Author: Haotian

After listening to the FLock 2025 annual performance report, I was particularly attracted to a mention of a Launchpad for AI large models.

What? Another Launchpad? How will large models issue assets? It's actually quite understandable; a comparison makes it clear:

The Launchpad for AI Agents like Virtuals Protocol is driven by the application layer, using a token incentive mechanism to help Agents evolve from "chatting" to x402 "paying," and ultimately to the goal of "autonomous trading" and providing complex services;

In contrast, the AI Model Launchpad that FLock plans to create is driven by the infrastructure layer, issuing assets to trained large models, specifically a large number of vertical scenario models, such as medical diagnosis, legal documents, financial risk control, and supply chain optimization, among others.

Although the training costs for these vertical models are relatively controllable, the commercialization paths are extremely narrow; they either get acquired by large companies or become open-source projects for love, with very few sustainable monetization methods.

FLock intends to reconstruct this value chain using Tokenomics, issuing assets to fine-tuned large models, thereby allowing data providers, computing nodes, validators, and others who contribute to model training to have a long-term possibility of earning rights. When the model is called and generates income, it can be continuously distributed according to contribution proportions.

Creating a Launchpad for large models sounds fresh at first, but essentially, it is about driving product development through financial means.

Once the model is assetized, the trainers will have the motivation for continuous optimization, and once the income can be continuously distributed, the ecosystem will have the ability to self-generate revenue.

The benefits of this approach are undeniable. For example, in the recent popular nof1 large model trading competition, only general large models participated, and there were no fine-tuned specialized large models in the competition. The reason lies in the lack of an incentive mechanism; excellent specialized models usually tend to earn quietly and cannot be exposed. However, if they have assets, the significance becomes extraordinary. This type of large model Arena competition would turn into a public showcase of strength, and competitive performance would directly impact the asset performance of large models, opening up a realm of imagination.

Of course, FLock has only proposed a direction at this point and has not yet truly implemented it. The specific differences between asset issuance for models and asset issuance for Agents remain unknown.

But one thing is certain: how to ensure that the model calls for asset issuance are based on real demand rather than volume manipulation, and how to effectively ensure PMF (Product-Market Fit) within vertical scenarios, etc., are all issues that should be addressed. It can be said that the challenges faced by the Agent application token issuance wave are likely to be similar.

I am just very much looking forward to seeing what unique gameplay will emerge from creating a Launchpad for Models?

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