution. TradFi possesses these but is constrained by legacy systems, limited operating hours, and slow settlement. Two primary convergence paths are highlighted: * **Path A (CEX to TradFi):** Exemplified by Gate, which has progressed from offering tokenized stocks and CFDs to providing direct, real stock trading (US, Hong Kong, South Korea) within its platform, using USDT. * **Path B (TradFi to Crypto):** Exemplified by Robinhood, which has integrated crypto trading, acquired exchanges like Bitstamp, and is moving traditional assets like stocks onto the blockchain via tokenization and its own Layer 2. Both paths are ultimately competing to become the next-generation, unified financial account—a "super account" where users can seamlessly trade cryptocurrencies, stocks, ETFs, RWA (Real World Assets), and tokenized treasury products in one interface. The growth of RWA and tokenized treasuries (e.g., BlackRock's BUIDL) is presented as the asset-layer fusion, providing stable, yield-bearing assets on-chain and acting as a bridge between the two worlds. In conclusion, the "Wall Street-ization" of crypto is framed as a mutual transformation. Decentralized ideals persist in the protocol layer, while at the application layer, a more efficient, global, and accessible unified capital market is emerging from this convergence. The future competition lies not between crypto exchanges and stockbrokers, but between platforms vying to offer the most comprehensive asset coverage, liquidity, and user experience within a single account.

marsbit9m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

marsbit9m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit14m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit14m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit15m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit15m ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片