Forwarded original title: Interpretation of Fluence: The rise of the co-processor concept makes decentralized computing more credible

AI X Crypto has become a hot topic in the industry this year.

Bankless founder Hoffman pointed out in a recent blog post that the foothold for the AI ​​x Crypto narrative begins at the “Decentralized Compute” layer.

A large number of AI and DePIN crypto projects almost all cannot bypass the basic homogeneity-mobilizing idle computing resources scattered around the world to provide support for AI computing.

From centralized data center storage and computing devices to cloud services, and now to decentralized computing, each new technology narrative has led to a wave of token price increases.

So what new stories can cryptocurrency and AI tell?

If you look closely, you will find that while decentralized computing talks about democratizing AI, making computing resources cheaper and more accessible, it ignores an important question:

How do you know that the results computed by different nodes are all correct? Who has verified them?

Most decentralized computing projects in the encryption field have different implementation methods, but they usually do not provide proof of the correctness of the execution results. Therefore, in this narrative gap, the concept of “coprocessor” has quietly emerged:

Treat decentralized computing nodes scattered in different places as one processor to jointly perform computing tasks. At the same time, we also need a co-processor to ensure and verify the correctness of the computation.

It is like two groups of people, one group is responsible for the work, and the other group is responsible for the acceptance. Only in this way can the work be more credible.

So what projects are currently on the market telling the story of coprocessors? What opportunities are there to participate?

Fluence: Verifiable Decentralized Computing

Fluence is a decentralized serverless platform and computing marketplace driven by blockchain economics.

With Fluence, developers can build applications and deploy them to a network of computing providers. Note that this provider can be a professional data center or even a home computer.

Providers compete on price and performance, and in order to get paid and rewarded, they continually prove that they are delivering computing resources to these applications.

This business is actually similar to many encrypted AI projects. It provides a decentralized computing resource network, assigns tasks to different nodes for calculation, and competes with traditional cloud services in a distributed manner.

Each of the technology stacks in the cloud service stack has a corresponding service in Fluence to replace it.

However, unlike most cloud services and decentralized computing projects, when using Fluence, developers can verify whether their application is running as expected and whether the computation is executed correctly by checking the proofs published by the providers on the chain.

The Fluence platform provides developers with a unique verifiable computing environment through its decentralized server architecture. On this platform, computing resource providers not only need to complete the computing tasks assigned to them, but also need to generate computing proofs. These proofs ensure the accuracy of the computing through encryption.

Each node will submit these proofs for the work it has completed, in order to demonstrate its ability and quality to complete the task.

The advantage of this approach is that it adds an important layer to decentralized computing — credibility. Developers can verify that their applications are executing as expected and that all computing processes have been verified and validated.

This is the concept of “coprocessor” we mentioned earlier —- “coprocessor” may not be the node that performs the main computation, but an auxiliary node or service that ensures and verifies the correctness of the computation for the entire decentralized computing network.

If we abstract the decentralized computing power into a unified processor responsible for computing, then the coprocessor on the other side is responsible for verifying the computing.

Specifically, Fluence is implemented with two core components: Aqua and Marine.

Aqua is responsible for distributing and executing scripts across servers. It can ensure that each step executed in a decentralized network is auditable and verifiable. Each time an Aqua script is executed, the participating nodes sign it.

Marine, on the other hand, allows multi-module execution within nodes and generates cryptographic proofs for each executed computation function. These proofs are then verified by other nodes in the network and chained as evidence of processing.

For more technical details, you can click on the official document to check. Here, only a brief and easy-to-understand implementation method is introduced.

So, what are the advantages of using the coprocessor approach for computing verification compared to other centralized and decentralized computing projects?

• Decentralized Verifiability: Developers can use the cryptographic proofs of the Fluence network to ensure that their computing tasks are executed correctly.
• Dynamic Resource Provider Selection: Developers can freely switch between any connected resource provider, not limited to a single cloud provider.
• Cost-effectiveness: Fluence helps to reduce overall costs by reducing the importance of specific providers, providing more economical computing resources.
• Web3 Native Management: Supports management and payment of infrastructure costs through decentralized organizations such as DAOs, strengthening community participation.
• Adapting to Diverse Computing Needs: Supports complex computations such as backend APIs and data processing. Tasks that are not suitable for direct execution on the chain can also be implemented on Fluence.

Token is About to TGE, OTC Valuation is Rising

Fluence is powered by its native token $FLT, which is used by compute providers as collateral for participation and as a monetary incentive. Providers earn Fluence tokens and payments for serving applications.

On March 7, Fluence official Twitter also announced the token economic model, with a total token supply of 1,000,000,000 FLT and an initial circulating supply of 50,000,000 (5%). About 45% of the total FLT supply will be unlocked within 24 months after the token launch.

The token address is: 0x236501327e701692a281934230AF0b6BE8Df3353

At the same time, the official also clearly stated that the FLT token currently has no public TGE, and only early contributors will receive airdrop rewards; the exact token listing time needs to be closely followed by the social media information in the next few weeks.

However, according to the analysis of Twitter user @atterX_, the current pre-trading price of FLT in the private market is about $16.30, and the daily trading volume is close to $400,000. Combined with the economic model mentioned above, it can be estimated that according to the current private market price, the Market Cap of FLT will reach about 800 million US dollars, and the overall FDV will be 16 billion US dollars.

It is also worth mentioning that as early as February 22, Multicoin Capital announced that it had led a US$9 million investment in the project. It’s just that the AI concept was not so popular at that time, and Fluence has not issued tokens.

So under the current price, volume and AI narrative, is FLT overvalued or undervalued?

There are also projects with the concept of coprocessor on the market, typical of Marlin Protocol and Phala Network.

The current market values ​​of these two projects are US$235 million and US$104 million respectively; but due to the fact that the token issuance time can be traced back to 21 years, the current FDV of these two projects is basically equal to the circulating market value.

Interestingly, it is precisely because of the token issuance in 21 years, that caught up with a good bull market cycle, the historical ATH of the two projects is about 10 times higher than now. After a round of narrative rotation, the two old projects still have a circulating market value of $100-200 million US dollars.

Therefore, considering the current bull market cycle and the momentum of the AI ​​narrative, the estimated circulating market value of Fluence token FLT of US$800 million does not seem to be overvalued compared to the peak period of similar projects, and there is a certain amount of room for upward growth.

What is certain is that the story of AI in the crypto market will continue to be told.

In the context of a largely similar main theme, telling more subdivided stories about AI, highlighting features that other projects do not have, will naturally usher in its own opportunities in the rising tide.

Let us wait and see how Fluence will perform in the future.

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