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Artificial intelligence is developing rapidly, but building reliable AI systems still fundamentally relies on deep human involvement. For companies that need to optimize models, improve reasoning quality, and scale data annotation and content evaluation, human labor remains irreplaceable.

Creating high-performance AI models is not simply achieved by stacking computing power. AI requires human intervention in the loop to optimize outputs, define quality standards, verify content accuracy, resolve semantic ambiguities, and ultimately ensure that AI systems truly serve humans and have practical value.

Pure machine reinforcement learning and automated training solutions have significant advantages in narrow, rule-based scenarios, helping companies reduce costs and improve operational efficiency at scale. However, these technologies have inherent limitations: algorithms tend to cater only to machine objectives rather than human preferences; they are prone to reward loopholes and rule exploitation; and they struggle to accurately capture language nuances, compliance boundaries, evolving social norms, and human subjective judgments in complex scenarios.

For this reason, even as automation technology continues to evolve, human participation remains a core necessity for refined AI iteration.

Challenges of Introducing Human Involvement into AI

The urgent need for human involvement in the AI industry presents significant operational challenges for many tech companies.

1. Scalability Challenges
AI businesses require massive, continuous human support, especially in emerging fields like robotics and physical intelligent devices. Breakthroughs in these areas heavily depend on real human behavior and interactions to build foundational datasets.
Just as vast online text data has fueled large language models like ChatGPT, physical world behavior data collected from real humans—via digital and virtual environments—may be key to breakthroughs in robotics. Real people can continuously provide core data such as movement trajectories, spatial navigation, object interactions, environmental navigation, and task execution in digital and virtual scenes.

2. Data Authenticity Challenges
The value of large-scale human data collection relies on genuine human participation combined with compliant quality checks. Companies must establish identity verification mechanisms, prevent script-based cheating, and ensure that human feedback is authentic, accurate, and effective. Without proper risk control, human-in-the-loop systems are vulnerable to mass fakes, low-quality inputs, and invalid training data, which can severely weaken model training outcomes.

3. High Cost Challenges
Building, maintaining, and deploying a high-quality, mature human-in-the-loop system incurs extremely high costs. Companies need to develop task distribution platforms, recruit and verify contributors, and flexibly allocate large volumes of tasks. Additionally, traditional fiat currency payments, cross-region workforce management, multi-party collaboration, and compliance reviews further increase operational burdens. The larger the scale, the higher the overall costs of platform maintenance, personnel verification, and salary settlement.

Large-Scale Implementation Case: Pi Network’s Distributed Human Verification System

To address the human resource shortfall in the AI industry, Pi Network has developed a mature solution: leveraging a global distributed network to establish a large-scale, identity-verified human workforce, with millions of real users deeply involved in Pi ecosystem tasks.

The scale and deployment capability of this human system have been validated: over 1 million users have completed 526 million verification tasks within Pi Network.
These tasks are part of Pi’s native KYC system, where verifiers are paid directly in Pi tokens. Unlike traditional third-party KYC tools, Pi’s innovative “AI automation + global distributed human” dual-track model provides efficient, accurate identity verification for over 18M users across more than 200 countries and regions, with the workforce still expanding.

Pi’s infrastructure offers a new foundational support for all AI companies and digital platforms requiring genuine human involvement. All contributors undergo strict KYC verification, helping partner companies effectively prevent machine cheating, fake data, and invalid labor risks—ensuring compliance and trust from the source.

Its value extends beyond basic human labor supply. The globally distributed workforce, with multilingual, cross-regional, and multicultural attributes, can provide more localized data sets, subjective judgments, and authentic user feedback for product deployment.
Unlike many industry solutions that rely solely on automation without real human support, Pi’s network of tens of millions of real users has completed over 500 million practical tasks, demonstrating mature large-scale human scheduling and collaboration capabilities, and providing a quantifiable, reusable human cooperation infrastructure for enterprises.

Pi Distributed Human Token Incentives and Global Settlement System

To sustain long-term, stable operation of massive human collaboration, an efficient, global, and scalable compensation and incentive mechanism is essential.
Pi ecosystem uses Pi Launchpad for salary settlement and supports companies issuing custom tokens as incentives, creating a new model for task distribution, user engagement, and ecosystem growth. Traditional fiat payment methods are increasingly inadequate for the flexible, fragmented, task-based gig economy worldwide.

1. Global Blockchain Payment Infrastructure
Cross-border fiat settlement often faces complex procedures, high transaction fees, strict compliance checks, and difficulties with small payments.
Pi leverages its own blockchain infrastructure and mature distribution system to streamline global salary payments. All ecosystem contributors have Pi wallets, eliminating the need for additional payment tools and significantly lowering cooperation barriers and user onboarding costs.

2. Cost Advantages
Compared to traditional fiat payment platforms, on-chain Pi settlements eliminate middleman commissions, cross-border remittance losses, bank fees, and extra costs for small transactions, offering a cost-performance ratio far superior to platforms like Amazon Mechanical Turk, avoiding multiple markups.

3. Launchpad Token: A New Business Deployment Tool
Companies can also issue dedicated ecosystem tokens via Pi Mainnet’s Pi Launchpad (currently in testnet phase) for labor incentives.
This represents Pi’s innovative Web3 business model tailored for the AI era: tokens are no longer just payment tools but are deeply integrated with product value, user rights, and real-world applications.
Companies can reduce reliance on cash by rewarding tasks, attracting users, and managing ecosystems through custom tokens, significantly lowering cash flow costs and transforming human input into a long-term growth strategy rather than mere operational expenses.
Meanwhile, ecosystem tokens issued by projects can be integrated into product systems for payments, feature unlocking, community governance, and rights exchange. Contributors who complete tasks naturally become core users, continuously engaging with the services they helped build.
These utility tokens focus on practical applications and scene empowerment, differing from purely speculative tokens in the Web3 market, offering stronger utility and stability, and increasing high-quality liquid assets for enterprises.
As AI continues to reshape global production and lifestyles, industry transformation compels companies to innovate business models to achieve long-term survival, sustained growth, and industry leadership.

If your AI enterprise needs compliant, scalable, and quickly deployable distributed human annotation, content verification, and data collection capabilities, you can contact Pi through official channels for collaboration. $PI