Crypto card payments overtake P2P stablecoin transfers: Artemis report

ambcryptoPublished on 2026-01-15Last updated on 2026-01-15

Abstract

According to a blockchain analytics report by Artemis, crypto card payments have surpassed peer-to-peer (P2P) stablecoin transfers as the primary driver of on-chain stablecoin activity. The data shows that crypto card payments now operate at a monthly run rate exceeding $15 billion, compared to approximately $11 billion for P2P transfers. This shift indicates that stablecoins are increasingly being used through traditional card networks rather than through direct on-chain transactions. Visa dominates this segment, accounting for over 80% of the tracked volume, while Mastercard holds a smaller but growing share. The growth is attributed to expanding merchant acceptance and integration with existing payment infrastructure, allowing users to spend stablecoins without requiring merchants to directly accept crypto. Although P2P transfers remain important for remittances and cross-border settlements, their growth has been slower. The report highlights a structural evolution in stablecoin usage—from infrastructure-led to interface-led adoption—where cards act as the primary user-facing access point, embedding crypto liquidity into global commerce and driving mainstream adoption.

Crypto-linked card payments have surpassed peer-to-peer [P2P] stablecoin transfers as the dominant driver of on-chain stablecoin activity. This is according to a new report published on 15 January by blockchain analytics firm Artemis.

The report, titled Stablecoin Payments at Scale: How Cards Bridge Digital Assets and Global Commerce, shows that stablecoin volumes routed through crypto cards now exceed direct wallet-to-wallet payments. It marks a structural shift in how stablecoins are being used in practice.

Artemis data indicates that crypto card payments have reached a monthly run rate of over $15 billion, compared with roughly $11 billion in P2P stablecoin transfers.

While P2P usage continues to grow steadily, card-based payments have accelerated faster. The growth is driven by expanding merchant acceptance and tighter integration with existing payment rails.

Cards emerge as stablecoins’ primary payment interface

Rather than replacing traditional payments outright, stablecoins are increasingly being used behind the scenes through familiar card networks.

The report highlights that most stablecoin-backed card transactions ultimately settle through major card processors.

This allows users to spend dollar-pegged tokens without requiring merchants to accept crypto directly.

Visa dominates this segment, accounting for more than 80% of stablecoin card volume tracked in the report. Mastercard represents a smaller but growing share, while regional card programs contribute marginally.

This model has allowed stablecoins to scale in consumer payments without requiring new merchant infrastructure. It effectively embeds crypto liquidity into existing global commerce systems.

P2P payments remain relevant but grow more slowly

Artemis notes that P2P stablecoin transfers continue to play a critical role in remittances, treasury movements, and cross-border settlements, particularly in emerging markets.

However, growth in this segment has been more incremental compared with the rapid expansion seen in card-linked spending.

The divergence suggests that while stablecoins are widely used for moving value between wallets, everyday consumer usage is increasingly mediated through cards rather than direct on-chain payments.

Stablecoin usage shifts from rails to interfaces

The report frames the trend as an evolution from infrastructure-led adoption to interface-led adoption.

Stablecoins remain the settlement layer. However, cards have become the dominant user-facing access point, lowering friction for mainstream users and businesses.

According to Artemis, this dynamic helps explain why stablecoin transaction volumes continue to rise even as direct on-chain payment activity grows at a slower pace.

The findings underline how stablecoins are integrating into traditional financial systems. They do this not by replacing them outrightly, but by quietly powering familiar payment experiences at scale.


Final Thoughts

  • The Artemis report shows a clear shift in how stablecoins are being used, with card-based payments now playing a central role in everyday transactions.
  • As traditional payment rails increasingly bridge digital assets and commerce, stablecoin adoption appears to be moving closer to mainstream consumer behavior rather than remaining a niche crypto-native activity.

Related Questions

QAccording to the Artemis report, which method has become the dominant driver of on-chain stablecoin activity?

ACrypto-linked card payments have surpassed P2P stablecoin transfers as the dominant driver of on-chain stablecoin activity.

QWhat is the monthly run rate of crypto card payments compared to P2P stablecoin transfers as reported by Artemis?

ACrypto card payments have reached a monthly run rate of over $15 billion, compared with roughly $11 billion in P2P stablecoin transfers.

QWhich card network dominates the stablecoin card payment segment and what is its market share?

AVisa dominates this segment, accounting for more than 80% of stablecoin card volume tracked in the report.

QWhat key role do P2P stablecoin transfers continue to play, according to the report?

AP2P stablecoin transfers continue to play a critical role in remittances, treasury movements, and cross-border settlements, particularly in emerging markets.

QHow does the report frame the evolution of stablecoin adoption in terms of infrastructure and interfaces?

AThe report frames the trend as an evolution from infrastructure-led adoption to interface-led adoption, where stablecoins remain the settlement layer but cards have become the dominant user-facing access point.

Related Reads

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

OpenAI engineer Weng Jiayi's "Heuristic Learning" experiments propose a new paradigm for Agentic AI, suggesting that intelligent agents can improve not just by training neural networks, but also by autonomously writing and refining code based on environmental feedback. In the experiment, a coding agent (powered by Codex) was tasked with developing and maintaining a programmatic strategy for the Atari game Breakout. Starting from a basic prompt, the agent iteratively wrote code, ran the game, analyzed logs and video replays to identify failures, and then modified the code. Through this engineering loop of "code-run-debug-update," it evolved a pure Python heuristic strategy that achieved a perfect score of 864 in Breakout and performed competitively with deep reinforcement learning (RL) algorithms in MuJoCo control tasks like Ant and HalfCheetah. This approach, termed Heuristic Learning (HL), contrasts with Deep RL. In HL, experience is captured in readable, modifiable code, tests, logs, and configurations—a software system—rather than being encoded solely into opaque neural network weights. This offers potential advantages in explainability, auditability for safety-critical applications, easier integration of regression tests to combat catastrophic forgetting, and more efficient sample use in early learning stages, as demonstrated in broader tests on 57 Atari games. However, the blog acknowledges clear limitations. Programmatic strategies struggle with tasks requiring long-horizon planning or complex perception (e.g., Montezuma's Revenge), areas where neural networks excel. The future vision is a hybrid architecture: specialized neural networks for fast perception (System 1), HL systems for rules, safety, and local recovery (also System 1), and LLM agents providing high-level feedback and learning from the HL system's data (System 2). The core proposition is that in the era of capable coding agents, a significant portion of an AI's learned experience could be maintained as an auditable, evolving software system.

marsbit27m ago

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

marsbit27m ago

Your Claude Will Dream Tonight, Don't Disturb It

This article explores the recent phenomenon of AI companies increasingly using anthropomorphic language—like "thinking," "memory," "hallucination," and now "dreaming"—to describe machine learning processes. Focusing on Anthropic's newly announced "Dreaming" feature for its Claude Agent platform, the piece explains that this function is essentially an automated, offline batch processing of an agent's operational logs. It analyzes past task sessions to identify patterns, optimize future actions, and consolidate learnings into a persistent memory system, akin to a form of reinforcement learning and self-correction. The article draws parallels to similar features in other AI agent systems like Hermes Agent and OpenClaw, which also implement mechanisms for reviewing historical data, extracting reusable "skills," and strengthening long-term memory. It notes a key difference from human dreaming: these AI "dreams" still consume computational resources and user tokens. Further context is provided by discussing the technical challenges of managing AI "memory" or context, highlighting the computational expense of large context windows and innovations like Subquadratic's new model claiming drastically longer contexts. The core critique argues that this strategic use of human-centric vocabulary does more than market products; it subtly reshapes user perception. By framing algorithms with terms associated with consciousness, companies blur the line between tool and autonomous entity. This linguistic shift can influence user expectations, tolerance for errors, and even perceptions of responsibility when systems fail, potentially diverting scrutiny from the companies and engineers behind the technology. The article concludes by speculating that terms like "daydreaming" for predictive task simulation might be next, continuing this trend of embedding the idea of an "inner life" into computational processes.

marsbit29m ago

Your Claude Will Dream Tonight, Don't Disturb It

marsbit29m ago

Trading

Spot
Futures
活动图片