Visa Effect

marsbitОпубликовано 2026-01-15Обновлено 2026-01-15

Введение

Visa Effect: A Blueprint for Stablecoin Network Growth The article draws parallels between the fragmented early days of the credit card industry and the current state of stablecoins. In the 1960s, numerous banks operated isolated payment networks, creating settlement chaos. Visa succeeded not just through technology, but by creating a cooperative, independent structure that aligned incentives. It acted as a neutral third party, allowing member banks to share profits proportionally, have governance rights, and initially adhere to exclusivity clauses. This fostered powerful network effects. Today, stablecoins face similar fragmentation, with over 300 stablecoins listed on Defillama. Services that enable protocols to issue their own branded stablecoins (e.g., Ethena, Anchorage Digital) are compared to the failed BankAmericard model, which only fractures liquidity and prevents any single stablecoin from achieving mainstream network effects. The proposed solution is a Visa-like model for stablecoins: an independent, third-party cooperative. Issuers and applications supporting a specific asset class would join, share in reserve yields, and have governance rights over the stablecoin's development. This structure would consolidate liquidity, create internal compounding network effects, and drive widespread adoption.

Author: Nishil Jain

Compiled by: Block unicorn

Preface

In the 1960s, the credit card industry was in chaos. Banks across the United States were trying to establish their own payment networks, but each network operated independently. If you held a Bank of America credit card, you could only use it at merchants that had a cooperation agreement with Bank of America. When banks tried to expand their business to other banks, all credit card payments encountered the problem of interbank settlement.

If a merchant accepted a card issued by another bank, the transaction had to be settled through its original check settlement system. The more banks that joined, the more settlement problems arose.

Then Visa emerged. Although the technology it introduced undoubtedly played a huge role in the credit card payment revolution, the more important success lay in its global universality and its success in getting global banks to join its network. Today, almost every bank in the world has become a member of the Visa network.

While this seems very normal today, imagine trying to convince the first thousand banks, both inside and outside the United States, that joining a cooperation agreement instead of building their own network was a wise move. Then you begin to realize the scale of this endeavor.

By 1980, Visa had become the dominant payment network, processing about 60% of credit card transactions in the United States. Currently, Visa operates in over 200 countries.

The key was not more advanced technology or more funding, but the structure: a model that could coordinate incentives, decentralize ownership, and create compound network effects.

Today, stablecoins face the same fragmentation problem. And the solution may be the same as what Visa did fifty years ago.

Experiments Before Visa

Other companies that appeared before Visa failed to develop.

American Express (AMEX) tried to expand its credit card business as an independent bank, but its scale expansion was limited to continuously adding new merchants to its banking network. On the other hand, BankAmericard was different; Bank of America owned its credit card network, and other banks only utilized its network effects and brand value.

American Express had to approach each merchant and user individually to open their bank accounts, while Visa achieved scale by accepting banks itself. Each bank that joined the Visa cooperative network automatically gained thousands of new customers and hundreds of new merchants.

On the other hand, BankAmericard had infrastructure issues. They did not know how to efficiently settle credit card transactions from one consumer bank account to another merchant bank account. There was no efficient settlement system between them.

The more banks that joined, the worse this problem became. Thus, Visa was born.

The Four Pillars of Visa's Network Effects

From the story of Visa, we learn about 2-3 important factors that led to the accumulation of its network effects:

Visa benefited from its status as an independent third party. To ensure that no bank felt threatened by competition, Visa was designed as a cooperative independent organization. Visa did not participate in competing for the distribution pie; the banks competed for it.

This incentivized participating banks to compete for a larger share of the profits. Each bank was entitled to a portion of the total profits, proportional to the total transaction volume it processed.

Banks had a say in the network's functions. Visa's rules and changes had to be voted on by all relevant banks and required 80% approval to pass.

Visa had exclusivity clauses with each bank (at least initially); anyone joining the cooperative could only use Visa cards and the network and could not join other networks—thus, to interact with Visa banks, you also needed to be part of its network.

When Visa's founder, Dee Hock, traveled across the United States to persuade banks to join the Visa network, he had to explain to each bank that joining the Visa network was more beneficial than building their own credit card network.

He had to explain that joining Visa meant more users and more merchants would be connected to the same network, which would promote more digital transactions globally and bring more benefits to all participants. He also had to explain that if they built their own credit card network, their user base would be very limited.

Implications for Stablecoins

In a sense, Anchorage Digital and other companies now offering stablecoin-as-a-service are replaying the BankAmericard story in the stablecoin space. They provide the underlying infrastructure for new issuers to build stablecoins, but liquidity continues to fragment into new tokens.

Currently, over 300 stablecoins are listed on Defillama. Moreover, each newly created stablecoin is limited to its own ecosystem. As a result, no single stablecoin can generate the network effects needed to go mainstream.

Since the same underlying assets support these new coins, why do we need more coins with new code?

In our Visa story, these are like BankAmericards. Ethena, Anchorage Digital, M0, or Bridge—each allows a protocol to issue its own stablecoin, but this only exacerbates industry fragmentation.

Ethena is another similar protocol that allows yield transmission and white-label customization of its stablecoin. Just like MegaETH issuing USDm—they issued USDm through tools that support USDtb.

However, this model failed. It only fragments the ecosystem.

In the credit card case, the brand differences between banks did not matter because it did not create any friction in user-to-merchant payments. The underlying issuance and payment layer was always Visa.

However, for stablecoins, this is not the case. Different token codes mean an infinite number of liquidity pools.

Merchants (or in this case, applications or protocols) will not add all stablecoins issued by M0 or Bridge to their list of accepted stablecoins. They will decide based on the liquidity of these stablecoins in the open market; the coins with the most holders and the highest liquidity should be accepted, while the rest will not.

The Way Forward: The Visa Model for Stablecoins

We need independent third-party institutions to manage stablecoins of different asset classes. Issuers and applications supporting these assets should be able to join cooperatives and access reserve earnings. At the same time, they should also have governance rights and be able to vote on the direction of their chosen stablecoin.

From a network effects perspective, this would be a superior model. As more issuers and protocols join the same token, it will facilitate the widespread adoption of a token that retains earnings internally rather than flowing into others' pockets.

Связанные с этим вопросы

QWhat was the main challenge faced by the credit card industry in the 1960s, and how did Visa address it?

AThe main challenge was fragmentation, with each bank operating its own isolated payment network. Visa addressed this by creating a cooperative network that allowed banks to join and share infrastructure, enabling universal acceptance and efficient interbank settlement.

QHow did Visa's structure differ from competitors like American Express and BankAmericard, and why was it more successful?

AVisa operated as an independent, cooperative organization where banks shared ownership and governance, avoiding competition among members. In contrast, American Express acted as a single bank expanding alone, and BankAmericard had infrastructure issues with interbank settlement. Visa's model incentivized participation and scaled network effects more effectively.

QWhat are the key pillars of Visa's network effects as described in the article?

AThe key pillars are: 1) Visa's independent third-party status to avoid competition among banks, 2) Profit sharing proportional to transaction volume, 3) Governance through voting by member banks requiring 80% approval, and 4) Initial exclusivity clauses preventing members from joining other networks.

QHow does the current stablecoin landscape resemble the pre-Visa credit card era, and what problem does this create?

AThe stablecoin landscape is fragmented, with over 300 stablecoins on Defillama, each limited to its own ecosystem. This prevents network effects, causes liquidity fragmentation, and hinders mainstream adoption, similar to how isolated bank networks struggled before Visa.

QWhat solution does the article propose for stablecoins, inspired by Visa's model?

AThe article proposes an independent third-party cooperative for stablecoins, where issuers and applications join to share reserve yields and governance rights. This would unify liquidity, enhance network effects, and keep benefits within the ecosystem rather than fragmenting them.

Похожее

Institutional Adoption of Prediction Markets Stuck at the Third Stage

Prediction markets are transitioning from niche platforms focused on elections and sports to mainstream financial tools, as highlighted at Kalshi Research's inaugural conference. While sports still dominate trading volume (around 80%), non-sports categories like macroeconomics, politics, and entertainment are growing faster, signaling a shift from entertainment-based trading to information and risk management tools. Institutions, including Wall Street firms, are increasingly using prediction markets for data reference (Stage 1 adoption), with some progressing to system integration (Stage 2). However, full-scale trading (Stage 3) is limited due to the lack of margin trading, requiring full collateral for positions—a barrier for leverage-dependent entities. Kalshi is working with regulators to introduce margin mechanisms. Key insights from participants like Goldman Sachs and CNBC emphasize the value of real-time pricing for events (e.g., Fed decisions, tariffs), providing benchmarks previously unavailable. The path to maturity mirrors historical financial instruments like options, with expectations that prediction markets will become institutional staples within five years. Political leaders, including Trump and Schumer, now cite Kalshi odds, underscoring its growing influence. The platform rewards domain expertise over traditional finance backgrounds, attracting diverse participants from fields like music and poker. Ultimately, prediction markets are evolving into critical infrastructure for pricing uncertainty.

marsbit15 мин. назад

Institutional Adoption of Prediction Markets Stuck at the Third Stage

marsbit15 мин. назад

The First Year of Computing Power Inflation: The Cheaper DeepSeek Gets, the Harder It Is to Stop This Round of Price Hikes

The year 2026 marks the beginning of "computing power inflation." While AI inference costs have dropped by over 80% in 18 months globally, China's three major cloud providers—Alibaba Cloud, Baidu AI Cloud, and Tencent Cloud—simultaneously announced price hikes of 20–30%. This reflects a deeper structural shift driven by Jevons Paradox: as unit costs fall (e.g., via models like DeepSeek-R1), demand explodes, especially with the rise of reasoning models and AI agents that consume 10–50x more tokens per task. Although DeepSeek open-sourced its model weights, it did not release its inference optimization stack, leaving a significant engineering efficiency gap between cloud providers and smaller players. The big three are leveraging this advantage to reposition: Alibaba focuses on high-margin premium clients, Baidu filters out low-value users, and Tencent capitalizes on ecosystem lock-in. Meanwhile, ByteDance’s Volcano Engine adopts a more moderate pricing strategy to capture displaced customers. Unexpectedly, the price surge is pushing large enterprises toward self-built computing solutions once their cloud bills exceed a certain threshold. While cloud providers aim to boost profitability, they risk driving away innovative startups and accelerating competition from GPU leasing and domestic hardware providers like Huawei. The涨价 trend is expected to persist for 2–3 years, fueled by rising token consumption from reasoning models, AI agent adoption, and NVIDIA export restrictions. The inflection point depends on whether domestic chips can match NVIDIA’s efficiency, likely around 2027–2028. Until then, cloud providers will maintain pricing power, and the key for AI companies is to optimize token usage—the real moat in this era.

marsbit1 ч. назад

The First Year of Computing Power Inflation: The Cheaper DeepSeek Gets, the Harder It Is to Stop This Round of Price Hikes

marsbit1 ч. назад

Торговля

Спот
Фьючерсы
活动图片