How will the projects favored by DAT before TGE reshape the AI ​​power structure?

Author: Chain News Source: Medium The Power Dilemma and Solutions in the AI ​​Era The history of the internet's development has been marked by the ongoing struggle between "openness" and "monopoly," and the inevitable shift of power and hegemony to the tech groups that control data and computing power. A similar scenario is currently unfolding in the AI ​​field, but even more so. As the scale of AI model parameters continues to expand at an astonishing rate, and as global AI chip computing power is dominated by the cloud services of a handful of companies, a profound dilemma has emerged: the emergence of a "digital oligopoly" even more powerful than that of the internet era, a triple monopoly comprised of "data, algorithms, and computing power." In this landscape, the vast amounts of personal data contributed by ordinary users have become "free production data" for training models. They are unable to share the enormous value created by this data, and innovation is consequently constrained. However, technological development always harbors the power of change. The emergence of blockchain offers us the potential for decentralization. Ethereum returns application development rights to developers, while Bitcoin decentralizes financial power to individual nodes. Following this trajectory, the 0G project emerged and is highly anticipated, designating a similar role for the AI ​​field. By building a decentralized AI incentive network as its core goal, 0G aims to fundamentally disrupt the existing power structure. By returning the value of computing power, data, and models to every participant, AI will truly become a universal public good. Leveraging Technological Solutions to Build AI Infrastructure To democratize AI, we must address the fundamental pain points of traditional centralized infrastructure: poor flexibility, performance bottlenecks, and inefficient storage. The development of a decentralized AI economy is fundamentally supported by 0G, leveraging a comprehensive and innovative technological architecture.

Architecture solves flexibility challenges

When the model needs to be upgraded from GPT-3 to GPT-4, and the parameter scale increases by nearly a hundred times, the shortcomings of the traditional AI system's overall architecture of "a single move affects the whole body" are fully exposed. Developers have no choice but to invest a huge amount of time and resources to restructure and reconstruct the entire system. However, the emergence of 0G replaces the AI ​​infrastructure for disassembly, and disassembles it into modules such as model training, data processing, and computing power. These modules can run and upgrade independently, and they communicate with each other through standardized interfaces. Developers can replace a module or the entire phone as needed, just like changing the phone case for a smartphone. This design greatly reduces the upgrade cost and the entire development cycle, ensuring that the infrastructure can quickly keep up with the evolution of AI technology, thereby reducing the negative value of implementing technology updates. A Revolutionary Breakthrough in Performance: Whether it's the instantaneous response of AI agents or the millisecond-level decision-making of autonomous driving, both place stringent demands on the processing speed and throughput of lower-level networks. To support future large-scale, real-time AI transactions, the 0G network is designed to handle 11,000 transactions per second and achieve a throughput of up to 50GB/s. Deploying urban traffic dispatch AI on 0G means it will be able to process thousands of real-time traffic light states and road conditions, making optimal decisions instantly. This eliminates biased judgments caused by network latency. This robust performance guarantee is key to ensuring AI applications move from the lab to real-world applications. The 0G mainnet utilizes an innovative architecture with erasure codes and systematic artifact processing, upgrading the traditional tiered model for hot and cold data. For example, a single original data block can be split into over 3,000 encrypted shards, leveraging a dynamic encoding algorithm to achieve the optimal balance between data availability and storage efficiency. Under ideal conditions, N nodes across the entire network can achieve a write capacity of N × 35MB/s. This is achieved by storing unique shard copies in storage nodes and employing a random sampling verification mechanism, enabling linear scalability across the entire network. This revolutionary global storage technology reshapes the AI ​​data management paradigm. The core technological breakthroughs are reflected in three key areas: 1. Data path decoupling: This completely separates the "storage verification channel" from the "data release channel," achieving consensus solely through aggregate signatures and KZG commitments, eliminating the broadcast bottlenecks inherent in traditional blockchains. 2. Tiered storage: Compute and storage components support independent deployment, allowing them to be combined based on demand to achieve optimal results. When training a model with hundreds of billions of parameters, the data set can be hashed to the L1 layer, and the sharded data can be processed with the help of loop nodes. 3. Unlimited shard expansion: Theoretically, it supports EB-level data storage needs and can perfectly equip AI for massive data throughput scenarios. With the help of a dynamic re-staking mechanism, the decision-making layer can be horizontally expanded without service interruption. The architecture compresses data access latency to microseconds, which reduces storage costs by 50% and initially supports multi-formula calculations. When dealing with complex tasks such as medical image analysis, the solution system can automatically schedule near-field shards to perform real-time reasoning instead of migrating the entire data set. It is this feature that makes 0G the only centralized storage solution that can meet the needs of AI training and real-time applications.

From Technology to Ecosystem: Market Verification and Application Implementation

Whether a cutting-edge technology can gain market recognition and build a thriving ecosystem ultimately determines its success. Through proven practical applications and collaboration, 0G is proving its commercial value.

In January of this year, Alibaba Cloud, a leading global cloud computing service provider, and 0G announced a partnership to significantly promote the development of next-generation Web3 and AI infrastructure in the Asia-Pacific region. This strategically significant collaboration combines 0G's standardized, centralized AI technology stack with Alibaba Cloud's industry-leading computing capabilities, vast customer base, and extensive regional influence. It's more than just a technological integration, creating a path for traditional enterprises and developers to adopt decentralized technologies at scale. It also serves as a crucial bridge between the Web2 and Web3 worlds. Specifically, the collaboration between Alibaba and 0G will focus on several key areas. 0G's cloud infrastructure will be integrated with 0G's decentralized AI storage to support data-intensive applications such as large-scale AI training, inference, and verifiable computation. Both parties will jointly launch initiatives such as workshops and hackathon mentorship programs to empower the next generation of developers and innovators in the Asia-Pacific region, inspiring them to explore the potential of the convergence of AI, cloud computing, and blockchain technologies. Cloudician Technologies, a professional Web3 infrastructure service provider, will also provide key support in this process, jointly promoting the accelerated integration of AI and Web3 in the Asia-Pacific region. This event is of extraordinary significance, comparable to ordinary technology. It signifies that 0G's technical architecture and ecosystem vision have been recognized by top cloud service providers. Subsequently, both parties are working together to build an open, composable, and innovation-driven digital future for developers and enterprises. The listed company is willing to actively engage in comprehensive cooperation, confident in the technological breakthroughs achieved by 0G. 0G recently successfully trained a 107 billion-parameter model using a cluster over a low-throughput internet connection. This achievement is considered a milestone in decentralized AI. Compared to related research conducted by Google, this efficiency improvement is 357 times. The entire market has been attracted to this foundational technology and Alibaba Cloud's business model. What is also surprising is the diversity and quality of 0G's ecosystem partners, including Layer 2 technology platforms like Optimistic and industries like IoTex that are deeply engaged in the Internet of Things (DePIN). In the commercial implementation process of specific scenarios, collaboration with industry leaders can accelerate progress, while cooperation with technology platforms can consolidate underlying capabilities. This dual-track collaboration model of "industry leaders + technology platforms" enables 0G deployment to unleash powerful ecological potential.

Future Vision: When AI Innovation Returns to the Masses

0G not only achieves innovation at the technological level but also embodies the implementation of production relations in the AI ​​era, promoting the practical return of AI innovation to the masses. Thanks to the decentralized network, developers truly possess technical autonomy, enabling them to innovate freely without being locked into platforms. Leveraging blockchain's entity rights mechanism, ordinary users can transform their digital footprints—whether chat logs, health data, or consumer habits—into licensable and tradable personal assets, earning fair financial rewards for every data contribution. The ultimate form of this vision is the ability to train AI models on consumer-grade devices. Imagine a middle school teacher on a 0G network leveraging years of accumulated teaching notes and classroom data to collaborate with peers to train a personalized tutoring AI that truly understands the needs of local students, thereby relying on specialized computing clusters for subsequent pricing. A farmer can share soil, climate, and crop growth data from their fields, collaborating with other farmers to train a precision agriculture AI model tailored to local soil and water conditions, and derive continuous benefits from the application of this model. When AI creativity is no longer the exclusive domain of a few tech elites, but becomes accessible to millions of ordinary people, the relationship between humans and AI shifts from being served to actively collaborating and creating. We may find that the true AI revolution lies not in how many more model parameters are added, but in everyone finding their own unique value in the constant growth and change, and sharing the rewards they deserve. This is the future of technological development that is more worth looking forward to.