Author: Haotian After listening to FLock's 2025 annual report, I was particularly intrigued by their mention of launching a large AI model using Laupac . What? Launchpad again? How do you issue assets for a large model? Actually, it's easy to understand; just make an analogy: Launchpad, an AI agent like Virtuals Protocol, is application-layer driven. It uses token incentives to incentivize agents by issuing assets, helping them evolve from "being able to chat" to "being able to make payments," and ultimately to "being able to trade autonomously" and provide complex services. FLock's AI Model Launchpad is driven by the infrastructure layer and distributes assets to large trained models , namely a large number of vertical scenario models, such as medical diagnosis, legal documents, financial risk control, and supply chain optimization. While the training cost of these vertical models is relatively controllable, their commercialization path is extremely narrow. They either sell themselves to large companies or open-source them out of passion, with very few sustainable ways to monetize them. FLock intends to restructure this value chain with Tokenomics, issuing assets to the finely tuned large model, thereby giving data providers, computing power nodes, validators, and others who contribute to the model training a long-term possibility of obtaining revenue. When the model is invoked and generates income, it can be continuously distributed according to the contribution ratio. Creating a launchpad for a large model might sound novel at first, but it's essentially using financial means to drive product development. Once a model is assetized, trainers have the motivation to continuously optimize it, and once the revenue can be continuously distributed, the ecosystem will have the ability to generate its own revenue. The benefits of doing this are undeniable. For example, the recently popular nof1 large model trading competition only accepts general large models for participation, and there are no large models with fine-tuning for participation. The reason is the lack of an incentive mechanism. Excellent specialized models usually tend to make money quietly and cannot be exposed. However, if they have assets, they are of great significance. Such large model Arena competitions have become a stage for publicly showing off one's strength, and the competitive performance will directly affect the performance of large model assets. The potential for imagination has been opened up. Of course, FLock has only proposed a direction so far and has not yet been truly implemented. The differences and similarities between the specific model for issuing assets and the agent-based asset issuance model are still unknown. However, one thing is certain: how to ensure that the model calls for issuing assets are based on real demand rather than inflated volume, and how to effectively ensure Product-Market Fit (PMF) in vertical scenarios are all problems. It is safe to say that the wave of token issuance by Agent applications will also encounter many of these issues. I'm really looking forward to seeing what different ways there will be to create a Launchpad for the Model.Author: Haotian After listening to FLock's 2025 annual report, I was particularly intrigued by their mention of launching a large AI model using Laupac . What? Launchpad again? How do you issue assets for a large model? Actually, it's easy to understand; just make an analogy: Launchpad, an AI agent like Virtuals Protocol, is application-layer driven. It uses token incentives to incentivize agents by issuing assets, helping them evolve from "being able to chat" to "being able to make payments," and ultimately to "being able to trade autonomously" and provide complex services. FLock's AI Model Launchpad is driven by the infrastructure layer and distributes assets to large trained models , namely a large number of vertical scenario models, such as medical diagnosis, legal documents, financial risk control, and supply chain optimization. While the training cost of these vertical models is relatively controllable, their commercialization path is extremely narrow. They either sell themselves to large companies or open-source them out of passion, with very few sustainable ways to monetize them. FLock intends to restructure this value chain with Tokenomics, issuing assets to the finely tuned large model, thereby giving data providers, computing power nodes, validators, and others who contribute to the model training a long-term possibility of obtaining revenue. When the model is invoked and generates income, it can be continuously distributed according to the contribution ratio. Creating a launchpad for a large model might sound novel at first, but it's essentially using financial means to drive product development. Once a model is assetized, trainers have the motivation to continuously optimize it, and once the revenue can be continuously distributed, the ecosystem will have the ability to generate its own revenue. The benefits of doing this are undeniable. For example, the recently popular nof1 large model trading competition only accepts general large models for participation, and there are no large models with fine-tuning for participation. The reason is the lack of an incentive mechanism. Excellent specialized models usually tend to make money quietly and cannot be exposed. However, if they have assets, they are of great significance. Such large model Arena competitions have become a stage for publicly showing off one's strength, and the competitive performance will directly affect the performance of large model assets. The potential for imagination has been opened up. Of course, FLock has only proposed a direction so far and has not yet been truly implemented. The differences and similarities between the specific model for issuing assets and the agent-based asset issuance model are still unknown. However, one thing is certain: how to ensure that the model calls for issuing assets are based on real demand rather than inflated volume, and how to effectively ensure Product-Market Fit (PMF) in vertical scenarios are all problems. It is safe to say that the wave of token issuance by Agent applications will also encounter many of these issues. I'm really looking forward to seeing what different ways there will be to create a Launchpad for the Model.

                                                                               Bitcoin gained fresh downward BTC price predictions on the back of an options expiry event and thickening bid liquidity on exchange order books.                     Key points:Bitcoin fails to close above $117,200, opening the door to support retests.A giant $4.9 trillion options expiry event adds further friction for Bitcoin bulls on Friday.Read more