Dune Stablecoin Research: The Flow and Demand of a $300 Billion Market

Author: Dune

Compiled by: Ken, Chaincatcher

Everyone is quoting supply data. It appears in every report, every earnings call, and every policy hearing. But aside from "the circulating supply exceeds $300 billion," how much do we really know about stablecoins?

Who holds them? What is the ownership concentration? How fast are they being transferred, and on which chains? What are their actual uses—DeFi liquidity, payments, or parking funds?

@Meta just announced plans to integrate third-party stablecoin payments across all its platforms; @Stablecoin received approval for a national trust bank charter from the Office of the Comptroller of the Currency (OCC). @Payoneer announced stablecoin payment capabilities for 2 million businesses. @Anchorage launched compliant stablecoin services for non-U.S. banks. Institutions and regulators are accelerating their entry, and they need answers that go beyond just supply data.

We used Dune's newly launched stablecoin dataset—developed in collaboration with @SteakhouseFi—to answer some of these questions. Here’s what the data reveals.

Supply Landscape

As of January 2026, the fully diluted supply of the top 15 stablecoins on EVM, Solana, and Tron reached $30.4 billion, a 49% year-over-year increase. @tether's USDT ($197 billion) and @circle's USDC ($73 billion) still account for 89% of the market share. By chain, @ethereum holds $176 billion (58%), Tron $84 billion (28%), @solana $15 billion (5%), and @BNBCHAIN $13 billion (4%). Despite the total supply nearly doubling, the distribution pattern across public chains has changed little over the year.

However, beneath the top two stablecoins, 2025 was a year for challengers. USDS (@SkyEcosystem/MakerDAO) grew by 376% to $6.3 billion. PYUSD (@PayPal) grew by 753% to $2.8 billion. RLUSD (@Ripple) surged from $58 million to $1.1 billion, an increase of 1,803%. USDG expanded 52 times. USD1 skyrocketed from zero to $5.1 billion. Not all challengers developed in the same direction. USD0 fell by 66%, while @ethena's USDe, after experiencing nearly threefold growth in October, ended the year up 23%. Even so, the competitive landscape beneath USDT and USDC has undergone a decisive expansion.

Who Holds Them

Most stablecoin datasets can tell you the total supply. Because our dataset combines address labels to track balances at the wallet level, we can tell you exactly who holds them.

On EVM and Solana, centralized exchanges (CEX) are the largest known category, holding $80 billion, up from $58 billion a year ago. The primary identity of stablecoins remains as the infrastructure for trading and settlement. Whale wallets hold $39 billion. Yield protocols' holdings nearly doubled to $9.3 billion, reflecting the growth of on-chain yield strategies. Issuer addresses—treasury and mint/burn contracts—soared 4.6 times from $2.2 billion to $10.2 billion, directly reflecting how much new supply has entered the market.

Regarding the quality of address labels: only 23% of the supply is held in completely unknown addresses. This is an extremely high identification rate for on-chain data, which is crucial for anyone trying to understand where the actual risks of stablecoins reside.

172 Million Holders, but High Concentration

As of February 2026, there are 172 million unique addresses holding at least one of these 15 stablecoins. Among them, USDT accounts for 136 million addresses, USDC for 36 million, and DAI for 4.7 million. These three stablecoins have a truly broad distribution: their top 10 wallets hold only 23-26% of the supply, and the HHI (Herfindahl-Hirschman Index, a standard measure of economic concentration, where 0 indicates complete dispersion and 1.0 indicates a single holder) is below 0.03.

Each of the other stablecoins tells a very different story. The top 10 wallets hold 60-99% of the supply. Despite having a circulating supply of $6.9 billion, 90% of USDS is concentrated in 10 wallets (HHI 0.48). The top 10 wallets of USDF hold 99% (HHI 0.54). USD0 is the most extreme: the top 10 wallets hold 99%, with an HHI of 0.84, indicating that even among these largest holders, the supply is almost monopolized by one or two wallets.

This does not mean that these stablecoins themselves have issues; some are newly launched, while others are intentionally structured by institutional investors. But it does mean that interpreting their supply data should be done in a way that is completely different from interpreting USDT or USDC. Concentration drives de-pegging risk, liquidity depth, and determines whether "supply" reflects natural demand or the actions of a few large participants. Only when you have the balance of each holder, rather than just the total supply derived from mint/burn events, can this depth of analysis become possible.

$10.3 Trillion Transferred in January

In January 2026, the transfer volume of stablecoins on EVM, Solana, and @trondao reached $10.3 trillion, more than double that of January 2025. The data breakdown by chain is astonishing and starkly contrasts with the supply landscape: Base leads with $5.9 trillion in transfers, despite its supply being only $4.4 billion. Ethereum accounts for $2.4 trillion. Tron $682 billion. Solana $544 billion. BNB Chain $406 billion.

By token, USDC dominates with $8.3 trillion in transfer volume—almost five times USDT's $1.7 trillion—despite its supply being 2.7 times smaller than USDT. USDC's transfer speed and frequency are simply faster than USDT. DAI's transfer volume is $138 billion, USDS $92 billion, and USD1 $43 billion.

Importantly, this data is intentionally kept neutral. The dataset does not pre-filter transfers based on a fixed interpretation of "real" economic activity, so the total may include flows related to arbitrage, bots, internal routing, or other automated behaviors. The dataset does not codify these subjective judgments but aims to present an objective view of on-chain activity, allowing users the flexibility to apply their own filters—whether they want to exclude bot-driven volumes, isolate real organic usage, or define an adjusted measure of transfer activity.

What Are Stablecoins Actually Used For

This is where the dataset's granularity shines. Transfers are no longer merely labeled as simple "transaction volume" but are categorized into different on-chain activities. This is the difference between "just knowing that $10 trillion was transferred" and "understanding why it was transferred."

January Data Breakdown:

1. Market Infrastructure (DEX Trading and Liquidity)

• Providing DEX liquidity and withdrawing from liquidity pools: $5.9 trillion. This is the largest single use case, reflecting the role of stablecoins as the foundational asset for on-chain market making.

• DEX swaps: $376 billion. Direct trading activity across automated market makers.

These two categories highlight that stablecoins primarily serve as collateral for trading and liquidity infrastructure. Interestingly, trading volume is mainly concentrated in incentive-driven activities (such as liquidity mining and active capital optimization), rather than pure trading demand.

2. Leverage and Capital Efficiency (Lending + Flash Loans)

• Flash loans (borrow and repay): $1.3 trillion. Automated arbitrage and liquidation cycles.

• Lending activities—providing, borrowing, repaying, withdrawing: $137 billion. This layer represents short-term capital efficiency and structured credit on-chain.

3. Access Channels (CEX and Cross-Chain Bridges)

• CEX fund flows—deposits ($224 billion), withdrawals ($224 billion), internal transfers ($151 billion): totaling $599 billion.

• Cross-chain bridge deposits and withdrawals: $28 billion. These fund flows indicate that stablecoins are serving as channels for settlement between centralized exchanges and across chains.

4. Issuer Layer (Monetary Operations)

• Issuer operations—minting ($28 billion), burning ($20 billion), peg rebalancing ($23 billion), and other issuer activities: totaling $106 billion. This is nearly five times the $42 billion recorded a year ago.

5. Yield Protocols

• Yield protocol activities: $2.7 billion. This is a smaller but structurally significant subfield closely related to structured strategies and on-chain asset management.

Overall, 90% of the transfer volume flows through identified activity categories, providing us with a granular perspective to understand the flow of stablecoins at every layer of the on-chain tech stack.

Circulation Speed: Same Tokens, Different Worlds

Daily circulation speed (transfer volume divided by supply) may be the most underutilized metric in stablecoin analysis. It tells us the extent to which stablecoins are actively used as a medium of exchange compared to simply being held.

Among the tokens we analyzed, USDC and USDT stand out again, although their performances differ.

USDC has the fastest circulation speed on L2 (Layer 2 networks) and Solana. On Base, USDC's daily median turnover rate reaches an astonishing 14 times, driven by high-frequency DeFi activity. On Solana and Polygon, its daily turnover rate stabilizes around 1 time. Even on Ethereum, USDC's turnover rate reaches 0.9 times, meaning nearly its entire supply circulates daily.

USDT has the fastest circulation speed on the BNB and Tron trading and payment networks. On BNB Chain, USDT's daily turnover rate reaches 1.4 times, reflecting active trading. On Tron, the turnover rate is lower at 0.3 times but remains remarkably stable day after day, aligning with its role as a dominant cross-border payment channel. However, on Ethereum, USDT's turnover rate is only 0.2 times, with over $100 billion of supply largely sitting idle.

USDe and USDS have slower circulation speeds, a result of their mechanism design. USDe's daily turnover rate on Ethereum is only 0.09 times, while USDS is at 0.5 times. Both are designed as yield-bearing stablecoins: USDe is typically staked as sUSDe to capture Ethena's delta-neutral funding rate strategy returns, while USDS is deposited into Sky Savings Rate to earn yields provided by the protocol. Therefore, a significant portion of the supply remains in savings contracts, lending markets like Aave, or structured yield loops. Here, low circulation speed is not a flaw but a feature: these assets are engineered to accumulate yield rather than circulate.

The public chain on which a token resides is more important than the token itself. PYUSD's daily turnover rate on Solana is 0.6 times, over four times its speed on Ethereum (0.1 times). The same token, with completely different usage patterns, depending on which ecosystem it exists in.

Supply and transfer volume each tell only part of the story. Circulation speed connects them, capturing with a single metric whether a particular stablecoin on a specific chain is functioning as active infrastructure or merely sitting as idle funds.

Beyond the Dollar

This analysis primarily focuses on 15 dollar-pegged stablecoins, but the complete dataset goes far beyond that. It tracks over 200 stablecoins representing more than 20 fiat currencies: Euro (17 tokens, supply $990 million), Brazilian Real ($141 million), Japanese Yen ($13 million), as well as tokens pegged to NGN (Nigerian Naira), KES (Kenyan Shilling), ZAR (South African Rand), TRY (Turkish Lira), IDR (Indonesian Rupiah), SGD (Singapore Dollar), and more.

The total supply of non-dollar stablecoins currently stands at only $1.2 billion, but 59 tokens have launched across 6 continents, accounting for nearly 30% of all tokens in our dataset. The infrastructure for local fiat stablecoins is currently being built on-chain, and the data to track it is ready.

Just the Tip of the Iceberg

Everything in this analysis comes from just a few queries of a single dataset. We only examined 15 stablecoins and a few core metrics, but the complete dataset covers nearly 200 stablecoins across more than 30 blockchains.

What sets this dataset apart, in addition to its broad coverage, is its classification layer. Each transfer is mapped to its on-chain triggers and categorized into one of nine activity categories using a deterministic prioritization framework. Each balance is segmented by holder type and employs a standardized classification system across all chains. Together, these two aspects transform noisy blockchain logs into structured, comparable data—revealing mechanisms of change, capital flows between different venues, concentration risks, and participation patterns.

This granularity can answer questions we haven't even thought to ask: Which wallets start accumulating a new stablecoin before it lands on an exchange? How does holder concentration shift days before a de-pegging event? What do cross-chain bridge fund flows look like for euro-pegged stablecoins? What correlation exists between issuer mint/burn patterns and market pressures? And many more.

This is precisely the kind of dataset designed to support institutional-level analysis, research report publication, risk modeling frameworks, compliance monitoring workflows, and executive dashboards. The depth is here. Start digging.

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