The Future Beyond LLMs: Why Small Models and Good Data Are the Key? 】 In the past two years, LLMs (large language models) have become synonymous with the AI world, with everyone from GPT to Claude, Gemini to Llama, competing for the number of parameters, emergence, and inference limits. But as the technology frenzy returns to cool, a new trend is emerging—where small language models (SLMs) and high-quality data are becoming the real focus of the next phase of AI evolution. This article will take a fresh look at the key role that OpenLedger plays in this trend and think about competing cryptocurrencies in the "post-LLM era". 1. The bottleneck of large models: not the larger the parameters, the better There is no doubt that large models have ushered in a new era of AI. However, as LLMs are further stacked and expanded, multiple bottlenecks become more apparent: (1) The inference cost is too high: large models generally require expensive computing resources, which are not suitable for edge deployment or high-frequency calls; (2) Slow response speed: especially in complex reasoning or long context processing scenarios, there are delays and inefficiency; (3) The dilemma of "average": large models pursue versatility but lack the ability to accurately respond to vertical domain problems; (4) Data is not traceable: The data used in the model training process is often mixed, with bias, abuse and opacity. These issues not only limit the large-scale implementation of LLMs, but also provide a breakthrough for SLM and data-driven innovation systems. Second, the advantages of the small model era: lightweight, professional, controllable The rise of SLM is not accidental, but a reflection on the uneconomical and unreliable nature of large models. In several real-world scenarios, SLM exhibits the following advantages: (1) Customizable: It can be fine-tuned around specific tasks (such as customer service, transactions, translation, etc.), and the performance is more focused; (2) Low cost: The inference overhead is smaller, and it is suitable for deployment on local, mobile phones, or edge nodes. (3) Strong controllability: the training process is shorter, and the data source used can be clearly recorded, which is conducive to traceability and compliance; (4) Decentralized deployment: It is easier to embed in the Web3 environment to form a network of callable and auditable models on the chain. This trend is also deeply aligned with OpenLedger's design philosophy. 3. OpenLedger's position: Reinventing the model paradigm with "good data". OpenLedger does not directly compete with the model layer of LLMs, but chooses to refactor data systems from the bottom up to serve the rise of SLM. Its core logic is: (1) Make data "valuable": Through the PoA mechanism and the Datanets network, it provides trusted, traceable, and tradable data assets for AI models; (2) Encourage model openness: Payable AI mode enables SLM to be invoked and connected to tasks, and revenue is distributed according to usage; (3) Incentives for real contributions: Through the reputation system and incentive mechanism, the interests of data producers, model developers and callers are bound. This means that OpenLedger is building an open ecosystem around "small model + good data", which provides a structural supplement for the post-LLM era. Fourth, the future picture: from "big and comprehensive" to "small and specialized" It is foreseeable that the future of AI will not be a one-size-fits-all model, but a network of "miniature intelligent units" that revolve around scenarios. These small models will: (1) Connect with high-quality data sources, rather than relying on capturing Internet noise; (2) verify the training process and call history through the on-chain mechanism to enhance credibility; (3) Linkage with different application protocols (DeFi, GameFi, social networking, etc.) to build an AI-driven Web3 tool layer. OpenLedger is building the infrastructure for this trend: it is not in the volume parameters, but in the volume "data value recognition mechanism" and "incentive distribution model", which is essentially a public platform that provides a trusted soil for AI models. OpenLedger's ambition is not to make the next GPT, but to provide the underlying support for the flow of data, reputation recognition, and incentives for the next generation of SLMs. Outside of the old paradigm of "parameters are power", it tries to answer a more fundamental question: "Who can provide credible ground for the future of AI?" In a new cycle where models are no longer omnipotent and data is critical, OpenLedger is at the right narrative inflection point. @OpenledgerHQ @cookiedotfun #OpenLedger #COOKIE #全面拆解OpenLedger系列