Although the meme supercycle theory has recently become a prominent topic in the crypto market, after filtering out market sentiment noise, you will find that the secondary market performance of AI+Crypto projects during this period is equally impressive. From the perspective of asset issuance, meme coins are Tokenized (Attention & Cult Culture), while AI+Crypto is Tokenized (Monetization (AI)). Both Tokenization objects currently possess strong momentum and sustainable growth potential.

As indicated by the Tokenization (AI Monetization) formula, the key to AI tokenization lies in first monetizing AI and then Tokenizing the ability to monetize AI. This path bears some similarity to traditional securities issuance; applying the Tokenization AI formula, stock issuance is securitization (future cash flow). However, there are also significant differences, as their asset issuance has its own specialized primitives.

The primitives for MeMe coin asset issuance are: standardization of MeMe creation - launch - Bonding Curve IBO financing - initial liquidity addition - market maker market making - conspiracy group calls - listing on CEX for exit.

The process of MeMe tokenization is already well understood and standardized in the market. However, how to tokenize AI is still in a stage of diverse opinions, with some solutions focusing on Tokenization (Monetization (AI Agent, Vector Knowledge Base)), others on Tokenization (Monetization (AGI)), some on Tokenization (Monetization (AI Production Factors)), and others on Tokenization (Monetization (AI Large Model Training, Optimization, Deployment, and Usage)).

Today, we will analyze Flock's AI tokenization solution.

A brief introduction to Flock: it is a decentralized AI development and deployment platform aimed at redefining the creation and distribution of AI 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 together form a complete deAI development lifecycle:

1. AI Arena: An arena for selecting and training foundational AI large models.

Its innovations include:

• Introducing a staking mechanism similar to POS, closely linking AI training with economic incentives.
• Designing multiple validation datasets to effectively prevent Sybil attacks and model overfitting.
• A dynamic reward distribution mechanism (Reward A and B) that balances short-term participation and long-term value creation.

1. FL Alliance: A model optimization platform based on federated learning.

Its technical highlights include:

• Decentralized supervisory nodes that address the centralization "snooping" problem in traditional federated learning.
• Blockchain-based random role assignment to ensure fairness and resistance to censorship.
• An innovative "proposer-voter" mechanism that improves the efficiency and quality of model updates.

For example, in a medical AI scenario: suppose we want to train a model to predict whether a patient has diabetes. Hospitals A, B, and C have patient data but cannot share it directly due to privacy concerns. Using FL Alliance:

1. The initial model is sent to all hospitals.
2. Each hospital trains the model using local data.
3. Hospitals only send updated model parameters to the blockchain network, not the raw data.
4. The network aggregates all hospitals' model updates through a consensus mechanism to generate a new global model.
5. The updated global model is sent back to each hospital, repeating steps 2-5.

This method not only protects patient privacy but also improves model accuracy by utilizing a broader dataset. Compared to traditional centralized methods, accuracy can increase by 15-20%.

3. AI Marketplace: A market platform for model deployment and usage.

Its uniqueness lies in:

• Introducing the concept of "use-to-mine," where the more a model is used, the more rewards the creator receives.
• Achieving "composability" of models, allowing developers to combine different models like building blocks.
• An 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 competitions is akin to betting on model potential, with excellent trainers and validators reaping substantial rewards.
• FL Alliance monetizes data value. In our medical AI example, contributing model updates indirectly monetizes the data value while protecting privacy.
• AI Marketplace directly monetizes model applications. Users pay to use models, and revenues are distributed to developers, data providers, and computing resource providers based on contributions.

In this way, Flock not only transforms abstract AI capabilities into quantifiable, tradable assets but also creates an ecosystem that incentivizes all participants to continuously contribute and improve. Finally, Flock issues its native token FML, further tokenizing after monetizing AI.

It is evident that Flock's tokenization solution adopts the approach of Tokenization (Monetization (AI Large Model Training, Optimization, Deployment, and Usage)).

Of course, the above is merely an observation and understanding of the Flock project from the perspective of tokenizing AI solutions. We can also gain deeper insights by comparing it with other well-known AI+Crypto projects in the market.

First, let's compare the technical focus, tokenization objects, degree of decentralization, market positioning, and ecosystem openness of Flock, Bittensor, Ritual, Sentient, and the Artificial Superintelligence Alliance.

1. Technical Focus:

• Flock and Bittensor focus more on the decentralization and incentive mechanisms of current AI technology.
• Ritual specializes in integrating AI capabilities into Web3 applications.
• Sentient and ASI Alliance focus more on the long-term development of AGI/ASI.

1. Tokenization Objects:

• Flock has the broadest tokenization scope, covering the entire lifecycle from training to usage.
• Bittensor primarily tokenizes the computational nodes and information value within the network.
• Ritual emphasizes tokenizing the inference capabilities of AI large models.
• Sentient and ASI Alliance focus more on tokenizing the AGI development process and contributions.

1. Degree of Decentralization:

• Bittensor may have the highest degree of decentralization, adopting a fully P2P structure.
• Flock and Sentient seek a balance between decentralization and efficiency.
• Ritual may be more centralized to ensure execution efficiency.
• ASI Alliance's degree of decentralization may vary due to its multi-party alliance structure.

1. Market Positioning:

• Flock targets a broad market for AI large model development and applications.
• Bittensor focuses on creating a self-organizing, self-optimizing AI network.
• Ritual targets Web3 developers who need AI capabilities.
• Sentient and ASI Alliance aim for the future AGI market.

1. Ecosystem Openness:

• Flock, Sentient, and ASI Alliance emphasize openness and community participation.
• Bittensor's openness is reflected in the ability of any node to join the network.
• Ritual's openness may be lower, focusing more on specific Web3 developers.

Next, let's compare the total market capitalization/funding of the above projects:

• Bittensor FDV: $12.15B
• ASI Alliance FDV: $3.65B
• Ritual funding: ~$30M, valuation not disclosed
• Flock funding: $6M, valuation not disclosed
• Sentient funding: $8500, valuation not disclosed

The AI+Crypto field is currently in the expectation inflation phase of the Gartner emerging technology curve. The current high valuations of Bittensor, Sentient, and ASI Alliance reflect optimism about the Tokenization (Monetization (AI Self-Organizing Network)) and Tokenization (Monetization (AGI)) asset issuance solutions. However, these high valuations and FDVs may more reflect investors' fervent imaginations about AGI and AI self-organizing networks rather than rational assessments of real technologies or confidence in the economic viability of Monetization (AI Self-Organizing Networks) and Monetization (AGI).

This irrational exuberance unsettlingly recalls the dot-com bubble of the early 2000s. For the entire AI+Crypto sector, we need to remain clear-headed. The current boom is likely a bubble. Most projects may disappear in the coming years. However, we should also recognize that after the dot-com bubble, those projects focused on solving real problems and having clear paths to achieve their visions may emerge after the "fantasy collapse." For investors, now is a good time to reassess and rebalance their AI+Crypto portfolios.

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