At the dawn of the internet, it took a while for people to get comfortable with trusting internet companies to do the things that non-internet companies did. First-generation internet companies generally took their traditional business models and ported them online: many magazine websites, for example, were initially just PDFs of the magazine itself. Over time, attitudes changed and companies today optimize for internet-first business models.
People like to use the analogy that blockchain will turn some of the biggest internet companies into protocols and outsource their roles to incentivize market participants. We tend to agree. Over time, orthogonal protocol-first ways of doing things will probably become the norm, but for now, there is a focus on getting people comfortable with trusting protocols to do the things that internet companies did.
Amazon started out with an ambitious but focused objective: to become the biggest bookseller in the world by offering unbeatable low prices and unparalleled service. This required immense innovation. Amazon needed to break the mold of traditional supply chains and find new, alternative sources to be able to offer books at lower prices, as well as to constantly refine their logistics to earn consumers’ trust.
Being a big, low-priced bookseller was hardly something new: there were specialists like Barnes & Nobles, Borders, and Waldenbooks who competed for scale. Some of them also had fledgling internet offerings, and there were even websites that aggregated and compared prices across various online stores. Amazon continued to strive for greater efficiency while relentlessly bringing down costs and delivery times for their customers, creating a superior buying experience. They pushed the boundaries for shipping speeds, more generous return policies, and even competitive price pressure by showing competing stores within their own marketplace, encouraging new and different entrants to the bookselling and producing business.
As we all know, after Amazon became the biggest bookseller, they were able to expand to other products, and the expertise and customer-centric focus they developed continued to serve them well.
Pyth Network is focused on bringing trustworthy, continuous, streaming financial market data on-chain in a highly efficient and elegant way.
We think this is an ambitious goal because financial market data is sufficiently unique: there are very few available sources, and those sources have very tightly controlled distributions. Furthermore, the challenge in combining such latency-sensitive data in a way that reduces the likelihood of errors is critically important to the growth of the blockchain industry, especially given how dependent many blockchain applications are on the accuracy of this type of data.
In our previous blog post, we explained how confidence intervals allow for more sophisticated real-time aggregation possibilities. In a future post, we will discuss additional mechanisms used to further secure data. This post outlines the various decentralized ecosystem roles.
Data Providers (Publishers)
Data providers are data owners that have legal access to distribute unique data sets.
There are two important categories: data owners that have not historically operated in the data distribution space, and primary data sources. The former can provide the network a new and cheaper way to source data. The latter helps avoid the latency cost of hops and allows for the most expedient way to get data on-chain.
Data providers will be incentivized to stake and publish correct data and earn fees and rewards for doing so. Likewise, if they publish incorrect data, they will see that stake slashed.
DeFi and CeFi applications use the data. The data could be used to secure a blockchain-based application or simply for informational purposes.
Applications that use the data to secure their value locked will be incentivized to pay fees to further secure the accuracy of that data.
The job of the delegator is to help demonstrate confidence in the data from a particular Pyth data provider by selecting them based on their historical performance and accuracy.
This helps level the playing field for those with low capital but very accurate data by decoupling these components to some extent. Delegators share in the good times and bad by earning a share of fees and being penalized by slashing events.
EDIT: January 18, 2022: The article has been edited to reflect the details of the Pyth whitepaper.
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