$BGB
Cooling DownSnapshot Window: 2026-07-24 12:20 UTC · ← Back to Crypto Overview
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IF YOU LISTEN CAREFULLY TO DR. FAN'S MESSAGE ON AI INNOVATION WITHIN THE PI ECOSYSTEM, YOU WILL UNDERSTAND THAT PI NETWORK IS THE BEST PROJECT FOR THE YEARS TO COME. ALWAYS BE OPTIMISTIC BECAUSE HAPPINESS WILL KNOCK ONE MORNING. [Spoken audio]: Why is PIE focusing on AI? What does AI have to do with blockchain? AI is fundamentally changing how value and production are created in the world. This shift inevitably introduces new challenges for humanity. As AI increasingly drives the production, traditional labor is no longer a reliable basis for how wealth is distributed. This raises a critical question. How do we ensure that value created by AI is distributed in a way that actually improves overall quality of life for everyone, rather than in becoming even more concentrated among those who already control capital? This is a core problem Py intends to address. Py sees the ultimate utility of blockchain technology as an auditable and scalable mechanism for societal distribution and redistribution. Over the past several years, Pi has built a widely distributed network as a foundation for that goal. But having a distributed network alone is not enough. To realize this ultimate utility, real production, both existing and new, must be brought on chain. Doing so presents significant challenges. For existing forms of production, one of the biggest obstacles is identity. identity and verified ownership. These are prerequisites for large-scale real-world production to move on-chain. Pi Network has invested heavily in solving this problem through its KYC solution, which enables identity verification as global scale and provides a foundation for trusted participation in on-chain economic activity. When it comes to new production, the picture is even clearer. The next generation of applications will almost certainly be either AI-powered or AI-native. Yet the AI application layer is still in its early stages. Today, most user interaction with AI remains centered around chatbots, even though the AI industry is actively experimenting with richer applications, new forms of social network, productivity tools, and AI-assisted workflows. In the short-to-meet term, Pi's focus at the intersection of AI and blockchain is therefore the AI application layer. Pi App Studio uses AI to empower app creation, simpler developer libraries and tools which are meant to enable easy integration by third-party AI builders to build on top of Pi. On the strategy level, Pi has combined bottom-up and top-down approaches. of applications to be created, even though the signal-to-noise ratio can be high. This is expected and acceptable. At the same time, Pi complements this with a more top-down high-signal strategy through Pi Network Ventures, which invests in carefully-vetted teams each year and works with them in a more high-touch way. teams each year and works with them in a more high-touch way. Between these two strategies are initiatives in the middle, such as hackathons, developer programs, which combine grassroots efforts with more structured guidance. Together, these strategies have sought to bootstrap, foster, and scale a vibrant ecosystem of AI-powered applications on Pi. Looking further ahead, Pi's involvement with AI extends into AI infrastructure itself. This includes distributed computing for AI training, inference, and reinforcement learning. Pi is uniquely positioned here because it already has a large network of computing nodes. Over 350,000 and a global network of identity-verified human participants who could opt-in to provide authentic human input for AI training, labeling, and reinforcement learning, in exchange for compensation in cryptocurrencies by third-party clients. Distributed AI training is still at the research stage in the world. Distributed AI training aligns naturally with the distributed network. And it may also help address some of the limitations of centralized training, such as data center constraints, energy concentration, and the issues like a catastrophic forgetting or global state bottlenecks. While significant challenges remain, these are precisely the kinds of problems that require sustained research and long-term commitment to solve. We're actively exploring why and how a paradigm shift from fully centralized AI training to more distributed approaches could be feasible and efficient. Turning this into a meaningful contribution to AI infrastructure requires much deeper research. Human participation is also a critical component. PI's network of verified humans offer a unique resource for scalable and authentic human input in AI systems with compensation handled natively through blockchain-based payments. Together, these elements, distributed computing, very hard-fired human input, and blockchain-based coordination provide new approaches to AI learning and infrastructure. Trying to solve the world's hard problems is a long-term endeavor for anyone in the world. It means starting early is even more crucial because anything truly defensible takes time to build, be it technology or network effect. For example, what Pi Network has achieved that provides strategic advantages and defensibility are PI's KYC system, its scale, networks of millions of identity-verified human beings, hundreds of thousands of computer nodes, and the attention and engagement of the community in the tens of millions. And they did not emerge overnight. Rather, they took years to build and efforts of the whole community. Similarly, contributing meaningfully to AI infrastructure through distributed system is extremely difficult, which is precisely why it must be explored early and persistently for long haul.
Waiting for $XRP to do something. Might be here a while.