On a certain deep night in 2021, in a small basement in San Francisco, Alex stared at the constantly flashing red error codes on the screen, his fingers flying over the keyboard. The coffee on the table had cooled for the third time, and the moonlight outside shone through the blinds, just enough to illuminate his draft paper filled with formulas—on it was drawn a crazy idea: "Can financial data no longer be held in the hands of the likes of Bloomberg?"

No one expected that a few years later, this project called @Pythnetwork would grow from a few lines of code in a basement to a "disruptor" capable of challenging the $50 billion market data industry.


First, starting point: The 'trust dilemma' in DeFi became their first battlefield.
At that time, DeFi was booming, but it hid a fatal problem: the price data provided by the oracle always had issues. Once, a lending protocol faced data delays; although Bitcoin had already crashed, the liquidation price hadn't updated, and many users woke up to find their assets had gone to zero. Alex and the team saw a flood of complaints in the community, and it felt like a stab to the heart: 'The core of finance is trust; if the data is inaccurate, everything is just empty talk.'

They decided to solve the problem from the root. Other oracles scraped data from public exchanges, like 'second-hand information vendors'; Pyth insisted on looking for 'first-hand sources' — directly negotiating with top trading firms like Jump Trading and Jane Street, and also persuading major exchanges like Binance and Cboe to directly integrate their internal real-time trading data into the network.

'At the beginning, they completely ignored us,' Alex later joked in an interview, 'We ran around 17 institutions with our code; once, in a café in New York, I talked to a technical director of an exchange until three in the morning, and he finally said, 'Your idea might actually work.'

After grinding for half a year, 125 institutions gradually joined. Pyth's 'first-party data' model finally ran through: data went directly from the source to the blockchain, updated at a speed of milliseconds, and could be verified for authenticity using cryptographic technology. During an extreme market condition, when Bitcoin fell 20% in ten minutes, Pyth's data synchronization rate was 100%, and no erroneous liquidations occurred in its DeFi protocols. Gradually, 'For safety, choose Pyth' became a consensus in the DeFi circle, with $8.3 billion of on-chain assets relying on it for security, and $16 trillion of trading backed by its data.


Second, turning point: Eyeing the $50 billion pie, they want to be 'the light for institutions'.
After establishing a foothold in DeFi, Alex's team did not stop. One day, they obtained an industry report: the global market data industry has a scale of $50 billion per year, yet it is monopolized by giants like Bloomberg and Refinitiv — institutions spend $250,000 per month for a full asset data package, and data delays and black box operations are common.

'Why should good data only be available to the wealthy?' Someone slammed the table during a team meeting. They decided to launch the second phase: to create an institutional-level data subscription product called 'Pyth Pro'.

To reassure institutions, they expanded the data coverage from cryptocurrencies to stocks, futures, and forex, covering a total of 1930 asset classes; they also specially developed a 'millisecond-level update' function to meet the needs of high-frequency trading. Even more aggressive is the pricing: the basic version of encrypted data is free, while the advanced version costs $5000 per month, which is 90% cheaper than traditional giants.

But convincing institutions is not easy. Once, when connecting with a certain Wall Street asset management company, the technical director directly asked: 'With a blockchain project, why should we hand over our core trading data to you?' The team did not argue, but simply opened the backend and demonstrated the entire encrypted record from the exchange to the blockchain, along with the real-time contribution trajectory of 125 institutions: 'Every data point we have can be traced and verified, which is something traditional giants cannot do.'

Three months later, that asset management company became the first client of Pyth Pro. Now, Jump Trading and institutions under Fidelity are testing it; a trader provided feedback: 'Previously, I had to switch between three platforms to look at data, but now Pyth is enough, and the time saved allows us to make three more trades.'


Third, confidence: $PYTH is not hype; it is a 'commitment to make money together'.
Someone asked Alex what the key to Pyth's long journey is. He pointed to the $PYTH token icon on the screen: 'It's this; it's not for speculation, but a 'commitment' for all builders.'

In the Pyth ecosystem, institutions that contribute data can receive $PYTH rewards — the more accurate and timely the data, the more rewards; if they dare to commit fraud, the staked $PYTH will be confiscated, which is like putting a 'safety lock' on the data. The subscription revenue of Pyth Pro will flow back to the DAO, with community votes deciding how to use it: last month, they used part of the revenue to repurchase $PYTH and subsidized five small projects developed based on Pyth.

'Previously, those of us in technology always thought that making money was a business person's job,' Alex said, 'but now we understand that a good ecosystem must allow everyone to make money — institutions find it worry-free, contributors get returns, and investors can share in the growth; only then can we go far.'


Now: the road is still long, but the light is getting brighter.
At the end of last year, Pyth's office moved from the basement to the Financial District of San Francisco, with Bloomberg Tower just outside the floor-to-ceiling windows. Alex's team wrote down new goals in #PythRoadmap: to expand asset classes to 10,000 by 2026, covering all mainstream financial categories.

Someone asked them if they were afraid of being suppressed by giants. Alex smiled and said: 'Now that we have come out of the basement, we are not afraid to face challenges again. The future of finance should be data transparency, accessible to everyone; this is not just our project's business, but the business of everyone who believes in 'fairness'.

Perhaps just like the slogan on the wall of their office says: 'It’s not because we see hope that we persist; it’s because we persist that we see hope.' The story of @Pythnetwork continues, and $PYTH is the most tangible 'merit badge' in this counterattack.@Pyth Network #PythRoadmapand $PYTH