Pyth Network is currently the largest first-hand data oracle in the blockchain, focusing mainly on low-latency financial market data.

This Monday, the $PYTH token officially launched on major exchanges. As a rising star in the oracle race, what makes Pyth unique, and what innovative possibilities does the oracle token $PYTH have? This Wednesday, we invited Ande from Pyth Data Association to join us for a discussion.

The discussion is divided into two main parts. First, we will cover the principles and core features of Pyth Network. The second part will focus on the hot topics in the market, including recent market dynamics, listings, and valuation related content. Below is a recap of this AMA.

TechFlow from Deep Tide: I am Zolo from Deep Tide. I am very happy to have Ande from Pyth Data Association join us today. This Monday, Pyth was listed on major exchanges and received a lot of attention. First, let's have Ande introduce himself briefly and tell us what Pyth Network is.

Ande: Hello everyone, I am Ande from Pyth Data Association, a contributor to Pyth Network, mainly responsible for market and partnership-related affairs. I am delighted to be invited by Deep Tide to participate in today's AMA.

Pyth Network is currently the largest first-hand data oracle in the blockchain, focusing mainly on low-latency financial market data. We collect this financial market data from first-hand sources and publish it to over 40 different blockchains after processing it with on-chain aggregation algorithms. The oracle itself is an intermediary facility for data because the on-chain world is a closed world, and we cannot directly access off-chain data to transmit data to these dApps on-chain. These on-chain dApps, especially DeFi, require real-world data to process on-chain transactions and drive DeFi innovation.

As an intermediary facility, the oracle does this. It brings off-chain real-world data to the chain in a fixed frequency and format, and then supplies it to on-chain dApps for integration and use. Data itself is also a very important part of DeFi innovation, a very important foundation, so the oracle plays such a role in between.

Pyth Network launched its V1 version in 2021, starting with Solana, and in less than a year, it basically occupied over 90% of Solana DeFi. In the second half of last year, Pyth Network released the V2 version, a cross-chain model called Pythnet. In less than a year, Pyth's price feed data oracle has been launched on over 40 different blockchains, with over 200 different types of dApps integrating Pyth's data on different chains. Pyth's data itself has over 350 different price feeds, all of which can be used in over 40 different blockchain ecosystems.

TechFlow from Deep Tide: Thank you, Ande. I personally learned about Pyth in 2021, when you probably didn't have as many customers as you do now, and the popularity wasn't as high as it is now. When it comes to oracles, everyone will definitely think of ChainLink. You mentioned that Pyth Network should currently be the largest first-hand data oracle network in the blockchain. I would like to ask, in your opinion, why do we still need Pyth after having oracles like ChainLink? And what are the core differences between Pyth and ChainLink?

Ande: Fundamentally, because Pyth is actually an intermediary facility, there are different innovations in the oracle market from both the data source and data user perspectives.

From the perspective of the data source, traditionally, financial data is considered free and can be obtained from any public data source and used. However, contrary to common sense, financial market data is a particularly special type of data and is often not free. In 2023, the value of the entire market for financial data alone has exceeded $60 billion.

In the traditional financial world, exchanges like the Hong Kong Stock Exchange, Nasdaq, and some specialized data institutions such as Bloomberg, treat data business as their main business. This traditional financial data is also very valuable and requires payment to obtain for use by financial practitioners. So the data itself is actually valuable, unlike other types of data that can be obtained freely in the public market. This type of data is very precious and comes from where?

The source of financial data actually comes from different exchanges, market makers, and many financial practitioners. For example, in the secondary market, funds generate this financial data through high-frequency trading or daily operational business. For instance, an exchange will have a bid-ask spread and real-time trading prices of financial assets, which is the real-time financial data they can provide. Large high-frequency trading institutions, for example, derive their data from every execution of order trading data as part of their daily business operations. They can commercialize and monetize this data in a certain way, so traditional financial data is very valuable.

Similarly, in the Crypto or Web3 field, we usually consider data to be free, but the value of this data is very high. Pyth Network's data all comes from first-hand data. For example, we do not obtain third-party data from public nodes because these nodes may be developers we do not know, who can write code to crawl financial data from various sources and publish it to the network. We do not rely on third-party data sources. Instead, we directly find the source of the data, which is the owner of the first-hand data, and invite these owners to continuously and stably publish financial market data at a high frequency to ensure the accuracy, high frequency data updates, and stability of our data.

After obtaining this data, Pyth aggregates and processes the data in Pythnet to generate an aggregated price feed, which integrates data from various sources. Once the data is obtained, it can be supplied to downstream decentralized applications (dApps) for data usage through the aggregation mechanism. When downstream applications receive this data, they need to access Pythnet on-chain through smart contracts to obtain the data. The benefit of this approach is that, unlike the traditional oracle model, which is a push model that continuously pushes data at a fixed frequency to different dApps in various blockchain ecosystems, Pyth's model addresses the scalability issue.

For example, at this stage, there may be 200 DeFi applications that require data to be pushed to them, or there may be a need to push data to 50 or 60 different blockchain ecosystems. While the costs of the oracle can cover this scenario, imagine the need to scale this to 2,000, 20,000, or even 200,000 different institutions or DeFi applications. If there are 100 or 200 different blockchain ecosystems in the future, the push model would require continuous data updates and push costs, leading to poor scalability.

The future of DeFi is a significantly large market that can grow to billions or even hundreds of billions. In this context, Pyth has innovated a pull-based oracle structure, where data is not continuously pushed to any blockchain or dApp. Instead, the data is uniformly updated and stored in Pythnet's proprietary application chain. Any DeFi application within any ecosystem can directly request data updates from Pythnet for seamless integration. This innovation ensures unlimited scalability for the oracle, eliminating the need for a cost-increasing framework to maintain a fixed push frequency and reducing the processing pressure on the oracle itself.

Furthermore, by not incurring significant costs, bandwidth, and maintenance for data push and updates, Pyth can focus more on data updates and data source acquisition, ensuring high-frequency, accurate, and stable price updates and data quality and availability. Additionally, all downstream DeFi applications are permissionless and can seamlessly access data sources from Pythnet, which is a significant innovation for Pyth.

In summary, Pyth ensures data stability and trustworthiness at the source, and its design can support the future expansion of DeFi to a range of billions of applications, representing two important innovations.

TechFlow from Deep Tide: I find it interesting that Pyth has a lot of first-hand data, directly accesses the data source, and can support the larger scale of financial data or DeFi growth. Behind these capabilities, for users using DeFi or protocols supported by Pyth, are there any differences in the user experience? For example, I saw someone in the group saying that many people would recommend using protocols supported by Pyth for contract trading, possibly because of faster update efficiency. Could you provide some practical examples from a user's perspective of using products or protocols supported by Pyth, and how it may result in actual changes or performance differences in the user experience?

Ande: To understand the necessity of oracle innovation and the future development of the entire DeFi world, we can classify it as follows:

First, during the DeFi Summer in 2020 or 2021, the most primitive and earliest batch of DeFi applications emerged, including DEX, on-chain swaps, some basic financial products, and even some lending protocols. In this part of DeFi, you will find that the frequency of on-chain transactions has relatively low requirements for data update frequency.

For example, in a DEX trade, which comes in many forms such as decentralized order books and on-chain AMMs, many of these DEXs may not require an external data source for price feeds because they may operate their own centralized order book. Additionally, on-chain swaps have slippage, and while there are innovative solutions now, in the initial trading structure, high-frequency data updates are not necessary to determine the current price, as it sets an acceptable price range based on different supply-demand relationships or the condition of on-chain tokens.

For lending, if you want to borrow, high-frequency data updates for accurate asset pricing are not necessary. Only when you need to execute a loan, having accurate data for that asset is sufficient. However, we are gradually transitioning to the second phase of DeFi, possibly from the second half of 2021 to 2022 or 2023, where many more advanced innovations are emerging on-chain. For example, the most significant innovation is on-chain perpetual contracts, which are financial derivatives that require real-time tracking of accurate price data for their underlying assets to execute high-frequency on-chain derivative trades. The derivatives themselves require an external data source to continuously feed the price of the underlying assets, enabling accurate on-chain derivative trade execution.

At this stage, we find that data accuracy and high-frequency updates are crucial for DeFi. This is where the first-generation DeFi innovative products pose a significant challenge to traditional oracle solutions because the traditional solutions are suitable for the first generation of DeFi products, where high-frequency data updates, cross-chain data updates, and overly accurate data updates are not required. As innovation progresses to this stage, we find that these previously unnecessary elements are now necessary, and traditional oracles have limitations due to their applicability to the previous generation of DeFi products, and they cannot quickly innovate a set of features suitable for the new generation of DeFi requirements.

At this point, Pyth has innovated a model that involves continuous on-chain updates and maintenance on Pythnet. When dApps need data, they can directly pull data from Pythnet. This model brings the value of almost no latency or very short latency to obtain the freshest and most accurate financial data.

We can take the example of on-chain execution of Perps, which is the largest data user of Pyth called Synthetix, and some ecosystem applications of Synthetix, such as Kwenta, Polynomial, and other Synthetix front-ends. Synthetix itself is a Perps, an on-chain contract product. When you need to execute a contract trade, we do it step by step.

Step one is to submit the order demand, and Synthetix will provide real-time price feeds based on this order. Only with the order demand and real-time price updates can trading be executed based on the current price. If there is a delay in the price feed during the execution of the trade, such as a 3 or 4-second delay, it means that the order was requested 3 or 4 seconds ago, but the trade is executed using price data from 3 or 4 seconds later. On-chain trading, especially high-frequency trading, is fast-moving, and prices can fluctuate significantly within three or four seconds. Such a time delay is unacceptable. This is where an innovative oracle solution is needed to provide the most accurate prices at sub-second speeds.

Pyth uses a set of Solana's technology models, allowing Solana to achieve price updates every 400 milliseconds, which is less than one second, or more than once per second. With this sub-second price update frequency and a data-pulling model, for example, in the case of Synthetix, when a user executes a trade and needs to pull real-time trading data, a timestamp is first applied, and then the data is pulled from Pythnet to the trade execution in less than one second, and then the trade is settled.

All of this can be executed at a very specific time, such as executing an order at 8:01:01 in the morning, minimizing the instability of order execution prices caused by price delays. This is actually a significant challenge posed by the new generation of DeFi to oracles and data itself. I believe that throughout the process, Pyth can meet the data needs of the new generation of DeFi, which is a significant real user case.

TechFlow from Deep Tide: Recently, there has been increasing discussion about Pyth, and we have found that many users seem to have just learned that Pyth already supports over 200 protocols. I first learned about Pyth in the second half of 2021. In the past two years, what key milestones have you achieved, and how did you weather the bear market? We are also very interested in the team behind Pyth. Could Ande please introduce some of the experiences and the team behind Pyth over the past two years?

Ande: Thank you. Regarding the team, all information about Pyth is public, and everyone can see all the core contributors to Pyth on the official website of the Pyth Data Association. The backgrounds of the contributors are diverse. For example, CEO Mike Cahill has a traditional financial background, previously working at Morgan Stanley and then at Jump, responsible for traditional financial product securitization, high-frequency trading, and the emergence of some emerging financial products in Europe, and then transitioning to on-chain research of financial products.

Like many other development teams, they come from large traditional tech companies such as AWS, Microsoft, or Google, or new high-frequency trading institutions. The team's background is diverse, but there is a common core point: everyone comes from the financial technology or traditional finance industry, and everyone has a deep understanding of finance. I can confidently say that all members of the Pyth team have a relatively deep understanding of financial operational logic. As I mentioned earlier, why do we think that data is not valuable now, or that data can be obtained for free on the internet? In reality, this is not the case.

For financial data, it is a very valuable market. For example, in the real world, none of us can directly obtain real-time market data for the US stock market for free. This data is available for free in public channels six hours later. If you need real-time trading data, you need to purchase it from the data owners, such as Nasdaq. Nasdaq can earn several billion US dollars a year by trading this data.

In this core assumption, financial market data is very valuable, which contradicts the common sense of our big data era. Most non-financial practitioners may naturally think that data itself does not have much commercial value, so data itself is not the focus of oracle work, or the data source itself is not a hindrance to oracle innovation.

However, this is why Pyth can distinguish itself from other oracles. The most important point is that we recognize the value of data and the value of accurate high-frequency financial market data. In this core setting, we have already acknowledged the value of data, so we will spend a lot of effort to find this data source, which can provide first-hand data. Only in this way can we achieve data accuracy from the source, leading to the subsequent innovative logic.

In short, the entire team understands finance and can integrate important elements of traditional finance into the chain through innovative technology, thereby building a future on-chain DeFi world using the most core technical means. Because we have always believed that on-chain DeFi and traditional finance are not opposites. We call it TradFi and DeFi, and at some point in the future, we may no longer have the distinction between TradFi and DeFi. The entire finance world should be able to integrate the convenience provided by on-chain finance into traditional finance.

The team has gone through many years, or at least five years, of preparation and construction to develop to where it is today. Before the mainnet launch in 2021, Pyth had been preparing slowly, possibly for more than a year, to achieve an ecological explosion. From the launch of the mainnet to the present, it has undergone the transition from V1 to V2, expanded to more than 40 different blockchains, and has over 90 first-hand data publishers upstream, as well as over 200 decentralized applications downstream, continuously integrating Pyth's data.

So, whether it's the growth curve we see or the innovative process of the entire product, Pyth is growing at an increasingly fast pace. Once the scale effect is generated, our growth rate will become faster and faster, and the speed and strength of innovation will also become faster and faster. This is the current growth stage and growth story of Pyth.

TechFlow from Deep Tide: Thank you for Ande's introduction. Next, we will ask some questions about market or token information. We have learned that Pyth places a strong emphasis on the entire positive feedback loop business logic and promotes the different practicalities of its token. How do you view the practicality of the Pyth Token in the entire model? Or what do you think are the differences between the Pyth Token and, for example, $LINK?

Ande: Regarding tokenomics and the design of the entire token, the practicality of the Pyth Token in various aspects is detailed in the Pyth whitepaper and official articles. If anyone is interested, they can directly refer to the corresponding official articles.

Here's a brief summary. Regarding governance, Pyth is currently the only decentralized on-chain oracle product in the entire oracle field. Along with token listing, we have also released a permissionless governance system, where users can exchange Pyth Tokens 1:1 for governance rights through staking, which may differ from other governance models.

Firstly, governance is a very serious matter for the organization. Pyth DAO is one of the few DAOs in the industry that has been legally registered and created in the form of a DAO. Additionally, governance determines critical parameters and issues within the entire Pyth network. For example, governance decides the fees that downstream protocols and applications need to pay to update Pyth's data.

Secondly, governance also decides when Pyth will release a new price feed. Thirdly, governance determines whether new data publishers can be onboarded onto Pyth. Fourthly, governance decides on any future product development or adjustments to product features.

You will find that staking Pyth Tokens provides significant governance rights. Additionally, Pyth's business model differs from traditional oracles. The traditional oracle business model is more Web2-oriented, where protocols or teams need to find and sign contracts with oracles to access data services. However, for Pyth, all data interfaces are open, and integration with Pyth's data does not require direct communication with the Pyth team. Instead, a data fee is paid on-chain for each price update, and the amount of this fee is determined by governance. The entire process of obtaining data fees is permissionless and transparent, without the need for contracts or formal agreements.

In terms of the role of Pyth Token in the process, Pyth can be used as a means of payment for data fees. Additionally, any future data staking or claims will be determined through governance. If the community believes that Pyth Staking can bring more token utility and benefits to token holders, a staking proposal can be put forward through governance. If approved, this feature can be added to the entire network ecosystem.

Deep Tide TechFlow: Because Pyth's contract has just been listed on Binance and has surpassed 0.42, I would like to ask, for example, whether it is reasonable to compare $LINK and $PYTH in terms of either market capitalization or FDV. How do you think Pyth Token can be better valued?

Ande: Firstly, we cannot comment too much on this issue, and we cannot provide any financial advice. We are a market participant in the oracle industry, and such comparisons in the market are inevitable.

However, you can also refer to other data for comparison. Currently, Pyth is the largest first-hand data oracle, and in terms of overall integration applications, Pyth ranks second in the entire oracle industry. This is some basic information. You can analyze Pyth Tokenomics and other related information based on this.

Deep Tide TechFlow: Pyth's token launch has received a lot of attention, but it seems that it has not been listed on Binance, or even more Korean exchanges. Can you share any relevant plans in this regard?

Ande: The process of exchange listing is not as negotiable as people might imagine. For example, Pyth's listing on Binance's contract did not involve any communication with Pyth. We were not aware that Binance would list Pyth's contract today, and I only found out after the announcement. Almost all exchanges directly list Pyth's tokens. We had previously announced the Pyth airdrop on November 20 for people to claim.

The entire process is not a dense negotiation or communication process. Instead, many exchanges saw that Pyth Token was already circulating in the market, with 15% of Pyth Tokens currently in circulation. Therefore, exchanges chose to list at this time because they were confident in their liquidity or in the listing itself.

What we can do is assure everyone that the listing news is confirmed and not a scam. As for plans to list on other exchanges, this is not something we approach the exchanges for, and it is the exchanges' own decision-making process. We also hope to see Pyth Token listed in more places, but the timing and occurrence of such listings depend on the exchanges themselves.

Deep Tide TechFlow: OK, thank you. I remember that you have not officially announced any funding information, right?

Ande: That's correct, we have not disclosed any information about funding.

Deep Tide TechFlow: We noticed that many leading institutions in the industry, whether exchanges or market makers, are partners or publishers of Pyth. Many users also feel that because Pyth has many market maker partners, they do not need to worry too much about the price. How do you view this, and what specific role do market makers play in the partnership?

Ande: Our only partnership with them is that they are our data publishers. So, all cooperation with these so-called on-chain financial service institutions is limited to them being the owners of first-hand data. Our only task is to try to obtain this data from them and bring it to the on-chain DeFi for application. Therefore, any other forms of cooperation beyond this are not decided by the organization itself, and I cannot provide much information on this.

All of this actually stems from or is based on becoming a data publisher for Pyth and then collaborating. After becoming a data publisher in the Pyth ecosystem, you can receive a portion of the data usage fee, so our interests are actually tied together. If the Pyth oracle develops well, attracting more users and more on-chain and off-chain protocols to apply Pyth's data, data publishers can also gain more benefits. From this perspective, our interests are integrated, and they can follow up and make their own decisions in this regard.

Deep Tide TechFlow: Thank you. I have one last question. Because we have Pyth Token now and can participate in governance in the future, and also potentially earn some rewards in the governance process, I would like to ask, does Pyth have any long-term development plans for the future? As an ordinary user, how can I participate in Pyth's future development?

Ande: First of all, in terms of development plans, listing is a very important milestone for Pyth, but it has not significantly affected the network's development process. First and foremost, we are an infrastructure. What we do at our core is very simple: continuously improve our technical capabilities and continuously innovate in the entire field of data or on-chain financial data. At the same time, in the future, we will continuously introduce new and more price feeds and seek out more data publishers to provide a more diverse range of price feed data.

Many traditional financial data, including market data for US stocks, stock indices, commodities, and forex, will continue to be listed.

Currently, the structure of Pyth's entire product is real-time price feed data. In the future, there will be more product features, such as many on-chain protocols that may require random numbers, so we will also have a product for random numbers. There will also be other products like on-chain risk management, which we call liquidity oracles. Many things are still being explored and developed. Pyth's focus is on building around on-chain financial data. As for how everyone can participate in the entire ecosystem, you can continue to stay informed and actively participate in the governance process.

Deep Tide TechFlow: Thank you very much for your answers and insights. Looking forward to the future development of the Pyth Network.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。