$PI Pi's Artificial Intelligence Infrastructure: A distributed team of one million humans has completed 526 million tasks
AI development is rapid, but the difficulty in building reliable systems still lies in human factors. For companies dedicated to improving models, optimizing inference quality, or expanding data annotation and evaluation scales, human participation remains indispensable.
Creating excellent models is not solely dependent on more powerful computing power: AI requires human involvement to optimize outputs, define quality standards, verify accuracy, and eliminate ambiguities, ensuring these systems are truly useful to people.
In scenarios with clear conditions and limited scope, non-human-assisted optimization methods and automated training techniques can indeed play a significant role in improving efficiency. However, they also have many limitations: they often only optimize certain surrogate metrics and cannot truly reflect human preferences; additionally, these methods are vulnerable to manipulation of reward mechanisms, and they struggle to grasp subtle differences, rationality of various behaviors, evolving norms, and human judgment standards in the real world.
For this reason, regardless of how automation technology develops, human participation remains essential for the continuous improvement of AI.
Practical Challenges of Human Input in Artificial Intelligence
Dependence on human intervention presents significant operational challenges for AI companies.
Scale/Extent
AI companies require large amounts of human input data. This is especially important in emerging fields like robotics and physical AI, as future breakthroughs are likely to depend on models trained on vast amounts of human-generated data—data involving physical environments and human interactions in the real world. Just as internet-scale data has been key to developing large language models like ChatGPT, large amounts of human data about the physical world could be crucial for breakthroughs in robotics. Real humans can provide such data, for example, by recording actions, movement patterns, object interactions, navigation processes, and task completion in digital or virtual environments.
Authenticity
Only human inputs from real individuals that meet reliable quality standards are valuable. AI companies need to find ways to verify user identities, eliminate malicious actors, and ensure that feedback is accurate, trustworthy, and useful. Without these safeguards, systems requiring human participation are vulnerable to fraud, low-quality input data, and unreliable training data.
Cost
Systems that truly enable human participation incur very high construction, operation, and maintenance costs. Companies need infrastructure to handle tasks, attract participants, verify contributor identities, assign work, and ensure large-scale, flexible participation mechanisms. Not to mention, they also need to pay human labor costs in fiat currency. Overall, operational costs include not only labor expenses but also the costs of maintaining platform infrastructure, coordinating tasks, verification processes, and payment systems.
Large-Scale Application Verification: Real Human Workforce Support in the Pi Network
The Pi Network has found a solution: leveraging a large number of globally distributed, verified participants active within the Pi ecosystem.
This data alone demonstrates the scale and capability of this team: over one million verified users have completed more than 526 million verification tasks. These tasks are part of Pi’s built-in KYC system, and the rewards for KYC verifiers are directly paid in Pi tokens. Unlike many other KYC tools, Pi’s system combines AI automation with a large human team, enabling accurate and efficient identity verification for over 1M people across more than 200 countries and regions. Those who complete verification can further participate in this talent marketplace.

Pi’s solution lays a new foundation for AI and digital platforms that require human participation, where contributions are genuine, motivated, and capable of handling tasks from simple to moderately complex. Since participants are verified, companies using Pi’s decentralized human resources can avoid threats from bots, fraud, and unreliable labor, while meeting various trust and compliance requirements from the outset.
Its significance goes far beyond that. A global workforce can facilitate cross-language, regional, and cultural localization, helping to provide more accurate data, more valuable analysis, and more useful feedback for practical applications. This is something many other methods cannot achieve.
