The 0G project has been followed for almost half a year now. Just today, I have time to come back and review 0G in detail with everyone, to see how @0G_labs is re-planning the future of artificial intelligence from centralized management to decentralized management. Let's go through it all at once!

Recently, I happened to chat with a friend about AI. My friend said, "Today's AI is increasingly like the 'private toy' of big companies." It does make some sense to me. The chatbots and image generation tools we use daily are basically controlled by a few tech giants, with the data in their hands. Outsiders have no idea how the algorithms really work. If something goes wrong, no one can clearly explain it. But if I told you that future AI might be based on "what everyone says counts," would you believe it?

The first thing that comes to my mind is the 0G project, and now I am trying to turn this "if" into reality.

1: Why is it necessary to pursue "decentralized AI"?

Let's talk about how "crowded" the current AI landscape is. The short video recommendations you scroll through every day and the "you might also like" sections in shopping apps are all powered by the AI models of a few big companies. However, these models are like "muffled gourds":

Your data has been used for training, has it been misused? I don't know;​

Why does AI recommend these things to you? The algorithm is secretive and hard to explain;​

If one day the big company’s server crashes, or the service is stopped for some reasons, those applications that rely on it may all go down.

This is the troubling issue with centralized AI: a small number of people hold the control, while the majority can only passively endure, facing risks of data breaches and service interruptions.

What decentralized AI wants to do is to take apart this "muffled gourd" and allow everyone to participate.
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Anyone can understand how AI is trained and how it works; the algorithms are clearly accessible.

What data was used and how it was used is all recorded on the chain, and it can be traced clearly from beginning to end.

Even if a certain node has issues, other nodes can continue to operate, and it won't collapse all at once.

In simple terms, it means transforming AI from "the private domain of large companies" into "a public resource for everyone," making it transparent and democratically decided, while also being more resilient to disruptions.

Two: What does 0G rely on to achieve? Modularity is the key.

To achieve decentralized AI, 0G relies on a "modular" architecture - breaking down complex systems into Lego blocks, where each part can be upgraded individually and combined efficiently.

This is also particularly flexible in terms of compliance, as the rules vary from country to country:

In some places, strict regulations on data storage lead to the DA module being stored on local compliant servers.

A certain country restricts overseas computing power, requiring enterprises to use only local computing power modules to handle business, while other modules can be added as needed;

In some places, tokens are banned but distributed computing is welcomed, so just remove the token-related module and keep the compliant parts like computing power scheduling and data encryption.

It's like building with blocks; don't use the non-compliant pieces for now. You can still build and use with the compliant ones, and put the non-compliant ones back when the rules change.

Three: Next, let's take a look at the core differences between centralized and decentralized AI.

Centralized AI is like an "integrated machine": it's fast and stable, but if you want to replace a part or add a function, you have to disassemble the entire machine, which is particularly troublesome. Moreover, the data is hidden inside the machine, unseen from the outside, and sometimes it may "talk nonsense" (what people refer to as "AI hallucination"), because no one can verify its reasoning process.

Decentralized AI is more like "building blocks": the parts (modules) are open, anyone can swap or add them, and both data and reasoning processes are on the chain, allowing for verification and reducing "hallucinations". However, the drawbacks are also obvious — coordinating so many parts may slow things down a bit, and if the "interfaces" between different blocks are not standardized, it could become a complete mess.

Four: What are the "bricks" of 0G?

0G has broken the entire system down into several key modules, each with its own clear tasks:

1) 0G Chain: This is the most basic chain that can be compatible with the existing decentralized applications (dApp), and core functions like execution and consensus can be upgraded independently without modifying the entire chain;

2) 0G Storage: This is a decentralized storage space that can hold a large amount of data, relying on special encoding and verification methods to ensure that data is not lost and is usable;

3) Data Availability Layer (DA): It manages whether the data is "sufficient", verifying by randomly selecting nodes, ensuring reliability while allowing for unlimited scalability;
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4) 0G Serving Architecture: Specifically manages the inference and training of AI models, and also provides developers with a toolkit (SDK). If you want to integrate AI functionality, you can use it directly without the need to set up a complex framework yourself;​

5) Alignment Nodes: Overseeing the entire system to ensure that the behavior of the AI is in accordance with ethical standards, and the management of these nodes is also decentralized, not determined by any single individual.
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The benefit of this architecture is its "flexibility"; for example, if stronger storage capacity is needed, the Storage module can be upgraded; if faster inference speed is needed, the Serving architecture can be optimized without affecting other parts. For developers, the barrier to entry has also been significantly lowered.

Five: Is 0G reliable now? Look at the financing team and ecological partners, it has been mentioned many times.

To determine whether a project is reliable, money and the actual progress of implementation are very important and quite straightforward.

0G said last January that it had raised 325 million dollars, which is mainly used to expand product scale and build a developer community.

Moreover, 0G has currently established a deep collaboration with HackQuest, focusing on the development of the developer community. It is important to note that HackQuest, as a developer education platform, has itself raised 4.1 million dollars, and the cooperation between the two parties can better promote the development of the developer ecosystem.

Having money and cooperation is not enough; the data from the testnet can explain the problem better.
Over 650 million transactions, 22 million accounts, and more than 8,000 validators;

During peak times, the TPS of each shard can reach 11,000, which is sufficient for processing the massive data required by AI.

Also, there is the number of nodes - 85,000 nodes have been sold, maintained by over 8,500 operators worldwide. The more nodes there are, the stronger the stability and security of the entire network. It's like having more than 8,500 people "on guard," making it difficult for any problems to arise.

Additionally, 0G, as one of the earliest decentralized AI modular public chains to engage in deep cooperation, is also the first decentralized AI modular public chain. Currently, there has not yet been a Token Generation Event (TGE), and the potential discussion value behind this is obvious to everyone!

Six: When talking about 0G, we certainly have to mention its iNFT. In simple terms, iNFT is "an NFT that can run AI functions"—what you are buying is not just an image, but a smart "little assistant."

Why is this thing considered fresh? Because it uses a new standard called ERC-7857:

When you buy an iNFT, not only do you receive ownership, but also the AI model and data (which is the "metadata") come with it, so you won't end up with just an empty shell.
Sensitive data is encrypted and stored, ensuring privacy, but the authenticity can be checked on the chain, so there is no fear of being scammed;

This AI assistant can also "grow"; the metadata can be updated at any time, and the longer you use it, the more valuable it may become.

This breaks the old model of traditional AI. In the past, you used AI by "borrowing" from big companies; now you can "own" your own AI assistant, and you can sell it or authorize others to use it, with all the profits going to you.

Seven: Learning threshold? 0G has already paved the way for you.

Recently, 0G collaborated with HackQuest @HackQuest_ to launch a dedicated course called 0G Learning Track. It covers everything from the data layer and storage mechanisms to how to integrate AI frameworks and operate across chains. All content is explained clearly, and upon completion, participants can receive a certification recognized by both parties. This is indeed a good stepping stone for developers who want to get started with decentralized AI.

Eight: To be honest, the idea of 0G is pretty good, but there are also quite a few challenges:

1) Decentralized systems are inherently slower than centralized ones. How can we find a balance between scalability and performance? It will depend on the actual situation after the mainnet launch.

2) What should we do if the module interface standards are not unified when different projects are integrated? This may lead to "fragmentation";
Compliance issues must also be considered, after all, it involves data and AI, and the policies vary.

3) But in any case, decentralized AI is a direction worth exploring. If it can truly achieve "transparency, participation from everyone, and risk resistance," then AI can be considered as genuinely serving everyone, rather than being a tool for a few.

Can 0G succeed? It's still too early to draw conclusions, but at least it has taken the first step. I will also continue to track this project, and I believe it is worth the time to pursue! Moreover, we have @Jtsong2 accompanying us in this endeavor!
#去中心化AI # 0GLabs #AI