FLock has proposed an innovative concept: building a Launchpad platform for AI large models, incentivizing the development and optimization of vertical models through assetization. This model aims to reconstruct the value chain using token economics, enabling data providers, computing nodes, and validators to continuously earn revenue based on their contributions.

Core Mechanism: Asset-izing trained large models (e.g., in vertical fields like healthcare, law, and finance) and using token distribution to incentivize participants to continuously optimize the models.
Potential Advantages: Model assetization can promote models to demonstrate their capabilities in open competitions while enhancing the ecosystem's self-sustaining ability, addressing the narrow commercialization paths for vertical models.
Challenges and Uncertainties: Measures are needed to prevent fake model calls and刷量, and to ensure product-market fit in vertical scenarios. The方案 is still in the conceptual stage, with specific implementation details yet to be clarified.
If successfully implemented, this direction could introduce a全新的 financialized驱动 model for AI model development.

Summary

I listened to FLock's 2025 performance report, and the idea of creating a Launchpad for AI large models particularly caught my attention.

What? Another Launchpad? How can large models issue assets? It's actually quite straightforward with an analogy:

• Launchpads like Virtuals Protocol for AI Agents are application-layer driven, issuing assets for Agents. They use token incentive mechanisms to help Agents evolve from "knowing how to chat" to "knowing how to pay" (x402), and ultimately to the goal of "autonomous trading" and providing complex services.

• In contrast, the AI Model Launchpad planned by FLock is infrastructure-layer driven, issuing assets for trained large models—specifically, a multitude of vertical scenario models, such as those for medical diagnosis, legal documentation, financial risk control, and supply chain optimization.

Although the training costs for these vertical models are relatively manageable, their commercialization paths are extremely narrow. They either get acquired by major corporations or open-source for free, with few sustainable monetization methods.

FLock intends to use Tokenomics to reconstruct this value chain: issuing assets for fine-tuned large models, thereby giving data providers, computing nodes, validators, and other contributors the possibility of long-term收益 rights. When the models are called and generate revenue, proceeds can be distributed持续 based on contribution proportions.

The idea of a Launchpad for large models sounds novel at first, but at its core, it's about using financialization手段 to drive product development.

Once a model is asset-ized, trainers gain motivation for持续 optimization. Moreover, if收益 can be持续分配, the ecosystem develops self-sustaining capabilities.

The benefits of this approach are undeniable. For example, take the recently popular nof1 large model trading competition. Currently, only general large models participate; fine-tuned specialized models don't, primarily due to a lack of incentive mechanisms. Excellent specialized models typically prefer to operate discreetly for profit rather than expose themselves. However, if they have assets tied to them, the significance changes entirely. Such large model Arena competitions could become public showcases of strength, and竞技表现 would directly impact the large model's asset performance—immediately opening up vast imaginative possibilities.

Of course, FLock has only proposed a direction at this stage; it hasn't been fully implemented yet. The specifics of how model asset issuance differs from Agent asset issuance remain unclear.

But one thing is certain: challenges such as ensuring that model calls are based on genuine demand rather than刷量, and effectively achieving product-market fit (PMF) in vertical scenarios, will need to be addressed. It's safe to say that the issues encountered during the Agent application token issuance wave will likely reappear here.

Nevertheless, it's exciting to anticipate what unique approaches might emerge in the direction of creating a Launchpad for Models.

FLock has proposed an innovative concept: building a Launchpad platform for AI large models, incentivizing the development and optimization of vertical models through assetization. This model aims to reconstruct the value chain using token economics, enabling data providers, computing nodes, and validators to continuously earn revenue based on their contributions.

Core Mechanism: Asset-izing trained large models (e.g., in vertical fields like healthcare, law, and finance) and using token distribution to incentivize participants to continuously optimize the models.
Potential Advantages: Model assetization can promote models to demonstrate their capabilities in open competitions while enhancing the ecosystem's self-sustaining ability, addressing the narrow commercialization paths for vertical models.
Challenges and Uncertainties: Measures are needed to prevent fake model calls and刷量, and to ensure product-market fit in vertical scenarios. The方案 is still in the conceptual stage, with specific implementation details yet to be clarified.
If successfully implemented, this direction could introduce a全新的 financialized驱动 model for AI model development.

Summary

I listened to FLock's 2025 performance report, and the idea of creating a Launchpad for AI large models particularly caught my attention.

What? Another Launchpad? How can large models issue assets? It's actually quite straightforward with an analogy:

• Launchpads like Virtuals Protocol for AI Agents are application-layer driven, issuing assets for Agents. They use token incentive mechanisms to help Agents evolve from "knowing how to chat" to "knowing how to pay" (x402), and ultimately to the goal of "autonomous trading" and providing complex services.

• In contrast, the AI Model Launchpad planned by FLock is infrastructure-layer driven, issuing assets for trained large models—specifically, a multitude of vertical scenario models, such as those for medical diagnosis, legal documentation, financial risk control, and supply chain optimization.

Although the training costs for these vertical models are relatively manageable, their commercialization paths are extremely narrow. They either get acquired by major corporations or open-source for free, with few sustainable monetization methods.

FLock intends to use Tokenomics to reconstruct this value chain: issuing assets for fine-tuned large models, thereby giving data providers, computing nodes, validators, and other contributors the possibility of long-term收益 rights. When the models are called and generate revenue, proceeds can be distributed持续 based on contribution proportions.

The idea of a Launchpad for large models sounds novel at first, but at its core, it's about using financialization手段 to drive product development.

Once a model is asset-ized, trainers gain motivation for持续 optimization. Moreover, if收益 can be持续分配, the ecosystem develops self-sustaining capabilities.

The benefits of this approach are undeniable. For example, take the recently popular nof1 large model trading competition. Currently, only general large models participate; fine-tuned specialized models don't, primarily due to a lack of incentive mechanisms. Excellent specialized models typically prefer to operate discreetly for profit rather than expose themselves. However, if they have assets tied to them, the significance changes entirely. Such large model Arena competitions could become public showcases of strength, and竞技表现 would directly impact the large model's asset performance—immediately opening up vast imaginative possibilities.

Of course, FLock has only proposed a direction at this stage; it hasn't been fully implemented yet. The specifics of how model asset issuance differs from Agent asset issuance remain unclear.

But one thing is certain: challenges such as ensuring that model calls are based on genuine demand rather than刷量, and effectively achieving product-market fit (PMF) in vertical scenarios, will need to be addressed. It's safe to say that the issues encountered during the Agent application token issuance wave will likely reappear here.

Nevertheless, it's exciting to anticipate what unique approaches might emerge in the direction of creating a Launchpad for Models.