While traditional data giants are still making easy money from 'intermediary spreads', Pyth Network has completely changed the track of the 50 billion market with its 'decentralized first-party oracle' approach! Previously, institutions had to spend millions to buy second-hand data, but now Pyth allows data to connect directly to the chain from the source; previously, giants monopolized pricing, but now PYTH tokens allow contributors to share in the profits—this transformation has even Wall Street institutions scrambling to get on board, and traditional methods are really going to be eliminated!
To understand Pyth's 'switching logic', first look at the 'deadlock' of the traditional data industry. The annual scale of the global market data exceeds 50 billion US dollars, but 70% of the profits are divided among 3 giants. The core model is 'buy low sell high': taking raw data from traders, forwarding it through intermediary nodes, and then tagging it with 'integrated service', making the price directly increase by 3-5 times. A quant fund manager complained: 'Previously, I spent 1.5 million annually on US stock and cryptocurrency data, with data delays of 200 milliseconds being the norm. Once, due to data errors, I lost 800,000 in arbitrage, and when I sought to protect my rights on the platform, I only got a reply of 'market risk.' What's worse, traditional data is severely fragmented, with stock, forex, and commodity data belonging to different platforms. If institutions want to conduct cross-market analysis, they have to buy 3 subscriptions at the same time, which increases costs sharply.
Pyth's breakthrough begins with 'decentralized first-party direct connection'. It bypasses all intermediary nodes, directly connecting with top global traders like DRW and Jump, with data going straight to the blockchain from the source—there are no middlemen marking up prices, and subscription costs are 40%-60% lower than traditional platforms; there is no need for multiple forwarding steps, updating speed is compressed to milliseconds, and high-frequency trading no longer has to be 'a step behind'; the traceability feature on the blockchain makes the source and timestamp of each piece of data clear and verifiable, completely solving the 'data black box' problem. Currently, Pyth has covered over 100 blockchains and supports over 1800 price feeds (including over 900 real-world assets), with 60% of the DeFi derivatives market relying on its data, and a cumulative trading volume exceeding $1.6 trillion, proving the correctness of the 'change of path'.
Pyth's vision to 'expand beyond the $50 billion market of DeFi' is not based on slogans but on the institution-level subscription products launched in the second phase. This product precisely addresses institutional pain points: for quantitative institutions, it offers a 'real-time market + historical backtesting' bundled service, increasing strategy backtesting efficiency threefold; for cross-border banks, it automatically generates on-chain compliance audit reports, meeting global regulatory requirements for data traceability; for asset management companies, it supports one-click integration of all asset data including stocks, forex, and commodities, eliminating the need to piece together 'data puzzles'. A certain Wall Street investment bank, after a month of trial, directly canceled two existing data subscriptions, stating, 'After using Pyth, data costs dropped by 50%, compliance efficiency improved by 40%, and much of the money spent previously was just 'intelligence tax' paid to intermediaries.'
Institutions are willing to regard Pyth as a 'trusted comprehensive data source', with the core being 'threefold reliability assurance'. The first layer is source reliability, as all cooperating traders are top industry players, and data is generated directly from the order book, with accuracy 10 times higher than the second-hand data forwarded by intermediaries; the second layer is technical reliability, as the decentralized architecture has no single point of failure, achieving 99.99% service stability under extreme market conditions, unlike centralized platforms that can crash data; the third layer is mechanism reliability, where the PYTH token ensures that 'the better the data, the more benefits one receives', forming a positive cycle of 'high-quality data → more rewards → even better data', fundamentally ensuring data quality.
The PYTH token is the 'engine' of the Pyth ecosystem, linking contributor incentives with DAO revenue distribution. Data publishers can receive PYTH rewards for uploading high-quality data, and staking tokens can also earn passive income; developers can integrate the Pyth protocol to build applications and apply for ecosystem fund support; subscription fees paid by institutions (partially settled in PYTH) flow into the Pyth DAO, where token holders can vote on the use of funds—whether for technical upgrades, market expansion, or to buy back tokens to enhance value. This 'incentive + co-governance' model allows ecosystem participants to share in growth dividends, rather than 'monopolizing profits' like traditional giants.
From the cornerstone of DeFi data to a 'path-changer' in the $50 billion market, Pyth uses a combination of 'direct connection to reduce costs, subscription to expand scenarios, and tokens to activate the ecosystem' to completely rewrite industry rules. As more and more institutions connect, the monopoly barriers of traditional giants are crumbling, and the data industry is transforming towards 'safety, transparency, and fairness'. In the future, when Pyth truly covers all market data scenarios, we will find that this 'change of path' is not accidental, but an inevitable outcome of industry development.#PythRoadmap
