Although the meme super cycle theory has become a popular topic in the crypto market recently, after reducing the noise of market sentiment, you will find that the secondary market performance of AI+Crypto projects during this period is also outstanding. From the perspective of asset issuance, meme coins are in Tokenize (attention & Cult culture), while AI+Crypto is in Tokenize (monetization (AI)). The objects of both Tokenizes have strong potential and sustainable growth at present.
As expressed in the Tokenize (AI monetization) formula, the key to AI tokenization is to first monetize the AI and then Tokenize the AI monetization capability. This path is somewhat similar to traditional securities issuance. Applying Tokenize AI's formula, stock issuance is securitization (future cash flow). But there are significant differences between them, and their asset issuance has their own dedicated primitives.
Primitives of MeMe coin asset issuance: Standardized creation of MeMe - Launch - Bonding Curve IBO financing - Initial liquidity addition - Market maker market making - Cabal call - Online CEX exit
The market is already very familiar with the process of MeMe tokenization, and it has been standardized. However, how to tokenize AI is still in the stage of contention among various schools of thought. Some solutions are Tokenize (monetization (AI Agent, vector knowledge base)), some are Tokenize (monetization (AGI)), some are Tokenize (monetization (AI production factors)), and some are Tokenize (monetization (AI large model training, optimization, deployment and use)).
Today we will analyze Flock’s AI tokenization solution.
Let me briefly introduce Flock, a decentralized AI development and deployment platform that aims to redefine the way AI is created and distributed through blockchain technology and federated learning. The grand narrative of Flock Follow is the democratization of AI.
Flock has built three core products: AI Arena, FL Alliance and AI Marketplace, which form a complete deAI development life cycle:
1. AI Arena: An arena for selecting and training basic AI models.
Its innovation lies in:
- A staking mechanism similar to POS is introduced to closely combine AI training with economic incentives.
- Multiple verification data set design effectively prevents Sybil attacks and model overfitting.
- Dynamic reward distribution mechanism (Reward A and B) that balances short-term engagement and long-term value creation.
2. FL Alliance: A model optimization platform based on federated learning.
Its technical highlights include:
- Decentralized supervision nodes solve the centralized "peek" problem in traditional federated learning.
- Blockchain-based random role assignment ensures fairness and censorship resistance.
- The innovative "proposer-voter" mechanism improves the efficiency and quality of model updates.
Let's take a medical AI example: suppose we want to train a model to predict whether a patient has diabetes. Hospitals A, B, and C all have patient data, but they cannot share it directly for privacy reasons. Using FL Alliance:
1. The initial model was sent to all hospitals.
2. Each hospital uses local data to train the model.
3. The hospital only sends the updated model parameters to the blockchain network, not the original data.
4. The network aggregates model updates from all hospitals through a consensus mechanism to generate a new global model.
5. The updated global model is sent back to each hospital and steps 2-5 are repeated.
This approach not only protects patient privacy, but also improves model accuracy by leveraging a wider data set, with accuracy improvements of up to 15-20% compared to traditional centralized methods.
3. AI Marketplace: A market platform for model deployment and use.
Its unique features include:
- The concept of "use is mining" is introduced. The more the model is used, the more rewards the creator will get.
- The "composability" of the model is realized, allowing developers to combine different models like building blocks.
- Innovative pricing mechanism that dynamically adjusts prices based on model usage frequency and computational complexity.
Flock cleverly monetizes AI throughout the deAI development lifecycle:
- AI Arena monetizes the model training process. Staking tokens to participate in the competition is like betting on the potential of the model, and excellent trainers and verifiers can get rich rewards.
- FL Alliance monetizes data value. In our medical AI example, the value of their data is monetized indirectly by contributing model updates while protecting privacy.
- AI Marketplace directly monetizes model applications. Users pay to use the model, and the revenue is distributed to developers, data providers, and computing resource providers based on their contributions.
In this way, Flock not only transforms abstract AI capabilities into quantifiable and tradable assets, but also creates an ecosystem that incentivizes all participants to continue to contribute and improve. Finally, Flock monetizes AI and then tokenizes it by issuing the platform's native token FML.
It can be seen that the tokenized AI solution adopted by Flock is tokenization (monetization (AI large model training, optimization, deployment and use)).
Of course, the above is just an observation and understanding of the Flock project from the perspective of tokenized AI solutions. We can also gain a deeper understanding of it by comparing and analyzing it with other well-known AI+Crypto projects on the market.
Let’s first compare the technical focus, tokenization objects, degree of decentralization, market positioning, ecosystem openness, and other technical features of Flock, Bittensor, Ritual, Sentient, and Artificial Superintelligence Alliance.
1. Technical focus:
- Flock and Bittensor focus more on the decentralization and incentive mechanism of current AI technology.
- Ritual focuses on introducing AI capabilities into Web3 applications.
- Sentient and ASI Alliance are more focused on long-term AGI/ASI development.
2. Tokenized objects:
- Flock has the widest scope of tokenization, covering the entire life cycle from training to use.
- Bittensor mainly tokenizes the computing nodes and information value in the network.
- Ritual focuses on the reasoning capabilities of tokenized AI large models.
- Sentient and ASI Alliance further tokenize the AGI development process and contributions.
3. Decentralization:
- Bittensor may be the most decentralized, adopting a completely P2P structure.
- Flock and Sentient seek a balance between decentralization and efficiency.
- Ritual may be more centralized to ensure efficient execution.
- ASI Alliance Since it is a multi-party alliance, the degree of decentralization may vary among different components.
4. Market positioning:
- Flock targets a wide range of AI large model development and application markets.
- Bittensor focuses on creating a self-organizing, self-optimizing AI network.
- Ritual is aimed at Web3 developers who need AI capabilities.
- Sentient and ASI Alliance target the future AGI market.
5. Ecosystem openness:
- Flock, Sentient, and ASI Alliance emphasize openness and community engagement.
- The openness of Bittensor is reflected in the fact that any node can join the network.
- Ritual may be less open and more targeted at specific Web3 developers.
Let’s compare the total market value/financing above
Bittensor VAT: $12.15B
ASI Alliance VAT: $3.65B
Ritual Funding: ~$30M, valuation undisclosed
Flock Funding: $6M, valuation undisclosed
Sentient Funding: $8,500, valuation undisclosed
The AI+Crypto field is in the expectation inflation period of the Gartner emerging technology curve. The current high market valuations of Bittensor, Sentient, and ASI Alliance show people's optimism about the two AI asset issuance solutions, Tokenize (monetization (AI self-organizing network)) and Tokenize (monetization (AGI)). However, this high valuation and high FDV may reflect investors' fanatical imagination of AGI and AI self-organizing networks, rather than a rational evaluation of real technology, nor confidence in the economic feasibility of monetization (AI self-organizing network) and monetization (AGI).
This irrational prosperity is disturbingly reminiscent of the dot-com bubble at the beginning of this century. For the entire AI+Crypto track, we need to stay sober. The current prosperity is likely to be a bubble. Most projects may disappear in the next few years. But we must also see that after the dot-com bubble, those projects that focus on solving practical problems and have a clear path to realize their vision may stand out after the "disillusionment period". For investors, now is a good time to re-evaluate and balance the AI+Crypto portfolio.
