With $35 million in funding, led by veteran VCs like PolyChain and Framework, Allora has been performing incredibly well recently. I've seen many people call it a "prediction market." Wrong. Let me share my understanding of this project: 1) To be precise, Allora is a decentralized AI reasoning service platform. Users can pay for AI agents to provide services for any needs requiring AI judgment, including price prediction, strategy optimization, and risk assessment. Therefore, the prediction market is only one application scenario of Allora, not the only one. 2) Given the inherently uneven inference and output capabilities of AI models, how can Allora become a mature upstream supplier of mass output? The answer lies in building an aggregation platform that leverages the collective power and competitive collaboration of AI models. The mechanism is straightforward. For example, if a user wants to predict whether ETH will rise or fall and decide how to set the LP price range, the traditional approach involves observing K-line charts, listening to key opinion leaders (KOLs) analysis, or purchasing customized AI model APIs for predictions, which often yield a variety of inconsistent answers. Is it possible to develop an aggregated inference service platform that can handle this comparative screening process? The key lies here. After the user sends the demand to Allora, the 280,000 nodes in the network architecture will compete to give answers. Some say it will rise, some say it will fall, and some say it will go sideways. Allora will vote for these models and record historical performance reports. It will give higher weight to AI models with a high prediction success rate and send token rewards. At the same time, it will deduct points and deposits from those who make blind guesses. This creates a positive flywheel: models with accurate predictions earn more, gain increasing weight, and take on more tasks; those that keep guessing are eliminated. 3) Therefore, I prefer Allora as the infrastructure layer for AI inference services, with the ability to call AI model combinations on demand. There are two main application scenarios: DeFAI: When the AI Agent executes on-chain transactions, it needs to determine whether a transaction is MEV, provide the optimal price range in real time when adjusting Uniswap LP, determine whether AAVE has liquidation risks, and how the Yield pool dynamically adjusts the leverage ratio, etc. Prediction market: Use AI models to dynamically adjust and update probabilities. Compared with the pricing mechanism based solely on trading volume, AI's aggregate reasoning can provide users with a smarter prediction starting point, avoiding pure follow-the-crowd. However, Allora is essentially just an infrastructure service facility. In the early stages, when there are few models, little data, and insufficient accuracy, it will also go through a long period of accumulating energy. However, if DeFAi and prediction markets, two promising markets, can become mainstream in the future, the value of its infrastructure services will be highlighted.With $35 million in funding, led by veteran VCs like PolyChain and Framework, Allora has been performing incredibly well recently. I've seen many people call it a "prediction market." Wrong. Let me share my understanding of this project: 1) To be precise, Allora is a decentralized AI reasoning service platform. Users can pay for AI agents to provide services for any needs requiring AI judgment, including price prediction, strategy optimization, and risk assessment. Therefore, the prediction market is only one application scenario of Allora, not the only one. 2) Given the inherently uneven inference and output capabilities of AI models, how can Allora become a mature upstream supplier of mass output? The answer lies in building an aggregation platform that leverages the collective power and competitive collaboration of AI models. The mechanism is straightforward. For example, if a user wants to predict whether ETH will rise or fall and decide how to set the LP price range, the traditional approach involves observing K-line charts, listening to key opinion leaders (KOLs) analysis, or purchasing customized AI model APIs for predictions, which often yield a variety of inconsistent answers. Is it possible to develop an aggregated inference service platform that can handle this comparative screening process? The key lies here. After the user sends the demand to Allora, the 280,000 nodes in the network architecture will compete to give answers. Some say it will rise, some say it will fall, and some say it will go sideways. Allora will vote for these models and record historical performance reports. It will give higher weight to AI models with a high prediction success rate and send token rewards. At the same time, it will deduct points and deposits from those who make blind guesses. This creates a positive flywheel: models with accurate predictions earn more, gain increasing weight, and take on more tasks; those that keep guessing are eliminated. 3) Therefore, I prefer Allora as the infrastructure layer for AI inference services, with the ability to call AI model combinations on demand. There are two main application scenarios: DeFAI: When the AI Agent executes on-chain transactions, it needs to determine whether a transaction is MEV, provide the optimal price range in real time when adjusting Uniswap LP, determine whether AAVE has liquidation risks, and how the Yield pool dynamically adjusts the leverage ratio, etc. Prediction market: Use AI models to dynamically adjust and update probabilities. Compared with the pricing mechanism based solely on trading volume, AI's aggregate reasoning can provide users with a smarter prediction starting point, avoiding pure follow-the-crowd. However, Allora is essentially just an infrastructure service facility. In the early stages, when there are few models, little data, and insufficient accuracy, it will also go through a long period of accumulating energy. However, if DeFAi and prediction markets, two promising markets, can become mainstream in the future, the value of its infrastructure services will be highlighted.