High-Quality Data Center: How does Sapien build a truly viable Web3 AI solution?

Written by: Shen Chao TechFlow

OpenAI CEO Sam Altman pointed out when talking about the GPT model:

The capabilities of AI directly depend on the quality and diversity of AI training data. Poor data can lead to biases and errors in the model, while high-quality data is the foundation for building strong AI.

However, in the face of this consensus, even as one of the most well-known AI companies in the world, OpenAI is deeply trapped in the dilemma of a shortage of high-quality AI training data. It is reported that the development of OpenAI’s next-generation flagship model GPT-5 (internal code name Orion) is lagging behind the original schedule, with the insufficient supply of high-quality text and data being a significant reason.

The importance of high-quality AI training data is self-evident. Even if one has the most intelligent model in the world, if the quality of the input data is poor, the results are meaningless. However, obtaining high-quality AI training data is a complex and challenging process due to issues such as the integration of data diversity, the high cost of data labeling, and the extremely high accuracy and professionalism requirements for data in certain specialized fields.

It is precisely for this reason that Sapien, which has secured $10.5 million in funding and has 1.2 million registered users across 110+ countries globally, is demonstrating its significant value in the intensifying competition of AI development.

As a decentralized data platform, how does Sapien specifically leverage the power of Web3 to build a unique reputation system and decentralized governance structure, incentivizing global users to participate and providing lower-cost, accurate, verifiable high-quality data for the development of AI?

Before the mainnet and TGE, how can users participate more efficiently in this AI data revolution and gain more ecological equity chips while earning their rewards?

As the points task progresses deeper, let’s take a look at Sapien’s problem-solving approach.

Cooperating with Alibaba and Amazon: Truly Implementing Web3 Data Solutions

In the past two years of the Crypto + AI craze, you may have seen many Web3 AI data projects. Most of them have used the two popular narratives of “blockchain” and “AI” as slogans to attract market attention and capital investment. However, very few of them can genuinely solve practical problems and achieve a deep integration of technology and application, leading to a demystification of Web3 AI data projects in the market.

The real adoption is also what distinguishes Sapien from other Web3 AI projects.

As an open, scalable, and decentralized data platform, Sapien is capable of truly transforming from concept to reality by providing high-quality data, empowering AI to optimize its development in multiple specific application scenarios.

Since its inception in 2023, Sapien has demonstrated strong growth potential and market recognition in less than two years. The platform’s user base and business scale have been rapidly expanding: it has over 1.2 million registered users across 165 countries/regions globally, and the completion volume of platform data tasks has exceeded 100 million.

In terms of corporate collaboration, Sapien has also performed remarkably. So far, Sapien has established deep partnerships with 27 enterprise-level clients, including well-known Web2 giants such as Amazon, Toyota, Alibaba, Baidu, and Lenovo. These collaborations not only further validate Sapien’s technological strength and commercial value but also lay a solid foundation for its continued development in the future.

Of course, behind the clever integration of the project with the Web3 concept and its application in multiple real-world scenarios is a team composed of experts in the AI field and elites in cryptographic technology, who possess deep insights into the pain points of the AI market and a strategic vision for the potential of Web3 AI.

As the founder and CEO, Rowan Stone has extensive experience in the blockchain field and was one of the main contributors to Base, a Layer 2 project launched by Coinbase. Now, he is turning his attention to the field of artificial intelligence, striving to realize the sharing and connection of human knowledge through Sapien, injecting momentum for the further development of AI.

Trevor Koverko is the co-founder of the on-chain digital securities platform Polymath, making pioneering contributions in the field of tokenization of real-world assets. Today, he serves as the Chief Strategy Officer (CSO) of Sapien, focusing on applying decentralized trust models to the field of artificial intelligence, promoting the trustworthy and transparent development of AI.

Henry Chen has extensive experience in market operations, serving as the Chief Operating Officer of Haller.ai (which is now publicly listed) and previously responsible for business growth at several tech unicorns including ClickUp, SAS, and Xsolla. As the Chief Operating Officer (COO) of Sapien, Henry is responsible for developing the platform’s market growth strategy and driving Sapien’s global expansion.

Kelly Ryan graduated from the University of Waterloo and is an experienced product and engineering leader, having worked at the startup FastAF, which received $80 million in funding. She currently serves as the Chief Technology Officer (CTO) at Sapien, where she leads technology architecture and product development, providing strong support for the platform’s technological innovation.

With the exceptional abilities and collaborative spirit of this elite team, Sapien not only excels in technology and the market but has also gained high recognition in the capital market. In October 2024, Sapien completed a $10.5 million seed round of financing, led by Variant, with participation from Primitive Ventures, Animoca, Yield Game Guild, and HF0.

Not only can it accumulate a large number of users and partners to promote the real transition of Web3 AI from concept to multi-scenario implementation, but also gain the favor of institutional capital, which is inseparable from Sapien’s clear business model and sophisticated operational logic. So how is all of this achieved?

Connect data contributors, annotators, and AI projects to build a high-quality data set hub.

When it comes to Web3 AI data services, many people will immediately have a stereotype: is this a data labeling platform driven by token economics?

It is important to clarify that data tagging is part of the Sapien business, but Sapien services go far beyond that.

In simple terms, the core operation of Sapien revolves around “high-quality data.”

Users can make two major contributions through Sapien:

On one hand, there is data contribution: users can contribute various types of data, including text, voice, images, videos, and even specialized knowledge. Beyond general data, Sapien’s data contribution system can provide customized data services. For instance, medical AI requires specialized and high-quality AI training data, and doctors can contribute medical data through Sapien to support the development of medical AI while receiving rewards. Based on a user base of 1.2 million registered users, Sapien will provide new data for the development of AI across various industries.

On the other hand, there is data annotation: people from any country/region in the world can participate in a decentralized manner. This contribution is similar to data labeling but more advanced, as Sapien can integrate artificial intelligence and human intelligence to collect and annotate all types of input for any model, while endowing AI with perception and understanding of language and context.

For example, when annotating text data, Sapien supports providing questions and answers based on the context and content of the text, thereby offering seamless and natural responses for chatbots, while further deepening AI understanding by annotating the text to determine the emotions expressed—positive, negative, or neutral.

For example, Sapien supports the recognition and differentiation of different objects, features, or areas in images, categorizing them into different classes, such as tagging people, cars, buildings, etc. in a picture. This higher-dimensional data processing provides higher quality data for AI training.

We can experience the difference of Sapien data through a vivid collaboration case: in the field of autonomous vehicles, Toyota provides a dataset from autonomous vehicles to Sapien, allowing Sapien users to deeply analyze these 3D data and annotate them, helping the model understand the position of the car in time and space, as well as the scenarios it faces, thus achieving safe driving.

Based on this higher quality data, Sapien easily connects data contributors, data processors, and AI projects, becoming a data resource center for the AI industry and a hub for high-quality AI datasets:

For data contributors: Anyone can upload data and receive rewards while contributing to AI development.

For data processors: Anyone can participate in data processing, contributing to the development of AI while earning rewards.

For AI projects: achieving higher quality data at a lower cost to enable rapid development of AI.

At the same time, with the power of blockchain, all contributions are recorded and managed on-chain, achieving distribution according to labor and avoiding the unequal distribution of benefits caused by intermediary exploitation.

The report “Unlocking the Chinese Artificial Intelligence Data Market: Trends, Challenges, and Opportunities” released by Sapien in May also pointed out that the foundation of any powerful AI system lies in the data used to train it. High-quality data has broad application prospects in areas such as automatic speech recognition (ASR), financial activities, autonomous vehicles, robotics, educational technology, and large language models (LLM).

Under the premise that the logic is valid, how to ensure high-quality data contributions and widely mobilize the enthusiasm of various participants has become the core challenge for the successful operation of the Sapien platform.

And all of this will be further realized through a decentralized task platform based on the SPN token.

Staking, validation, and matching are interlinked: the higher the quality, the higher the returns.

In simple terms, the core logic of the Sapien decentralized task platform is: register on the task platform → select a task → complete the task → receive rewards.

SPN token, as the native token of Sapien, plays an important role as an ecological incentive.

In this process, how can we ensure that users truly complete tasks with high quality? Sapien addresses this issue by introducing a staking mechanism and an on-chain reputation system.

Users who want to participate in the task need to stake SPN tokens as collateral.

After the task is completed, it will enter the peer review stage, where high-reputation users review the task quality of low-reputation users.

If the task is completed with high quality, users can receive rewards and improve their reputation;

If the task completion quality is low, the tokens pledged by the user will be confiscated, and it will also affect their subsequent task permissions.

Through continuous evaluation of users’ task completion status, a comprehensive on-chain reputation system emerges: on one hand, users with higher reputation will be able to unlock more task permissions and receive more rewards, while richer rewards will attract more users to join in the effort to complete tasks and enhance their reputation, creating a positive cycle within the ecosystem; on the other hand, based on users’ reputation and task completion status, Sapien will also filter and certify users to establish clearer user profiles, achieving precise matching between tasks and users, thereby further enhancing the operational efficiency of the entire ecosystem.

With a global scale of 1.2 million registered users and adoption by dozens of top enterprises, the feasibility of the Sapien high-quality AI training data solution is well demonstrated in terms of data dimensions. So, how can one better participate in this amidst the current lack of mainnet and TGE?

The Cookie x Sapien special event is ongoing, earn points to accumulate airdrop chips.

Sapien has just concluded the third phase of the Sapien Squad, an initiative aimed at collaborating with top projects in the Ethereum and Base ecosystems, including Uniswap, AAVE, Morpho, Pendle, etc. Users who have registered with Sapien and hold eligible tokens at the time of the snapshot will have the opportunity to earn badges and receive ally airdrops in the future.

However, there’s no need to worry if you missed the third phase of Sapien Squad. The most direct way to earn points before the TGE is to participate in tasks.

Currently, there are three sections on the Sapien official website where you can earn points: the task panel, the points panel, and the training center.

The task panel is where data tasks are displayed, and users can select their preferred tasks based on time, task type, and task points. In the future, Sapien will continuously launch more tasks.

The points panel is where Sapien guides users to learn more about Sapien. Users can earn 100 - 500 points by completing tasks such as following Twitter, binding Twitter, connecting Farcaster, and binding World ID.

The training center provides users with beginner science popularization videos and tutorials. By watching the videos and tutorials, users can earn points multipliers and point rewards.

Points serve as proof of participation in Sapien and as a chip for earning SPN token rewards in the future. They can be exchanged for SPN tokens during the official TGE.

Meanwhile, the Cookie DAO’s Sapien-themed SNAPS event is also underway. After registering an account on the Cookie.fun platform, you can help expand the project’s influence by posting tweets on platform X that introduce Cookie DAO and Sapien with tags such as #Sapien、#snaps. In the future, you will share 0.5% of the SPN token supply.

In addition, to maintain the orderly growth and healthy sustainable development of the entire ecosystem, Sapien has also designed an invitation fission mechanism and staking rewards.

In the staking mechanism, the longer the staking period, the higher the points multiplier: users who choose a 1-month lock-up period will receive a 1.05x reward multiplier; users who choose a 3-month lock-up period will receive a 1.10x reward multiplier; users who choose a 6-month lock-up period will receive a 1.25x reward multiplier; users who choose a 12-month lock-up period will receive a 1.50x reward multiplier.

In the invitation mechanism, the more new ecosystem participants a user invites, the higher the rewards they can obtain, with a maximum of 5% of their referrer’s rewards.

Conclusion

Data is the new electricity, and this is an undeniable consensus.

Sapien, which focuses on providing high-quality AI training data for AI development, is a powerhouse in this data revolution, encouraging global users to participate in data contributions with decentralized power and rewarding them based on the quality of their contributions, thus solving the AI data dilemma. Sapien’s mission is not only to train machines but also to coordinate global intelligence, allowing AI to truly serve the interests of all humanity.

It is worth mentioning that on July 7, 2025, Sapien announced a brand refresh, and when opening the official Sapien Twitter, the progress bar on its introduction page was changed from 40% to 50%. Many community members speculate that this progress bar is an indication of important milestones for the project (mainnet and TGE).

According to the roadmap disclosed by Sapien’s official documentation, 2025 will be a key year for its development. Sapien’s focus includes the mainnet launch (covering the reputation system and user qualification certification), the token TGE (Token Generation Event), and further promoting the continuous growth of data contributors in the ecosystem while attracting more enterprise-level partners to join. With the project’s brand image being refreshed, the orderly advancement of the points program, and the continuous expansion of the ecosystem scale, we look forward to Sapien redefining the rules of data sharing and value creation with high-quality data, becoming an important force in promoting AI development in the future.