Pi’s Human Infrastructure for AI: 526 Million Tasks Completed by Distributed Workforce of 1 Million Humans

AI is advancing quickly, but the hardest part of building reliable systems is still deeply human. For companies improving models, tuning inference quality, or scaling data labeling and evaluation, human input remains essential.

Building strong models is not only a matter of more compute: AI needs human-in-the-loop input to refine outputs, define quality, verify correctness, resolve ambiguity, and ensure systems are actually useful to people.

Non-human reinforcement and automated training methods can be powerful in narrow or well-defined settings, helping to scale optimization and improve efficiency. But they are still limited in important ways: they often optimize proxies rather than true human preferences, can be vulnerable to reward hacking, and struggle to fully capture nuance, legitimacy, changing norms, and real-world human judgment.

That is why, regardless of advances in automated methods, human input remains essential to the refinement of AI.

Practical Challenges of Human Input in AI

The need for human input creates significant operational challenges for AI companies.

1. Scale: AI companies need human input at scale. This becomes even more important in emerging areas such as robotics and physical AI, where a future breakthrough may depend on foundation models trained on massive amounts of human-generated data about physical environments and real-world interactions. Just as internet-scale data was a key condition for the rise of large language models like ChatGPT, large-scale human data about the physical world may be a key condition for a similar breakthrough in robotics. Real people can help provide this kind of data, including through digital or virtual environments that capture human actions, movement, object interaction, navigation, and task completion in space.
2. Authenticity: Scaled human input is only valuable if it comes from real people and meets a reliable quality standard. AI companies need ways to verify identity, eliminate bots, and ensure responses are accurate, trustworthy, and useful. Without those protections, human-in-the-loop systems become vulnerable to fraud, low-quality inputs, and weak training signals.
3. Cost: Quality, authentic human-in-the-loop systems are expensive to build, operate and use. Companies need infrastructure to host tasks, attract participants, verify contributors, distribute work, and support large-scale but flexible participation, not to mention the cost for the labor itself in fiat currencies. At scale, the operational burden is not just the labor itself, but the platform, coordination, verification, and payment systems needed to make that labor usable.

Demonstrated at Scale: Pi Network’s Verified Human Workforce

Pi Network has already built the solution: introducing the large-scale, globally distributed workforce of identity-verified human participants already active inside the Pi ecosystem.

In just one example of the scale and ability of this workforce, over one million verified individuals completed over 526 million validation tasks on the network. These tasks were part of Pi’s native KYC system, and the KYC validators’ work was paid directly in Pi tokens. Unlike many other KYC tools, Pi’s KYC uniquely combines AI automation with the power of its massive distributed human workforce to accomplish accurate and efficient verification for over 18 million people in over 200 countries and regions. The over 18 million identity verified people, in turn, may also further join the marketplace of such a workforce.

Pi’s solution creates a new foundation for AI and digital platforms that need human input that is authentic, active, and ready to participate across simple to medium-complexity tasks. Because contributors are KYC-verified, companies using Pi’s distributed human workforce can reduce exposure to bots, fraud, and unverifiable labor while meeting important trust and compliance requirements from the start.

The significance of this goes further. A global workforce brings built-in localization across languages, regions, and cultural contexts, making it possible to generate more relevant data, judgments, and feedback for products intended for real-world use. And unlike many alternatives in the market without a substantial number of real humans, Pi’s network with tens of millions of real people has already demonstrated its ability to provide human input at scale, having accomplished over half a billion tasks. That means companies are not just gaining access to labor, but to measurable human coordination infrastructure.

Pi’s Payment and Incentive Infrastructure for Distributed, Global Human Work

Large-scale human labor is only useful if it can be paid efficiently, globally, and at the scale of millions of people completing hundreds of millions of tasks. With compensation supported in Pi, or in a company’s own token through Pi Launchpad, Pi Network’s model opens a new way to align work, incentives, and ecosystem growth. This is essential as traditional fiat models may become less well-suited to global, flexible, task-based participation.

Global payout infrastructure

Paying millions of people across jurisdictions in fiat can create major friction in payment processing, cross-border transfers, compliance, and the handling of very small payouts. Pi already has the platform, infrastructure, and blockchain-based distribution system that can help simplify this logistics layer. Plus, the Pi workforce already has active Pi wallets, reducing onboarding friction and eliminating the need to introduce users to a new payment system.

Cost efficiency

Payments in Pi may offer a cost advantage over many fiat-based systems by reducing intermediary fees, cross-border payout friction, banking and payment operations, and small-payment overhead. This may compare favorably with platforms such as Mechanical Turk, where requester fees are added on top of worker payments.

Launchpad token as a business model tool

Companies can also compensate contributors in their own token on Pi Mainnet through Pi Launchpad, which is currently being iterated on Testnet. This is part of Pi’s innovation around new business models catered to the AI age and enabled by blockchain: a token that is not just a payment instrument, but is designed for user acquisition and product utility, tied to real usage. A Pi Launchpad token can reduce costs for companies by allowing rewards, participation, user growth, and ecosystem engagement to be supported through the token rather than funded entirely through cash, thus making the payments part of a broader growth strategy rather than only an operating expense.

The token can also function as a tool to continuously engage and interact with people completing work and getting paid who may convert to the company’s users consuming the service they help contribute to. Tokens can be integrated into the company’s product itself as payments, discounts for services offered, access, governance, or other participation mechanisms. For the company, issuing such a token can also mean having another liquid asset at hand for business needs at times. In a break from the common approach to tokens in Web3, Pi Launchpad positions tokens as utility tools tied to working apps and real usage rather than speculative fundraising assets.

AI does not just change how we live and work, but demands new business models for companies to survive, grow and thrive.

Explore Pi’s Human Infrastructure for Your AI Company

Interested AI companies exploring Pi Network’s verified human input at scale can contact Pi here.